Analyzing Supply and Demand on a General Internal Medicine Ward: A Cross-Sectional Study
Bibliographic record
Abstract
ABSTRACT Background The capacity of the general internal medicine clinical teaching units has been strained by decreasing resident supply and increasing patient demand. The objective of our study was to quantitatively compare the number of residents (supply) with the volume and duration of patient care activities (demand) to identify inefficiency. Methods Using the most recently available data from an academic teaching hospital, we identified each occurrence of a set of patient care activities that took place on the clinical teaching unit. We completed a descriptive analysis of the frequencies of these activities and how the frequencies varied by hour, day, week, month, and year. Patient care activities included admissions, rounds, responding to pages, meeting with patients and their families, patient transfers, discharges, and responding to cardiac arrests. The estimated time to complete each task was based on the available data in our electronic healthcare system and interviews with general internal medicine physicians or trainees. To calculate resident utilization, the person-hours of patient care tasks was divided by the person-hours of resident supply. Resident utilization was computed for three scenarios corresponding to varying levels of resident absenteeism. Results Between 2015 and 2019 there were 14,581 consultations to general internal medicine from the emergency department. Patient volumes tended to be highest during January and lowest during May and June; and highest on Monday morning and lowest on Friday night. Daily admissions into hospital from the emergency department were higher on weekdays than on weekends, and hourly admissions peaked at 8:00 AM and between 3:00 PM and 1:00 AM. Weekday resident utilization was generally highest between 8:00 AM and 2:00 PM and lowest between 1:00 AM and 8:00 AM. In a scenario where all residents were present apart from those who were post-call, resident utilization generally never exceeded 100%; in scenarios where at least one resident was absent due to illness and/or vacation, it was common for resident utilization to approach or exceed 100%, particularly during daytime working hours. Interpretation Analyzing supply and demand on a general internal medicine ward has allowed us to identify periods where supply and demand are not aligned and to empirically demonstrate the vulnerability of current staffing models. These data have the potential to inform and optimize scheduling on an internal medicine ward.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".