Undercounting diagnoses in Australian general practice: a data quality study with implications for population health reporting

Abstract

Abstract Background Diagnosis can often be recorded in electronic medical records (EMRs) as free text or using a term with a diagnosis code from a dropdown list. Researchers, governments, and agencies, including organisations that deliver incentivised primary care quality improvement programs, frequently utilise coded data only and ignore free text entries. Diagnosis data are reported for population healthcare planning including resource allocation for patient care. This study sought to determine if diagnosis counts based on coded diagnosis data only led to under-reporting of disease prevalence and if so, to what extent for six common or important chronic diseases. Methods This cross-sectional data quality study used de-identified EMR data from 84 general practices in Victoria, Australia. Data represented 456,125 patients who attended one of the general practices three or more times in two years between January 2021 and December 2022. We reviewed the percentage and proportional difference between patient counts of coded diagnosis entries alone and patient counts of clinically validated free-text entries for asthma, chronic kidney disease, chronic obstructive pulmonary disease, dementia, type 1 diabetes and type 2 diabetes. Results Undercounts were evident in all six diagnoses when using coded diagnoses alone (2.57–36.72% undercount), of these, five were statistically significant. Overall, 26.4% of all patient diagnoses had not been coded. Conclusion In Australia the reporting of aggregated patient diagnosis data to government relies on using coded diagnoses which can lead to significant undercount in diagnosis compared to counts that also incorporate clinically validated free-text diagnoses. Diagnosis under-reporting can impact on population health, healthcare planning, resource allocation, and patient care. We propose the use of phenotypes that draw diagnoses from clinically validated text entered improves the accuracy of reports of diagnoses and disease. There are existing technologies and collaborations from which to build trusted mechanisms to provide greater reliability of general practice EMR data used for secondary purposes.