Clinical microbiology informatics.

Clinical Microbiology Informatics Daniel D. Rhoads, Vitali Sintchenko, Carol A. Rauch and Liron Pantanowitz Clin. Microbiol. Rev. 2014, 27(4):1025. DOI: 10.1128/CMR.00049-14.

These include: REFERENCES

CONTENT ALERTS

This article cites 176 articles, 49 of which can be accessed free at: http://cmr.asm.org/content/27/4/1025#ref-list-1 Receive: RSS Feeds, eTOCs, free email alerts (when new articles cite this article), more»

Information about commercial reprint orders: http://journals.asm.org/site/misc/reprints.xhtml To subscribe to to another ASM Journal go to: http://journals.asm.org/site/subscriptions/

Downloaded from http://cmr.asm.org/ on October 21, 2014 by NYU MEDICAL CENTER LIBRARY

Updated information and services can be found at: http://cmr.asm.org/content/27/4/1025

Clinical Microbiology Informatics Daniel D. Rhoads,a Vitali Sintchenko,b,c Carol A. Rauch,d Liron Pantanowitza

SUMMARY. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1026 INTRODUCTION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1026 UNIQUE FEATURES OF THE MICROBIOLOGY LABORATORY INFORMATION SYSTEM. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1026 Multiple-Derivative Tracking . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1026 Laboratory Electronic Notes. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1027 Results Reporting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1027 Informatics tools that facilitate standardized results entry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1027 Caveats to consider in reporting preliminary and final results. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1028 Quality Assurance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1028 TOOLS TO FACILITATE APPROPRIATE ANTIMICROBIAL THERAPY. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1028 The Antibiogram . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1028 Advanced Clinical Decision Support Tools . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1029 Postdischarge Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1029 Preventing Release of Inappropriate Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1029 EXPERT SYSTEMS IN THE LABORATORY . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1029 The Need for Expert Systems. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1029 The Definition of an Expert System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1030 Utility of Expert Systems in Antimicrobial Susceptibility Testing and Reporting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1030 Expert Systems as Alert Tools . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1030 Expert Systems as Data Analysis Tools . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1031 The Future of Expert Systems in the Microbiology Laboratory. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1031 INSTRUMENT INTERFACES WITH THE LIS. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1031 Interface Commonalities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1031 Interfaces of Common Instruments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1031 BD instruments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1031 bioMeriéux instruments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1032 Siemens MicroScan WalkAway . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1032 Molecular Testing Interfaces . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1032 Instrument Interfaces of the Future . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1033 TOTAL LABORATORY AUTOMATION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1033 Informatics Challenges in TLA. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1033 TELEMICROBIOLOGY AND AUTOMATED DIGITAL IMAGE ANALYSIS. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1034 Telemicrobiology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1034 Automated Digital Image Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1034 The Future of Telemicrobiology and Automated Digital Image Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1035 MICROBIAL IDENTIFICATION AND CHARACTERIZATION USING DATABASES. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1035 Biochemical Databases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1035 MALDI-TOF Databases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1036 Nucleic Acid Databases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1036 Database challenges . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1036 Considerations in using public databases for microorganism identification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1037 WGS and MGS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1037 (i) Potential advantages of routine clinical WGS and MGS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1037 (ii) Informatics challenges. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1038 DNA data in the future . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1039 REPORTING TO PUBLIC HEALTH AGENCIES AND DETECTING OUTBREAKS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1039 Reporting to Public Health Agencies. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1039 Outbreak Detection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1039 Detection of evolving antimicrobial susceptibility patterns . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1039 WHONET . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1040 (continued)

Address correspondence to Daniel D. Rhoads, [email protected]. Copyright © 2014, American Society for Microbiology. All Rights Reserved. doi:10.1128/CMR.00049-14

October 2014 Volume 27 Number 4

Clinical Microbiology Reviews

p. 1025–1047

cmr.asm.org

1025


Department of Pathology, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania, USAa; Marie Bashir Institute for Infectious Diseases and Biosecurity and Sydney Medical School, The University of Sydney, Sydney, New South Wales, Australiab; Centre for Infectious Diseases and Microbiology-Public Health, Institute of Clinical Pathology and Medical Research, Westmead Hospital, Sydney, New South Wales, Australiac; Department of Pathology, Microbiology and Immunology, Vanderbilt University School of Medicine, Nashville, Tennessee, USAd

Rhoads et al.

Detection of regional and global outbreaks. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1040 Detection of local outbreaks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1041 Surveillance. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1041 Integrative Public Health Informatics Approaches of the Future. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1041 CONCLUSIONS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1041 ACKNOWLEDGMENTS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1041 REFERENCES. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1041 AUTHOR BIOS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1047

The clinical microbiology laboratory has responsibilities ranging from characterizing the causative agent in a patient’s infection to helping detect global disease outbreaks. All of these processes are increasingly becoming partnered more intimately with informatics. Effective application of informatics tools can increase the accuracy, timeliness, and completeness of microbiology testing while decreasing the laboratory workload, which can lead to optimized laboratory workflow and decreased costs. Informatics is poised to be increasingly relevant in clinical microbiology, with the advent of total laboratory automation, complex instrument interfaces, electronic health records, clinical decision support tools, and the clinical implementation of microbial genome sequencing. This review discusses the diverse informatics aspects that are relevant to the clinical microbiology laboratory, including the following: the microbiology laboratory information system, decision support tools, expert systems, instrument interfaces, total laboratory automation, telemicrobiology, automated image analysis, nucleic acid sequence databases, electronic reporting of infectious agents to public health agencies, and disease outbreak surveillance. The breadth and utility of informatics tools used in clinical microbiology have made them indispensable to contemporary clinical and laboratory practice. Continued advances in technology and development of these informatics tools will further improve patient and public health care in the future. INTRODUCTION

T

he local clinical microbiology laboratory’s responsibilities range from characterizing the causative agent of a patient’s infection to helping detect global disease outbreaks. These processes are becoming increasingly more complex. Every laboratory is obliged to continually improve quality while operating more efficiently. The clinical microbiology laboratory is being challenged to do more work, identify more microorganisms, report complex and changing drug-related information, automate procedures, integrate traditional lab data with molecular findings, and participate in public health reporting and outbreak detection. Informatics provides the tools and processes to satisfy most of these demands and also offers unique opportunities to advance the clinical microbiology laboratory, allowing the lab to do more with less. Therefore, it is important for microbiologists to be familiar with informatics (Table 1) (1). Although many informatics components are already widely used in clinical microbiology, there are many emerging tools that are not being used routinely but that could be leveraged by the laboratory. Studies have demonstrated that implementation of informatics tools can improve the efficiency, accuracy, precision, and rapidity of microbiology testing and reporting (2–7). In this review, we describe the broad impact of informatics on clinical microbiology and highlight bur-

1026

cmr.asm.org

geoning areas of clinical microbiology informatics, such as its role in total laboratory automation (TLA), telemicrobiology, and microbial whole-genome sequencing (WGS). “Clinical microbiology informatics” is the use of information (e.g., data, knowledge, and results) and information tools (e.g., software, databases, and rules) in the “science and service dealing with detection, identification, and antimicrobial susceptibility testing” of clinically relevant microbes and the communication of these results to clinicians (8). A more practical definition includes the application of information technology (IT) to solve problems in clinical microbiology by improving workflow, efficiency, reliability, and, ultimately, patient care. It is important to stipulate that the practice of informatics not only involves technology but also includes the people who use, implement, and maintain information systems, and it includes the workflow processes that are affected by this technology. This review discusses those diverse informatics components that are uniquely relevant to the clinical microbiology laboratory. Specifically, the following topics are addressed: the microbiology laboratory information system (LIS), decision support tools, expert systems, instrument interfaces with the LIS, total laboratory automation, remote and automated image analysis, nucleic acid sequence databases, reporting of infectious agents to public health agencies, and outbreak surveillance. Systems, algorithms, and published studies are not exhaustively studied in this review. Instead, this article focuses on the breadth of connections that informatics and clinical microbiology share and the potential improvements that can be realized by increasing the implementation of informatics tools in clinical microbiology. Some of these informatics solutions have been incorporated into the workflow of only a few hospitals or laboratories, while other informatics components are likely to be more familiar to many microbiologists, as they are ingrained into the routine practice of clinical microbiology. It is important to continually explore new informatics tools which have the potential to improve the efficiency, accuracy, cost, and quality of care related to clinical microbiology. UNIQUE FEATURES OF THE MICROBIOLOGY LABORATORY INFORMATION SYSTEM

The microbiology LIS has been in development and use for about half a century (9, 10). Like every LIS, the microbiology LIS needs to be secure, user-friendly, and able to interface with other information systems. However, there are several unique features of the microbiology LIS which are not used in other clinical laboratories. Multiple-Derivative Tracking

Samples sent to the microbiology laboratory often produce more than a single result, and the final type and number of results are typically not known until the testing is under way. One sample



SUMMARY

Clinical Microbiology Informatics

TABLE 1 Key informatics resources for the clinical microbiology laboratory Resource (reference)

often creates multiple derivatives with unique parent-child relationships (10, 11). For example, consider a singly accessioned container that is sent to the laboratory. This container is the laboratory’s original asset. Inside the original asset may be a polymicrobial abscess sample, which is the original specimen. The original specimen will be inoculated into or onto various media. These media immediately become derivative assets that need to be linked to the original accession number. Similarly, each organism that is cultivated from the original polymicrobial specimen becomes a derivative specimen. These derivative specimens may be aerobic bacteria, anaerobic bacteria, mycobacteria, and/or fungi, and all of these need to be linked to the original accession number. Additional derivative assets, such as subculture plates or antimicrobial susceptibility testing (AST) plates, may need to be created in order to fully characterize the isolated derivative specimens. Properly handling the electronic information associated with a sample, such as tracking its derivatives, modifying descriptions of its derivatives, and linking its derivatives with their accession number, is a unique and essential aspect of the microbiology LIS. The physical derivatives also require accurate tracking and identification on the bench, and the best practice for proper identification employs the use of bar-coded labels on all assets (12, 13) (Fig. 1).


Notes need to be made routinely regarding cultured specimens so that the previously performed work-up is evident to the microbiologist who is presently examining the derivatives. Historically, these notes were recorded on physical note cards, but now the LIS has largely replaced these physical notes with electronic notes. Having archived electronic notes on each specimen allows for more permanent and easily searchable record keeping and easier auditing, which can potentially be used in quality improvement efforts. Results Reporting

The microbiology LIS requires unique features involving how results are conveyed to clinicians. Most clinical laboratory disciplines report numerical results, such as concentration, titer, or quantity. However, other clinical microbiology laboratory needs include reporting nonnumerical results, such as the genus and species name of an identified organism (11). Microbiology results often include qualitative, semiquantitative, and/or quantitative data, which can complicate data structures in the LIS (14). Multiple organisms can grow in culture, each with susceptibility results, interpretive comments, and potentially different clinical relevancies, so report design is paramount to supporting safe interpretation by health care providers (15). Optimizing the information design of a report, for example, by summarizing and grouping the microbiology results to improve data visualization, can improve interpretation of the data (16). Expert systems may also be used in reporting results, and these are discussed in a separate section. Informatics tools that facilitate standardized results entry. The breadth of potential results that may need to be reported requires freedom to enter text as a result. However, there need to be strict input requirements, more than simply permitting a free text field for results entry. Standardization in the system is needed to facilitate the use of appropriate nomenclature, to improve consistency of reporting, and to simplify auditing so that, for example, the identification of Staphylococcus epidermidis is not reported as “Staph epidermidis,” “S. epidermidis,” “coagulase-negative staphylococcus,” or “coag-neg staph” (17).

FIG 1 Image demonstrating the increased information density that can be obtained using 2-dimensional (2-D) bar codes over 1-dimensional (1-D) bar codes and demonstrating that minor damage to a 2-D bar code can be compensated for by the remaining portion of the bar code. Bar codes use solid lines (1-D bar codes) or blocks (2-D bar codes) in combination with intervening spaces to encode data, which can be translated to text via a bar code scanner and its software. The words “Staphylococcus aureus” are depicted in a 1-D bar code using Code 128 symbology (A) and a 2-D bar code using DataMatrix symbology (B). These symbologies are commonly used to label specimens in clinical laboratories, although numerous bar code formats are available. 2-D bar codes are becoming the preferred symbology because of their smaller footprint and robust error correction. For example, even if part of the bar code is slightly damaged (C), the integrity of the information remains intact and can be read accurately. Bar codes can also be used to enter microbiology results or comments into an LIS, and the use of bar codes can help to decrease typographical errors and standardize results reporting.

cmr.asm.org 1027


Resources for general knowledge and reference Biomedical Informatics: Computer Applications in Health Care and Biomedicine, 3rd ed (188) Clinical Diagnostic Technology. The Total Testing Process, vol 1, 2, and 3 (189–191) Infectious Disease Informatics (192) Informatics for the Clinical Laboratory: a Practical Guide for the Pathologist (193) “The Laboratory Information System: Making the Most of It in the Clinical Microbiology Laboratory” (194) Pathology Informatics: Theory and Practice (195) Practical Informatics for Cytopathology (196) Practical Pathology Informatics: Demystifying Informatics for the Practicing Anatomic Pathologist (197) Health Informatics. Practical Guide for Healthcare and Information Technology Professionals, 6th ed (198) Public Health Informatics and Information Systems, 2nd ed (199) Standards and guidelines for operations, maintenance, and compliance Laboratory Automation: Bar Codes for Specimen Container Identification (200) Laboratory Automation: Communications with Automated Clinical Laboratory Systems, Instruments, Devices, and Information Systems (201) Managing and Validating Laboratory Information Systems (202) “CLIA Program and HIPAA Privacy Rule; Patients’ Access to Test Reports. Final Rule” (203) Clinical Guidelines for Telepathology (204) Digital Pathology Resource Guide, version 4.0 (205) “Laboratory Computer Services” (20) “Publication of OIG Compliance Program Guidance for Clinical Laboratories—HHS” (206) “Standards for Secure Data Sharing across Organizations” (207) “Validating Whole Slide Imaging for Diagnostic Purposes in Pathology: Guideline from the College of American Pathologists Pathology and Laboratory Quality Center” (67) “Validation of Digital Pathology in a Healthcare Environment” (208)

Laboratory Electronic Notes

Rhoads et al.

Quality Assurance

Like all areas of the clinical microbiology laboratory, the quality of the LIS and its interface with clinicians needs to be ensured. Several components of the LIS and HIS need to be operational before implementing a new microbiology test. First, an interface needs to be present between the LIS and HIS. Second, orders submitted via the HIS need to be received by the LIS. Third, results in the LIS need to be transmitted downstream to the HIS. Fourth, results in the HIS, including preliminary reports, final reports, and antimicrobial susceptibility test results, need to be displayed correctly. The College of American Pathologists requires that these interfaces be verified every 2 years (GEN.48500) (20). Software upgrades and network improvements often have unintended negative consequences that may not be identified without proactive investigation, so it is important to check all the interfaces that the clinician experiences at regular intervals, including interfaces beyond the results interface. For example, it is necessary to check and maintain the order entry interface that clinicians use to be certain that the tests that are visible to the clinicians are truly those tests that are currently being offered by the microbiology laboratory. TOOLS TO FACILITATE APPROPRIATE ANTIMICROBIAL THERAPY

Aiding in the appropriate selection of an antimicrobial therapy regimen for a patient is a primary purpose of the clinical microbiology laboratory, and two important variables should be considered in working to select appropriate antimicrobial therapy for a

1028

cmr.asm.org

potential infection. The first variable is whether or not an infection is actually present. If an infection is present, the second important variable is whether or not a drug will be an effective therapy. Several informatics tools can help the laboratory and clinicians to best determine these variables. The most rudimentary and universally used informatics tool for empirical antimicrobial therapy selection is the classic annual antibiogram. However, some have proposed that more advanced informatics tools should be incorporated into the LIS or HIS and have demonstrated that such algorithms can improve the quality and decrease the cost of care (5, 21–23). Expert systems are useful in helping the laboratory to avoid reporting inappropriate antimicrobial susceptibility test results that could lead to inappropriate therapy, and these systems are discussed in another section. Tools can be used to alert physicians to important microbiology results that may require changing or commencing antimicrobial therapy, but studies suggest that the best way to communicate important microbiology results is still direct communication of those results to a treating clinician via a telephone call (24, 25). Rapid notification of a clinical pharmacist can also be used as a means to expedite appropriate therapy alterations, and electronic tools are available to rapidly notify these individuals (26, 27). The Antibiogram

The antibiogram is helpful in facilitating the selection of an appropriate therapy for an infecting organism prior to knowledge of the antimicrobial susceptibility test results for the specific isolate infecting the patient. The antibiogram is a helpful utility that organizes the susceptibility data of the local microbiology laboratory, but the decision as to which antimicrobials are potentially clinically useful depends on more variables than the likelihood of antimicrobial susceptibility. Other key variables include the site of infection, cost of therapy, patient allergies, route of administration, and hospital antimicrobial stewardship policy. However, these variables are often analyzed separately from the antibiogram instead of via an integrated approach. Although an antibiogram is the simplest means of facilitating empirical antimicrobial selection, creating the most accurate and helpful antibiogram still requires refinement of raw data. Although expert systems can be used to generate antibiograms (14), software limitations may hinder this refinement (28). In compiling data for construction of the antibiogram, care needs to be taken to prevent repeated incorporation of the same organism from the same patient from skewing the report (28–30). The Clinical Laboratory and Standards Institute (CLSI) guideline recognizes that “the methods used to create, record, and analyze the data” need to be “reliable and consistent” in order to maximize the quality and utility of the antibiogram (31). It has been suggested that each species isolated from each patient be incorporated into the annual antibiogram calculation only once per period, per site of infection, or per unique phenotype (28, 29, 31). Others suggest averaging the susceptibility of repeat isolates from a patient. That way, each repeat isolate contributes equally to a patient’s susceptibility profile, and each patient’s susceptibility profile contributes equally to the antibiogram (28). Guidelines currently recommend incorporating the results for a species only the first time it is isolated from a patient in the period being considered (28, 31). In creating an antibiogram, data stratification may be necessary for some bacteria in some patient populations. For example, Pseudomonas aeruginosa isolates from cystic fibrosis patients may be



One way to facilitate standardized input of text into the LIS is by implementing the use of rapid input methods, such as bar codes or keyboard shortcuts that expand into canned text phrases (4, 18, 19). Examples of bar codes that can be used for rapid input of results are depicted in Fig. 1. LIS software typically has the ability to use keyboard shortcuts to enter repetitive texts. For example, the software can be programmed so that when “;sa” is entered by the scientist on the bench, the entry expands into “Staphylococcus aureus.” These data entry methods allow results and comments to be entered quickly and accurately and are most useful for entering lengthy, repetitive texts, such as commonly used footnotes or disclaimers. Using computers to facilitate rapid input of results and comments can improve standardization of results reporting, decrease error rates, and increase productivity (4, 18, 19). Additionally, bar-coded accession numbers can be used on culture plates, which can help to improve workflow (14). Caveats to consider in reporting preliminary and final results. Because of the time and processes required for microbial culture and identification, results are often initially reported preliminarily but subsequently revised or refined. For example, a yeast isolate may be identified as Candida sp., which is preliminarily reported to the hospital information system (HIS) via the LIS. After additional testing, the isolate might be classified further as Candida glabrata. It is important that the most recent and specific findings are accessible in the HIS and that the preliminary results are suppressed but traceable so the clinician viewing the chart can easily identify the most accurate and up-to-date results. It is necessary that the final results replace the preliminary results in the accessible electronic medical record, but it is also necessary that these preliminary results be archived (and not overwritten) in case they need to be revisited (11).


Advanced Clinical Decision Support Tools

Advanced clinical decision support tools for clinical microbiology can be helpful in determining if an infection is present and/or determining which therapy is most appropriate. These tools have been under development for more than 4 decades (32), but they are not yet widely used. The decision as to which antimicrobials are potentially clinically useful depends on multiple variables, such as the patient’s underlying disease (e.g., cystic fibrosis) or location (e.g., emergency department), and advanced computerbased decision support tools have been used to aid clinicians in choosing the most appropriate antimicrobial regimen. There is a recognized need for the development of more software tools to optimize the clinical selection of appropriate antimicrobial therapy (22). These tools range in complexity and function. Some simpler tools work to provide improved antibiograms (33) or work similarly to an antibiogram to help in the selection of an appropriate empirical antimicrobial agent (34). Other software can work to support the determination of whether or not a true infection is present (35) or can alert a physician if a patient is failing therapy (36). Some software tools use rules that are based in Bayesian logic to predict both the presence of an infection and an appropriate empirical antimicrobial (37). Other tools are complex expert systems, which consider patient allergies, laboratory data, physical findings, radiological findings, antibiograms, drug interactions, cost, and findings in similar patients to aid in deciding which treatment might be most appropriate (21). Although the traditional antibiogram is a helpful decision support tool for empirical antimicrobial selection, other informatics tools have proven capable of improving upon the antibiogram and offering clinicians more complete support in assessing the information that is available and determining the most appropriate antimicrobial therapy regimen. However, their implementation remains sparse, probably because of the large amount of up-front resources required for implementation (38). Overcoming the initial cost of investment to implement clinical decision support tools is a common challenge in attempting to institute any new informatics tool that holds potential, but these tools’ long-term return on investment needs to be considered carefully. Furthermore, maintaining these tools and keeping them current with newly published data add significantly to their cost and sustainability. Postdischarge Results

Properly addressing postdischarge microbiology results can help to ensure that discharged patients with infections are managed appropriately. Upon discharge of an inpatient, microbiology test results are often pending. Depending on the final test results, a change in therapy for the discharged patient may be warranted; however, appropriate follow-up often does not occur (39). It has


been demonstrated that the use of an automated email system to alert the appropriate physician(s) that a discharged patient has new microbiology results and may require a change in antimicrobial therapy is a helpful tool in improving physician follow-up in these cases (2). Another option is to use a more hands-on approach in which software flags a chart for review by an infectious disease physician when a result of interest is reported after discharge. Once the infectious disease specialist has reviewed the result and the patient chart, the specialist can subsequently contact the treating physician if a change in therapy is potentially indicated (40). Preventing Release of Inappropriate Results

Reporting of positive results for patients who have a low pretest probability of infection but may produce positive laboratory findings, for example, urine cultures for hospitalized patients with asymptomatic bacteriuria, may lead to suboptimal patient management. Some laboratories have gone as far as withholding the routine release of such results in an attempt to decrease inappropriate antimicrobial therapy (41). Others have attempted to guide the interpretation of positive urine cultures by adding instructive comments which encourage clinicians to verify that signs or symptoms of infection are present before treating the patient (C. A. Rauch, presented at Preventing Healthcare-Associated Infections: Whose Problem Is It?, Lenox, MA, 3 November 2010). However, in many clinical situations, positive microbiology results help to verify and characterize an infection, and preventing the release of inappropriate susceptibility test results is paramount in facilitating selection of an effective antimicrobial therapeutic regimen. The laboratory is expected to identify “unusual or inconsistent antimicrobial testing results,” such as vancomycin-resistant staphylococci, and subsequently to investigate such findings more thoroughly (42). It is important that laboratory scientists be well trained and capable of identifying unusual results. However, humans should not be the first-line gatekeepers to recognize these unusual results. The identification of these unusual results can and should be performed by laboratory instrumentation software, middleware, or the LIS. Similarly, the laboratory is responsible for suppressing antimicrobial susceptibility test results that are not appropriate for clinical consideration (e.g., clindamycin for enterococci), and this suppression can be performed more reliably by software created by skilled laboratory professionals. Software tools can also be incorporated into the workflow to identify and characterize potential contamination in molecular testing (43). The use of these types of software is a practical means by which informatics tools can offload some of the work from humans and thereby increase efficiency and improve the consistent quality of results reporting, which supports appropriate antimicrobial usage. The implementation of an expert system is a way by which antimicrobial susceptibility interpretations can be determined and reported appropriately, and expert systems are described in the next section. EXPERT SYSTEMS IN THE LABORATORY The Need for Expert Systems

Traditionally, simple rules have been used by microbiologists for linking the detected phenotype of an organism to a clinically actionable finding. For example, the detection of cefoxitin resistance in Staphylococcus aureus is used to infer resistance of the organism

cmr.asm.org 1029


more resistant than isolates from other patients. In instances in which stratification yields significantly different findings, it is appropriate to list the likelihood of antimicrobial susceptibility for each subpopulation, whether defined by patient demographics (e.g., patients with cystic fibrosis), organism features (e.g., mucoid strains of P. aeruginosa), or patient location (e.g., emergency department). Antibiograms should also include 95% confidence intervals with the reported susceptibility likelihoods so clinicians can better interpret the data (28).

Rhoads et al.

form of artificial intelligence that allows a user to use software rules (inference engine) together with a knowledge database to make a conclusion (output) about an input.

to penicillins and cephalosporins. However, identifying resistance mechanisms can be less straightforward in other situations, such as identifying and distinguishing extended-spectrum-beta-lactamase (ESBL)-producing, AmpC-hyperproducing, and wild-type Enterobacteriaceae bacteria (38, 44). Because of the variability of microorganism phenotypes and in vitro test results, as well as the possibility of multiple resistance mechanisms, a simple flow chart or table can quickly evolve into a complex algorithm. Rules for reporting results can be dependent upon patient demographics, the specimen’s source, or antimicrobial resistance. Instead of relying on humans to investigate these criteria, recall the associated rules, and accurately implement the rules, the rules can be built into the system to selectively release results. It is no surprise that the microbiology laboratory has turned to expert systems (aka knowledge-based systems) to attempt to organize algorithms into more usable and useful systems. The Deﬁnition of an Expert System

An expert system is a form of artificial intelligence. It contains three main parts: a knowledge base (i.e., known facts), an inference engine (i.e., the rules), and a user interface (38). An expert system is software that combines a database of information with a set of rules to help make a conclusion about an input (Fig. 2). The same conclusion can potentially be achieved by a human. However, the advantage of using an expert system is that the system always “remembers” all of the rules involved in the decision-making process, so it is able to quickly and consistently produce the same objective output for a given input. Utility of Expert Systems in Antimicrobial Susceptibility Testing and Reporting

In the clinical microbiology laboratory, expert systems may be employed most commonly in automated antimicrobial susceptibility testing (AST). These expert systems can alert the user to an unusual AST pattern for an identified organism, alter the AST interpretation of one antimicrobial based upon the interpretation of test results for a second antimicrobial, suppress the report for the AST of an antimicrobial if appropriate, add a footnote to an interpretation, and allow laboratories to customize the rules that are used by the expert system. Winstanley and Courvalin recently wrote a thorough review of clinical microbiology expert systems (38). In practice, a user (or instrument) inputs MIC values into an

1030

cmr.asm.org

1. An organism with intrinsic resistance to an antimicrobial may produce a result that suggests that the organism is sensitive to the antimicrobial, but the expert system will convert the result to “resistant.” 2. Inferred susceptibility of an organism to an antimicrobial that has not been tested but which is based on the susceptibility test result of a different antimicrobial may be reported. 3. A comment may be added to provide clinically relevant interpretation of a result. For example, an S. aureus isolate with cefoxitin resistance will receive an additional educational comment explaining that penicillins and cephalosporins should not be used for therapy. Additional comments may be added to support infection control- and preventionrelated efforts according to the needs of the institution. In addition to altering AST results when necessary, expert systems can withhold inappropriate results or release appropriate results that are typically not reported for a number of reasons. For example, antimicrobial test results can be withheld when the patient’s age or the specimen source suggests that the drug’s use would be inappropriate (14). In other cases, AST results that are typically suppressed can be released if the patient is critically ill (e.g., residing in an intensive care unit) or if the patient’s isolate is resistant to first-line therapy (14). This general approach has been referred to as “cascade” reporting and has been used in efforts to optimize the use of antimicrobial drugs. Expert Systems as Alert Tools

Expert systems can also be used to inform the appropriate personnel of new or important information. An expert system can alert a laboratory scientist on the bench when additional work-up needs to be performed on an isolate. A system can also alert a laboratory



FIG 2 Schematic representation of an expert system. An expert system is a

expert system. The software then compares the input to the knowledge database by using the inference engine, and the system then outputs the appropriate susceptible, intermediate, or resistant (SIR) interpretation. Expert systems can also be used to infer the mechanism of antimicrobial resistance by comparing a clinical isolate’s MIC values to a curated database of strains which have had their MIC values and mechanism(s) of resistance characterized (45). In addition to having the antimicrobial breakpoints in its database, the expert system also has rules which check to see if some drug results need to be reported as resistant even though the organism might test as sensitive. For example, if S. aureus is resistant to cefoxitin, then the cephalosporin results need to be reported as resistant, even if these results appear individually to be sensitive in vitro, as the cefoxitin result is a more robust indicator of resistance. Becton, Dickinson, and Company (BD) uses an expert system (BDXpert) to analyze the identification and AST of microorganisms from its BD Phoenix system to recognize if modifications or additions should be made to the AST results (i.e., MIC) or interpretation (i.e., SIR) before they are reported to clinicians (46). Similar expert systems are employed by the Siemens MicroScan and bioMeriéux Vitek systems (38). BDXpert examines the AST interpretations initially identified by the instrument and may make changes to the interpretations before communicating the results to the LIS. Some examples of modifications or additions to results include the following (46):


scientist if a similar isolate was recently worked up by the laboratory, which would suggest that the current isolate may not need a repeat AST performed. An expert system can send email notifications to hospital staff when a patient needs to be placed in contact precautions, or it can email the microbiology director when the health department needs to be contacted (14).

testing, interfaces between laboratory instruments and the LIS should be employed for data transfer whenever possible. Interface Commonalities

INSTRUMENT INTERFACES WITH THE LIS

Interfaces of Common Instruments

An interface comprises a combination of software and hardware that facilitates the electronic exchange of data over a network. This communication between devices or computers is accomplished by using a protocol, i.e., a set of digital rules and functions. Today’s clinical microbiology laboratory routinely uses instrumentation for three components of analysis: taxonomic identification of an isolated colony, determination of the AST of an isolated colony, and detection of microbial growth in blood cultures. Phenotypic or biochemical identification and susceptibility testing instrumentation is typically combined into a single instrument (e.g., MicroScan [Siemens], Phoenix [BD], or Vitek [bioMérieux]). A separate instrument is often used for the detection of microbial growth in continuously monitored blood cultures. Electronically transmitting information (e.g., computerized physician test orders) from the LIS to these devices and bidirectional exchange of information determined by such instruments with the LIS are important means for streamlining laboratory workflow, decreasing human errors, and expediting results reporting (14). An increasing number of stand-alone niche instruments are used in microbiology for nucleic acid testing or antigen testing, and some of the tests for which they are used are laboratory developed. Unfortunately, interfacing each of these peripheral instruments with the LIS is not always pursued or is deemed too resource intensive, because these interfaces may need to be built and maintained by in-house support teams, but the lack of an instrument-LIS interface can result in inefficient and error-prone daily workflow in the laboratory. It has been demonstrated that manual entry of microbiology results is a source of laboratory errors (47, 48), and other clinical laboratories have demonstrated that the development of in-house interfaces between instruments and the LIS can be useful in reducing manual entry errors and hands-on time (49, 50). In order to optimize the efficiency and accuracy of all instrument

Instrument interfaces are the nuts and bolts of informatics for clinical laboratories. This component of informatics typically requires more technical expertise than clinical expertise, but the proper functioning of these interfaces is essential for smooth operations. It is important that data be arranged in a standard fashion according to specific protocols when being relayed between systems. Health level 7 (HL7) is the health care electronic messaging standard used by most laboratories (Fig. 3). Increasingly, middleware components and expert systems are being incorporated into the IT infrastructure and analysis pipelines, so important reporting decisions and autoverification are often made before results data ever reach the LIS. The interfaces of some common instruments are briefly discussed below. BD instruments. BD instruments include blood culture (BacTec FX or 9000 series), mycobacterial detection and AST (BacTec MGIT 960), and identification and AST (BD Phoenix) instruments. Workstations that directly interface with the instrument by using a private network are supplied by BD. BD uses a data management system (EpiCenter) hosted by a server that is designed to integrate patient demographics it receives from the LIS, results it receives from BD instruments, and results from manually performed microbiology tests. EpiCenter can be networked to multiple clients to allow many workstations in the laboratory to access EpiCenter. EpiCenter and the LIS send messages to each other containing patient information, order information, and test results. EpiCenter is designed to allow results to be pushed to the LIS or pulled by the LIS, depending on user preferences. The system also allows the LIS to inform EpiCenter of an ordered test, and EpiCenter can associate this ordered test with results in its database as the results become available. EpiCenter can request patient information from the LIS if it encounters an accession number for which it does not have demographics. Newer BD

Expert Systems as Data Analysis Tools

Expert systems can be used to aggregate and report metrics of interest. For example, MIC trends of an organism can be visualized, infection rates in a given hospital unit can be identified, or blood culture contamination rates associated with a particular clinical area or phlebotomist of interest can be examined (14). The Future of Expert Systems in the Microbiology Laboratory


cmr.asm.org 1031


There is unrealized potential for full integration of the microbiology laboratory’s expert systems with clinician alerts and advanced clinical decision support tools to optimize the flow of microbiology data and to translate those data into improved patient care. By decreasing suboptimal or unnecessary antimicrobial therapy, development of antimicrobial resistance and therapy-related complications can be minimized. Potential also exists to incorporate expert systems into total laboratory automation systems, in which expert systems could have the ability to “read” plate cultures and report autoverified results in some instances.

Most instruments and databases used in clinical microbiology laboratories within the United States are cleared by the U.S. Food and Drug Administration (FDA) for in vitro diagnostic testing, and they have software designed and maintained to interface with the microbiology LIS, which enables bidirectional communication between the instrument and the LIS. This communication typically occurs via a serial port (RS-232) and follows the CLSI specification guidelines LIS01-A2 and LIS02-A2 (formerly maintained as guidelines ASTM E1381 and E1394). These guidelines specify communication protocols for structuring content and the data elements contained within those structures, as well as data transfer requirements. The LIS02-A2 standard is applicable to all text-oriented instruments. Electronic microbiology testing can be complex, as messages may have different test priorities (e.g., stat, routine, or callback), contain multiple requests (i.e., a battery of test orders) and/or results (e.g., MICs) for one patient, include different report types (e.g., preliminary and final results), incorporate comments of various lengths, or be flagged to trigger alerts or display in a desired manner in a downstream electronic health record. Communicating this information between instruments and information systems requires robust interfaces designed to handle these complex messages.

Rhoads et al.

instruments have been designed be able to interact directly with an LIS. bioMeriéux instruments. bioMeriéux instruments include blood culture (e.g., BacT/ALERT3D) and identification and AST (e.g., Vitek 2) instruments. These instruments can interface with the LIS, or results from these instruments can be centralized in an Observa workstation, which is then interfaced with the LIS (51). Some instrument interfaces follow the ASTM E1381-specified guidelines, but other interfaces rely on a nonstandard “bioMeriéux communications protocol” (52). bioMeriéux’s blood culture instruments allow for varied levels of LIS integration, depending upon the instrument capabilities and user preferences. There are options for no integration with an LIS, direct LIS interface, or LIS interface by means of bioMeriéux data management systems (BacT/VIEW or Observa). Interfacing with the LIS through any means requires the use of BacT/LINK (middleware). BacT/LINK enables bidirectional communication between BacT/Alert and the LIS. Test orders can be pushed to the BacT/Alert by the LIS, or the BacT/Alert can pull test orders from the LIS. Subsequently, the instrument can push results to the LIS as changes in the detected results are identified. bioMeriéux’s Vitek instruments allow for similar bidirectional interface with the LIS via data management software (bioLIAISON or Observa). Siemens MicroScan WalkAway. The MicroScan system performs identification and AST, and it uses Siemens’ LabPro software for interfacing with the LIS. LabPro allows for bidirectional sharing of information between the MicroScan system and the LIS. LabPro can push and pull information to and from an LIS. LabPro “[t]ransmits and receives patient, specimen, and isolate order information to/from the laboratory system manually or automatically” (53). This allows LabPro to collect patient informa-

1032

cmr.asm.org

tion and results from manually performed test results recorded in the LIS and to collect results from automated tests performed by the MicroScan system. The expert system can identify results that need further review (38), and LabPro can communicate results to the LIS. Molecular Testing Interfaces

A growing number of FDA-approved molecular tests and laboratory-developed tests are used in clinical microbiology, ranging from PCR assays designed to detect single pathogens to highthroughput parallel sequencing of DNA designed to detect multiple species simultaneously (54–56). Many of these systems are laboratory developed, but FDA-approved instruments are not uncommon. One FDA-approved example is Gen-Probe’s (Hologic’s) Tigris DTS system, which can be used for the detection of Neisseria gonorrhoeae and Chlamydia trachomatis. Although the Tigris DTS receives orders sent by the LIS, a Tigris DTS user can still manually request the LIS to send orders to the instrument. Subsequently, when a work list is complete, the Tigris DTS pushes results to the LIS (57). Instruments employed for laboratory-developed molecular tests vary in their interoperability with the LIS. On one hand are systems that have no direct communication link with the LIS and require results from the instrument to be entered manually into the LIS, and on the other hand are instruments that are fully integrated with the LIS system by means of bidirectional interfaces, whereby data flow automatically, avoiding the need for manual data entry. Factors that play a role in determining the level of instrument-LIS integration include the capabilities of the instrument and LIS being used, the technical support (vendor and laboratory IT staff) available for establishing and maintaining cus-



FIG 3 Anonymized HL7 message, which would be sent from an LIS to an HIS. Information technology staff often work with this type of message, but clinical microbiologists are seldom exposed to the HL7 messaging format. It is helpful for clinical microbiologists to know what an HL7 message might look like, so they can communicate more effectively with information technology staff. The depicted example relays the positive result of a Chlamydia trachomatis test that was determined using a Tigris DTS instrument (Hologic Gen-Probe, Inc., San Diego, CA). The test that was ordered and performed is represented by an LOINC code within the message, i.e., 21190-4 (bolded and underlined for emphasis). A SNOMED code within the message, i.e., G-A200 (bolded and double underlined for emphasis), indicates that the result is positive. Information in the message is divided into sections which include patient identifying information (PID), information about where the order originated (ORC or “common order”), information about the test that was ordered (OBR or “observation request”), and information about results and the reporting of the results (OBX or “observation of results”).


tom interfaces, and the level of priority placed on integration by the laboratory director and administration. Preferably, results from instruments, whether performing FDA-approved tests or laboratory-developed tests, should be integrated seamlessly into the LIS, which can decrease the chance for random human errors and decrease the time spent performing and checking manual data entry. As the demand for sophisticated laboratory instrumentation, automation, and electronic reporting increases, so too does the need for better software to clearly collect, display, and integrate ongoing specimen processes and reported results. Middleware (e.g., bioMeriéux’s Observa and Myla) has been developed to address this need. Middleware not only connects legacy to newer systems but also permits data exchange and management of complex data that the LIS cannot handle. Middleware solutions can also help to unify microbiology results into a more manageable and userfriendly centralized system. This approach will continue to promote lab automation, increase productivity, expedite workflow, and promote standardization while minimizing the opportunity for human errors. Unifying interfaces with middleware will eventually blur the line between the LIS and instrument software. TOTAL LABORATORY AUTOMATION

The clinical microbiology laboratory faces “an ever-increasing load of work not matched by an increase in the number of people available to do it,” as Williams and Trotman described in the 1960s, and which still seems to hold true today (58). The laboratory has been looking to automation to overcome this shortcoming for the last half century. Williams and Trotman envisioned a more totally automated laboratory where “[i]t should be possible to mechanize the inoculation of specimens on to culture plates and their transfer to the incubator, and similarly, when the bacteriologist has picked the colony of interest and selected its identification program, it should be possible to transfer it to the various identification media, or set up cultures for antibiotic sensitivity determination mechanically without undue difficulty” (58). Automated instruments have become the workhorses of many clinical laboratories, but automation has traditionally been used only for discrete portions of the analysis pipeline, which typically has been limited to identifying the presence of growth in blood cultures and determining the identity and susceptibility of isolated organisms. Only recently is Williams and Trotman’s dream of more complete laboratory automation beginning to be realized. This approach is commonly termed “total laboratory automation” (TLA) (59, 60). Several commercial entities are now manufacturing products and systems (e.g., WASP [Copan], Previ-Isola [bioMeriéux], Innova [BD], and InoqulA [Kiestra]) that they hope will expand automation in the microbiology laboratory (59–61). These systems have been implemented in some laboratories to automate processes including inoculation, identification of growth on plated specimens, the subculture of colonies of interest, and inoculation for organism identification and susceptibility testing. Europe has some laboratories that have already moved to TLA (62), and some laboratories in the United States are preparing to implement TLA (63). The potential advantages of TLA are plentiful and include decreased operational costs, improved standardization of processing and testing, increased throughput capability, increased


Informatics Challenges in TLA

Advances in informatics have allowed for TLA to become a realistic option for the clinical microbiology laboratory (66). The flow of information from instrument to instrument in the automated microbiology laboratory is a new challenge for clinical microbiology informatics. Traditionally, information has been transmitted between a single instrument and the LIS, sometimes only unidirectionally (61). Now, however, informatics pipelines are needed to facilitate the automated flow of data through and between multiple instrument components (or workbenches), the LIS, and the HIS, so that the test ordered in the HIS is translated to the appropriate inoculation of culture media by the automated instrumentation. Additionally, findings from one instrument in the system may need to be able to direct the actions of subsequent instruments in the work-up process, and this may include tasks such as bar code-driven automated specimen handling, which is common in other areas of the clinical laboratory. As the options for instrument automation increase and the implementation of these systems increases, the necessity for interfaces between different components from different vendors will also increase (59). Automated acquiring of digital images or topography (x, y, and z coordinates) of culture plates is an emerging area of informatics that is often linked to TLA. Using analysis software to compare growth between scans can help to detect and characterize growth of microorganisms. The human component of digital culture plate analysis can be undertaken remotely, which may result in moving a microbiologist’s workspace from the benchtop to the office. It is possible that these new and increasingly automated instruments, which are able to capture images and monitor growth, may also be able to quantify microbial growth in ways that were previously interpreted qualitatively by humans (e.g., colony color, shape, texture, size, odor, time to growth, and pattern of

cmr.asm.org 1033


Instrument Interfaces of the Future

numbers of isolated colonies per plate, management of various specimen types simultaneously, decreased turnaround times, greater specimen traceability, and decreased human workloads (59, 64). However, these systems are only beginning to be evaluated in formal studies. The systems may not be as efficient as advertised, and their real impact on routine testing needs to be evaluated (65). Initial studies have demonstrated that TLA can help to double the sample throughput per technologist per day, which has enabled laboratories to handle increased specimen numbers with decreased staff (N. Bentley, M. Farrington, R. Doughton, and D. Pearce, poster 1792 presented at the 21st EECMID, Milan, Italy, 7 to 10 May 2011 [http://www.poster -submission.com/search/download/13592]; G. Humphrey, C. Malone, H. Gough, and F. M. Awadel-Kariem, poster 1793 presented at the 21st EECMID, Milan, Italy, 7 to 10 May 2011 [http: //www.poster-submission.com/search/download/13593]) (59). However, fully realizing TLA requires “intelligent instruments” with flexible solutions and open architecture that are not yet available. These intelligent instruments would be able to act on their own interpretation of cultured samples and then make decisions independently of human analysis by using software rules. No instrument is yet capable of handling diverse specimens and interpreting all of the data that a culture plate provides, but once an instrument can interpret the growth on plates, then true TLA in the microbiology laboratory can occur. Until that time, automation will work to aid humans on the bench with the processing and analysis of specimens.

Rhoads et al.

hemolysis on sheep blood agar media). Incorporating the use of pattern recognition software that can interpret these images could enable the automated system to make decisions about how to work up, report results, and interpret the findings of a specimen without direct human input. Creating this type of intelligent instrument would allow for even more complete TLA.

The capture, transmission, and remote or automated analysis of photomicrographs is becoming a reality in pathology (67–70). Remote analysis of microbiology (telemicrobiology) has been implemented only sparsely, but there is growing interest in the transmission of microbiology images for remote analysis and consultation (71). Remote analysis might be performed by an off-site expert or could be performed by a technologist in an office down the hall. Studies that have used telemicrobiology have demonstrated its usefulness, and they have identified shortcomings that need to be considered in moving forward. Automated analysis of microbiology images has the potential to transfer the burden of repetitive, time-consuming visual examinations from humans to computers. Automated image analysis is being explored for the routine diagnosis of malaria and may also be employed for reading culture plates. With the advancement of telemicrobiology and automated image analysis comes the need for implementation and validation guidelines, which are not yet available. The future of telemicrobiology and automated digital image analysis is uncertain. However, interest in these technologies is growing, and as indicated above, there is a vision to integrate telemicrobiology into TLA systems. Telemicrobiology

Microbiology testing often requires the visual analysis of an expert in order to properly interpret a sample or culture. However, expertise is not always available at the location of need. For example, expert parasitologists are scarce, and the likelihood of a laboratory having on-site expertise is low. The Centers for Disease Control and Prevention’s (CDC’s) DPDx service from the Division of Parasitic Diseases fills this gap in expertise by freely offering aid in remotely diagnosing parasitic infections by telepathology. Telemicrobiology requires a team approach where the local group can supply the clinical history and electronic images and the remote consultant can provide the diagnostic expertise after interpreting the image(s). An image without relevant metadata is of limited value. For this reason, it is important that DPDx requires that all image submissions be accompanied by CDC Form 50.34 (version 1.2; www.cdc.gov/laboratory/specimen-submission/pdf/form-50 -34.pdf). This form requires patient information, including source site, stage of illness (e.g., asymptomatic, acute, or chronic), type of infection (e.g., lower respiratory tract, soft tissue, or sepsis), therapeutic agents used during the disease course, epidemiological extent of disease (e.g., carrier, contact, or outbreak), travel history, vector exposure, and immunization history. The importance of a patient’s clinical history is often essential in providing the best diagnostic evaluation in medicine, and the clinical history remains an important component in the remote analysis of microbiology. McLaughlin et al. performed a study to evaluate the use of telemicrobiology in analyzing captured images of Gram stains from fresh specimens (72). While their study suggests that the remote

1034

cmr.asm.org

Automated Digital Image Analysis

Automated or semiautomated digital image analysis has been implemented successfully in cytology, cytogenetics, and hematology laboratories, in which repetitive, high-volume image analysis occurs (69, 70). However, automated image interpretation in the clinical microbiology laboratory has not been widely implemented. The majority of the work published in the area of microbiology digital image analysis has been focused on tests requiring a large amount of human time for analysis, such as the analysis of sputum smears for acid-fast bacilli or the analysis of blood smears for Plasmodium spp. Although automated microscopic digital image analysis tools



TELEMICROBIOLOGY AND AUTOMATED DIGITAL IMAGE ANALYSIS

interpretation of Gram stains is feasible and accurate, it also revealed four potential shortcomings which need to be considered carefully before implementing telemicrobiology as a part of routine clinical analysis: suboptimal field selection, suboptimal number of fields analyzed, insufficient image quality, and inappropriate case identification due to technical error. In this study, discrepant results were attributed to shortcomings in all four of these areas. Scheid et al. authored the first report describing the routine use of telemicrobiology in clinical practice. Their telemicrobiology system was first implemented in a German military field hospital in 2003 (3). The system employed in this study used teleconference image software (i.e., DISKUS), which allowed the person in the field (the photographer) to collect the images and to discuss the images with an off-site expert (the consultant). The study employed the use of both high-power microscopic images for cellular morphology analysis and low-power microscopic images for bacterial colony morphology analysis. In the study, multiple microbiology samples were analyzed, including thick and thin blood smears for malaria, stool samples for parasites, diluted bacterial suspensions, Gram stains of patient specimens, and bacterial colonies on plates. The authors reported that the telemicrobiology system was used as part of everyday microbiology testing, and it was instrumental in identifying important diagnoses, such as malaria and dermal leishmaniasis. This study is important because it confirmed the utility of routine telemicrobiology in a real-world setting in which the remotely stationed photographer was reliant upon the off-site consultant for analysis. The study also demonstrated one of the main shortcomings of static (“store-and-forward”) telemicrobiology, which is that the testing sensitivity is limited by the acuity and expertise of the photographer. As the authors state, “when transmitting static images in bacteriology and parasitology, the expertise of transmitting users is of decisive importance, for they cannot transmit what they do not notice.” Overcoming this shortcoming could potentially be achieved by allowing the off-site consultant to analyze the entire glass slide via robotic telemicroscopy and/or whole-slide imaging (WSI) instead of only analyzing a few selected static microscopic fields of interest. To date, studies validating the use of WSI have been limited largely to anatomical pathology (i.e., surgical pathology, frozen section use, and cytopathology). Moreover, hardly any of the current WSI scanners are capable of digitizing glass slides while using 100⫻ (oil magnification) objectives, which are often needed to best visualize microorganisms. This may explain why prior studies using WSI scanners to routinely digitize glass slides at a magnification of ⫻20 reported that pathologists were unable to clearly visualize microscopic organisms (73).


The Future of Telemicrobiology and Automated Digital Image Analysis

Regulations, guidelines, and administrative matters (e.g., accreditation and malpractice matters) related to the practice of telepathology are beginning to emerge (67). While many of the documents related to these topics do not specifically address telemicrobiology, they are applicable to the practice of digital microbiology. Like the CDC’s DPDx consultation program, remote telemicrobiology allows microbiology laboratories to have rapid access to off-site expert opinions. Currently, formal microbiology expert consults are typically performed by sending an isolate or sample to another laboratory for repeated or additional work-up. This type of consult can drastically lengthen the specimen’s turnaround time and often results in some amount of redundant testing. In contrast, informal expert consults occur daily between colleagues (e.g., ASM’s ClinMicroNet Listserv), but this type of consultation may in some instances increase the laboratory’s liability by relying on an unofficial opinion. Building a virtual infrastructure for remote telemicrobiology could help to prevent such liability, delays, and redundant testing that accompany traditional consultations. Some authors believe that telemicrobiology and automated digital image analysis should be incorporated within the local laboratory as part of a larger system of TLA where cultures are examined on a computer monitor in an office or by automated pattern recognition software instead of at the bench (64, 91). Initially, the simplest of culture interpretations (i.e., no growth) could be interpreted by automated image analysis software and reported accordingly, without the need for human verification, which is similar to how negative blood cultures are currently reported. It has been suggested that this type of system may be helpful in increasing productivity and accuracy and decreasing exposure to patho-


gens (64, 91). A notable potential advantage to this approach is the ability to electronically preserve and recall images of a sample’s colony morphology at earlier time points, recall images from a patient’s previous testing, and quickly cross-reference images of concurrent culture specimens from the patient that were sampled from different sources (59, 64). Providing image records of culture plates is one way in which telemicrobiology and TLA could provide information beyond what is currently available in the clinical microbiology laboratory. These images could also be used in quality assurance, proficiency testing, and training programs. MICROBIAL IDENTIFICATION AND CHARACTERIZATION USING DATABASES

Databases are commonly used for the identification of microorganisms. Common databases include biochemical reaction databases, matrix-assisted laser desorption ionization–time of flight (MALDI-TOF) mass spectrum databases, and nucleic acid sequence databases, and less frequently, high-performance liquid chromatography databases are used for the identification of mycobacteria (92–94). In the clinical microbiology laboratory, one of the more familiar and tangible databases utilized may be that of the analytical profile index (API; bioMérieux), which identifies isolates by their pattern of biochemical reactions. Currently, many microorganisms are identified using biochemical testing on automated instruments that use databases and software for profile matching. In some laboratories and situations, this may turn the instrument database and software into somewhat of a black box, but humans need to maintain their knowledge and understanding of biochemical reaction interpretations in order to recognize potential errors, to troubleshoot, and to ensure that accurate results are obtained. New to the scene of clinical microbiology are MALDI-TOF instruments such as the bioMérieux Vitek MS and Bruker MALDI Biotyper instruments, which use spectral databases for the identification of microorganisms. These instruments identify an organism by comparing its mass spectrum to a spectral database of organism “molecular fingerprints.” These instruments have spectral databases curated by their manufacturers, but a user may be able to add spectra to the database. The third commonly used type of database for microbial identification is the nucleic acid sequence database (e.g., NCBI’s GenBank), which is used to identify microbes by their unique nucleic acid sequences. Although identification of an organism by its genetic material is now a gold standard, using these databases to interpret the identity of a microbe is not always as straightforward as one might hope. Biochemical Databases

Manufacturers have developed and maintain databases that are used for the identification of microorganisms via their biochemical metabolism. The identification system compares the qualitative findings of multiple biochemical tests to its database of known reactions that are specific for a group of taxa, and by this means the system is able to determine the most probable identity of an unknown isolate. Often, different test panels are required to be used for different groups of microorganisms (e.g., Gram-positive bacteria, anaerobic bacteria, yeast, etc.), and the selection of an inappropriate test panel can affect the sensitivity and specificity of the test. A proprietary database is supplied as part of a manufacturer’s method for identification, or the database is incorporated into the manufacturer’s expert system. Except for verification or valida-

cmr.asm.org 1035


are not being used routinely in clinical microbiology, one can imagine their potential utility for screening slides for acid-fast bacilli (74), interpretation of colony Gram stains (75), or simple bacterial culture interpretations (e.g., colony counts) (76–79). The use of automated digital image analysis can also increase the amount of information obtained from samples (80), standardize the interpretation of samples (77–79), and/or decrease the human time required to analyze samples (81–83). Outside the United States, the potential role of automated microscopic image analysis in detecting blood parasites, such as Plasmodium, has been recognized because of the repetitive and highvolume nature of such testing. Although blood parasites are typically identified by the hematology laboratory, not the microbiology laboratory, the role of detection of parasites by automated image analysis is relevant to the discussion of microbiology informatics. Numerous studies have been published which describe the use of automated digital image capture and analysis to determine parasite presence, load, and/or species (84–90). Work in this area is ongoing to facilitate rapid and convenient remote analysis. For example, one study describes the use of automated image collection, analysis, and subsequent remote consultation by short message service (SMS) messaging a composite image of infected cells to an off-site expert for confirmation (85). Studies have identified that automated digital image capture and analysis have the potential to be used as a low-cost screening tool to detect malarial infections in resource-poor settings (86), and results are encouraging, with sensitivities approaching 100% in early studies (89).

Rhoads et al.

MALDI-TOF Databases

A MALDI-TOF identification system compares an averaged mass spectrum from an unidentified microorganism to its database of known spectra. Pattern-matching software identifies the taxon(s) to which the unidentified microorganism is most similar. Generally, any bacterium or yeast that grows routinely in the microbiology laboratory can be identified using the same preparation method, and unlike the case with biochemical identification databases, prescreening of the isolate to select an appropriate test panel and database is not routinely necessary. Currently, Bruker or Vitek systems are used for microbial identification by MALDI-TOF. The software in these systems compares a clinical sample to the system’s respective database, but these two systems use different algorithms for organism identification. Bruker’s system converts the raw spectra from a test sample into a numerical list of peaks (MALDI Biotyper software, version 3.1 [Bruker Daltonik GmbH]; Help menu ¡ MALDI Biotyper OC workflows ¡ classifying unknown samples by matching their spectra or MSPs). These peaks are then compared to the reference database. First, if all of the most prominent peaks in a reference spectrum (typically 70 peaks) are present in the test sample, then a score of 1 is assigned. If none of the peaks in a reference spectrum are present in the test sample, then a score of 0 is assigned. Second, and similarly, if all of the most prominent peaks in the test sample are present in a reference spectrum, then a score of 1 is assigned, and if none of the peaks in a test sample are present in a reference spectrum, then a score of 0 is assigned. Third, the symmetry of the peak intensity (peak height) of the reference spectrum and the test sample is determined. Perfect symmetry is assigned a score of 1, and complete asymmetry is assigned a score of 0. These three scores, ranging from 0 to 1, are multiplied together and multiplied by 1,000. The product is converted to a logarithmic value to base 10, and this gives the final score. Bruker suggests that a final score of ⱖ2.0 is typically sufficient to classify a test sample to the species level. Vitek’s MALDI-TOF system approaches the identification of microbes slightly differently from the Bruker method. Vitek’s algorithm is well described in the supplemental material prepared by Rychert and colleagues (96). The Vitek algorithm classifies each identified mass peak as belonging within a “mass bin,” where the average bin contains peaks that fall within a range of about 10 Da. The software uses supervised machine learning to identify bins that correlate with a taxon. For example, a bin that is always filled (sensitive) and only filled (specific) when a given taxon is analyzed will be assigned a large positive weight. In contrast, a bin that is never filled when a given taxon is analyzed but is often filled when other taxa are analyzed will be assigned a large negative weight. Bruker’s software is also capable of positively weighting peak im-

1036

cmr.asm.org

portance, but machine learning is not used to facilitate appropriate weighting of peaks. In the Vitek system, in comparing a test sample to a reference spectrum, each peak-containing bin in the test sample is multiplied by the weighted coefficient assigned to the bin as determined by the reference spectrum, and these products are summed. A confidence value as to whether or not the test sample matches the reference spectrum is determined based upon the sum from the test sample. A confidence value of ⱖ60% with sufficient difference from the confidence values for other taxa is typically sufficient to classify a test sample to the species level. If a user chooses to be proactive in developing a laboratory’s MALDI-TOF identification system, the user can add to the system’s spectral database as he or she encounters microorganisms that are not well represented in the database. In recent reviews of MALDI-TOF analysis, authors repeatedly emphasized the importance of actively adapting the MALDI-TOF spectral database to meet the local needs of the laboratory (97, 98). Although MALDITOF analysis is very accurate overall, errors or failures in identification that do occur can be traced to inadequate databases or clerical errors in building these databases, so development of an in-house database should be performed carefully, emphasizing the need to maintain and improve quality (6, 97). In identifying an organism, it may be most appropriate to compare the spectrum of an unknown isolate to the most rigorously curated database first (e.g., a database that is part of an FDAapproved system) and subsequently to compare the spectrum to potentially less rigorously curated databases (e.g., a research use only [RUO] database, an in-house database, or a database shared by a colleague) if the well-curated database poorly identifies the organism. MALDI-TOF analysis also has the ability to attempt to identify multiple organisms from a single inoculum, but it is not yet clear in which circumstances this type of database query is most appropriate. Although MALDI-TOF databases currently being used for clinical applications are either proprietary from the manufacturer, developed in-house, or a mixture of the two, efforts are being made to create public spectral databases that are freely available (99). However, many variables are involved in generating spectra, so the quality and utility of a public database for clinical use remain to be proven. Nucleic Acid Databases

The use of genotyping to identify microorganisms is becoming increasingly common in the clinical microbiology laboratory (55, 100–102). Nucleic acid databases are commonly used for the identification of microbes. Typically, genotyping assays target DNA regions with high interspecies variation and low intraspecies variation that are flanked by conserved regions of DNA for which primers can be designed (e.g., the 16S rRNA gene, hsp65, and the internal transcribed spacer [ITS]). These regions are employed to identify a microorganism by comparing the isolate’s nucleic acid sequence with a reference database. However, using these databases is not as straightforward as using the databases of biochemical reactions or MALDI-TOF spectra. Additionally, WGS of microorganisms is moving toward becoming a routine test in clinical microbiology, with informatics challenges of its own (101, 103– 106). Database challenges. Nucleic acid sequence analysis is usually a laboratory-developed test, with the local microbiology laboratory being responsible for developing, validating, and maintaining the



tion testing, the end user (e.g., the clinical microbiology laboratory) has little responsibility for maintaining these databases. An advantage of these databases is that they are curated by the manufacturer and are part of an FDA-approved in vitro diagnostic method, but this can also be a disadvantage. Because only the manufacturer can manipulate the database after receiving FDA approval, it can be cumbersome to make database changes. It can be a decade or more before a newly described and/or rarely isolated species makes its way into the database (e.g., Serratia ureilytica was described in 2005 [95] and is not yet in some of the major manufacturers’ databases [as of 2014]).


1. Specimen source. Reference strains can provide more reliable identification than clinical or environmental strains, and it may be most appropriate to identify an organism as a more poorly matched reference strain than a more closely matched nonreference strain. 2. Date of submission. Microbial taxonomy and nucleic acid methods are frequently changing, and findings from more current submissions may be preferred over older submissions. 3. Submitter. The reliability (or lack thereof) of the person who submitted the sequence to the database should be considered. 4. Maximum identity. Basic Local Alignment Search Tool (BLAST) searches can rank the alignment of the queried sequence to that of sequences in the database by using different parameters, and the search results may be ranked by similarity score by default. However, it is best to rank the


alignments by “maximum identity” instead of “similarity score.” One consistent difficulty in identifying microorganisms by their DNA is determining when an imperfect database match is “close enough” to allow the microbiologist to identify the queried sequence as belonging to the same taxon as the imperfect match in the database. Commonly, arbitrary percentages of sequence homology are employed as cutoff values (100, 116), but the amount of homology that should be required to deem a sequence a match should ideally be based on both the specific region of DNA being sequenced and also the taxon being considered the microorganism’s potential identity. For example, the 16S rRNA gene is commonly useful in delineating bacterial species, but the 16S rRNA genes of mycobacteria are notoriously similar between species. So a 99% match of a 16S rRNA gene to a staphylococcal species should be interpreted differently than a 99% match to a mycobacterial species (100). Additionally, it is recommended to consider the phenotypic and genotypic findings together (not independently) in attempting to identify a microorganism, as this may prevent misidentifications (100, 108). Identifying organisms by their DNA sequences has become a gold standard in microbial identification. However, interpretation of a sequence by comparing it to a reference database has caveats, and it is important to consider these challenges in attempting to interpret a DNA sequence. WGS and MGS. As the cost of nucleic acid sequencing declines and the analysis pipelines improve in speed and ease of use, more and more voices are heralding the advent of clinical microbial WGS and metagenomic sequencing (MGS) (101, 105, 106, 117– 122). WGS is typically performed by using high-throughput sequencing (HTS) to analyze nucleic acid fragments from an isolated pathogen; however, WGS can also be attempted on a pathogen that has not been isolated and cultivated. The sequenced nucleic acid fragments are aligned with each other and assembled into contiguous DNA sequences by use of genome assembly and alignment software. WGS is typically done in an attempt to characterize an infectious agent. In contrast, MGS is performed using HTS to analyze nucleic acid fragments directly from a patient sample or after enriching the sample for nucleic acids of interest. MGS is typically done in an attempt to characterize an infection or microbial community. The advent of WGS and MGS in the clinical microbiology laboratory will hinge on informatics capabilities and resources that will be greater than those of any other clinical microbiology informatics endeavor to date. Clinical WGS and MGS provide potential advantages for better characterizing pathogens and the microbiome, but they also present new informatics challenges. (i) Potential advantages of routine clinical WGS and MGS. HTS methods provide large amounts of high-quality information in a relatively short period. WGS and MGS can offer information about microorganisms, including antimicrobial resistance genes or pertinent mutations, virulence and toxin genes, organism identification, and epidemiological typing by single nucleotide polymorphism (SNP) or multilocus sequence typing (MLST) analysis (118, 121). WGS and MGS may provide clinically relevant information regarding exactly which unique genes, pathways, and organisms are present in an infection (123–127). Emerging evidence suggests that WGS improves the sensitivity and resolution of laboratory-based surveillance (106, 128). Specifically, WGS enhances

cmr.asm.org 1037


test. There is no single nucleic acid database upon which users rely, and it is common that large laboratories use a hybrid database comprising reference sequences created in-house and reference sequences derived from external databases. Possibly the biggest challenge in using DNA sequence data to identify microorganisms and their genomes is that the largest and most comprehensive nucleic acid databases (e.g., GenBank) are also the most poorly curated and error-prone databases because they are created by a community of users with no peer review process. It has been well established that errors are present in open public databases, and these can lead to incorrect microbial identification (107–109). Conversely, the most trustworthy databases that are more closely controlled (e.g., MicroSeq) contain fewer sequences and are therefore less exhaustive in their coverage, and potentially less frequently useful (100, 110, 111). Considerations in using public databases for microorganism identification. The current lack of freely available, robust, and reliable methods for identifying clinical bacteria by genotyping is recognized as a barrier (108). Tools are available that can facilitate and streamline the traditional means of analyzing and comparing public database sequences with microbial DNA sequences of interest, but these approaches require significant hands-on time and are not designed for high-throughput clinical work (112). Others are making efforts to create more user-friendly and clinically oriented tools that translate raw sequence data into an identified microorganism result (113–115), but these tools are not widely used or accepted. Multiple papers have proposed algorithms or guidelines suggesting how best to approach nucleic acid database queries and to identify an appropriate result (100, 107, 108, 111, 113, 116). These varied proposals highlight the lack of consensus regarding how best to use DNA sequence data together with reference databases to identify microorganisms and to further ensure clinical reliability of results. Tortoli cautions that even a perfect match of a queried nucleic acid sequence with a reference database sequence should not be assumed to correctly identify the microorganism. Rather, he suggests that users of public databases perform additional investigations before deciding that the sequence being queried is truly positively identified (111). It is appropriate to consider the following information in the reference database regarding the reference sequence being considered a potential match (111):

Rhoads et al.

1038

cmr.asm.org

FIG 4 Proposed informatics pipeline for microbial genomic analysis in the clinical microbiology laboratory. The clinical purposes of genomic sequencing can be varied, and multiple parallel pipelines and their associated expert systems can be used to analyze data depending on which endpoints are desired. One goal of the microbiology laboratory is to unite the information obtained from these various pipelines into a clinically relevant and concise report that can guide patient care. A second goal is to characterize a potential agent of an emerging outbreak so that future samples can be monitored for this agent of interest. The genomic characterization of this outbreak agent can be shared with public health agencies, which can also alert other laboratories to monitor their samples for this emerging agent. Solid arrows show the flow of analysis. Dotted arrows show the transfer of information about outbreak agents into expert system knowledge databases. Wet laboratory analysis is shown in red, and dry laboratory analysis is shown in blue. Information available to clinicians is shown in yellow. Asterisks mark data that may be appropriate for long-term storage.

sequencing run need to be stored, or only the aligned sequences? Or only the identified virulence genes and drug resistance markers? In using next-generation sequencing, many laboratories have opted for in-house solutions instead of cloud computing for storing data. At present, Health Insurance Portability and Accountability Act (HIPAA)-compliant cloud storage solutions to handle clinical work are limited. Currently, significant variability in the storing of sequence and secondary data files exists among laboratories. The most common approach to data storage is to rely on external hard drives, but this is not a sustainable plan going forward. A scalable capacity for warehousing, data compression, and systematic backup is needed. As noted, external cloud services are currently not considered a viable option because they typically do not comply with medical confidentiality regulations.



the identification and tracking of outbreaks in community and hospital settings through the recognition of covert clusters as well as reconstruction of transmission events. Recent proof-of-concept studies have demonstrated the superiority of WGS to current subtyping methods (119, 129, 130). Work is actively being done to create a Global Microbial Identifier (GMI) system that will enable users to submit a genome sequence as a query to compare to the GMI database (http://www.globalmicrobialidentifier.org/About -GMI). The GMI system would then report information about the strain, such as its identity, treatment options, and the global location(s) where the strain or similar strains have been identified, to the user. MGS also has the power to investigate the microbiome and complex infections (55, 56, 131, 132), and it can detect fastidious and difficult-to-culture organisms, such as that reported in a case of neuroleptospirosis (133). (ii) Informatics challenges. Bioinformatics approaches are varied across the global community. Currently, there is no platform offering complete data processing, database management, and data warehousing capabilities; therefore, institutions are currently required to establish their own data analysis pipelines or to link together a variety of commercial, open-source, and in-house software packages and data sets that contain information about microbial genomes of interest. Establishing an efficient informatics pipeline for data generation, analysis, and storage for WGS or MGS is absolutely necessary if clinical laboratories are going to accurately and cost-effectively interpret the data within a reasonable time frame. These pipelines should work to improve the quality of data and to minimize the amount of human time required for analysis by maximizing the use of software to eliminate technically poor data (e.g., chimeras and misreads) (134–136), meaningfully compare the sequences to an appropriate database or reference strain, and annotate and/or interpret the comparison (Fig. 4) (137–140). (a) Data analysis challenges. Analysis of WGS and MGS data requires multidisciplinary teams of microbiologists, informaticians, clinicians, and epidemiologists, with institutional support for resources and personnel. Microbiologists and epidemiologists typically need to be upskilled in genomic sequencing and its applications. The goals of using WGS and MGS are not always the same, but the goals should guide the analyses. In using WGS to identify a newly identified microorganism, aligning and closing the genome are useful steps to ensure full characterization and optimal primer design for future rapid nucleic acid tests. However, in using WGS or MGS to elucidate virulence genes within a strain, aligning the genome is not as important as annotating virulence genes’ functions. In using genomic sequencing for outbreak surveillance purposes, the most important step in analysis is the identification of discriminatory features (e.g., SNPs, mutations, or a unique gene) (141). The motivations for clinical WGS and MGS can be varied, and it is important to consider the goal(s) of the assay in determining how the data will be analyzed. If multiple analysis goals are pursued, then it may be most effective to use multiple parallel pipelines for the analyses, as reported for the genomic analysis of cancer (142). (b) Data storage and sharing challenges. Routine and frequent use of WGS and MGS has the potential to rapidly produce large volumes of digital information that requires storage. However, it is not yet clear which information needs to be stored and which can be discarded. For example, does the entire data set from a



REPORTING TO PUBLIC HEALTH AGENCIES AND DETECTING OUTBREAKS

Local clinical microbiology laboratories are responsible for reporting the identification of certain infectious agents to various public health agencies (city, county, and state). These reports are used by public agencies to track incidences and attempt to identify outbreaks. The reportable findings are somewhat dynamic, often with annual modifications, so informatics support requires ongoing vigilance to keep up with expectations of public health authorities. Additionally, the local clinical microbiology laboratory is involved in recognizing local institutional outbreaks and working to prevent them (158). Informatics tools and electronic communication are key in efficiently communicating with public health agencies and rapidly identifying outbreaks (Fig. 5). Once identified, there are needs for data and reports throughout the incident, often with evolving parameters of interest. Having informatics experts participate in incident management from the beginning, to assist those in the microbiology laboratory as well as those managing the outbreak outside the laboratory, is optimal. Reporting to Public Health Agencies

Laboratory reporting of reportable infectious agents to public health agencies or departments is not currently a seamless electronic process. A first step in streamlining the reporting process is to have all the stakeholders share a nomenclature. The frontrunner for this standard is a combination of using Laboratory Logical Observation Identifiers Names and Codes (LOINC) to identify the microbiology test that was ordered, using the Systematized Nomenclature of Medicine (SNOMED) to identify the results associated with the test that was ordered, and arranging the transmission syntax in accordance with health level 7 (HL7) standards (Fig. 3) (159–161). Recently, work began to formally link LOINC and SNOMED (162). The CDC has been working to facilitate the electronic transmission of information from the local microbiology laboratory to public health agencies through the use of the National Electronic Disease Surveillance System (NEDSS) (163, 164). The goal of NEDSS is to automate the reporting and analysis of public health surveillance data (163). As of 2012, 46 of 50 states in the United States use information systems that are NEDSS compatible for reporting notifiable microbial findings to the CDC (161). The goal of electronic reporting to public health agencies is to capture more reportable events, with increased timeliness and completeness, than those obtained with paper reporting, and this possibility has been demonstrated (7, 159). Outbreak Detection

Timely detection of infectious disease outbreaks is needed globally, regionally, and locally. The responsibility of identifying widespread outbreaks is largely shouldered by state public health agencies, the CDC, and the World Health Organization (164). Detecting, tracking, and modeling outbreaks are areas of continued interest in academia and public health (165, 166). Rapid detection is of key importance, because decreasing the time to detection can significantly decrease the adverse impact of an outbreak (167). Detection of evolving antimicrobial susceptibility patterns. Detection of the emergence or increasing prevalence of antimicrobial-resistant organisms is a concern for both local and global clinical microbiology. Software exists that can link the local laboratory with other regional and global laboratories in the bidirec-

cmr.asm.org 1039


The complications of sharing microbial organisms and their sequences recently made headline news as a disagreement between referring and reference laboratories occurred (143). Ownership of nucleic acid sequences and its relevance to clinical laboratories are areas with ongoing legal debate and ramifications (144). The intellectual property rights related to the sequencing of Middle East respiratory syndrome coronavirus (MERS-CoV) and the subsequent patenting of a diagnostic assay have reinforced the legal and ethical challenges of sharing microbial genetic material and data transfer (143). (c) The need for guidelines. The standardization of quality metrics, such as calibration standards, validation methods, acceptable data reliability, test robustness, result reproducibility, and data storage, are critically needed for microbial WGS. Additionally, proficiency testing programs that cover both wet (in vitro testing) and dry (in silico analysis) portions of genomic assays are urgently required. Appropriate data storage guidelines regarding the type of data that should be stored, the means of storage, and the duration of storage are necessary, as the cost of storage will soon exceed the cost of data generation. Another question that is relevant in this era, which is placing a growing emphasis on patients’ genetic rights, is the unresolved question of who owns the data and whose responsibility it is to procure it. Are there certain sequences that are reportable to public health agencies? Should an individual’s microbiomic data be protected as carefully as an individual’s human genomic data (145, 146)? DNA data in the future. Although current efforts to improve microbial identification are still focused on creating better ways to culture organisms (121), the use of culture-free identification by means of massively parallel nucleic acid sequencing has shown potential in clinical studies (55, 56, 131). The use of nucleic acid analysis directly from samples can overcome culture bias (55, 147, 148). In the future, genomic or metagenomic microbial analyses might replace or augment the current approach in clinical microbiology of culturing and identifying isolated microbes. Also, our perspective of what constitutes a species or a taxon might fundamentally change as DNA sequencing continues to become more commonplace and as the computational tools associated with these analyses continue to improve. Microbiology systematics might fundamentally shift to a taxonomic system based upon genotyping (149). The identification and classification of infection might also change dramatically. Computer algorithms can analyze and classify complex infections by using more variables than can currently be considered by a clinician or microbiologist. Analyses may unravel the variables that enable a pathogenic microbial ecosystem to take hold in a susceptible host. In the future, the microbiology laboratory might be expected to understand, evaluate, and recommend therapies based upon the analysis of pathobiomes (150), functionally equivalent pathogroups (126), supragenomes (124, 151), epigenomes (152, 153), or impaired or pathogenic microbiomes (154–157). The use of WGS and MGS may provide more complete quantitative and qualitative identification of all of the microbes and relevant resistance and virulence genes that are present in a sample, and these types of analyses have the potential to better direct patient care (55, 56, 101, 122, 124). Therapies such as probiotics or fecal microbiota transplantation may be indicated depending on the results of the laboratory evaluation of a patient’s microbiome (154, 155).

Rhoads et al.

registries. This generic figure depicts the flow of information associated with a patient diagnosed with gastroenteritis who is tested for Salmonella. Although human input or interpretation may be required at various nodes, information is often generated, transmitted, and received digitally (solid arrows). Various terminology and messaging standards, such as SNOMED, LOINC, and HL7, are employed to ensure the standardization, efficiency, and security of the process. However, many clinical laboratories still use paper to transmit information. The more often information is exchanged between two nodes, the more incentive exists to create a robust electronic interface between these nodes. Therefore, transmitting information by paper (via fax or post) is most likely to be used between nodes that rarely communicate with each other. Electronic reporting can expedite information communication and therefore expedite the detection of outbreak clusters. Information between the patient and clinician is often exchanged verbally (dotted arrows), although electronic communication with patients is likely to become more common. (Courtesy of The Royal College of Pathologists of Australasia, adapted with permission.)

tional exchange of antimicrobial susceptibility data. Two of these programs which have been used since the 1990s are WHONET and The Surveillance Network (TSN) (30). Until recently, both WHONET and TSN were used to analyze antimicrobial susceptibility data from multiple laboratories to determine patterns of microbial susceptibility, and these networks continue to be used to share information (164, 166, 168). However, as of 2014, the TSN is not supported and is no longer available. WHONET. The WHONET system (http://www.who.int /drugresistance/whonetsoftware/en/) was developed a quarter century ago by the WHO Collaborating Centre for Surveillance of Antimicrobial Resistance. At the center of the system is the WHONET software, which is freely downloadable (http://www.who net.org/dnn/Software/tabid/68/language/en-US/Default.aspx). The WHONET system works by translating laboratory data that are currently available in an LIS to a WHONET universal file format (169). A software utility, BacLink, can be used to facilitate this translation. Because the data are translated into a universal file format, the local data can be shared and compiled so that regional,

1040

cmr.asm.org

national, or international groups can analyze the data (164). The system is designed to enable individual laboratories (or groups of laboratories) to manage their AST results, identify the emergence of resistant microbes, identify the spread of resistant strains, and identify trends in AST quality control testing (170, 171). Analyses which WHONET can facilitate include examples such as the identification of changes in Escherichia coli isolate resistance within a country over time (172, 173), prospective surveillance of Shigella outbreaks in a country (166, 174), and, potentially, a means of global strain tracking (175). Detection of regional and global outbreaks. Although local laboratories are not usually considered directly responsible for identifying regional and global outbreaks, local laboratories are essential in providing information to public health agencies so that identification of these outbreaks is possible. The role that the local laboratory plays in the detection of global outbreaks is typically confined to its responsibility to report the detection of notifiable infectious agents to the public health agencies which oversee the laboratory’s region (176). However, the detection of some



FIG 5 Agents of notifiable infectious diseases and their associated data often travel through layers of agencies, including clinics, laboratories, and public health



syndromic surveillance would alert the local laboratory to increase its vigilance or adjust its testing protocols so as to increase the laboratory’s sensitivity and rapidity of identifying an outbreak (167). Although this type of integrated system is feasible and would improve patient care, it has not yet been employed. Integrative Public Health Informatics Approaches of the Future

Increasingly, standardized electronic reporting, such as NEDSS, is being used by local laboratories to report to their regional health agencies, and standardized electronic reporting correlates with improved completeness and rapidity of reporting, which allow for quicker detection of regional outbreaks. Additionally, data analysis surveillance software has demonstrated improved detection of local disease outbreaks beyond that which humans have been able to identify without these tools. With the growing clinical use of WGS and MGS, the need for globally integrated informatics tools that can identify and characterize whole genomes, such as GMI, are needed for outbreak detection (187). Integration of prospective, laboratory, and syndromic surveillance systems in combination with rapid standardized reporting of events has the potential to improve outbreak detection beyond what any one of these systems can do independently. CONCLUSIONS

The clinical microbiology laboratory is required to generate, analyze, and interpret an ever-increasing amount of information. Incorporating the use of informatics tools to improve the quality of laboratory workflow and processes is paramount for data to effectively and efficiently be evaluated and communicated. Informatics tools can help microbiologists to more capably keep track of specimen work-ups in the laboratory, automate their workloads, identify clinically relevant characteristics of microorganisms, remotely share digital images for teleconsultation, quickly distribute accurate and appropriate results, perform more thorough and rapid disease surveillance, and (most importantly) provide patients and the public with better health care. The continued development and implementation of informatics tools are needed in order to continue to help the laboratory to produce, interpret, and communicate the most useful information. Guidance is required in order to best develop and implement these informatics tools, specifically in areas of telemicrobiology and microbial MGS and WGS. When used properly, informatics tools can help the clinical microbiology laboratory to do more with less while improving the quality of patient and public health care. ACKNOWLEDGMENTS V.S. was supported by the National Health and Medical Research Council’s Career Development Award. None of the authors have any financial interests or support from institutions or companies mentioned in the article.

REFERENCES 1. Campos JM. 2003. Clinical microbiology informatics—its time has come. Rev. Med. Microbiol. 14:105–108. http://dx.doi.org/10.1097 /00013542-200310000-00001. 2. El-Kareh R, Roy C, Williams DH, Poon EG. 2012. Impact of automated alerts on follow-up of post-discharge microbiology results: a cluster randomized controlled trial. J. Gen. Intern. Med. 27:1243–1250. http://dx .doi.org/10.1007/s11606-012-1986-8. 3. Scheid P, Lam DM, Thommes A, Zoller L. 2007. Telemicrobiology: a novel telemedicine capability for mission support in the field of infec-

cmr.asm.org 1041


local outbreaks carries global public health significance, and all identification of global outbreaks begins locally. One way to facilitate timely identification of regional outbreaks is to provide the regional public health agencies with clinical and microbiological information from the local laboratory in as timely a way as possible. The rapidity of regional outbreak identification can be enhanced by using electronic reporting by local laboratories (7, 159). Detection of local outbreaks. Local clinical microbiology laboratories actively play a role in the detection of local disease outbreaks. Local outbreak identification is best achieved by active surveillance by infection preventionists in partnership with the clinical microbiology laboratory and in combination with “virtual surveillance,” in which mathematical algorithms are used to identify possible outbreaks independently of hypothesis-based or query-based investigations (29). These local outbreaks could be community-acquired infections (e.g., annual seasonal influenza outbreaks) or hospital-associated infections (e.g., nosocomial infections). Software has been used successfully to mine the data of clinical microbiology laboratories in search of aberrancies that may be an indication of a community outbreak (177). Software has also been used to identify nosocomial infections by autonomously reviewing laboratory data and medical records, and this software has demonstrated increased sensitivity over the manual analysis of records for the identification of nosocomial infections (17, 51, 178). Surveillance. Outbreak detection can be accomplished by at least these three ways: prospective organism surveillance, laboratory surveillance (including virtual surveillance), and syndromic surveillance. Prospective surveillance using environmental biosensors is the most rapid means of detection (167), but the local microbiology laboratory does not typically play a role in this type of screening. The local laboratory is integral to the traditional surveillance systems that are in place, which typically identify outbreaks after they have occurred. A goal of laboratory outbreak surveillance is to transform this surveillance from a retrospective means of surveillance into a real-time means of proactive surveillance by using informatics tools to continually analyze laboratory data, with the goal of identifying outbreaks as they are occurring (179). Syndromic surveillance is increasingly being used to attempt early identification of outbreaks or bioterrorism (180, 181). One of the most highly publicized systems using syndromic surveillance is Google Flu Trends (182, 183). Unlike prospective organism surveillance and laboratory surveillance, syndromic surveillance does not rely on definitive clinical diagnoses or the identification of microbes. Instead, syndromic surveillance infers the presence of an outbreak by analyzing patient signs and symptoms or analyzing public behavior (184, 185). Syndromic surveillance is performed completely in silico and has the potential to identify an outbreak before the laboratory can identify a causative agent. This surveillance does not require the implementation of new physical tests but relies on the analysis of already available information. Obtaining information from Internet users is often a part of syndromic surveillance (185). For example, Yelp.com has been used successfully by public health officials to identify food-borne illness outbreaks in New York City (186). Syndromic surveillance has a lower specificity of detection than laboratory surveillance, so it has been suggested that syndromic surveillance can be used as a screening tool for detecting an outbreak, which can then be confirmed using laboratory surveillance. In this situation, detection of a potential outbreak by

Rhoads et al.

4.

5.

7.

8.

9. 10. 11. 12.

13. 14. 15. 16.

17. 18. 19. 20. 21.

22. 23. 24.

25.

1042

cmr.asm.org

26. 27.

28.

29.

30. 31. 32.

33.

34. 35.

36. 37.

38. 39.

40.

41.

42. 43.

44. 45.

Does electronic clinical microbiology results reporting influence medical decision making: a pre- and post-interview study of medical specialists. BMC Med. Inform. Decis. Mak. 11:19. http://dx.doi.org/10.1186/1472 -6947-11-19. Leuthner KD, Doern GV. 2013. Antimicrobial stewardship programs. J. Clin. Microbiol. 51:3916 –3920. http://dx.doi.org/10.1128 /JCM.01751-13. Pogue JM, Mynatt RP, Marchaim D, Zhao JJ, Barr VO, Moshos J, Sunkara B, Chopra T, Chidurala S, Kaye KS. 2014. Automated alerts coupled with antimicrobial stewardship intervention lead to decreases in length of stay in patients with gram-negative bacteremia. Infect. Control Hosp. Epidemiol. 35:132–138. http://dx.doi.org/10.1086/674849. Hindler JF, Stelling J. 2007. Analysis and presentation of cumulative antibiograms: a new consensus guideline from the Clinical and Laboratory Standards Institute. Clin. Infect. Dis. 44:867– 873. http://dx.doi.org /10.1086/511864. Peterson LR, Brossette SE. 2002. Hunting health care-associated infections from the clinical microbiology laboratory: passive, active, and virtual surveillance. J. Clin. Microbiol. 40:1– 4. http://dx.doi.org/10.1128 /JCM.40.1.1-4.2002. Trevino S. 2000. Antibiotic resistance monitoring: a laboratory perspective. Mil. Med. 165:40 – 42. CLSI. 2014. M29-A4. Analysis and presentation of cumulative antimicrobial susceptibility test data; approved guideline, 4th ed. Clinical and Laboratory Standards Institute, Wayne, PA. Shortliffe EH, Davis R, Axline SG, Buchanan BG, Green CC, Cohen SN. 1975. Computer-based consultations in clinical therapeutics: explanation and rule acquisition capabilities of the MYCIN system. Comput. Biomed. Res. 8:303–320. http://dx.doi.org/10.1016/0010-4809(75)90009-9. Wilson G, Badarudeen S, Godwin A. 2010. Real-time validation and presentation of the cumulative antibiogram and implications of presenting a standard format using a novel in-house software: ABSOFT. Am. J. Infect. Control 38:e25– e30. http://dx.doi.org/10.1016/j.ajic.2010.02.015. Evans RS, Classen DC, Pestotnik SL, Lundsgaarde HP, Burke JP. 1994. Improving empiric antibiotic selection using computer decision support. Arch. Intern. Med. 154:878 – 884. Trick WE, Zagorski BM, Tokars JI, Vernon MO, Welbel SF, Wisniewski MF, Richards C, Weinstein RA. 2004. Computer algorithms to detect bloodstream infections. Emerg. Infect. Dis. 10:1612–1620. http: //dx.doi.org/10.3201/eid1009.030978. Polis MA. 2013. ACP Journal Club: a clinical decision-support system with interactive alerts improved CD4 cell count in HIV. Ann. Intern. Med. 158: JC11. http://dx.doi.org/10.7326/0003-4819-158-8-201304160-02011. Schurink CA, Lucas PJ, Hoepelman IM, Bonten MJ. 2005. Computerassisted decision support for the diagnosis and treatment of infectious diseases in intensive care units. Lancet Infect. Dis. 5:305–312. http://dx .doi.org/10.1016/S1473-3099(05)70115-8. Winstanley T, Courvalin P. 2011. Expert systems in clinical microbiology. Clin. Microbiol. Rev. 24:515–556. http://dx.doi.org/10.1128/CMR .00061-10. El-Kareh R, Roy C, Brodsky G, Perencevich M, Poon EG. 2011. Incidence and predictors of microbiology results returning postdischarge and requiring follow-up. J. Hosp. Med. 6:291–296. http://dx.doi .org/10.1002/jhm.895. Wilson JW, Marshall WF, Estes LL. 2011. Detecting delayed microbiology results after hospital discharge: improving patient safety through an automated medical informatics tool. Mayo Clin. Proc. 86:1181–1185. http://dx.doi.org/10.4065/mcp.2011.0415. Leis JA, Rebick GW, Daneman N, Gold WL, Poutanen SM, Lo P, Larocque M, Shojania KG, McGeer A. 2014. Reducing antimicrobial therapy for asymptomatic bacteriuria among noncatheterized inpatients: a proof-of-concept study. Clin. Infect. Dis. 58:980 –983. http://dx .doi.org/10.1093/cid/ciu010. College of American Pathologists. 2012. Microbiology checklist. College of American Pathologists, Northfield, IL. Ebbert MT, Mallory MA, Wilson AR, Dooley SK, Hillyard DR. 2013. Application of a new informatics tool for contamination screening in the HIV sequencing laboratory. J. Clin. Virol. 57:249 –253. http://dx.doi.org /10.1016/j.jcv.2013.03.013. Paterson DL, Bonomo RA. 2005. Extended-spectrum beta-lactamases: a clinical update. Clin. Microbiol. Rev. 18:657– 686. http://dx.doi.org/10 .1128/CMR.18.4.657-686.2005. Sanders CC, Peyret M, Moland ES, Shubert C, Thomson KS, Boeuf-



6.

tious medicine. Telemed. J. E Health 13:108 –117. http://dx.doi.org/10 .1089/tmj.2007.0043. Shaw R, Coia JE, Michie J. 1999. Use of bar code readers and programmable keypads to improve the speed and accuracy of manual data entry in the clinical microbiology laboratory: experience of two laboratories. J. Clin. Pathol. 52:54 – 60. http://dx.doi.org/10.1136/jcp.52.1.54. Yu VL, Fagan LM, Wraith SM, Clancey WJ, Scott AC, Hannigan J, Blum RL, Buchanan BG, Cohen SN. 1979. Antimicrobial selection by a computer. A blinded evaluation by infectious diseases experts. JAMA 242:1279 –1282. Lau AF, Drake SK, Calhoun LB, Henderson CM, Zelazny AM. 2013. Development of a clinically comprehensive database and a simple procedure for identification of molds from solid media by matrix-assisted laser desorption ionization–time of flight mass spectrometry. J. Clin. Microbiol. 51:828 – 834. http://dx.doi.org/10.1128/JCM.02852-12. Effler P, Ching-Lee M, Bogard A, Ieong MC, Nekomoto T, Jernigan D. 1999. Statewide system of electronic notifiable disease reporting from clinical laboratories: comparing automated reporting with conventional methods. JAMA 282:1845–1850. http://dx.doi.org/10.1001/jama.282.19 .1845. Thomson RB, Jr, Wilson ML, Weinstein MP. 2010. The clinical microbiology laboratory director in the United States hospital setting. J. Clin. Microbiol. 48:3465–3469. http://dx.doi.org/10.1128/JCM .01575-10. Bergqvist F, Bengtsson S. 1975. The BACTLAB system—a data system for bacteriological routine. Comput. Programs Biomed. 4:144 –157. http://dx.doi.org/10.1016/0010-468X(75)90016-1. Forsum U, Kronvall G. 2007. Clinical microbiology informatics— developments in Sweden. 1939. APMIS 115:415– 421. http://dx.doi.org /10.1111/j.1600-0463.2007.apm_682.x. Pantanowitz L, Tuthill JM, Balis UGJ. 2012. Pathology informatics: theory and practice, p 162–164. ASCP, Chicago, IL. Snyder SR, Favoretto AM, Derzon JH, Christenson RH, Kahn SE, Shaw CS, Baetz RA, Mass D, Fantz CR, Raab SS, Tanasijevic MJ, Liebow EB. 2012. Effectiveness of barcoding for reducing patient specimen and laboratory testing identification errors: a laboratory medicine best practices systematic review and meta-analysis. Clin. Biochem. 45: 988. http://dx.doi.org/10.1016/j.clinbiochem.2012.06.019. Pantanowitz L, Tuthill JM, Balis UGJ. 2012. Pathology informatics: theory and practice, p 286 –296. ASCP, Chicago, IL. Paxton A. 2012. Finally, microbiology labs getting a lift from IT. CAP Today 26:32– 40. Ackerman VP, Pritchard RC, Groot Obbink DJ, Bradbury R, Lee A. 1980. Reporting practices of microbiology laboratories. J. Clin. Pathol. 33:830 – 835. http://dx.doi.org/10.1136/jcp.33.9.830. Willard KE, Connelly DP, Johnson JR. 1996. Radical improvements in the display of clinical microbiology results: a Web-based clinical information system. Am. J. Med. 101:541–549. http://dx.doi.org/10.1016 /S0002-9343(96)00253-7. Brossette SE, Hymel PA, Jr. 2008. Data mining and infection control. Clin. Lab. Med. 28:119 –126. http://dx.doi.org/10.1016/j.cll.2007.10.007. Aller RD, Friedman W. 1996. Rapid accurate entry of microbiology results. Arch. Pathol. Lab. Med. 120:57– 61. Willard KE, Shanholtzer CJ. 1995. User interface reengineering. Innovative applications of bar coding in a clinical microbiology laboratory. Arch. Pathol. Lab. Med. 119:706 –712. College of American Pathologists. 2014. Laboratory general checklist. College of American Pathologists, Northfield, IL. Evans RS, Pestotnik SL, Classen DC, Clemmer TP, Weaver LK, Orme JF, Jr, Lloyd JF, Burke JP. 1998. A computer-assisted management program for antibiotics and other antiinfective agents. N. Engl. J. Med. 338:232–238. http://dx.doi.org/10.1056/NEJM199801223380406. Schreckenberger PC, Binnicker MJ. 2011. Optimizing antimicrobial susceptibility test reporting. J. Clin. Microbiol. 49:S15–S19. http://dx.doi .org/10.1128/JCM.00712-11. Evans RS, Pestotnik SL, Classen DC, Burke JP. 1993. Development of an automated antibiotic consultant. M.D. Comput. 10:17–22. Perez KK, Olsen RJ, Musick WL, Cernoch PL, Davis JR, Land GA, Peterson LE, Musser JM. 2013. Integrating rapid pathogen identification and antimicrobial stewardship significantly decreases hospital costs. Arch. Pathol. Lab. Med. 137:1247–1254. http://dx.doi.org/10.5858/arpa .2012-0651-OA. Bruins MJ, Ruijs GJ, Wolfhagen MJ, Bloembergen P, Aarts JE. 2011.


46. 47.

49.

50.

51.

52. 53. 54.

55.

56.

57. 58. 59. 60. 61. 62.

63. 64. 65.

66.


67.

68.

69. 70.

71.

72. 73.

74. 75. 76. 77.

78.

79. 80. 81.

82.

83.

84. 85.

Automation in the clinical microbiology laboratory. Clin. Chem. 59: 1696 –1702. http://dx.doi.org/10.1373/clinchem.2012.201038. Pantanowitz L, Sinard JH, Henricks WH, Fatheree LA, Carter AB, Contis L, Beckwith BA, Evans AJ, Otis CN, Lal A, Parwani AV. 2013. Validating whole slide imaging for diagnostic purposes in pathology: guideline from the College of American Pathologists Pathology and Laboratory Quality Center. Arch. Pathol. Lab. Med. 137:1710 –1722. http: //dx.doi.org/10.5858/arpa.2013-0093-CP. Pantanowitz L, Wiley CA, Demetris A, Lesniak A, Ahmed I, Cable W, Contis L, Parwani AV. 2012. Experience with multimodality telepathology at the University of Pittsburgh Medical Center. J. Pathol. Inform. 3:45. http://dx.doi.org/10.4103/2153-3539.104907. Lee LH, Mansoor A, Wood B, Nelson H, Higa D, Naugler C. 2013. Performance of CellaVision DM96 in leukocyte classification. J. Pathol. Inform. 4:14. http://dx.doi.org/10.4103/2153-3539.114205. Levi AW, Chhieng DC, Schofield K, Kowalski D, Harigopal M. 2012. Implementation of FocalPoint GS location-guided imaging system: experience in a clinical setting. Cancer Cytopathol. 120:126 –133. http://dx .doi.org/10.1002/cncy.20187. Suhanic W, Crandall I, Pennefather P. 2009. An informatics model for guiding assembly of telemicrobiology workstations for malaria collaborative diagnostics using commodity products and open-source software. Malar. J. 8:164. http://dx.doi.org/10.1186/1475-2875-8-164. McLaughlin WJ, Schifman RB, Ryan KJ, Manriquez GM, Bhattacharyya AK, Dunn BE, Weinstein RS. 1998. Telemicrobiology: feasibility study. Telemed. J. 4:11–17. http://dx.doi.org/10.1089/tmj.1.1998.4.11. Campbell WS, Lele SM, West WW, Lazenby AJ, Smith LM, Hinrichs SH. 2012. Concordance between whole-slide imaging and light microscopy for routine surgical pathology. Hum. Pathol. 43:1739 –1744. http: //dx.doi.org/10.1016/j.humpath.2011.12.023. Sadaphal P, Rao J, Comstock GW, Beg MF. 2008. Image processing techniques for identifying Mycobacterium tuberculosis in Ziehl-Neelsen stains. Int. J. Tuberc. Lung Dis. 12:579 –582. Pandolfi D, Pons MN. 2004. Gram-staining characterisation of activated sludge filamentous bacteria by automated colour analysis. Biotechnol. Lett. 26:1841–1846. http://dx.doi.org/10.1007/s10529-004-5280-7. Ingels N, Daughters G. 1968. New design for an automated bacterial colony counter. Rev. Sci. Instrum. 39:115–120. http://dx.doi.org/10 .1063/1.1683083. Marotz J, Lubbert C, Eisenbeiss W. 2001. Effective object recognition for automated counting of colonies in Petri dishes (automated colony counting). Comput. Methods Programs Biomed. 66:183–198. http://dx .doi.org/10.1016/S0169-2607(00)00128-0. Brugger SD, Baumberger C, Jost M, Jenni W, Brugger U, Muhlemann K. 2012. Automated counting of bacterial colony forming units on agar plates. PLoS One 7:e33695. http://dx.doi.org/10.1371/journal.pone .0033695. Geissmann Q. 2013. OpenCFU, a new free and open-source software to count cell colonies and other circular objects. PLoS One 8:e54072. http: //dx.doi.org/10.1371/journal.pone.0054072. Wilson BK, Vigil GD. 2013. Automated bacterial identification by angle resolved dark-field imaging. Biomed. Optics Express 4:1692–1701. http: //dx.doi.org/10.1364/BOE.4.001692. Lewis JJ, Chihota VN, van der Meulen M, Fourie PB, Fielding KL, Grant AD, Dorman SE, Churchyard GJ. 2012. “Proof-of-concept” evaluation of an automated sputum smear microscopy system for tuberculosis diagnosis. PLoS One 7:e50173. http://dx.doi.org/10.1371/journal.pone.0050173. Patel B, Douglas TS. 2012. Creating a virtual slide map from sputum smear images for region-of-interest localisation in automated microscopy. Comput. Methods Programs Biomed. 108:38 –52. http://dx.doi .org/10.1016/j.cmpb.2011.12.017. Khutlang R, Krishnan S, Dendere R, Whitelaw A, Veropoulos K, Learmonth G, Douglas TS. 2010. Classification of Mycobacterium tuberculosis in images of ZN-stained sputum smears. IEEE Trans. Inf. Technol. Biomed. 14:949 –957. http://dx.doi.org/10.1109/TITB.2009 .2028339. Di Ruberto C, Dempster A, Khan S, Jarra B. 2002. Analysis of infected blood cell images using morphological operators. Image Vision Comput. 20:133–146. http://dx.doi.org/10.1016/S0262-8856(01)00092-0. Kaewkamnerd S, Uthaipibull C, Intarapanich A, Pannarut M, Chaotheing S, Tongsima S. 2012. An automatic device for detection and classification of malaria parasite species in thick blood film. BMC Bioinform. 13(Suppl 17):S18. http://dx.doi.org/10.1186/1471-2105-13-S17-S18.

cmr.asm.org 1043


48.

gras JM, Sanders WE, Jr. 2000. Ability of the VITEK 2 advanced expert system to identify beta-lactam phenotypes in isolates of Enterobacteriaceae and Pseudomonas aeruginosa. J. Clin. Microbiol. 38:570 –574. Becton, Dickinson, and Company. 2008. LIS vendor interface document. Becton, Dickinson, and Company, Sparks, MD. Astion ML, Shojania KG, Hamill TR, Kim S, Ng VL. 2003. Classifying laboratory incident reports to identify problems that jeopardize patient safety. Am. J. Clin. Pathol. 120:18 –26. http://dx.doi.org/10.1309 /8U5D0MA6MFH2FG19. Morris AJ, Haremza E, Walker DA. 2011. The frequency and potential clinical impact of non-analytical errors in the RCPA Microbiology QAP 1987–2008. Pathology 43:346 –349. http://dx.doi.org/10.1097/PAT .0b013e32834634f4. Blank GE, Virji MA. 2011. Development and implementation of an electronic interface for complex clinical laboratory instruments without a vendor-provided data transfer interface. J. Pathol. Inform. 2:14. http: //dx.doi.org/10.4103/2153-3539.77176. French D, Terrazas E. 2013. The successful implementation of a licensed data management interface between a Sunquest(R) laboratory information system and an AB SCIEX mass spectrometer. J. Pathol. Inform. 4:1. http://dx.doi.org/10.4103/2153-3539.106682. Huang SS, Yokoe DS, Stelling J, Placzek H, Kulldorff M, Kleinman K, O’Brien TF, Calderwood MS, Vostok J, Dunn J, Platt R. 2010. Automated detection of infectious disease outbreaks in hospitals: a retrospective cohort study. PLoS Med. 7:e1000238. http://dx.doi.org/10.1371 /journal.pmed.1000238. bioMérieux. 2006. Bidirectional computer interface specification for VITEK systems. bioMérieux, Inc., Durham, NC. Siemens. 2010. Siemens LabPro interface implementation guide 90207424, rev A. Siemens, West Sacramento, CA. Espy MJ, Uhl JR, Sloan LM, Buckwalter SP, Jones MF, Vetter EA, Yao JD, Wengenack NL, Rosenblatt JE, Cockerill FR, 3rd, Smith TF. 2006. Real-time PCR in clinical microbiology: applications for routine laboratory testing. Clin. Microbiol. Rev. 19:165–256. http://dx.doi.org/10.1128 /CMR.19.1.165-256.2006. Rhoads DD, Cox SB, Rees EJ, Sun Y, Wolcott RD. 2012. Clinical identification of bacteria in human chronic wound infections: culturing vs. 16S ribosomal DNA sequencing. BMC Infect. Dis. 12:321. http://dx .doi.org/10.1186/1471-2334-12-321. Salipante SJ, Sengupta DJ, Rosenthal C, Costa G, Spangler J, Sims EH, Jacobs MA, Miller SI, Hoogestraat DR, Cookson BT, McCoy C, Matsen FA, Shendure J, Lee CC, Harkins TT, Hoffman NG. 2013. Rapid 16S rRNA next-generation sequencing of polymicrobial clinical samples for diagnosis of complex bacterial infections. PLoS One 8:e65226. http://dx.doi.org/10.1371/journal.pone.0065226. Gen-Probe. 2011. Tigris DTS system LIS1-A & LIS2-A2 interface guide for LIS programmers, 902621, rev C. Gen-Probe, San Diego, CA. Williams RE, Trotman RE. 1969. Automation in diagnostic bacteriology. J. Clin. Pathol. Suppl. Coll. Pathol. 3:8 –13. Bourbeau PP, Ledeboer NA. 2013. Automation in clinical microbiology. J. Clin. Microbiol. 51:1658 –1665. http://dx.doi.org/10.1128/JCM .00301-13. Novak SM, Marlowe EM. 2013. Automation in the clinical microbiology laboratory. Clin. Lab. Med. 33:567–588. http://dx.doi.org/10.1016/j.cll .2013.03.002. Greub G, Prod’hom G. 2011. Automation in clinical bacteriology: what system to choose? Clin. Microbiol. Infect. 17:655– 660. http://dx.doi.org /10.1111/j.1469-0691.2011.03513.x. Mutters NT, Hodiamont CJ, de Jong MD, Overmeijer HP, van den Boogaard M, Visser CE. 2014. Performance of Kiestra total laboratory automation combined with MS in clinical microbiology practice. Ann. Lab. Med. 34:111–117. http://dx.doi.org/10.3343/alm.2014.34.2.111. Paxton A. 2014. Labs weighing pros, cons of micro TLA. CAP Today 28:1, 26 –29. Matthews S, Deutekom J. 2011. The future of diagnostic bacteriology. Clin. Microbiol. Infect. 17:651– 654. http://dx.doi.org/10.1111/j.1469 -0691.2011.03512.x. Froment P, Marchandin H, Vande Perre P, Lamy B. 2014. Automated versus manual sample inoculations in routine clinical microbiology: a performance evaluation of the fully automated InoqulA instrument. J. Clin. Microbiol. 52:796 – 802. http://dx.doi.org/10.1128/JCM.02341-13. Burnham CA, Dunne WM, Jr, Greub G, Novak SM, Patel R. 2013.

Rhoads et al.

1044

cmr.asm.org

105.

106.

107.

108.

109.

110.

111. 112. 113.

114.

115.

116.

117. 118.

119.

120.

121.

clinical samples. J. Clin. Microbiol. 52:139 –146. http://dx.doi.org/10 .1128/JCM.02452-13. Reuter S, Ellington MJ, Cartwright EJ, Koser CU, Torok ME, Gouliouris T, Harris SR, Brown NM, Holden MT, Quail M, Parkhill J, Smith GP, Bentley SD, Peacock SJ. 2013. Rapid bacterial whole-genome sequencing to enhance diagnostic and public health microbiology. JAMA Intern. Med. 173:1397–1404. http://dx.doi.org/10.1001/jamainternmed.2013.7734. Dunne WM, Jr, Westblade LF, Ford B. 2012. Next-generation and whole-genome sequencing in the diagnostic clinical microbiology laboratory. Eur. J. Clin. Microbiol. Infect. Dis. 31:1719 –1726. http://dx.doi .org/10.1007/s10096-012-1641-7. Janda JM, Abbott SL. 2007. 16S rRNA gene sequencing for bacterial identification in the diagnostic laboratory: pluses, perils, and pitfalls. J. Clin. Microbiol. 45:2761–2764. http://dx.doi.org/10.1128/JCM .01228-07. Boudewijns M, Bakkers JM, Sturm PD, Melchers WJ. 2006. 16S rRNA gene sequencing and the routine clinical microbiology laboratory: a perfect marriage? J. Clin. Microbiol. 44:3469 –3470. http://dx.doi.org/10 .1128/JCM.01017-06. Conville PS, Murray PR, Zelazny AM. 2010. Evaluation of the integrated database network system (IDNS) SmartGene software for analysis of 16S rRNA gene sequences for identification of Nocardia species. J. Clin. Microbiol. 48:2995–2998. http://dx.doi.org/10.1128/JCM.00681-10. Clarridge JE, 3rd. 2004. Impact of 16S rRNA gene sequence analysis for identification of bacteria on clinical microbiology and infectious diseases. Clin. Microbiol. Rev. 17:840 – 862. http://dx.doi.org/10.1128 /CMR.17.4.840-862.2004. Tortoli E. 2010. Standard operating procedure for optimal identification of mycobacteria using 16S rRNA gene sequences. Stand. Genomic Sci. 3:145–152. http://dx.doi.org/10.4056/sigs.932152. Hall BG. 2013. Building phylogenetic trees from molecular data with MEGA. Mol. Biol. Evol. 30:1229 –1235. http://dx.doi.org/10.1093 /molbev/mst012. Woo PC, Teng JL, Yeung JM, Tse H, Lau SK, Yuen KY. 2011. Automated identification of medically important bacteria by 16S rRNA gene sequencing using a novel comprehensive database, 16SpathDB. J. Clin. Microbiol. 49: 1799 –1809. http://dx.doi.org/10.1128/JCM.02350-10. Mohamed AM, Kuyper DJ, Iwen PC, Ali HH, Bastola DR, Hinrichs SH. 2005. Computational approach involving use of the internal transcribed spacer 1 region for identification of Mycobacterium species. J. Clin. Microbiol. 43:3811–3817. http://dx.doi.org/10.1128/JCM.43.8 .3811-3817.2005. Hartman AL, Riddle S, McPhillips T, Ludascher B, Eisen JA. 2010. Introducing W.A.T.E.R.S.: a workflow for the alignment, taxonomy, and ecology of ribosomal sequences. BMC Bioinform. 11:317. http://dx.doi .org/10.1186/1471-2105-11-317. Woo PC, Lau SK, Teng JL, Tse H, Yuen KY. 2008. Then and now: use of 16S rDNA gene sequencing for bacterial identification and discovery of novel bacteria in clinical microbiology laboratories. Clin. Microbiol. Infect. 14:908 –934. http://dx.doi.org/10.1111/j.1469-0691.2008.02070.x. Didelot X, Bowden R, Wilson DJ, Peto TE, Crook DW. 2012. Transforming clinical microbiology with bacterial genome sequencing. Nat. Rev. Genet. 13:601– 612. http://dx.doi.org/10.1038/nrg3226. Koser CU, Ellington MJ, Cartwright EJ, Gillespie SH, Brown NM, Farrington M, Holden MT, Dougan G, Bentley SD, Parkhill J, Peacock SJ. 2012. Routine use of microbial whole genome sequencing in diagnostic and public health microbiology. PLoS Pathog. 8:e1002824. http://dx .doi.org/10.1371/journal.ppat.1002824. Koser CU, Holden MT, Ellington MJ, Cartwright EJ, Brown NM, Ogilvy-Stuart AL, Hsu LY, Chewapreecha C, Croucher NJ, Harris SR, Sanders M, Enright MC, Dougan G, Bentley SD, Parkhill J, Fraser LJ, Betley JR, Schulz-Trieglaff OB, Smith GP, Peacock SJ. 2012. Rapid whole-genome sequencing for investigation of a neonatal MRSA outbreak. N. Engl. J. Med. 366:2267–2275. http://dx.doi.org/10.1056 /NEJMoa1109910. Loman NJ, Constantinidou C, Christner M, Rohde H, Chan JZ, Quick J, Weir JC, Quince C, Smith GP, Betley JR, Aepfelbacher M, Pallen MJ. 2013. A culture-independent sequence-based metagenomics approach to the investigation of an outbreak of Shiga-toxigenic Escherichia coli O104:H4. JAMA 309:1502–1510. http://dx.doi.org/10.1001/jama.2013 .3231. Fournier PE, Drancourt M, Colson P, Rolain JM, Scola BL, Raoult D.



86. Le MT, Bretschneider TR, Kuss C, Preiser PR. 2008. A novel semiautomatic image processing approach to determine Plasmodium falciparum parasitemia in Giemsa-stained thin blood smears. BMC Cell Biol. 9:15. http://dx.doi.org/10.1186/1471-2121-9-15. 87. Murray CK, Mody RM, Dooley DP, Hospenthal DR, Horvath LL, Moran KA, Muntz RW. 2006. The remote diagnosis of malaria using telemedicine or e-mailed images. Mil. Med. 171:1167–1171. 88. Prasad K, Winter J, Bhat UM, Acharya RV, Prabhu GK. 2012. Image analysis approach for development of a decision support system for detection of malaria parasites in thin blood smear images. J. Digit. Imaging 25:542–549. http://dx.doi.org/10.1007/s10278-011-9442-6. 89. Purwar Y, Shah SL, Clarke G, Almugairi A, Muehlenbachs A. 2011. Automated and unsupervised detection of malarial parasites in microscopic images. Malar. J. 10:364. http://dx.doi.org/10.1186/1475-2875-10-364. 90. Ross NE, Pritchard CJ, Rubin DM, Duse AG. 2006. Automated image processing method for the diagnosis and classification of malaria on thin blood smears. Med. Biol. Eng. Comput. 44:427– 436. http://dx.doi.org /10.1007/s11517-006-0044-2. 91. Comina G, Mendoza D, Velazco A, Coronel J, Sheen P, Gilman RH, Moore DA, Zimic M. 2011. Development of an automated MODS plate reader to detect early growth of Mycobacterium tuberculosis. J. Microsc. 242:325–330. http://dx.doi.org/10.1111/j.1365-2818.2010.03477.x. 92. Jeong J, Kim SR, Lee SH, Lim JH, Choi JI, Park JS, Chang CL, Choi JY, Richman DD, Smith DM. 2011. The use of high performance liquid chromatography to speciate and characterize the epidemiology of mycobacteria. Lab. Med. 42:612– 617. http://dx.doi.org/10.1309/LMDDEHPSYE6ZDM3C. 93. Butler WR, Guthertz LS. 2001. Mycolic acid analysis by high-performance liquid chromatography for identification of Mycobacterium species. Clin. Microbiol. Rev. 14:704 –726. http://dx.doi.org/10.1128/CMR.14.4.704-726 .2001. 94. Kellogg JA, Bankert DA, Withers GS, Sweimler W, Kiehn TE, Pfyffer GE. 2001. Application of the Sherlock Mycobacteria identification system using high-performance liquid chromatography in a clinical laboratory. J. Clin. Microbiol. 39:964 –970. http://dx.doi.org/10.1128/JCM.39 .3.964-970.2001. 95. Bhadra B, Roy P, Chakraborty R. 2005. Serratia ureilytica sp. nov., a novel urea-utilizing species. Int. J. Syst. Evol. Microbiol. 55:2155–2158. http://dx.doi.org/10.1099/ijs.0.63674-0. 96. Rychert J, Burnham CA, Bythrow M, Garner OB, Ginocchio CC, Jennemann R, Lewinski MA, Manji R, Mochon AB, Procop GW, Richter SS, Sercia L, Westblade LF, Ferraro MJ, Branda JA. 2013. Multicenter evaluation of the Vitek MS matrix-assisted laser desorption ionization–time of flight mass spectrometry system for identification of Gram-positive aerobic bacteria. J. Clin. Microbiol. 51:2225–2231. http: //dx.doi.org/10.1128/JCM.00682-13. 97. Clark AE, Kaleta EJ, Arora A, Wolk DM. 2013. Matrix-assisted laser desorption ionization–time of flight mass spectrometry: a fundamental shift in the routine practice of clinical microbiology. Clin. Microbiol. Rev. 26:547– 603. http://dx.doi.org/10.1128/CMR.00072-12. 98. Patel R. 2013. Matrix-assisted laser desorption ionization–time of flight mass spectrometry in clinical microbiology. Clin. Infect. Dis. 57:564 – 572. http://dx.doi.org/10.1093/cid/cit247. 99. Bohme K, Fernandez-No IC, Barros-Velazquez J, Gallardo JM, Canas B, Calo-Mata P. 2012. SpectraBank: an open access tool for rapid microbial identification by MALDI-TOF MS fingerprinting. Electrophoresis 33:2138 –2142. http://dx.doi.org/10.1002/elps.201200074. 100. CLSI. 2008. MM18-A. Interpretive criteria for identification of bacteria and fungi by DNA target sequencing; approved guideline. Clinical and Laboratory Standards Institute, Wayne, PA. 101. Long SW, Williams D, Valson C, Cantu CC, Cernoch P, Musser JM, Olsen RJ. 2013. A genomic day in the life of a clinical microbiology laboratory. J. Clin. Microbiol. 51:1272–1277. http://dx.doi.org/10.1128 /JCM.03237-12. 102. O’Farrell B, Haase JK, Velayudhan V, Murphy RA, Achtman M. 2012. Transforming microbial genotyping: a robotic pipeline for genotyping bacterial strains. PLoS One 7:e48022. http://dx.doi.org/10.1371/journal .pone.0048022. 103. Fricke WF, Rasko DA. 2014. Bacterial genome sequencing in the clinic: bioinformatic challenges and solutions. Nat. Rev. Genet. 15:49 –55. http: //dx.doi.org/10.1038/nrg3624. 104. Hasman H, Saputra D, Sicheritz-Ponten T, Lund O, Svendsen CA, Frimodt-Moller N, Aarestrup FM. 2014. Rapid whole-genome sequencing for detection and characterization of microorganisms directly from


122. 123. 124.

126.

127. 128.

129.

130.

131. 132.

133.

134. 135. 136. 137.

138. 139.


140. 141.

142.

143. 144. 145.

146. 147.

148.

149.

150.

151. 152.

153.

154.

155.

156.

Gevers D, Knight R. 2012. Experimental and analytical tools for studying the human microbiome. Nat. Rev. Genet. 13:47–58. http://dx.doi.org /10.1038/nrg3129. Lee JH, Yi H, Jeon YS, Won S, Chun J. 2012. TBC: a clustering algorithm based on prokaryotic taxonomy. J. Microbiol. 50:181–185. http://dx.doi.org/10.1007/s12275-012-1214-6. Harris SR, Torok ME, Cartwright EJ, Quail MA, Peacock SJ, Parkhill J. 2013. Read and assembly metrics inconsequential for clinical utility of whole-genome sequencing in mapping outbreaks. Nat. Biotechnol. 31: 592–594. http://dx.doi.org/10.1038/nbt.2616. Duncavage EJ, Abel HJ, Szankasi P, Kelley TW, Pfeifer JD. 2012. Targeted next generation sequencing of clinically significant gene mutations and translocations in leukemia. Mod. Pathol. 25:795– 804. http://dx .doi.org/10.1038/modpathol.2012.29. Schmidt KK. 28 May 2013, posting date. As outbreak continues, confusion reigns over virus patents. ScienceMag.org. http://news.sciencemag.org/people -events/2013/05/outbreak-continues-confusion-reigns-over-virus-patents. Kesselheim AS, Cook-Deegan RM, Winickoff DE, Mello MM. 2013. Gene patenting—the Supreme Court finally speaks. N. Engl. J. Med. 369:869 – 875. http://dx.doi.org/10.1056/NEJMhle1308199. Schloissnig S, Arumugam M, Sunagawa S, Mitreva M, Tap J, Zhu A, Waller A, Mende DR, Kultima JR, Martin J, Kota K, Sunyaev SR, Weinstock GM, Bork P. 2013. Genomic variation landscape of the human gut microbiome. Nature 493:45–50. http://dx.doi.org/10.1038 /nature11711. Sweeney L, Abu A, Winn J. 24 April 2013. Identifying participants in the personal genome project by name. White paper 1021-1. dataprivacylab.org. Benschop K, Thomas X, Serpenti C, Molenkamp R, Wolthers K. 2008. High prevalence of human parechovirus (HPeV) genotypes in the Amsterdam region and identification of specific HPeV variants by direct genotyping of stool samples. J. Clin. Microbiol. 46:3965–3970. http://dx .doi.org/10.1128/JCM.01379-08. Rodriquez-R LM, Konstantinidis KT. 2014. Bypassing cultivation to identify bacterial species. Microbe 9:111–118. http://www.microbemagazine .org/index.php?option⫽com_content&view⫽category&layout⫽blog&id ⫽1318&Itemid⫽1591. Gevers D, Cohan FM, Lawrence JG, Spratt BG, Coenye T, Feil EJ, Stackebrandt E, Van de Peer Y, Vandamme P, Thompson FL, Swings J. 2005. Opinion: re-evaluating prokaryotic species. Nat. Rev. Microbiol. 3:733–739. http://dx.doi.org/10.1038/nrmicro1236. Vayssier-Taussat M, Albina E, Citti C, Cosson JF, Jacques MA, Lebrun MH, Le Loir Y, Ogliastro M, Petit MA, Roumagnac P, Candresse T. 2014. Shifting the paradigm from pathogens to pathobiome: new concepts in the light of meta-omics. Front. Cell. Infect. Microbiol. 4:29. http: //dx.doi.org/10.3389/fcimb.2014.00029. Fux CA, Shirtliff M, Stoodley P, Costerton JW. 2005. Can laboratory reference strains mirror “real-world” pathogenesis? Trends Microbiol. 13:58 – 63. http://dx.doi.org/10.1016/j.tim.2004.11.001. Krebes J, Morgan RD, Bunk B, Sproer C, Luong K, Parusel R, Anton BP, Konig C, Josenhans C, Overmann J, Roberts RJ, Korlach J, Suerbaum S. 2014. The complex methylome of the human gastric pathogen Helicobacter pylori. Nucleic Acids Res. 42:2415–2432. http://dx.doi .org/10.1093/nar/gkt1201. Murray IA, Clark TA, Morgan RD, Boitano M, Anton BP, Luong K, Fomenkov A, Turner SW, Korlach J, Roberts RJ. 2012. The methylomes of six bacteria. Nucleic Acids Res. 40:11450 –11462. http://dx.doi .org/10.1093/nar/gks891. Lee CH, Belanger JE, Kassam Z, Smieja M, Higgins D, Broukhanski G, Kim PT. 2014. The outcome and long-term follow-up of 94 patients with recurrent and refractory Clostridium difficile infection using single to multiple fecal microbiota transplantation via retention enema. Eur. J. Clin. Microbiol. Infect. Dis. 33:1425–1428. http://dx.doi.org/10.1007 /s10096-014-2088-9. Cammarota G, Ianiro G, Gasbarrini A. 2014. Fecal microbiota transplantation for the treatment of Clostridium difficile infection: a systematic review. J. Clin. Gastroenterol. 48:693–702. http://dx.doi.org/10.1097 /MCG.0000000000000046. van der Gast CJ, Cuthbertson L, Rogers GB, Pope C, Marsh RL, Redding GJ, Bruce KD, Chang AB, Hoffman LR. 5 March 2014. Three clinically distinct chronic pediatric airway infections share a common core microbiota. Ann. Am. Thorac. Soc. http://dx.doi.org/10.1513 /AnnalsATS.201312-456OC.

cmr.asm.org 1045


125.

2013. Modern clinical microbiology: new challenges and solutions. Nat. Rev. Microbiol. 11:574 –585. http://dx.doi.org/10.1038/nrmicro3068. Bertelli C, Greub G. 2013. Rapid bacterial genome sequencing: methods and applications in clinical microbiology. Clin. Microbiol. Infect. 19: 803– 813. http://dx.doi.org/10.1111/1469-0691.12217. Ehrlich GD, Hu FZ, Shen K, Stoodley P, Post JC. 2005. Bacterial plurality as a general mechanism driving persistence in chronic infections. Clin. Orthop. Relat. Res. 2005:20 –24. Shen K, Sayeed S, Antalis P, Gladitz J, Ahmed A, Dice B, Janto B, Dopico R, Keefe R, Hayes J, Johnson S, Yu S, Ehrlich N, Jocz J, Kropp L, Wong R, Wadowsky RM, Slifkin M, Preston RA, Erdos G, Post JC, Ehrlich GD, Hu FZ. 2006. Extensive genomic plasticity in Pseudomonas aeruginosa revealed by identification and distribution studies of novel genes among clinical isolates. Infect. Immun. 74:5272–5283. http://dx .doi.org/10.1128/IAI.00546-06. Costello EK, Stagaman K, Dethlefsen L, Bohannan BJ, Relman DA. 2012. The application of ecological theory toward an understanding of the human microbiome. Science 336:1255–1262. http://dx.doi.org/10 .1126/science.1224203. Dowd SE, Wolcott RD, Sun Y, McKeehan T, Smith E, Rhoads D. 2008. Polymicrobial nature of chronic diabetic foot ulcer biofilm infections determined using bacterial tag encoded FLX amplicon pyrosequencing (bTEFAP). PLoS One 3:e3326. http://dx.doi.org/10.1371/journal.pone .0003326. Jiao D, Ye Y, Tang H. 2013. Probabilistic inference of biochemical reactions in microbial communities from metagenomic sequences. PLoS Comput. Biol. 9:e1002981. http://dx.doi.org/10.1371/journal.pcbi.1002981. Gilmour MW, Graham M, Reimer A, Van Domselaar G. 2013. Public health genomics and the new molecular epidemiology of bacterial pathogens. Public Health Genomics 16:25–30. http://dx.doi.org/10.1159 /000342709. Gardy JL, Johnston JC, Ho Sui SJ, Cook VJ, Shah L, Brodkin E, Rempel S, Moore R, Zhao Y, Holt R, Varhol R, Birol I, Lem M, Sharma MK, Elwood K, Jones SJ, Brinkman FS, Brunham RC, Tang P. 2011. Whole-genome sequencing and social-network analysis of a tuberculosis outbreak. N. Engl. J. Med. 364:730 –739. http://dx.doi.org/10 .1056/NEJMoa1003176. Underwood AP, Dallman T, Thomson NR, Williams M, Harker K, Perry N, Adak B, Willshaw G, Cheasty T, Green J, Dougan G, Parkhill J, Wain J. 2013. Public health value of next-generation DNA sequencing of enterohemorrhagic Escherichia coli isolates from an outbreak. J. Clin. Microbiol. 51:232–237. http://dx.doi.org/10.1128/JCM.01696-12. Rhoads DD, Wolcott RD, Sun Y, Dowd SE. 2012. Comparison of culture and molecular identification of bacteria in chronic wounds. Int. J. Mol. Sci. 13:2535–2550. http://dx.doi.org/10.3390/ijms13032535. Chumpitazi BP, Hollister EB, Oezguen N, Tsai CM, McMeans AR, Luna RA, Savidge TC, Versalovic J, Shulman RJ. 2014. Gut microbiota influences low fermentable substrate diet efficacy in children with irritable bowel syndrome. Gut Microbes 5:165–175. http://dx.doi.org/10.4161 /gmic.27923. Wilson MR, Naccache SN, Samayoa E, Biagtan M, Bashir H, Yu G, Salamat SM, Somasekar S, Federman S, Miller S, Sokolic R, Garabedian E, Candotti F, Buckley RH, Reed KD, Meyer TL, Seroogy CM, Galloway R, Henderson SL, Gern JE, DeRisi JL, Chiu CY. 2014. Actionable diagnosis of neuroleptospirosis by next-generation sequencing. N. Engl. J. Med. 370: 2408 –2417. http://dx.doi.org/10.1056/NEJMoa1401268. Schloss PD, Gevers D, Westcott SL. 2011. Reducing the effects of PCR amplification and sequencing artifacts on 16S rRNA-based studies. PLoS One 6:e27310. http://dx.doi.org/10.1371/journal.pone.0027310. Gaspar JM, Thomas WK. 2013. Assessing the consequences of denoising marker-based metagenomic data. PLoS One 8:e60458. http://dx.doi.org /10.1371/journal.pone.0060458. Wright ES, Yilmaz LS, Noguera DR. 2012. DECIPHER, a search-based approach to chimera identification for 16S rRNA sequences. Appl. Environ. Microbiol. 78:717–725. http://dx.doi.org/10.1128/AEM.06516-11. Hwang K, Oh J, Kim TK, Kim BK, Yu DS, Hou BK, Caetano-Anolles G, Hong SG, Kim KM. 2013. CLUSTOM: a novel method for clustering 16S rRNA next generation sequences by overlap minimization. PLoS One 8:e62623. http://dx.doi.org/10.1371/journal.pone.0062623. Zheng Z, Kramer S, Schmidt B. 2012. DySC: software for greedy clustering of 16S rRNA reads. Bioinformatics 28:2182–2183. http://dx.doi .org/10.1093/bioinformatics/bts355. Kuczynski J, Lauber CL, Walters WA, Parfrey LW, Clemente JC,

Rhoads et al.

1046

cmr.asm.org

175. O’Brien TF, Stelling J. 2014. The world’s microbiology laboratories can be a global microbial sensor network. Biomedica 34(Suppl 1):9 –15. 176. Sahm DF, Thornsberry C, Karlowsky JA. 2003. The application of information technology to regional, national, and global surveillance of antimicrobial resistance. Curr. Pharm. Des. 9:969 –974. http://dx.doi.org /10.2174/1381612033455215. 177. Peterson LR, Hacek DM, Rolland D, Brossette SE. 2003. Detection of a community infection outbreak with virtual surveillance. Lancet 362: 1587–1588. http://dx.doi.org/10.1016/S0140-6736(03)14765-4. 178. Bouam S, Girou E, Brun-Buisson C, Karadimas H, Lepage E. 2003. An intranet-based automated system for the surveillance of nosocomial infections: prospective validation compared with physicians’ self-reports. Infect. Control Hosp. Epidemiol. 24:51–55. http://dx.doi.org/10.1086/502115. 179. Voldstedlund M, Haarh M, Molbak K. 2014. The Danish Microbiology Database (MiBa) 2010 to 2013. Euro Surveill. 19:20667. http://www .eurosurveillance.org/ViewArticle.aspx?ArticleId⫽20667. 180. Buckeridge DL. 2007. Outbreak detection through automated surveillance: a review of the determinants of detection. J. Biomed. Inform. 40: 370 –379. http://dx.doi.org/10.1016/j.jbi.2006.09.003. 181. Buehler JW, Berkelman RL, Hartley DM, Peters CJ. 2003. Syndromic surveillance and bioterrorism-related epidemics. Emerg. Infect. Dis. 9:1197–1204. http://dx.doi.org/10.3201/eid0910.030231. 182. Carneiro HA, Mylonakis E. 2009. Google trends: a web-based tool for real-time surveillance of disease outbreaks. Clin. Infect. Dis. 49:1557– 1564. http://dx.doi.org/10.1086/630200. 183. Ortiz JR, Zhou H, Shay DK, Neuzil KM, Fowlkes AL, Goss CH. 2011. Monitoring influenza activity in the United States: a comparison of traditional surveillance systems with Google Flu Trends. PLoS One 6:e18687. http://dx.doi.org/10.1371/journal.pone.0018687. 184. Tsui FC, Espino JU, Dato VM, Gesteland PH, Hutman J, Wagner MM. 2003. Technical description of RODS: a real-time public health surveillance system. J. Am. Med. Inform. Assoc. 10:399 – 408. http://dx.doi.org /10.1197/jamia.M1345. 185. Milinovich GJ, Williams GM, Clements AC, Hu W. 2014. Internetbased surveillance systems for monitoring emerging infectious diseases. Lancet Infect. Dis. 14:160 –168. http://dx.doi.org/10.1016/S1473 -3099(13)70244-5. 186. Harrison C, Jorder M, Stern H, Stavinsky F, Reddy V, Hanson H, Waechter H, Lowe L, Gravano L, Balter S. 2014. Using online reviews by restaurant patrons to identify unreported cases of foodborne illness—New York City, 2012–2013. MMWR Morb. Mortal. Wkly. Rep. 63:441– 445. 187. Aarestrup FM, Brown EW, Detter C, Gerner-Smidt P, Gilmour MW, Harmsen D, Hendriksen RS, Hewson R, Heymann DL, Johansson K, Ijaz K, Keim PS, Koopmans M, Kroneman A, Lo Fo Wong D, Lund O, Palm D, Sawanpanyalert P, Sobel J, Schlundt J. 2012. Integrating genome-based informatics to modernize global disease monitoring, information sharing, and response. Emerg. Infect. Dis. 18:e1. http://dx.doi .org/10.3201/eid/1811.120453. 188. Shortliffe EH, Cimino JJ (ed). 2012. Biomedical informatics: computer applications in health care and biomedicine, 3rd ed. Springer, New York, NY. 189. Ward-Cook KM, Lehmann CA, Schoeff LE, Williams RH (ed). 2003. Clinical diagnostic technology. The total testing process, vol 1. AACC Press, Washington, DC. 190. Ward-Cook KM, Lehmann CA, Schoeff LE, Williams RH (ed). 2005. Clinical diagnostic technology. The total testing process, vol 2. AACC Press, Washington, DC. 191. Ward-Cook KM, Lehmann CA, Schoeff LE, Williams RH (ed). 2006. Clinical diagnostic technology. The total testing process, vol 3. AACC Press, Washington, DC. 192. Sintchenko V (ed). 2010. Infectious disease informatics. Springer, New York, NY. 193. Cowan D (ed). 2002. Informatics for the clinical laboratory: a practical guide for the pathologist. Springer, New York, NY. 194. Campos JM. 2014. Chapter 24. The laboratory information system: making the most of it in the clinical microbiology laboratory, p 458 – 470. In Garcia LS (ed), Clinical laboratory management, 2nd ed. ASM Press, Washington, DC. 195. Pantanowitz L, Tuthill JM, Balis UGJ (ed). 2012. Pathology informatics: theory and practice. ASCP Press, Chicago, IL. 196. Pantanowitz L, Parwani AV (ed). 2014. Practical informatics for cytopathology. Springer, New York, NY. 197. Sinard JH. 2006. Practical pathology informatics: demystifying infor-



157. Taniuchi M, Sobuz SU, Begum S, Platts-Mills JA, Liu J, Yang Z, Wang XQ, Petri WA, Jr, Haque R, Houpt ER. 2013. Etiology of diarrhea in Bangladeshi infants in the first year of life analyzed using molecular methods. J. Infect. Dis. 208:1794 –1802. http://dx.doi.org/10.1093/infdis /jit507. 158. Kho AN, Doebbeling BN, Cashy JP, Rosenman MB, Dexter PR, Shepherd DC, Lemmon L, Teal E, Khokar S, Overhage JM. 2013. A regional informatics platform for coordinated antibiotic-resistant infection tracking, alerting, and prevention. Clin. Infect. Dis. 57:254 –262. http://dx.doi.org/10.1093/cid/cit229. 159. Panackal AA, M’Ikanatha NM, Tsui FC, McMahon J, Wagner MM, Dixon BW, Zubieta J, Phelan M, Mirza S, Morgan J, Jernigan D, Pasculle AW, Rankin JT, Jr, Hajjeh RA, Harrison LH. 2002. Automatic electronic laboratory-based reporting of notifiable infectious diseases at a large health system. Emerg. Infect. Dis. 8:685– 691. http://dx.doi.org/10 .3201/eid0807.010493. 160. Wurtz R, Cameron BJ. 2005. Electronic laboratory reporting for the infectious diseases physician and clinical microbiologist. Clin. Infect. Dis. 40:1638 –1643. http://dx.doi.org/10.1086/429904. 161. CDC. 10 December 2013, posting date. National notifiable diseases surveillance system (NNDSS). CDC, Atlanta, GA. http://wwwn.cdc.gov /nndss/script/nedss.aspx. 162. The International Health Terminology Standards Development Organisation and The Regenstrief Institute. 2013. Cooperation agreement 48465531-6754v6. http://loinc.org/collaboration/ihtsdo/agreement.pdf. 163. National Electronic Disease Surveillance System Working Group. 2001. National Electronic Disease Surveillance System (NEDSS): a standards-based approach to connect public health and clinical medicine. J. Public Health Manag. Pract. 7:43–50. http://dx.doi.org/10.1097 /00124784-200107060-00005. 164. O’Brien TF, Stelling J. 2011. Integrated multilevel surveillance of the world’s infecting microbes and their resistance to antimicrobial agents. Clin. Microbiol. Rev. 24:281–295. http://dx.doi.org/10.1128/CMR .00021-10. 165. Pinner RW, Rebmann CA, Schuchat A, Hughes JM. 2003. Disease surveillance and the academic, clinical, and public health communities. Emerg. Infect. Dis. 9:781–787. http://dx.doi.org/10.3201/eid0907.030083. 166. Stelling J, Yih WK, Galas M, Kulldorff M, Pichel M, Terragno R, Tuduri E, Espetxe S, Binsztein N, O’Brien TF, Platt R. 2010. Automated use of WHONET and SaTScan to detect outbreaks of Shigella spp. using antimicrobial resistance phenotypes. Epidemiol. Infect. 138:873– 883. http://dx.doi.org/10.1017/S0950268809990884. 167. Sintchenko V, Gallego B. 2009. Laboratory-guided detection of disease outbreaks: three generations of surveillance systems. Arch. Pathol. Lab. Med. 133:916 –925. 168. Sahm DF, Marsilio MK, Piazza G. 1999. Antimicrobial resistance in key bloodstream bacterial isolates: electronic surveillance with the Surveillance Network Database—USA. Clin. Infect. Dis. 29:259 –263. http://dx .doi.org/10.1086/520195. 169. Stelling JM, O’Brien TF. 1997. Surveillance of antimicrobial resistance: the WHONET program. Clin. Infect. Dis. 24(Suppl 1):S157–S168. http: //dx.doi.org/10.1093/clinids/24.Supplement_1.S157. 170. O’Brien TF, Stelling JM. 1996. WHONET: removing obstacles to the full use of information about antimicrobial resistance. Diagn. Microbiol. Infect. Dis. 25:162–168. 171. O’Brien TF, Stelling JM. 1995. WHONET: an information system for monitoring antimicrobial resistance. Emerg. Infect. Dis. 1:66. 172. Cullen IM, Manecksha RP, McCullagh E, Ahmad S, O’Kelly F, Flynn RJ, McDermott T, Murphy P, Grainger R, Fennell JP, Thornhill JA. 2012. The changing pattern of antimicrobial resistance within 42,033 Escherichia coli isolates from nosocomial, community and urology patient-specific urinary tract infections, Dublin, 1999 –2009. BJU Int. 109: 1198 –1206. http://dx.doi.org/10.1111/j.1464-410X.2011.10528.x. 173. Polwichai P, Dejsirilert S, Panpetch S, Sawanpanyalert P, Aswapokee N, Mootsikapun P. 2009. Antimicrobial resistance of Escherichia coli isolated from urine in Thailand from 2000 to 2005. J. Med. Assoc. Thai. 92(Suppl 4):S59 –S67. 174. Vinas MR, Tuduri E, Galar A, Yih K, Pichel M, Stelling J, Brengi SP, Della Gaspera A, van der Ploeg C, Bruno S, Roge A, Caffer MI, Kulldorff M, Galas M. 2013. Laboratory-based prospective surveillance for community outbreaks of Shigella spp. in Argentina. PLoS Negl. Trop. Dis. 7:e2521. http://dx.doi.org/10.1371/journal.pntd.0002521.


198. 199. 200.

202. 203.

Daniel D. Rhoads is a Chief Resident who is training in clinical pathology at the University of Pittsburgh Medical Center. In 2004, he received a B.S. in biology from Millersville University of Pennsylvania and completed medical technology training at Lancaster General College of Nursing and Health Sciences. In 2006, Dan began working as a research scientist at the Southwest Regional Wound Care Center and the Research and Testing Laboratory in Lubbock, TX. While studying chronic wounds there, he acquired his interest in biofilm infections and his desire to unravel the complexity of microbial communities by using informatics tools. In 2012, Dan received his M.D. from Texas Tech University Health Sciences Center, and he has recently taken coursework in the Department of Biomedical Informatics at the University of Pittsburgh. Dan has accepted a Medical Microbiology Fellowship at the Cleveland Clinic for the 2015–2016 academic year.

Vitali Sintchenko is a tenured Associate Professor of the Sydney Medical School at the University of Sydney and Director of the Centre for Infectious Diseases and Microbiology-Public Health at Westmead Hospital in Sydney, Australia. He is a Fellow of the Royal College of Pathologists of Australasia and earned his Ph.D. in medical informatics from the University of New South Wales. He is a member of the Marie Bashir Institute for Infectious Diseases and Biosecurity. His research focuses on infectious disease informatics, bacterial genomics-guided public health laboratory surveillance, and disease control. He has authored two books and more than 150 scientific papers. He has been actively involved in disease outbreak investigations and the design of biosurveillance systems. He currently chairs the Public Health Laboratory Network of Australia.


204. 205. 206. 207.

208.

Rights (OCR), HHS. 6 February 2014. CLIA program and HIPAA privacy rule; patients’ access to test reports. Final rule. Fed. Regist. 79:7289 –7316. American Telemedicine Association. 2014. Clinical guidelines for telepathology. American Telemedicine Association, Washington, DC. College of American Pathologists. 2013. Digital pathology resource guide, version 4.0. College of American Pathologists, Northfield, IL. Federal Register. 24 August 1998. Publication of OIG compliance program guidance for clinical laboratories—HHS. Fed. Regist. 63:45076 – 45087. Harris D, Khan L, Paul R, Thuraisingham B. 2007. Standards for secure data sharing across organizations. Comput. Stand. Interfaces 29:86 –96. Lowe A, Chlipala E, Elin J, Kawano Y, Long RE, Tillman D. 2011. Validation of digital pathology in a healthcare environment. Digital Pathology Association, Madison, WI.

Carol A. Rauch received her M.D. and Ph.D. at Johns Hopkins University and continued her education with residency training in pathology and laboratory medicine and fellowship training in medical microbiology at Yale New Haven Hospital. At Baystate Health, the Western Campus of Tufts University School of Medicine, her roles included Medical Director of Clinical Microbiology, Chief of Clinical Pathology, and Medical Director of Laboratory Information Systems. She is currently Medical Director of Clinical Pathology and Associate Professor of Pathology, Microbiology & Immunology at Vanderbilt University School of Medicine. She recently chaired Division C for ASM. Through 20 years in clinical laboratories and clinical microbiology, her interests have included patient safety, quality in laboratory testing, and bioterrorism preparedness. Her professional experience has led to an appreciation of the critical role of pathology informatics in healthcare, as well as the special needs of microbiology in information systems as drivers of quality medical and public health information.

Liron Pantanowitz is an Associate Professor in the Departments of Pathology and Biomedical Informatics at the University of Pittsburgh. Dr. Pantanowitz obtained his M.D. in South Africa and specialized in pathology at Harvard University in Boston, MA. He subsequently completed cytopathology and hematopathology fellowships. Dr. Pantanowitz is currently the Director of Cytopathology at the University of Pittsburgh Medical Center (UPMC) Shadyside. He is also the Director of the Pathology Informatics Fellowship and Associate Director of the Pathology Informatics Division at UPMC. He is the immediate Past President of the Association for Pathology Informatics (API), and he has also served on several key committees for other societies, such as the CAP, ASCP, USCAP, and ATA. He has published many peer-reviewed articles and book chapters, written several textbooks, and given talks around the world. Dr. Pantanowitz is current Editor-in-Chief of the Journal of Pathology Informatics.

cmr.asm.org 1047


201.

matics for the practicing anatomic pathologist. Springer, New York, NY. Hoyt RE, Yoshihashi AK (ed). 2014. Health informatics. Practical guide for healthcare and information technology professionals, 6th ed. Lulu, Raleigh, NC. Magnuson JA, Fu PC, Jr (ed). 2014. Public health informatics and information systems, 2nd ed. Springer, New York, NY. CLSI. 2005. AUTO02-A2. Laboratory automation: bar codes for specimen container identification. CLSI, Wayne, PA. CLSI. 2009. AUTO03-A2. Laboratory automation: communications with automated clinical laboratory systems, instruments, devices, and information systems. CLSI, Wayne, PA. CLSI. 2006. AUTO08-A. Managing and validating laboratory information systems. CLSI, Wayne, PA Centers for Medicare & Medicaid Services (CMS), HHS, Centers for Disease Control and Prevention (CDC), HHS, Office for Civil

Clinical microbiology of azithromycin.

Informatics and the clinical laboratory.

Optimizing Clinical Research Participant Selection with Informatics.

Proteomics boosts translational and clinical microbiology.

Pediatric Clinical Microbiology: It's the Little Things.

Guest-editorial: Biomedical informatics in clinical environments.

Lectins and their application to clinical microbiology.

A computer system for clinical microbiology.

Building blocks for a clinical imaging informatics environment.

Milestones: Critical Elements in Clinical Informatics Fellowship Programs.

Medical student awareness of and interest in clinical informatics.

Biobanking informatics infrastructure to support clinical and translational research.

A review of analytics and clinical informatics in health care.

Clinical Research Informatics: Recent Advances and Future Directions.

Clinical proteome informatics workbench detects pathogenic mutations in hereditary amyloidoses.

Clinical informatics: prospects for a new medical subspecialty.

Perspectives on clinical informatics: integrating large-scale clinical, genomic, and health information for clinical care.

Epidemiology, clinical history and microbiology of peritonsillar abscess.

[Applications of MALDI-TOF technology in clinical microbiology].

Whole genome sequencing in clinical and public health microbiology.

Application of MALDI-TOF mass spectrometry in clinical diagnostic microbiology.

Interpretation of Blood Microbiology Results - Function of the Clinical Microbiologist.

Good laboratory practice for clinical next-generation sequencing informatics pipelines.

Clinical research informatics and electronic health record data.