Data quality in electronic medical records in Manitoba: Do problem lists reflect chronic disease as defined by prescriptions?

OBJECTIVE: To determine if the problem list (health conditions) in primary care electronic medical records (EMRs) accurately reflects the conditions for which chronic medications are prescribed in the EMR. DESIGN: A retrospective analysis of EMR data. SETTING: Eighteen primary care clinics across rural and urban Manitoba using the Accuro EMR. PARTICIPANTS: Data from the EMRs of active patients seen in an 18-month period (December 18, 2011, to June 18, 2013, or December 3, 2012, to June 3, 2014) were used. MAIN OUTCOME MEASURES: The likelihood of documentation in the EMR problem list of those specific chronic diseases for which drug prescriptions were documented in the EMR. Regression modeling was performed to determine the effect of clinic patient load and remuneration type on the completeness of EMR problem lists. RESULTS: Overall problem-list completeness was low but was highest for diabetes and lowest for insomnia. Fee-for-service clinics generally had lower problem-list completeness than salaried clinics did for all prescription medications examined. Panel size did not affect problem-list completeness rates. CONCLUSION: The low EMR problem-list completeness suggests that this field is not reliable for use in quality improvement initiatives or research until higher reliability has been demonstrated. Further research is recommended to explore the reasons for the poor quality and to support improvement efforts.

Data quality of electronic medical records in Manitoba: do problem lists accurately reflect chronic disease billing diagnoses?

OBJECTIVE: To determine problem list completeness related to chronic diseases in electronic medical records (EMRs) and explore clinic and physician factors influencing completeness. METHODS: A retrospective analysis of primary care EMR data quality related to seven chronic diseases (hypertension, diabetes, asthma, congestive heart failure, coronary artery disease, hypothyroidism, and chronic obstructive pulmonary disorder) in Manitoba, Canada. We included 119 practices in 18 primary care clinics across urban and rural Manitoba. The main outcome measure was EMR problem list completeness. Completeness was measured by comparing the number of EMR-documented diagnoses to the number of billings associated with each disease. We calculated odds ratios for the effect of clinic patient load and salary type on EMR problem list completeness of the 7 chronic diseases. RESULTS: Completeness of EMR problem list for each disease varied widely among clinics. Factors that significantly affected EMR problem list completeness included the primary care provider, the patient load, and the clinic's funding and organization model (ie, salaried, fee-for-service, or residency training clinics). Average rates of completeness were: hypertension, 72%; diabetes, 80%; hypothyroidism, 63%; asthma, 56%; chronic obstructive pulmonary disorder, 43%; congestive heart failure, 54%; and coronary artery disease, 64%. CONCLUSION: This study demonstrates the high variability but generally low quality of problem lists (health condition records) related to 7 common chronic diseases in EMRs. There are systematic physician- and clinic-level factors associated with low data quality completeness. This information may be useful to support improvement in EMR data quality in primary care.