Connecting the Dots Bridging Patient and Population Health Data Systems Patrick L. Remington, MD, MPH, William C. Wadland, MD, MS

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he digital age has arrived in full force. Searching “big data” on Google returns more than 4 million hits. There is even a Wikipedia page that defines big data as “an all-encompassing term for any collection of data sets so large and complex that it becomes difficult to process using traditional data processing applications.”1 But big data could be too much of a good thing. Like a dot-to-dot puzzle, it is impossible to interpret big data without connecting the dots. The electronic health record (EHR) is transforming how information is gathered and used in clinical care and will become an increasingly important source of big data in the future. In 2013, 78% of office-based physicians used any type of EHR system, up from only 18% in 2001.2 If this rate of adoption continues, nearly all health systems will be using EHRs soon, providing an opportunity to improve the quality of health care, lower healthcare costs, and permit patients to become more involved in their own health care.3 Similarly, there has been an explosion of information available to describe the characteristics of populations, ranging from air and water quality to neighborhood walkability. These population-based data systems are often linked to specific addresses. The IOM’s recent report supports the use of these data, and points out that geographically linked data can capture health-relevant information that cannot be obtained directly from the patient.4 Bridging patient and population health data systems can enhance our ability to care for patients and improve the health of populations (Figure 1). Population health data can be linked to individual patients to provide the “context” that each patient experiences, to not only improve the quality of care but also provide the context for assessing the quality of care provided. On the other hand, communities can develop higher-quality and lower-cost population health surveillance systems by “building up” from the EHRs. From the Department of Population Health Sciences (Remington), School of Medicine and Public Health, University of Wisconsin-Madison, Madison, Wisconsin; and the Department of Family Medicine (Wadland), Michigan State University, East Lansing, Michigan Address correspondence to: Patrick L. Remington, MD, MPH, University of Wisconsin-Madison, Health Science Learning Center, Room 4263, 750 Highland Ave, Madison WI 53705. E-mail: [email protected]. 0749-3797/$36.00 http://dx.doi.org/10.1016/j.amepre.2014.10.021

& 2015 American Journal of Preventive Medicine

Improving Patient Care Using Population Data Incorporating population data into the EHR for each patient can capture aspects of the social and physical context in which an individual lives, above and beyond the individuallevel characteristics of the patient. Gottlieb et al.5 described how to move electronic medical records “upstream” by incorporating information about social determinants. Through a series of three case studies, they demonstrated the feasibility of integrating information that (1) connects patient needs to community services; (2) identifies support for legal services; and (3) assists veterans who are homeless. Bailey and colleagues6 illustrated how pooled data in EHRs can be used to evaluate barriers to access and quality of care, such as limitation in insurance coverage. They combined EHR and Medicaid data sets to obtain detailed information about each patient’s precise duration of coverage. By combining these two data sources, they were able to learn that continuously uninsured patients had lower odds of receiving services at visits when due, compared to those who were continuously insured. However, these recent studies by Gottlieb et al.5 and Bailey et al.6 only scratch the surface of potential opportunities related to the use of population data to improve the quality of patient care. It is now feasible and relatively inexpensive to geocode each patient’s address and then link a variety of variables to that patient which describe the context in which they live. A color-coded page could be included in each patient’s EHR showing potential risks related to crime, lead poisoning, social deprivation, or neighborhood walkability, categorizing risk as high (red), medium (yellow), or low (green). This type of easily interpreted information could help providers to conduct more thorough and targeted assessments and deliver care that considers the context in which people live. In addition, such contextual information could be used in quality of care assessments, to stratify or control for patient risks.

Improving Population Health Surveillance Using Data From Electronic Health Records Just as population health data can improve the quality of healthcare, EHR data could be used to improve the quality of population health surveillance. For example,

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Figure 1. Connecting the dots: Bridging patient and population health data systems. EHR, electronic health record

population health surveillance systems, such as the County Health Rankings, often rely on telephone surveys, such as the Behavioral Risk Factor Surveillance System, to assess and monitor trends in risk factors like smoking, obesity, or physical activity.7,8 Collecting these data is often expensive and the estimates may be unreliable owing to small sample sizes or survey nonresponse. These data could instead be provided by health systems, in collaboration with local public health organizations.9 Haegerich et al.10 outlined this potential in the area of injury prevention and control through a review of the literature and examples that focused on older adult falls, prescription drug overdose, and intimate partner violence.10 They noted that certain aspects of meaningful use requirements have potential for improving the use of EHRs for public health improvement, including (1) the core objective of implementing computerized clinical decision support; (2) the clinical quality measures requirement; and (3) the menu objective of submitting electronic syndromic surveillance data to public health agencies. Flood and colleagues11 demonstrated the feasibility of using de-identified EHRs in obesity surveillance in Wisconsin. Data on 93,130 children and adolescents (aged 2–19 years) were used to estimate prevalence estimates of childhood obesity overall, and by racial and ethnic subgroups. This study showed that EHRs are a cost-effective and promising tool for local obesity

prevention efforts, providing a model for the surveillance of other chronic conditions or health factors that exist in the EHR. Despite evidence of successful integration of patient and population health data systems described herein, significant barriers exist, including privacy concerns, costs related to data analysis and management, and competing proprietary interest. However, breaking down these barriers to allow integration of data, as well as bridging the EHR with population health data, will help “connect the dots” to reveal the bigger picture, which will not only improve clinical care but also advance our ability to measure and monitor, while enhancing the health of populations. This work (Dr. Remington) was supported in part by a grant from the Robert Wood Johnson Foundation (ID 69835), for the County Health Rankings. No additional financial disclosures were reported by the authors of this paper.

References 1. Wikipedia. Big data. en.wikipedia.org/wiki/Big_data. 2. Hsiao C-J, Hing E. Use and characteristics of electronic health record systems among office-based physician practices: United States, 2001– 2013. NCHS data brief, no 143. Hyattsville, MD: National Center for Health Statistics; 2014. 3. Xierali IM, Hsiao CJ, Puffer JC, et al. The rise of electronic health record adoption among family physicians. Ann Fam Med. 2013;11(1): 14–19. 4. IOM. Capturing Social and Behavioral Domains in Electronic Health Records: Phase 1. Washington, DC: National Academies Press; 2014. 5. Gottlieb LM, Tirozzi KJ, Manchanda R, Burns AR, Sandel MT. Moving electronic medical records upstream: incorporating social determinants of health. Am J Prev Med. 2015;48(2):215–218. 6. Bailey SR, O’Malley JP, Gold R, Heintzman J, Marino M, DeVoe JE. Receipt of diabetes preventive services differs by insurance status at visit. Am J Prev Med. 2015;48(2):229–233. 7. Mokdad AH, Remington PL. Measuring health behaviors in populations. Prev Chronic Dis. 2010;7(4):A75. Epub 2010 Jun 15. PMID: 20550833. 8. Remington PL, Booske BC. Measuring the health of communities— how and why? J Public Health Manag Pract. 2011;17(5):397–400. 9. IOM. Living Well with Chronic Illness: A Call for Public Health Action. Washington, DC: National Academies Press; 2012. 10. Haegerich TM, Sugerman DE, Annest JL, Klevens J, Baldwin GT. Improving injury prevention through health information technology. Am J Prev Med. 2015;48(2):219–228. 11. Flood TL, Zhao YQ, Tomayko EJ, Tandias A, Carrel AL, Hanrahan LP. Electronic health records and community health surveillance of childhood obesity. Am J Prev Med. 2015;48(2):234–240.

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Connecting the dots: bridging patient and population health data systems.

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