Addressing Electronic Clinical Information in the Construction of Quality Measures

• Published in: Academic pediatrics 2014-09, Vol.14 (5), p.S82-S89
• Main Authors: Bailey, L. Charles, MD, PhD, Mistry, Kamila B., PhD, MPH, Tinoco, Aldo, MD, MPH, Earls, Marian, MD, MTS, Rallins, Marjorie C., DPM, Hanley, Kendra, MS, Christensen, Keri, MS, Jones, Meredith, MPH, Woods, Donna, EdM, PhD
• Format: Article
• Language: English
• Subjects: Child; CHIPRA; e-measurement; Electronic Health Records; Humans; Neonatal and Perinatal Medicine; Pediatrics; PQMP; Quality Assurance, Health Care - methods; Quality Indicators, Health Care - standards; quality measurement; United States
• ISSN: 1876-2859; 1876-2867
• DOI: 10.1016/j.acap.2014.06.006

Abstract Electronic health records (EHR) and registries play a central role in health care and provide access to detailed clinical information at the individual, institutional, and population level. Use of these data for clinical quality/performance improvement and cost management has been a focus of policy initiatives over the past decade. The Children's Health Insurance Program Reauthorization Act of 2009 (CHIPRA)-mandated Pediatric Quality Measurement Program supports development and testing of quality measures for children on the basis of electronic clinical information, including de novo measures and respecification of existing measures designed for other data sources. Drawing on the experience of Centers of Excellence, we review both structural and pragmatic considerations in e-measurement. The presence of primary observations in EHR-derived data make it possible to measure outcomes in ways that are difficult with administrative data alone. However, relevant information may be located in narrative text, making it difficult to interpret. EHR systems are collecting more discrete data, but the structure, semantics, and adoption of data elements vary across vendors and sites. EHR systems also differ in ability to incorporate pediatric concepts such as variable dosing and growth percentiles. This variability complicates quality measurement, as do limitations in established measure formats, such as the Quality Data Model, to e-measurement. Addressing these challenges will require investment by vendors, researchers, and clinicians alike in developing better pediatric content for standard terminologies and data models, encouraging wider adoption of technical standards that support reliable quality measurement, better harmonizing data collection with clinical work flow in EHRs, and better understanding the behavior and potential of e-measures.