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

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.

Publikationsverlauf

Eingereicht: 23. Juni 2022

Angenommen: 11. Oktober 2022

Artikel online veröffentlicht:
14. Dezember 2022

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