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- Title
Differences in Health Professionals' Engagement With Electronic Health Records Based on Inpatient Race and Ethnicity.
- Authors
Yan, Chao; Zhang, Xinmeng; Yang, Yuyang; Kang, Kaidi; Were, Martin C.; Embí, Peter; Patel, Mayur B.; Malin, Bradley A.; Kho, Abel N.; Chen, You
- Abstract
Key Points: Question: Are there differences in health professionals' engagement with hospitalized patients' electronic health records (EHRs) with respect to race and ethnicity? Findings: In this cross-sectional study of 243 216 adult patients hospitalized at 2 major US academic medical institutions between 2018 and 2020, the EHRs of minoritized racial and ethnic populations were more likely to receive less health professional engagement compared with those of White patients. Meaning: The findings highlight unequal EHR engagement by race and ethnicity in the inpatient setting, and the analytic methods introduced in this study can be used to assess institutional EHR usage patterns across patient subpopulations. This cross-sectional study investigates differences in the level of health professionals' engagement with electronic health records according to inpatient race and ethnicity. Importance: US health professionals devote a large amount of effort to engaging with patients' electronic health records (EHRs) to deliver care. It is unknown whether patients with different racial and ethnic backgrounds receive equal EHR engagement. Objective: To investigate whether there are differences in the level of health professionals' EHR engagement for hospitalized patients according to race or ethnicity during inpatient care. Design, Setting, and Participants: This cross-sectional study analyzed EHR access log data from 2 major medical institutions, Vanderbilt University Medical Center (VUMC) and Northwestern Medicine (NW Medicine), over a 3-year period from January 1, 2018, to December 31, 2020. The study included all adult patients (aged ≥18 years) who were discharged alive after hospitalization for at least 24 hours. The data were analyzed between August 15, 2022, and March 15, 2023. Exposures: The actions of health professionals in each patient's EHR were based on EHR access log data. Covariates included patients' demographic information, socioeconomic characteristics, and comorbidities. Main Outcomes and Measures: The primary outcome was the quantity of EHR engagement, as defined by the average number of EHR actions performed by health professionals within a patient's EHR per hour during the patient's hospital stay. Proportional odds logistic regression was applied based on outcome quartiles. Results: A total of 243 416 adult patients were included from VUMC (mean [SD] age, 51.7 [19.2] years; 54.9% female and 45.1% male; 14.8% Black, 4.9% Hispanic, 77.7% White, and 2.6% other races and ethnicities) and NW Medicine (mean [SD] age, 52.8 [20.6] years; 65.2% female and 34.8% male; 11.7% Black, 12.1% Hispanic, 69.2% White, and 7.0% other races and ethnicities). When combining Black, Hispanic, or other race and ethnicity patients into 1 group, these patients were significantly less likely to receive a higher amount of EHR engagement compared with White patients (adjusted odds ratios, 0.86 [95% CI, 0.83-0.88; P <.001] for VUMC and 0.90 [95% CI, 0.88-0.92; P <.001] for NW Medicine). However, a reduction in this difference was observed from 2018 to 2020. Conclusions and Relevance: In this cross-sectional study of inpatient EHR engagement, the findings highlight differences in how health professionals distribute their efforts to patients' EHRs, as well as a method to measure these differences. Further investigations are needed to determine whether and how EHR engagement differences are correlated with health care outcomes.
- Subjects
LENGTH of stay in hospitals; CONFIDENCE intervals; CROSS-sectional method; HISPANIC Americans; RACE; RETROSPECTIVE studies; REGRESSION analysis; JOB involvement; SOCIOECONOMIC factors; HOSPITAL care; ACCESS to information; DESCRIPTIVE statistics; ELECTRONIC health records; PATIENT care; SOCIODEMOGRAPHIC factors; LOGISTIC regression analysis; ODDS ratio; WHITE people; DATA analysis software
- Publication
JAMA Network Open, 2023, Vol 6, Issue 10, pe2336383
- ISSN
2574-3805
- Publication type
Article
- DOI
10.1001/jamanetworkopen.2023.36383