TY - JOUR
T1 - Addressing electronic clinical information in the construction of quality measures
AU - Bailey, L. Charles
AU - Mistry, Kamila B.
AU - Tinoco, Aldo
AU - Earls, Marian
AU - Rallins, Marjorie C.
AU - Hanley, Kendra
AU - Christensen, Keri
AU - Jones, Meredith
AU - Woods, Donna
N1 - Funding Information:
The authors are supported by Pediatric Quality Measurement Program Center of Excellence grants from the Agency for Healthcare Research and Quality to the Children's Hospital of Philadelphia (C.B.), the National Collaborative for Innovation in Quality Measurement (A.T.), and the Pediatric Measurement Center of Excellence (M.R., K.H., K.C., M.J, D.W.).
Funding Information:
Publication of this article was supported by the US Department of Health and Human Services and the Agency for Healthcare Research and Quality.
PY - 2014
Y1 - 2014
N2 - 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.
AB - 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.
KW - CHIPRA
KW - PQMP
KW - e-measurement
KW - electronic health records
KW - quality measurement
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U2 - 10.1016/j.acap.2014.06.006
DO - 10.1016/j.acap.2014.06.006
M3 - Comment/debate
C2 - 25169464
AN - SCOPUS:84906850061
SN - 1876-2859
VL - 14
SP - S82-S89
JO - Academic Pediatrics
JF - Academic Pediatrics
IS - 5 SUPPL.
ER -