Service Systems with Heterogeneous Customers: Investigating the Effect of Telemedicine on Chronic Care
Bibliographic record
Abstract
Medical specialists treating chronic conditions typically face a heterogeneous set of patients. Such heterogeneity arises because of differences in medical conditions as well as the travel burden each patient faces to visit the clinic periodically. Given this heterogeneity, we compare the strategic behavior of revenue-maximizing and welfare-maximizing specialists and prove that the former will serve a smaller patient population, spend more time with the patients, and have shorter waiting times. We also analyze the impact of telemedicine technology on patient utility and the specialists' operating decisions. We consider both the case when specialists can freely set their own fee for service and the case when fees are set exogenously by a third-party payer. We prove that with the introduction of telemedicine the specialists become more productive and the overall social welfare increases, though some patients, unexpectedly, will be worse off. Our analytical results lead to some important policy implications for facilitating the further deployment of telemedicine in the care of chronically ill patients.
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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 | 0.007 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".