Identifying opioid-related electronic health record phenotypes for emergency care research and surveillance: An expert consensus driven concept mapping process

Abstract

Background: Case identification for many areas of opioid research and surveillance in the emergency department (ED) is challenging as patients are often undifferentiated with nonspecific symptoms and diagnostic codes have proven to be inaccurate. Opioid-related phenotypes based on combinations of electronic health record data are a promising method to address this gap but lack a consensus-based conceptual framework to aid organization and prioritization. Methods: To achieve consensus around opioid-related phenotype topics in the ED, we used a hybrid scheme that employed modified Delphi and conceptual mapping methods. The combined iterative process used three rounds of electronic meetings and questionnaires to generate consensus recommendations and concept mappings based on the opinions and feedback of the 9 member Delphi panel. Mean importance and feasibility scores based on 5-point Likert scales (1 = relatively unimportant (infeasible) to 5 = extremely important (feasible)) for each statement/phenotype were calculated. We used multidimensional scaling to produce a point map of the phenotype concepts and hierarchical cluster analysis to generate concept maps. Results: After the first round, 120 initial phenotype concepts were proposed which were reduced to 73 concepts after normalization by the research team. Opioid overdose (9.54, SD = 0.9) had the highest combined importance and feasibility score. A final labeled 12-cluster solution was determined to be the most parsimonious description of the content by the research team. Three key groups emerged: opioid overdose, other opioid-specific phenotypes (opioid use disorder, opioid misuse, and opioid withdrawal) with significant concept overlap and opioid use-related phenotypes (homelessness, falls, infections, and suicidality). Conclusions: Using an expert consensus driven concept mapping process we identified specific opioid phenotype concepts within an overlapping schema that carry high priority for development and validation to advance emergency care opioid-related research and surveillance.

Keywords:

• Phenotype
• opioid
• informatics
• emergency medicine

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No potential conflict of interest was reported by the author(s).

Correction Statement

This article has been republished with minor changes. These changes do not impact the academic content of the article.

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Funding

This research was supported by the National Institutes of Health through the NIH Heal Initiative under award number [3UG1DA015831-18S7]. The funding organization had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health or its Heal Initiative.