Using Electronic Medical Records and Health Claim Data to Develop a Patient Engagement Score for Patients With Multiple Chronic Conditions: An Exploratory Study.

Autor: Ngorsuraches S; Department of Health Outcomes Research and Policy, Auburn University, Harrison School of Pharmacy, Auburn, AL, USA., Michael S; Department of Mathematics and Statistics, Jerome J. Lohr College of Engineering, South Dakota State University, Brookings, SD, USA., Poudel N; Department of Health Outcomes Research and Policy, Auburn University, Harrison School of Pharmacy, Auburn, AL, USA., Djira G; Department of Mathematics and Statistics, Jerome J. Lohr College of Engineering, South Dakota State University, Brookings, SD, USA., Griese E; Department of Pediatrics, University of South Dakota Sanford School of Medicine, Sanford Research, Sioux Falls, SD, USA.; Behavioral Sciences Group, Sanford Research, Sioux Falls, SD, USA., Selya A; Department of Pediatrics, University of South Dakota Sanford School of Medicine, Sanford Research, Sioux Falls, SD, USA.; Behavioral Sciences Group, Sanford Research, Sioux Falls, SD, USA., Da Rosa P; Population Health Evaluation Center, Office of Nursing Research, College of Nursing, South Dakota State University, Brookings, SD, USA.
Jazyk: angličtina
Zdroj: Journal of patient experience [J Patient Exp] 2021 Jan 18; Vol. 8, pp. 2374373520981480. Date of Electronic Publication: 2021 Jan 18 (Print Publication: 2021).
DOI: 10.1177/2374373520981480
Abstrakt: The study objective was to (1) develop a statistical model that creates a novel patient engagement score (PES) from electronic medical records (EMR) and health claim data, and (2) validate this developed score using health-related outcomes and charges of patients with multiple chronic conditions (MCCs). This study used 2014-16 EMR and health claim data of patients with MCCs from Sanford Health. Patient engagement score was created based on selected patients' engagement behaviors using Gaussian finite mixture model. The PES was validated using multiple logistic and linear regression analyses to examine the associations between the PES and health-related outcomes, and hospital charges, respectively. Patient engagement score was generated from 5095 patient records and included low, medium, and high levels of patient engagement. The PES was a significant predictor for low-density lipoprotein, emergency department visit, hemoglobin A 1c , estimated glomerular filtration rate, hospitalization, and hospital charge. The PES derived from patient behaviors recorded in EMR and health claim data can potentially serve as a patient engagement measure. Further study is needed to refine and validate the newly developed score.
Competing Interests: Declaration of Conflicting Interests: The author(s) declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: Emily Griese and Ariella Selya received funding from the National Institutes of General Medical Sciences (NIGMS) of the NIH, grant number 1P20GM121341.
(© The Author(s) 2021.)
Databáze: MEDLINE
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