Family physician perspectives on managing indirect patient care activities in the electronic inbox: a systematic mixed studies review
Bibliographic record
Abstract
INTRODUCTION: Family physicians spend approximately half their professional time on indirect patient care activities (IPCA). Essential to patient care, inbox IPCA includes renewing prescriptions, checking lab results, and reviewing messages. However, IPCA detracts time from direct patient care and has been linked to burnout, potentially contributing to the family medicine crisis in Canada. Our objective was to understand the range of family physicians' experiences and perspectives regarding electronic inbox management. METHODS: We conducted a systematic review of peer-reviewed articles, published in English between 1 January 2012, and 22 April 2024, that addressed family physicians' perspectives on tasks related to the electronic inbox and used any method of primary data collection and analysis. Data analysis used a constant comparative method. RESULTS: Fifty-four articles were included. The combination of fragmented technical systems and an overwhelming volume of complex tasks has created a system where family physicians struggle to manage the administrative work of patient care. Selected impacts include excessive time spent on duplicated or unnecessary tasks, inadvertently making uninformed clinical decisions, and perceived tension between patient accessibility and workload. Strategies for management were described, including the re-design of electronic medical record systems, task delegation, and synchronizing prescription renewals with patient visits. CONCLUSIONS: The intersection of inefficient systems and high workloads makes inbox management labour-intensive and frustrating, lowering job satisfaction and efficacy. Downloading administrative tasks to family physicians, combined with the growing complexity of patient management, has generated a tremendous burden. Solutions are needed to improve the sustainability and appeal of family medicine.
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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 | 0.018 | 0.083 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".