Real-World Evidence of COVID-19 Patients’ Data Quality in the Electronic Health Records

Abstract

Despite the importance of electronic health records data, less attention has been given to data quality. This study aimed to evaluate the quality of COVID-19 patients’ records and their readiness for secondary use. We conducted a retrospective chart review study of all COVID-19 inpatients in an academic healthcare hospital for the year 2020, which were identified using ICD-10 codes and case definition guidelines. COVID-19 signs and symptoms were higher in unstructured clinical notes than in structured coded data. COVID-19 cases were categorized as 218 (66.46%) “confirmed cases”, 10 (3.05%) “probable cases”, 9 (2.74%) “suspected cases”, and 91 (27.74%) “no sufficient evidence”. The identification of “probable cases” and “suspected cases” was more challenging than “confirmed cases” where laboratory confirmation was sufficient. The accuracy of the COVID-19 case identification was higher in laboratory tests than in ICD-10 codes. When validating using laboratory results, we found that ICD-10 codes were inaccurately assigned to 238 (72.56%) patients’ records. “No sufficient evidence” records might indicate inaccurate and incomplete EHR data. Data quality evaluation should be incorporated to ensure patient safety and data readiness for secondary use research and predictive analytics. We encourage educational and training efforts to motivate healthcare providers regarding the importance of accurate documentation at the point-of-care.

Electronic health record data quality assessment and tools: a systematic review

“…Forty-six (45%) papers assessed concordance most often using data element agreement or data source agreement ( Tables 2 and 3 ). Similar to correctness, comparison data sets included other EHR data from the same system, 17 , 18 , 25 , 30 , 31 , 33 , 46 , 50 , 53 , 63–69 unstructured data, 34 , 55 , 70–73 manual review by a physician, 38 , 41 , 42 , 46 , 48 , 74–76 billing data, 46 , 68 , 69 , 77–80 or an external data source. 18 , 25 , 53 , 54 , 56 , 57 , 60 , 80–87 Common terms used to describe concordance were consistency, agreement, sensitivity, discrepancy, and correlation.…”

Information on medical records of covid-19 patients in Indonesia

“…Zali et al (2018) added that Health professionals contribute to recording patient care, and the Medical Records Department leads those responsible for managing daily patient records. Binkheder et al (2021) stated that in assessing the accuracy of the data, it is necessary to evaluate and analyze the recording of Covid-19 patients further. Hospitals have implemented Electronic Health Records, abbreviated as HER data, as a model for collecting patient data via electronic data.…”

Health Informatics: The Foundations of Public Health

“…The pandemic has also generated extensive datasets related to COVID-19. The vast amount of COVID-19 data presents numerous health informatics research opportunities [ 4 , 5 , 6 ].…”

“…Since the quality of the input dataset for constructing a model or performing an analysis will affect the overall reliability of the results, Binkheder et al [ 5 ] discussed the data quality of COVID-19 patient records in their study. They evaluated the quality of the records and their readiness for secondary use.…”