Using EMR data to describe administrative workload of primary care providers in Nova Scotia, Canada

Context: Primary care providers in Canada face significant workload challenges, including managing prescriptions, referrals, and laboratory tests alongside patient visits. This study aims to analyze electronic medical record (EMR) data to understand these workload dynamics.

Objective: Describe trends in prescriptions, referrals, and laboratory tests per encounters using electronic medical record (EMR) data in Nova Scotia, Canada

Study design and analysis: Retrospective cohort design

Setting: We used de-identified Canadian primary care EMR data available from the Maritime Family Practice Research Network (MaRNet-FP)

Population studied: Clinicians with at least 500 patient encounters per year in Nova Scotia.

Intervention/Instrument: We analyzed EMR data to report means and associated standard deviations and described trends over time.

Outcome measures: Average number of prescriptions, referrals, and laboratory tests per encounters among primary care providers in Nova Scotia from 2007 to 2022.

Results: Clinicians with 500 or more patient contacts had an average of 2.7 (0.9SD) encounters per patient since 2007. On average, each encounter resulted in 1.7 (0.7) prescriptions, 1.1 (0.5) referrals, and 6.6 (2.8) laboratory tests. Trends in prescriptions, referrals, and laboratory tests per encounter seem consistent over time, though fell in the context of the COVID-19 pandemic. However, since 2020, encounters per patient increased, perhaps compensating for care delayed during the pandemic.

Conclusions: Taken together, the number of prescriptions, referrals, and laboratory tests per encounter point to a substantial volume of administrative work over and above time with patients. In addition, this province-specific investigation supports the use of EMR data to describe trends in administrative workload and informs the need for further analysis within national EMR data available through the Canadian Primary Care Sentinel Surveillance Network (CPCSSN) to understand pan-Canadian trends.