Influencing Primary Care Antibiotic Prescription Behavior Using Financial Incentives
Bibliographic record
Abstract
Antibiotic resistance is an ongoing public health crisis fueled by overuse and misuse of antibiotics. The goal of this article is to examine the impact of action-based incentive payments on reducing inappropriate antibiotic prescriptions in primary care, where 30%-50% of antibiotic prescriptions are inappropriate. Various financial incentive programs to reduce the rate of inappropriate antibiotic prescriptions have been implemented and studied empirically. However, there have not been analytical studies to evaluate payment model contract design features and the potential for payment models to impact diagnosis decision making. We develop a stylized physician compensation model to study the interaction between a payer and a provider. The payer offers a payment contract, with a bonus tied to the prescription, to maximize social welfare, considering total costs of providing care and social harm from antibiotic resistance. Given the contract offered and their own opportunity cost associated with factors such as fear of misdiagnosis and time spent explaining to patients why antibiotics are not indicated, the provider chooses whether or not to prescribe antibiotics to patients for whom antibiotics are not clinically indicated. We consider four cases: when diagnostic accuracy relies on symptom presentation versus additional diagnostic testing and when the opportunity cost of not prescribing antibiotics is public versus private information of the provider. When there is no information asymmetry, an action-based incentive payment can co-ordinate care and achieve the first-best policy, decreasing the rate of inappropriate prescribing, even when incentive payments can affect the diagnosis behavior. However, when the diagnosis depends on additional testing, the first-best policy results in fewer inappropriate antibiotic prescriptions, when the test has high specificity. Therefore, when an accurate technical diagnostic is available, a simple to implement action-based incentive payment can be effective in reducing inappropriate antibiotic prescribing. In the realistic setting where the provider's opportunity cost is private information, an action-based incentive payment cannot eliminate inappropriate antibiotic prescribing. In these settings, the introduction of point of care diagnostics to aid in objective diagnostic criteria will reduce the unintended consequences of the contract.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".