Comparing the Quality of Primary Care Electronic Health Record Data in Australia and Canada: Case Study in Osteoarthritis.

backgroundGeneral practice electronic health records (EHRs) contain a wealth of patient information. However, these data are collected for clinical purposes. Hence, questions remain around the suitability of using these data for other purposes, including epidemiological research, developing and validating clinical prediction models, conducting audits, and informing policy.

objectiveThis study aimed to compare the quality of osteoarthritis-related data in Australian and Canadian general practice EHRs for externally validating a clinical prediction model for total knee replacement surgery.

methodsA data quality assessment was conducted on 201,462 patient general practice EHRs from Australia provided by National Prescribing Service MedicineWise, and 92,425 from Canada provided by the Canadian Primary Care Sentinel Surveillance Network. Completeness, plausibility, and external validity of data elements relevant to osteoarthritis were assessed. Completeness and plausibility were evaluated using counts and proportions. For external validity, prevalence was estimated using proportions, and knee replacement summarized as a rate per 100,000 population.

resultsThere were minimal incomplete and implausible data fields for age and sex (<1%), geographic location (<5%), and commonly cooccurring comorbidities (<10%) in both datasets. However, weight, height, BMI, and Canadian Index of Multiple Deprivation contained >50% missing data. The recording of osteoarthritis by age and sex in both datasets were similar to national estimates, except for patients aged >80 years (Australia: 16.6%, 95% CI 16%-17.3% vs 13.1%, 95% CI 11.2%-15.4%; Canada: 36.7%, 95% CI 36.1%-37.2% vs 50.8%, 95% CI 50.7%-50.9%). Total knee replacement rates were substantially lower in both EHR datasets compared with national estimates (Australia: 72 vs 218 per 100,000; Canada: 0.84 vs 200 per 100,000).

conclusionsAge, sex, geographic location, commonly cooccurring comorbidities, and prescribing of osteoarthritis medications in Australian and Canadian general practice EHRs are suitable for use in clinical prediction model validation studies. However, BMI and the Canadian Index of Multiple Deprivation are unfit for such use due to large proportions of missing data. Rates of total knee replacement surgery were substantially underreported and should not be used for prediction model validation. Better harmonization of patient data across primary and tertiary care is required to improve the suitability of these data. In the meantime, data linkage with national registries and other health datasets may overcome some of the data quality challenges in general practice EHRs.