Contracts to Promote Optimal Use of Optional Diagnostic Tests in Cancer Treatment
Bibliographic record
Abstract
In this study, we examine performance‐based payment contracts to promote the optimal use of an optional diagnostic test for newly diagnosed cancer patients. Our work is inspired by three trends: tremendous increases in the cost of new, advanced cancer drugs; development of new diagnostic tests to allow physicians to tailor treatment to patients; and changes in healthcare funding models that reward quality care. We model the interaction between two parties—a healthcare payer and an oncologist, in which the oncologist has private information about patients’ characteristics (adverse selection) and the payer does not know whether the oncologist takes the optimal course of action (moral hazard). We show that, in the presence of information asymmetry, a healthcare payer should never incentivize an oncologist to use a diagnostic test for all patients, even if the diagnostic test is available for free. Moreover, although the oncologist has additional information about a patient's risk, he cannot always benefit from this private information. We also find that social welfare may not increase as a result of a decrease in the oncologist's concerns regarding the health outcome of patients. Finally, we show that it is not always socially optimal to make a diagnostic test compulsory even if such a policy can be implemented for free.
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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 | 0.033 | 0.100 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".