Validity of Medical Record Abstraction and Electronic Health Record-Generated Reports to Assess Performance on Cardiovascular Quality Measures in Primary Care.


Journal

JAMA network open
ISSN: 2574-3805
Titre abrégé: JAMA Netw Open
Pays: United States
ID NLM: 101729235

Informations de publication

Date de publication:
01 07 2020
Historique:
entrez: 29 7 2020
pubmed: 29 7 2020
medline: 29 12 2020
Statut: epublish

Résumé

Cardiovascular disease is the leading cause of death in the United States. To improve cardiovascular outcomes, primary care must have valid methods of assessing performance on cardiovascular clinical quality measures, including aspirin use (aspirin measure), blood pressure control (BP measure), and smoking cessation counseling and intervention (smoking measure). To compare observed performance scores measured using 2 imperfect reference standard data sources (medical record abstraction [MRA] and electronic health record [EHR]-generated reports) with misclassification-adjusted performance scores obtained using bayesian latent class analysis. This cross-sectional study used a subset of the 2016 aspirin, BP, and smoking performance data from the Healthy Hearts for Oklahoma Project. Each clinical quality measure was calculated for a subset of a practice's patient population who can benefit from recommended care (ie, the eligible population). A random sample of 380 eligible patients were included for the aspirin measure; 126, for the BP measure; and 115, for the smoking measure. Data were collected from 21 primary care practices belonging to a single large health care system from January 1 to December 31, 2018, and analyzed from February 21 to April 17, 2019. The main outcomes include performance scores for the aspirin, BP, and smoking measures using imperfect MRA and EHRs and estimated through bayesian latent class models. A total of 621 eligible patients were included in the analysis. Based on MRA and EHR data, observed aspirin performance scores were 76.0% (95% bayesian credible interval [BCI], 71.5%-80.1%) and 74.9% (95% BCI, 70.4%-79.1%), respectively; observed BP performance scores, 80.6% (95% BCI, 73.2%-86.9%) and 75.1% (95% BCI, 67.2%-82.1%), respectively; and observed smoking performance scores, 85.7% (95% BCI, 78.6%-91.2%) and 75.4% (95% BCI, 67.0%-82.6%), respectively. Misclassification-adjusted estimates were 74.9% (95% BCI, 70.5%-79.1%) for the aspirin performance score, 75.0% (95% BCI, 66.6%-82.5%) for the BP performance score, and 83.0% (95% BCI, 74.4%-89.8%) for the smoking performance score. Ensuring valid performance measurement is critical for value-based payment models and quality improvement activities in primary care. This study found that extracting information for the same individuals using different data sources generated different performance score estimates. Further research is required to identify the sources of these differences.

Identifiants

pubmed: 32721028
pii: 2768724
doi: 10.1001/jamanetworkopen.2020.9411
pmc: PMC7388024
doi:

Substances chimiques

Platelet Aggregation Inhibitors 0
Aspirin R16CO5Y76E

Types de publication

Journal Article Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, P.H.S.

Langues

eng

Sous-ensembles de citation

IM

Pagination

e209411

Subventions

Organisme : NIGMS NIH HHS
ID : U54 GM104938
Pays : United States

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Auteurs

Juell Homco (J)

Department of Medical Informatics, School of Community Medicine, University of Oklahoma Health Sciences Center, Tulsa.
Department of Biostatistics and Epidemiology, University of Oklahoma Health Sciences Center, Oklahoma City.

Hélène Carabin (H)

Department of Biostatistics and Epidemiology, University of Oklahoma Health Sciences Center, Oklahoma City.
Département de Pathologie et Microbiologie, Université de Montréal, Montréal, Quebec, Canada.
Département de Médecine Sociale et Préventive, Université de Montréal, Montréal, Quebec, Canada.
Centre de Recherche en Santé Publique, Université de Montréal et Centre Intégré Universitaire de Santé et de Services Sociaux du Centre-Sud-de-l'Île-de-Montréal, Montréal, Quebec, Canada.

Zsolt Nagykaldi (Z)

Department of Family and Preventive Medicine, University of Oklahoma Health Sciences Center, Oklahoma City.

Tabitha Garwe (T)

Department of Biostatistics and Epidemiology, University of Oklahoma Health Sciences Center, Oklahoma City.

F Daniel Duffy (FD)

Department of Medical Informatics, School of Community Medicine, University of Oklahoma Health Sciences Center, Tulsa.

David Kendrick (D)

Department of Medical Informatics, School of Community Medicine, University of Oklahoma Health Sciences Center, Tulsa.

Sydney Martinez (S)

Department of Biostatistics and Epidemiology, University of Oklahoma Health Sciences Center, Oklahoma City.

Yan Daniel Zhao (YD)

Department of Biostatistics and Epidemiology, University of Oklahoma Health Sciences Center, Oklahoma City.

Julie Stoner (J)

Department of Biostatistics and Epidemiology, University of Oklahoma Health Sciences Center, Oklahoma City.

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