Patient‐controlled use of nonphysician providers: Appointment scheduling in mixed‐provider settings
Bibliographic record
Abstract
The aging population and increasing chronic disease load are rapidly changing the face of primary care delivery, with mid‐level (e.g., nurse) practitioners providing growing proportion of patient care. Potential differences in the quality of care offered by physicians and nurse practitioners may affect patient preferences, thus leading to patient choice behavior. This paper focuses on the problem of appointment scheduling for physician–nurse teams in the presence of patient choice and no‐shows. We propose a novel model that accounts for patient choices in a system with two provider types. Despite the increased structural complexity of the model, we derive sufficient conditions under which the problem is efficiently solvable. To counter the computational challenges arising in the general setting, we propose an easy‐to‐implement heuristic, which is proven to be optimal in the absence of patient no‐shows. Our numerical study shows how the ratio of qualities of care delivered by nurses and physicians affect the profitability of the medical practice, enabling the analysis of the trade‐offs involved in hiring a nurse practitioner. This paper introduces a patient‐controlled approach to incorporating nonphysician providers into physician‐led outpatient care delivery systems and compares it to widely used “ice breaker” and “standalone” modes of using nonphysician providers. Our findings reveal that clinical practices that employ mixed (physicians and nonphysicians) provider pools can significantly improve their financial and operational performance by moving away from the “ice breaker” and “standalone” use of nonphysician providers by delaying the selection of an appropriate care provider till the actual day of care delivery.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".