Using EMR data to describe administrative workload of primary care providers in Nova Scotia, Canada
Bibliographic record
Abstract
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".