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Appl Clin Inform 2022; 13(05): 1172-1180
DOI: 10.1055/s-0042-1758737
Research Article

Quantifying Electronic Health Record Data Quality in Telehealth and Office-Based Diabetes Care

Kevin K. Wiley
1   Department of Healthcare Leadership and Management, Medical University of South Carolina, Charleston, South Carolina, United States
,
Eneida Mendonca
2   University of Cincinnati, Cincinnati, Ohio, United States
,
Justin Blackburn
3   Department of Health Policy and Management, Richard M. Fairbanks School of Public Health, Indiana University, Indianapolis, Indiana, United States
,
Nir Menachemi
3   Department of Health Policy and Management, Richard M. Fairbanks School of Public Health, Indiana University, Indianapolis, Indiana, United States
,
Mary De Groot
4   Indiana University School of Medicine, Indianapolis, Indiana
,
Joshua R. Vest
3   Department of Health Policy and Management, Richard M. Fairbanks School of Public Health, Indiana University, Indianapolis, Indiana, United States
› Author Affiliations Funding This research was supported by the National Library of Medicine of the National Institutes of Health (NIH) under award T15LM012502. Additional support was provided by the Health Policy Research Scholars of the Robert Wood Johnson Foundation (RWJF).
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Abstract

Objective Data derived from the electronic health record (EHR) are commonly reused for quality improvement, clinical decision-making, and empirical research despite having data quality challenges. Research highlighting EHR data quality concerns has largely been examined and identified during traditional in-person visits. To understand variations in data quality among patients managing type 2 diabetes mellitus (T2DM) with and without a history of telehealth visits, we examined three EHR data quality dimensions: timeliness, completeness, and information density.

Methods We used EHR data (2016–2021) from a local enterprise data warehouse to quantify timeliness, completeness, and information density for diagnostic and laboratory test data. Means and chi-squared significance tests were computed to compare data quality dimensions between patients with and without a history of telehealth use.

Results Mean timeliness or T2DM measurement age for the study sample was 77.8 days (95% confidence interval [CI], 39.6–116.4). Mean completeness for the sample was 0.891 (95% CI, 0.868–0.914). The mean information density score was 0.787 (95% CI, 0.747–0.827). EHR data for patients managing T2DM with a history of telehealth use were timelier (73.3 vs. 79.8 days), and measurements were more uniform across visits (0.795 vs. 0.784) based on information density scores, compared with patients with no history of telehealth use.

Conclusion Overall, EHR data for patients managing T2DM with a history of telehealth visits were generally timelier and measurements were more uniform across visits than for patients with no history of telehealth visits. Chronic disease care relies on comprehensive patient data collected via hybrid care delivery models and includes important domains for continued data quality assessments prior to secondary reuse purposes.

Keywords

electronic health record - data quality - telehealth - chronic disease

Protection of Human and Animal Subjects

This study was approved by the Indiana University Institutional Review Board (IRB) as exempt research. This research analyzed secondary electronic health record (EHR) data and did not involve Human Subjects.


Supplementary Material



Publication History

Received: 23 June 2022

Accepted: 11 October 2022

Article published online:
14 December 2022

© 2022. Thieme. All rights reserved.

Georg Thieme Verlag KG
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