Data gaps in electronic health record (EHR) systems: An audit of problem list completeness during the COVID-19 pandemic

Abstract

Objective: To evaluate the completeness of diagnosis recording in problem lists in a hospital electronic health record (EHR) system during the COVID-19 pandemic.

Design: Retrospective chart review with manual review of free text electronic case notes.

Setting: Major teaching hospital trust in London, one year after the launch of a comprehensive EHR system (Epic), during the first peak of the COVID-19 pandemic in the UK.

Participants: 516 patients with suspected or confirmed COVID-19.

Main outcome measures: Percentage of diagnoses already included in the structured problem list.

Results: Prior to review, these patients had a combined total of 2841 diagnoses recorded in their EHR problem lists. 1722 additional diagnoses were identified, increasing the mean number of recorded problems per patient from 5.51 to 8.84. The overall percentage of diagnoses originally included in the problem list was 62.3% (2841 / 4563, 95% confidence interval 60.8%, 63.7%).

Conclusions: Diagnoses and other clinical information stored in a structured way in electronic health records is extremely useful for supporting clinical decisions, improving patient care and enabling better research. However, recording of medical diagnoses on the structured problem list for inpatients is incomplete, with almost 40% of important diagnoses mentioned only in the free text notes.

Keywords: COVID-19; Data completeness; EHR systems; Electronic health record; Health information technology; Problem lists.

Fig. 1

Proportion of diagnoses recorded on the problem list, aggregated at the ICD-10 block level, for conditions with at least 15 additional entries from manual review of electronic notes.