Indian J Pediatr (January 2015) 82(1):71–79 DOI 10.1007/s12098-014-1585-2

REVIEW ARTICLE

The Pediatrix BabySteps® Data Warehouse — A Unique National Resource for Improving Outcomes for Neonates Alan R. Spitzer & Dan Ellsbury & Reese H. Clark

Received: 5 August 2014 / Accepted: 9 September 2014 / Published online: 17 October 2014 # Dr. K C Chaudhuri Foundation 2014

Abstract The Pediatrix Medical Group Clinical Data Warehouse represents a unique electronic data capture system for the assessment of outcomes, the management of quality improvement (CQI) initiatives, and the resolution of important research questions in the neonatal intensive care unit (NICU). This system is described in detail and the manner in which the Data Warehouse has been used to measure and improve patient outcomes through CQI projects and research is outlined. The Pediatrix Data Warehouse now contains more than 1 million patients, serving as an exceptional tool for evaluating NICU care. Examples are provided of how significant outcome improvement has been achieved and several papers are cited that have used the “Big Data” contained in the Data Warehouse for novel observations that could not be made otherwise. Keywords Pediatrix Clinical Data Warehouse . Neonatal outcomes . Continuous quality improvement . Neonatal research . Big data

Introduction There is an increasing evidence that the introduction of the electronic health record (EHR) and the extraction of data from those systems have had a profound impact upon medical care, especially in the NICU. Furthermore, the use of such “Big Data” for the assessment of outcomes in thousands of infants and the evaluation of practice variation upon outcomes have already affected care in the NICU in a profound way. It is the purpose of this paper to examine how this change in medical A. R. Spitzer (*) : D. Ellsbury : R. H. Clark The Center for Research, Education, and Quality Improvement, Pediatrix Medical Group, 1301 Concord Terrace, Sunrise, FL, USA e-mail: [email protected]

care is progressing, what has already evolved with respect to neonatal outcome measures, and what can be expected in the near future.

The Electronic Health Record (EHR) in the NICU The overall goals for EHRs, as outlined in the concept of “meaningful use” [1] by the United States Health IT Policy Committee, are relatively straightforward: & & & & &

Improve quality, safety, efficiency, and reduce health care disparities. Engage patients and families. Improve care coordination. Improve population and public health. Ensure adequate privacy and security protection of personal health information [2]

In 1996, Pediatrix Medical Group anticipated the need for an EHR as the number of practices it managed continued to expand. With a growing patient population under its care (at present, nearly 25 % of neonates in intensive care units in the U.S. are cared for by Pediatrix clinicians), the company recognized the possibilities for both extraction of “meaningful” data in measuring outcomes, as well as the potential for examining how practice variation might affect those outcomes. Thus, the opportunity to develop a strategy for research investigations into comparative effectiveness measures was an early consideration in the development of a dedicated neonatal EHR and Data Warehouse. Pediatrix has developed a proprietary EHR system called BabySteps® (Fig. 1). This EHR has served to gather data on a rapidly expanding patient population, while accurately coding for care according to American Academy of Pediatrics (AAP) Perinatal Section Coding Committee guidelines. Most

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importantly, however, early consideration was given for the best way to extract data for both clinical and research use, ultimately giving rise to the Pediatrix BabySteps Clinical Data Warehouse (CDW). At the present time, the CDW is believed to be one of the largest repositories of data on the neonate in the world, containing information on more than 1,025,000 infants and approximately 18,600,000 patient days. Because of the extent and depth of the data collected, the CDW has been queried not only within the organization for novel research observations, but also by the Food and Drug Administration (FDA), the National Institutes of Health (NIH), and the National Institute of Child Health and Human Development (NICHD), and numerous academic neonatology programs [3–6]. Many of these queries have resulted in publication in the peer-reviewed literature, and to date, more than 100 papers have been published that use the data from the CDW. Perhaps the most significant attestation to the value of this electronic system was provided by the American Board of Pediatrics (ABP), when it designated the CDW as an acceptable tool for use in their Maintenance of Certification (MOC) process, and named Pediatrix Medical Group as the initial Portfolio Sponsor in the MOC system introduced by the ABP in 2011.

BabySteps® Development and the Approach to Data Extraction When Pediatrix Medical Group first implemented the installation of a proprietary electronic health record throughout its practices, several goals emerged. First, the EHR was intended to create a clear, easily-readable admission, discharge, and daily medical record note that was consistent throughout the organization (now numbering more than 200 practices, covering approximately 340 hospitals in 34 states plus Puerto Rico). In order to meet this goal, a series of concepts was defined as being critical for optimal EHR documentation, known as the 4 “C’s”: &

Conciseness of notes

– –

Reduce the daily note to the specific needs of the patient. The daily note should, in general, only contain that day’s information. Avoid carryover of excessive amounts of information from previous days. Excessive verbiage should be excluded, since it breeds inconsistency in charting and increases liability.

– – &

Convey information to other caregivers

– –

Notes must be easily readable. Daily changes in the patient’s condition or the management plan should be immediately apparent to the reader.

– –

Problems should be diagnosed, assessed, treated, resolved, and removed from daily notes as appropriate. Simple recitation of numbers or laboratory reports does not constitute patient assessment, and further evidence of physician thought processes must be provided in the record.

&

Confirm clinical decisions



Notes should not simply recount numbers or events, but assess the patient’s clinical condition and provide a coherent approach. Notes should be read carefully before being placed into the chart. Confirmation of the clinical plan and the reasons for that plan are essential in assigning proper codes for the care being delivered.

– –

&

Consistent internally



Notes should be consistent from the physical examination through the management plan. There should not be any discrepancies between the physical examination, laboratory values, radiographic studies, the assessment, or the plan for the patient. Entry of information into fields, rather than as comments, is essential in refining the daily progress note and limiting inconsistency. Inconsistencies are the most common problematic issue within the chart and are difficult to defend in malpractice cases.

– –

There is a tendency for the EHR documentation to contain excessive detail, making evaluation of the daily note difficult, especially for consultants and reviewers, while potentially increasing liability risks. Clinicians sometimes erroneously believe that voluminous documentation aids in the care of the patient. Unfortunately, non-representative information is often carried forward unnecessarily, enhancing risks for liability. For example, cloning a physical examination over several days may indicate a “normal” exam from a prior period, yet later the note may have the child headed to the operating room. These inconsistencies undermine care and increase the risks for errors. To offset these problems, documentation training should be provided to all physicians and nurses in order to insure consistency. In planning for automated data extraction, several important decisions had to be made. These processes are often overlooked during the adoption of the EHR, but are critical in the concept of “meaningful use.” Because every medical record note consists of drop down menus and text entryfields, questions arose if both parts of the note could be reliably used for data

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Fig. 1 Part of the BabySteps admission note to the NICU

extraction. Ultimately, the issue was resolved in favor of drop downfield entry extraction only. First, text areas of notes often are highly inconsistent in recording data. With no standardized definitions for many conditions, clinicians record diagnoses in a highly variable manner. Secondly, the authors assessed how a few specific diagnoses were designated within the text areas of the note to assess the variability of documentation. As an example, neonatal intraventricular hemorrhage (IVH) was mentioned in the text in more than 1,700 different ways! A small, partial list of these is shown in Table 1, all of which would need to be examined for if the text boxes were to be accurately reviewed. This process would dramatically slow data extraction, limiting the value of the CDW itself. It was therefore felt that limiting extraction to the drop down menu fields would be the optimal approach.

Goals for Data Extraction from BabySteps© and Any Electronic Record The ultimate objectives for data management were also identified. The goals for data must be carefully considered, since it is frustrating to have large volumes of data that are not useful. Most importantly, outcome data from any EHR should be automatically extracted from the record, not transcribed by hand for input into a database. Repeated transcription of data provides a serious potential source for error and undermines the value of the information in the database. Automated data extraction has several additional benefits. Automated data extraction eliminates the bias that might creep into the information with multiple data extractors. For

example, some referral centers may exclude some patients referred for high-level tertiary or quaternary care, since they affect outcomes negatively. Babies sent for laser surgery for Retinopathy of Prematurity (ROP), or infants with complex congenital malformations tend to adversely influence NICU outcomes and lengthen stays because of complex management issues. Shouldering the burden for these patients in a database often leads to results that appear less than optimal and they may be edited out. Automated extraction of information, therefore, leads to a much more precise data set, from which more accurate conclusions can be drawn. With any attempt at data extraction, a number of issues become evident. Each practice needs to be aware of its evolving outcomes. Often, when one views outcome data for the first time, one is often left with a sense of disbelief. The goals of authors, therefore, have been to assist physicians in assessing their data so that they can focus on outcomes and work towards improvement, the primary reason for this information. It is essential to provide outcome data as a graphic report, which yields a more indelible impression than a table. In addition, outcome data in the abstract are not meaningful, and the data must have some basis for comparison. Even with riskadjusted data, if you do not know how your neonatal colleagues are doing, outcomes have little value. In authors company of that big size, the obvious comparison would be the rest of the patient population in Pediatrix Medical Group, at least as a starting point. Each report, therefore, would have to present outcomes from not only the individual practice, but from other Pediatrix practices as well. These outcomes had to be provided in an easily understandable format. One should be able to see at a glance how one’s NICU is doing compared to other NICUs.

74 Table 1 Designation variability for intraventricular hemorrhage, partial listing Intraventricular hemorrhage IVH Intracranial hemorrhage ICH Gr. 1, 2, 3, or 4 IVH Gr. 1, 2, 3, or 4 ICH Gr. 1, 2, 3, or 4 Grade 1, 2, 3, or 4 IVH Grade 1, 2, 3, or 4 ICH Gr. 1, 2, 3, or 4 IVH Gr. 1, 2, 3, or 4 ICH Head bleed (with or without grading – all listed also may be graded) CNS bleed Periventricular hemorrhage Ventricular bleed Vent. Bleed Periventricular bleed White matter bleed WM bleed Brain bleed Br. Bleed Possible bleed Possible CNS bleed Possible IVH Probable IVH Probable IVH bleed Probable CNS bleed Indeterminate bleed Etc. Note: Numerous other combinations and abbreviations of this event were noted as well

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Elimination of selection bias was also important, since there is a tendency to avoid taking credit for patients who might have adverse outcomes, especially in transport situations. By collecting data on all patients, and allowing comparisons of inborn and outborn patient populations and NICUs of similar size, controls were built into the CDW to avoid situations in which large referral institutions were somewhat unfairly penalized for accepting the most difficult cases in transport. While some databases either risk-adjust or assign severity indices to outcomes, it was authors belief that the actual data, with selection of appropriate comparison groups, was a preferable way to evaluate outcomes. Finally, to examine any regional variations in outcomes that might be of interest, the CDW also allows grouping of all hospitals covered by an individual practice to allow comparison to other groups within the state or a region of the country.

BabySteps Data Warehouse Reports The CDW is constantly evolving to provide the practicing physician with the most current data possible and as many report types as is practical for the management of the patient in that NICU. From clinician feedback, the CDW has become amarvelous tool for assessment of outcomes and quality improvement efforts. Data within the CDW is refreshed on a weekly basis. At the present time, Table 2 shows the list of CDW reports that are currently available. Reports are categorized into several types: Activity Reports indicate basic demographic types of information. Management Reports reflect decision-making processes in patient care.

Since outcome data were going to be used in a comparative manner, it was essential that the Clinical Data Warehouse be made HIPAA (Health Insurance and Portability and Accountability Act of 1996) compliant. The data set required eliminating all patient-identifying information. To accomplish this result, data “cleansing” excluded information such as day, month and year of birth, date of admission, date of specific therapy initiation, etc. Furthermore, events were recorded as days since birth to eliminate any possibility of discovery of a baby having an unusual event on a specific birth date that might be traceable. Research use of the de-identified data set is approved annually by the national Western IRB. In the publication of studies, however, authors also require IRB approval from any institution, company, or university whose investigators query the Data Warehouse. More than 100 peer-reviewed papers have been published during the last fifteen years with information from the CDW.

Morbidity and Mortality Reports documentation of a variety of common types of outcomes of greatest interest in NICU patients. Summary Reports reveal a selected one-page snapshot of outcomes for a specific NICU, or network trends in a variety of outcomes that are constantly tracked. The Summary Reports were developed as a request from the regional management teams, who are each responsible for approximately 40–70 practices in one of six regions of the country. So that they could get a sense of the quality of care being delivered in an individual unit, or all units within the region, the Summary Report was developed to provide a snapshot picture of overall outcomes and is shown in Fig. 2. Overall, the Summary Reports provide an instant snapshot of

Indian J Pediatr (January 2015) 82(1):71–79 Table 2 Current Pediatrix Clinical Data Warehouse reports • Activity Reports: Types of Discharges (home, transfer, in-hospital, etc.); Admissions by Gestational Age; Admissions by Birth Weight; Length of Stay; Average Daily Census; Type of Delivery (vaginal vs. cesarean section); Transport Report • Morbidity and Mortality Reports: Mortality; Survival; Oxygen at 28 d of life (BPD); Oxygen at 36 wks’ gestational age (BPD); Intraventricular Hemorrhage (IVH); Late-onset sepsis; Necrotizing Enterocolitis (NEC); Patent Ductus Arteriosus (PDA); Periventricular Leukomalacia (PVL); Respiratory Distress Syndrome (RDS) and Surfactant use; Retinopathy of Prematurity (ROP); Severe IVH; Severe ROP; Pneumothorax; Catheter-related bloodstream infections/1,000 catheter days • Management Reports: Maximal ventilator support; Median ventilator days; Temperature from delivery room to NICU; Types of lines inserted and duration of use; Median daily weight gain during the first 28 d; Hepatitis B immunization rates; TPN use day one; Percent infants breastfeeding in first week of life; Continuation of breastfeeding during NICU stay; Percent of infants breastfeeding at discharge; Bilirubin reports; Late onset sepsis rates; Discharge needs (Oxygen, NG feeds, Monitoring) o Infection Reports: Percent of NICU admissions treated with antibiotics; Median days of antibiotic therapy with negative cultures; Use of cefotaxime; Percent of patients treated without cultures o Medication Reports: All commonly-used medications in the NICU, frequency of use o Comparison Reports: Unique institutional comparisons of outcomes • Summary Dashboard Report and Network Trends Reports o Annual quality gauge report o Three year trend report o Network trends

a year’s important outcome measures and can be assessed within minutes. Should the observer desire more data, he or she could then turn to the more detailed reports. A typical Clinical Data Warehouse Report is illustrated in Fig. 3. Reports can be viewed for yearly, quarterly, or monthly time periods. Many morbidities, even in large NICUs, occur so infrequently that annual reports often work best, but the clinician has the option to examine an outcome during various time frames, which is helpful in quality improvement projects. The clinician may filter report results by multiple combinations of birth weight and gestational age selections, to facilitate detailed “drill down” for various outcomes. Results can also be filtered by admission status - inborn, outborn, or combined. The selected filtering parameters are used to provide the specific network comparison group. This can be even further refined by selection of high (>450 discharges annually), medium (225–450 discharges annually) or low (450 discharges per year) Pediatrix Medical Group NICUs as a comparison

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Fig. 4 The panel on the left shows the various options for reports that can be selected: Overall Report Group, Specific Report, and Report Format at the top of the Figure. The period of time can be selected by year, quarter, or month. Various gestational age or birth weight combinations can also be chosen to filter reports. Admission status (inborn or outborn) is available as an additional filter. Only surviving infants may also be chosen as a group to be examined. The comparison network group may be selected based on annual NICU volume, specific region, or state

Fig. 5 Network trend reports between 2000–2013 for severe retinopathy for prematurity (ROP) – Stages 3–5 and infants surgically treated

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sepsis, enhanced growth, improved temperature stability from the delivery room to the NICU, decreased use of drugs with limited evidence of therapeutic value for the neonate (e.g., metoclopramide, H2 blockers, spironolactone), and reduction of necrotizing enterocolitis. One of the most interesting recent reports recently made available in the CDW is the individual NICU comparison report (Fig. 6). While comparisons have been part of the visual reports in the Data Warehouse, the new report allows the identification of “outliers,” whose outcomes fall below acceptable standards. In practice, Pediatrix corporate quality improvement projects have started with the identification of a pressing outcome concern to be addressed. The CQI team reviews the literature, defines the appropriate evidence necessary to support the project, and proceeds to build a toolkit. The toolkit contains reprints of key publications from the literature, slide presentations for the medical and nursing staffs, an operations manual that describes the methodology for rolling out the project, and any ancillary materials needed for project management. Toolkits are finally posted on the Pediatrix web site for the practices to review, use, or modify as needed. The toolkits do not represent specific recommendations on how to practice, but rather provide a series of suggested approaches for NICUs looking to improve their outcomes. Each practice can tailor the toolkit to their particular unit’s needs. Timelines are then established for projects and enrollment is initiated for units desiring to participate. The Data Warehouse serves as the primary method of following outcome data. As seen from the data extracted from the Data Warehouse, a tool at this level of sophistication allows for meaningful healthcare improvement and cost savings. It further allows identification of practices that fail to meet (or exceed) goals for outcomes, referred to as an “outlier.” Many questions revolve around outlier practices: are they outliers that are doing exceptionally well, or exceptionally poorly; are they outliers in several areas or just an isolated part of practice; can

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Fig. 6 Comparison report showing an individual NICU compared to other regional NICUs with respect to human milk use in the first week of life. The dark horizontal line is the average for all NICUs examined. The lighter lines represent one standard deviation from the mean. The group of

NICUs on the right hand side of the figure that lie more than one standard deviation below the mean for human milk feeding may be considered “outliers” and be targeted for quality initiatives to improve human milk use rates

relationships be drawn between a particular practice strategy and the outcome results; how can adverse outcomes be corrected, etc. The thoughtful application of evidence-based approaches with data can also add immeasurably to parent satisfaction. Hospital partners also appreciate knowing that the services they offer in the NICU produce the best possible results.

knowing the 30 most commonly used medications in the NICU. These data were furnished to the FDA and published in Pediatrics [3]. It was also noted that three of the five most commonly used medications were antibiotics: ampicillin, gentamicin, and cefotaxime. Since the most common neonatal admission to the NICU is suspected septicemia, these drugs represented the two most common antibiotic regimens used for suspected sepsis, ampicillin and gentamicin or ampicillin

The BabySteps© Research Data Warehouse Given that the BabySteps record is scoured daily for 563 data fields, when combined with the extraordinary volume of patients in this database (now >1,000,000), the potential for novel research observations from “big data” cannot be overlooked. In fact, the Data Warehouse has been queried by the NIH (National Institutes of Health) and the NICHD Neonatal Network, the Food and Drug Administration (FDA), major universities, and private corporations, who have sought data not available elsewhere. Some publications from the Data Warehouse are provided in the references. As an example of the utility of the CDW, in 2005, the authors were contacted by the FDA, which was interested in

Fig. 7 Adjusted odds ratios for mortality rate of ampicillin and cefotaxime compared to ampicillin and gentamicin. 95th confidence intervals are indicated [7]

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and cefotaxime. The two approaches were initially thought to be equivalent. When the authors examined the outcomes in more detail, however, they discovered that the use of cefotaxime had nearly a two-fold (100 %) greater association with mortality at certain gestational ages than did gentamicin in this patient population [7] (Fig. 7). Extensive attempts to eliminate confounding variables through the use of logistic regression analysis strongly suggested that this finding was indeed correct. Because of this risk, the use of cefotaxime has fallen to an extremely low rate in the NICUs across the country. This observation, which might have gone unnoticed otherwise, became apparent only when large numbers of patients were evaluated. Most recently, the research CDW has been used to develop a screening tool for the potential development of NEC [8], examine the impact of duration of rupture of membranes on outcome of premature infants [9], assess the intrauterine growth [10] and growth of premature infants [11], examine clinical outcomes and caffeine use [12], follow trends in discharge planning needs for premature infants [13], re-examine changes in medication use in the NICU [14], evaluate renal function in the pre-term infant [15], and several others. The authors continue to expand the Data Warehouse capabilities for research and constantly invite inquiries for it use. One of the greatest assets of this type of database is that not only does it provide answers to many outcome questions, but it can also serve to model prospective trials and studies in which targeted data collection is required. It is authors’ belief that there is much more information that can continue to be extracted and evaluated that will have additional significant benefits for the practice of newborn medicine. Conflict of Interest and Source of Funding The BabySteps electronic health record (EHR) and Pediatrix Clinical Data Warehouse are proprietary programs developed and funded internally by MEDNAX, Inc., the parent organization of Pediatrix Medical Group. These programs are currently available and used only in NICUs that are staffed by Pediatrix Medical Group physicians and they are not available for sale outside of this company. The examples cited in this article are used to show the value of a detailed EHR and data repository in newborn medicine and are not provided to promote sales of any product.

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References 1. DHHS.gov News Release, June 16, 2009. 2. Health IT Policy Council Recommendations to National Coordinator for Defining Meaningful Use Final- August 2009; http://healthit.hhs.gov. 3. Clark RH, Bloom BT, Spitzer AR, Gerstmann DR. Reported medication use in the NICU: data from a large national data set. Pediatrics. 2006;117:1979–87. 4. Abrams ME, Meredith KS, Kinnard P, Clark RH. Hydropsfetalis: a retrospective review of cases reported to a large national database and identification of risk factors associated with death. Pediatrics. 2007;120:84–9. 5. Yoder BA, Harrison M, Clark RH. Time-related changes in steroid use and bronchopulmonary dysplasia in preterm infants. Pediatrics. 2009;124:673–9. 6. Singh BS, Clark RH, Powers RJ, Spitzer AR. Meconium aspiration syndrome remains a significant problem in the NICU: outcomes and treatment patterns in term neonates admitted for intensive care during a ten-year period. J Perinatol. 2009;29:497–503. 7. Clark RH, Bloom BT, Spitzer AR, Gerstmann DR. Empiric use of ampicillin and cefotaxime, compared with ampicillin and gentamicin, for neonates at risk for sepsis is associated with an increased risk of neonatal death. Pediatrics. 2006;117:67–74. 8. Gephart SM, Spitzer AR, Effken JA, Dodd E, Halpern M, McGrath JM. Discrimination of GutCheck NEC: a clinical risk index for necrotizing enterocolitis. J Perinatol. 2014;34:468–75. 9. Walker MW, Picklesheimer AH, Clark RH, Spitzer AR, Garite TJ. Impact of duration of rupture of membranes on outcomes of premature infants. J Perinatol. 2014; in press. 10. Olsen IE, Grovemen SA, Lawson ML, Clark RH, Zemel BS. New intrauterine growth curves based on United States data. Pediatrics. 2010. doi:10.1542/peds.2009-0913. 11. Clark RH, Olsen IE, Spitzer AR. Assessment of neonatal growth in prematurely born infants. Clin Perinatol. 2014;41:295–307. 12. Dobson NR, Patel RM, Smith PB, Kuehn DR, Clark J, Vyas-Read S, et al. Trends in caffeine use and association between clinical outcomes and timing of therapy in very low birth weight infants. J Pediatr. 2014;164:992–8. 13. Lagatta JM, Clark RH, Brousseau DC, Hoffmann RG, Spitzer AR. Varying patterns of home oxygen use in infants at 23–43 wks’ gestation discharged from United States neonatal intensive care units. J Pediatr. 2013;163:976–82. 14. Hsieh EM, Hornik CP, Clark RH, Laughon MM, Benjamin DK Jr, Smith PB, et al. Medication use in the neonatal intensive care unit. Am J Perinatol. 2014; In Press. 15. Walker MW, Clark RH, Spitzer AR. Elevation of plasma creatinine and renal failure in premature infants without major anomalies: terminology, occurrence and factors associated with risk. J Perinatol. 2011;31:199–205.

The Pediatrix BabySteps® Data Warehouse--a unique national resource for improving outcomes for neonates.

The Pediatrix Medical Group Clinical Data Warehouse represents a unique electronic data capture system for the assessment of outcomes, the management ...
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