Defining the gold standard: What is success in electronic health record documentation?

Author(s):  
Blair P. Golden ◽  
Michelle M. Kelly ◽  
Andrew P. J. Olson
2020 ◽  
Vol 17 (4) ◽  
pp. 402-404
Author(s):  
Jill Schnall ◽  
LingJiao Zhang ◽  
Jinbo Chen

For utilizing electronic health records to help design and conduct clinical trials, an essential first step is to select eligible patients from electronic health records, that is, electronic health record phenotyping. We present two novel statistical methods that can be used in the context of electronic health record phenotyping. One mitigates the requirement for gold-standard control patients in developing phenotyping algorithms, and the other effectively corrects for bias in downstream analysis introduced by study samples contaminated by ineligible subjects.


2016 ◽  
Vol 24 (e1) ◽  
pp. e121-e128 ◽  
Author(s):  
Susan E Spratt ◽  
Katherine Pereira ◽  
Bradi B Granger ◽  
Bryan C Batch ◽  
Matthew Phelan ◽  
...  

Objective: We assessed the sensitivity and specificity of 8 electronic health record (EHR)-based phenotypes for diabetes mellitus against gold-standard American Diabetes Association (ADA) diagnostic criteria via chart review by clinical experts. Materials and Methods: We identified EHR-based diabetes phenotype definitions that were developed for various purposes by a variety of users, including academic medical centers, Medicare, the New York City Health Department, and pharmacy benefit managers. We applied these definitions to a sample of 173 503 patients with records in the Duke Health System Enterprise Data Warehouse and at least 1 visit over a 5-year period (2007–2011). Of these patients, 22 679 (13%) met the criteria of 1 or more of the selected diabetes phenotype definitions. A statistically balanced sample of these patients was selected for chart review by clinical experts to determine the presence or absence of type 2 diabetes in the sample. Results: The sensitivity (62–94%) and specificity (95–99%) of EHR-based type 2 diabetes phenotypes (compared with the gold standard ADA criteria via chart review) varied depending on the component criteria and timing of observations and measurements. Discussion and Conclusions: Researchers using EHR-based phenotype definitions should clearly specify the characteristics that comprise the definition, variations of ADA criteria, and how different phenotype definitions and components impact the patient populations retrieved and the intended application. Careful attention to phenotype definitions is critical if the promise of leveraging EHR data to improve individual and population health is to be fulfilled.


Diabetes ◽  
2019 ◽  
Vol 68 (Supplement 1) ◽  
pp. 211-LB ◽  
Author(s):  
M. SUE KIRKMAN ◽  
JENNIFER G. NOONEY ◽  
STEPHEN R. BENOIT ◽  
KATHERINE A. BERGAMO ◽  
KAI M. BULLARD ◽  
...  

2011 ◽  
Vol 21 (1) ◽  
pp. 18-22
Author(s):  
Rosemary Griffin

National legislation is in place to facilitate reform of the United States health care industry. The Health Care Information Technology and Clinical Health Act (HITECH) offers financial incentives to hospitals, physicians, and individual providers to establish an electronic health record that ultimately will link with the health information technology of other health care systems and providers. The information collected will facilitate patient safety, promote best practice, and track health trends such as smoking and childhood obesity.


2012 ◽  
Author(s):  
Robert Schumacher ◽  
Robert North ◽  
Matthew Quinn ◽  
Emily S. Patterson ◽  
Laura G. Militello ◽  
...  

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