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.

Methods to improve the quality of smoking records in a primary care EMR database: exploring multiple imputation and pattern-matching algorithms.

BACKGROUND: Primary care electronic medical record (EMR) data are emerging as a useful source for secondary uses, such as disease surveillance, health outcomes research, and practice improvement. These data capture clinical details about patients' health status, as well as behavioural risk factors, such as smoking. While the importance of documenting smoking status in a healthcare setting is recognized, the quality of smoking data captured in EMRs is variable. This study was designed to test methods aimed at improving the quality of patient smoking information in a primary care EMR database. METHODS: EMR data from community primary care settings extracted by two regional practice-based research networks in Alberta, Canada were used. Patients with at least one encounter in the previous 2 years (2016-2018) and having hypertension according to a validated definition were included (n = 48,377). Multiple imputation was tested under two different assumptions for missing data (smoking status is missing at random and missing not-at-random). A third method tested a novel pattern matching algorithm developed to augment smoking information in the primary care EMR database. External validity was examined by comparing the proportions of smoking categories generated in each method with a general population survey. RESULTS: Among those with hypertension, 40.8% (n = 19,743) had either no smoking information recorded or it was not interpretable and considered missing. Those with missing smoking data differed statistically by demographics, clinical features, and type of EMR system used in the clinic. Both multiple imputation methods produced fully complete smoking status information, with the proportion of current smokers estimated at 25.3% (data missing at random) and 12.5% (data missing not-at-random). The pattern-matching algorithm classified 18.2% of patients as current smokers, similar to the population-based survey (18.9%), but still resulted in missing smoking information for 23.6% of patients. The algorithm was estimated to be 93.8% accurate overall, but varied by smoking status category. CONCLUSION: Multiple imputation and algorithmic pattern-matching can be used to improve EMR data post-extraction but the recommended method depends on the purpose of secondary use (e.g. practice improvement or epidemiological analyses).

Prevalence and Incidence of Diagnosed Chronic Rhinosinusitis in Alberta, Canada.

Importance: Reported prevalence rates of chronic rhinosinusitis (CRS) range from 1% to 12% worldwide. To facilitate appropriate health service delivery and resource allocation, it is important to improve the estimated burden of CRS to the health care system. Objectives: To assess the prevalence and incidence of diagnosed CRS in Alberta, Canada, from the perspective of the health care system and to evaluate the 10-year temporal trend and geographic variation of diagnosed CRS. Design, Setting, and Participants: From provincial-wide physicians' claim data, a CRS cohort was identified using a validated case definition. The population at the midpoint (2008-2009) of the study period (2 925 930) was used as the reference. The crude as well as age- and sex-standardized incidence and prevalence rates were calculated. The age-specific incidence and prevalence by sex were also assessed in each study year. Small-area variation analysis was conducted using extremal quotient, weighted coefficient of variation, χ2 statistic, systematic component of variation, and empirical Bayes variance estimate. Results: Of the 2 925 930 individuals in the study at midpoint (2008-2009), 1 451 261 (49.6%) were women, and the mean (SD) age was 45 (17) years. From fiscal year 2004-2005 to fiscal year 2013-2014, the mean age- and sex-standardized incidence of diagnosed CRS was 2.5 (range, 2.3-2.7) per 1000 population. The estimated prevalence based on age-specific incidence varied between 18.8 (95% CI, 18.7-18.9) and 23.3 (95% CI, 23.1-23.5) per 1000 population during 2004-2005 to 2013-2014, and no obvious growing trend was found. There was high geographic variation in the diagnosed incidence and prevalence of CRS (mean systematic component of variation, 19.4 and 12.3, respectively). Conclusions and Relevance: Although the incidence and prevalence rates of diagnosed CRS were lower compared with earlier published estimates obtained from population-based survey analysis, outcomes from this study may more accurately reflect the disease burden of CRS to the health care system. Given that the prevalence of CRS within a single province is expected to be uniformly distributed, the large geographic variation in diagnosed CRS indicates a potential gap in quality of care and justifies further investigation into the reasons for the variation.