Clinical Informatics Researcher's Desiderata for the Data Content of the Next Generation Electronic Health Record

Abstract

Objective Clinical informatics researchers depend on the availability of high-quality data from the electronic health record (EHR) to design and implement new methods and systems for clinical practice and research. However, these data are frequently unavailable or present in a format that requires substantial revision. This article reports the results of a review of informatics literature published from 2010 to 2016 that addresses these issues by identifying categories of data content that might be included or revised in the EHR.

Materials and Methods We used an iterative review process on 1,215 biomedical informatics research articles. We placed them into generic categories, reviewed and refined the categories, and then assigned additional articles, for a total of three iterations.

Results Our process identified eight categories of data content issues: Adverse Events, Clinician Cognitive Processes, Data Standards Creation and Data Communication, Genomics, Medication List Data Capture, Patient Preferences, Patient-reported Data, and Phenotyping.

Discussion These categories summarize discussions in biomedical informatics literature that concern data content issues restricting clinical informatics research. These barriers to research result from data that are either absent from the EHR or are inadequate (e.g., in narrative text form) for the downstream applications of the data. In light of these categories, we discuss changes to EHR data storage that should be considered in the redesign of EHRs, to promote continued innovation in clinical informatics.

Conclusion Based on published literature of clinical informaticians' reuse of EHR data, we characterize eight types of data content that, if included in the next generation of EHRs, would find immediate application in advanced informatics tools and techniques.

Keywords

electronic health records - information storage and retrieval - health system - clinical informatics research - data quality

Funding

This study was funded in part by the Center for Clinical and Translational Sciences (CCTS) at the University of Alabama at Birmingham (UAB) under grant 1TL1TR001418–01, partly by the NIH Medical Student Training Program grant to the University of Alabama at Birmingham under grant 5T32GM008361–23, and partly by the CCTS NCATS grant and by research funds from the UASOM Informatics Institute.

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