Assessing the suitability of general practice electronic health records for clinical prediction model development: a data quality assessment.

BACKGROUND: The use of general practice electronic health records (EHRs) for research purposes is in its infancy in Australia. Given these data were collected for clinical purposes, questions remain around data quality and whether these data are suitable for use in prediction model development. In this study we assess the quality of data recorded in 201,462 patient EHRs from 483 Australian general practices to determine its usefulness in the development of a clinical prediction model for total knee replacement (TKR) surgery in patients with osteoarthritis (OA). METHODS: Variables to be used in model development were assessed for completeness and plausibility. Accuracy for the outcome and competing risk were assessed through record level linkage with two gold standard national registries, Australian Orthopaedic Association National Joint Replacement Registry (AOANJRR) and National Death Index (NDI). The validity of the EHR data was tested using participant characteristics from the 2014-15 Australian National Health Survey (NHS). RESULTS: There were substantial missing data for body mass index and weight gain between early adulthood and middle age. TKR and death were recorded with good accuracy, however, year of TKR, year of death and side of TKR were poorly recorded. Patient characteristics recorded in the EHR were comparable to participant characteristics from the NHS, except for OA medication and metastatic solid tumour. CONCLUSIONS: In this study, data relating to the outcome, competing risk and two predictors were unfit for prediction model development. This study highlights the need for more accurate and complete recording of patient data within EHRs if these data are to be used to develop clinical prediction models. Data linkage with other gold standard data sets/registries may in the meantime help overcome some of the current data quality challenges in general practice EHRs when developing prediction models.

A data quality assessment to inform hypertension surveillance using primary care electronic medical record data from Alberta, Canada.

BACKGROUND: Hypertension is a common chronic condition affecting nearly a quarter of Canadians. Hypertension surveillance in Canada typically relies on administrative data and/or national surveys. Routinely-captured data from primary care electronic medical records (EMRs) are a complementary source for chronic disease surveillance, with longitudinal patient-level details such as sociodemographics, blood pressure, weight, prescribed medications, and behavioural risk factors. As EMR data are generated from patient care and administrative tasks, assessing data quality is essential before using for secondary purposes. This study evaluated the quality of primary care EMR data from one province in Canada within the context of hypertension surveillance. METHODS: We conducted a cross-sectional, descriptive study using primary care EMR data collected by two practice-based research networks in Alberta, Canada. There were 48,377 adults identified with hypertension from 53 clinics as of June 2018. Summary statistics were used to examine the quality of data elements considered relevant for hypertension surveillance. RESULTS: Patient year of birth and sex were complete, but other sociodemographic information (ethnicity, occupation, education) was largely incomplete and highly variable. Height, weight, body mass index and blood pressure were complete for most patients (over 90%), but a small proportion of outlying values indicate data inaccuracies were present. Most patients had a relevant laboratory test present (e.g. blood glucose/glycated hemoglobin, lipid profile), though a very small proportion of values were outside a biologically plausible range. Details of prescribed antihypertensive medication, such as start date, strength, dose, frequency, were mostly complete. Nearly 80% of patients had a smoking status recorded, though only 66% had useful information (i.e. categorized as current, past, or never), and less than half had their alcohol use described; information related to amount, frequency or duration was not available. CONCLUSIONS: Blood pressure and prescribed medications in primary care EMR data demonstrated good completeness and plausibility, and contribute valuable information for hypertension epidemiology and surveillance. The use of other clinical, laboratory, and sociodemographic variables should be used carefully due to variable completeness and suspected data errors. Additional strategies to improve these data at the point of entry and after data extraction (e.g. statistical methods) are required.

Prevalence and management of dementia in primary care practices with electronic medical records: a report from the Canadian Primary Care Sentinel Surveillance Network.

BACKGROUND: The proportion of Canadians living with Alzheimer disease and related dementias is projected to rise, with an increased burden on the primary health care system in particular. Our objective was to describe the prevalence and management of dementia in a community-dwelling sample using electronic medical record (EMR) data from the Canadian Primary Care Sentinel Surveillance Network (CPCSSN), which consists of validated, national, point-of-care data from primary care practices. METHODS: We used CPCSSN data as of Dec. 31, 2012, for patients 65 years and older with at least 1 clinical encounter in the previous 2 years. A validated case definition for dementia was used to calculate the national and provincial prevalence rates, to examine variations in prevalence according to age, sex, body mass index, rural or urban residence, and select comorbid conditions, and to describe patterns in the pharmacologic management of dementia over time at the provincial level. RESULTS: The age-standardized prevalence of dementia among community-dwelling patients 65 years and older was 7.3%. Prevalence estimates increased with age; they also varied between provinces, and upward trends were observed. Dementia was found to be associated with comorbid diabetes, depression, epilepsy and parkinsonism. Most of the patients with dementia did not have a prescription for a dementia-related medication recorded in their EMR between 2008 and 2012 inclusive. Those who had a prescription were most often prescribed donepezil by their primary care provider. INTERPRETATION: Overall prevalence estimates for dementia based on EMR data in this sample managed in primary care were generally in line with previous estimates based on administrative data, survey results or clinical sources.