January 29, 2020: Open-Source Tool From the ADAPTABLE Supplement Enables Comparisons of EHR and Patient-Reported Data

The ADAPTABLE Supplement project team released user documentation and source code for an open-source tool that enables rapid assessment of concordance between electronic health record (EHR) data and information reported directly by patients. The tool is part of a larger effort supported by the NIH Collaboratory Coordinating Center to develop and test methods for integrating patient-reported data into the EHR and to streamline data for use in pragmatic clinical trials.

ADAPTABLE, the first major randomized comparative effectiveness trial conducted by the National Patient-Centered Clinical Research Network (PCORnet), aims to identify the optimal dose of aspirin therapy for secondary prevention of atherosclerotic cardiovascular disease. The trial relies on both existing EHR data sources and direct patient report.

The ADAPTABLE Supplement project team developed a menu-driven query (MDQ) tool to enable comparison of patient-reported data with analogous EHR data. Using data for patients enrolled in ADAPTABLE at the trial’s largest US site, the team tested the MDQ tool by using it to compare patient-reported hospitalizations with hospitalizations recorded in the EHR. In this test, 46% of the encounters recorded in the EHR were an exact match with patient-reported encounters, and 85% of the EHR-recorded encounters fell within 5 days of the patient-reported encounter dates.

The study demonstrates the feasibility of using the MDQ tool to assess concordance between patient-reported data and EHR data. Because the tool is based on the PCORnet Common Data Model, it will be useful to participating sites across the network and can be used for querying this widely available data source.

The MDQ tool user documentation describes the features of the tool and provides links to the source code. A summary of the MDQ tool’s development describes how the tool performed with data from ADAPTABLE.

This work was supported by a supplemental grant award to the NIH Collaboratory Coordinating Center from the National Center for Complementary and Integrative Health.

Computer Adaptive Testing Approach to Patient-Reported Outcomes


Michael Bass and Maria Varela Diaz of the Department of Social Sciences, Feinberg School of Medicine, Northwestern University, have kindly given the Living Textbook permission to post their presentation (link opens as a PDF) about how to use an application programming interface (API) to create a computer adaptive testing (CAT) program that integrates patient-reported outcome (PRO) measures with an institution’s electronic health record (EHR) system.

With a CAT approach, PRO assessment can cover a wide range of question/response items with increased precision. In their CAT application, the authors describe a clinical use case for a mobile health solution, using measures from the NIH-sponsored PRO Measurement Information System (PROMIS®) domain framework, in which a health assessment is issued by a physician, administered to a patient via phone, and then sent back to the EHR.

You can read more about CAT in the Patient-Reported Outcomes chapter of the Living Textbook.