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

Abstract

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-atrandom). 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.

Healthcare providers’ knowledge, attitudes and practices on smoking cessation intervention in the Northern Cape

“…In their daily practice, it is recommended that HCPs ask for and record the smoking status of their patients (Garies et al 2020 ; Van Schayck et al 2017 ; Van Zyl-Smit et al 2013 ). This may also help to monitor the impact of smoking cessation interventions (Garies et al 2020 ).…”

“…In their daily practice, it is recommended that HCPs ask for and record the smoking status of their patients (Garies et al 2020 ; Van Schayck et al 2017 ; Van Zyl-Smit et al 2013 ). This may also help to monitor the impact of smoking cessation interventions (Garies et al 2020 ). This study found poor and inconsistent record-keeping; less than half of the HCPs always ask for and record their patients’ smoking status.…”

Dense phenotyping from electronic health records enables machine learning-based prediction of preterm birth

“…We also note that many lifestyle factors, such as smoking, alcohol consumption, diet, and physical activity, have been implicated for increasing preterm birth risk [ 1 , 68 ]. Many of these data are recorded in unstructured fields in EHRs, and there are active efforts to develop accurate algorithms to extract these data from EHR [ 69 , 70 ]. As these approaches become robust, including lifestyle factors may further improve preterm birth prediction.…”

A multi-step approach to managing missing data in time and patient variant electronic health records

“…Simpler approaches toward EHR imputation must consider whether missing values are missing completely at random (MCAR), missing at random (MAR), or missing not at random (MNAR) [ 14 ]. Conditional imputation methods may be used to account for these dependencies, most effectively if missing data are MAR [ 10 , 12 , 15 ]. While they may improve completeness and predictive precision, these methods may be computationally intensive when applied to large-scale EHR data with significant amounts of missing values.…”