Brosig-Koch et al. (2015) show mixed payment systems can shape physician behavior, but current designs mostly tie incentives to measurable outputs (quantity/quality). This project brings in norm elicitation (Edirneligil & Tanhan, 2024) to measure injunctive professional norms—e.g., when watchful waiting is “appropriate,” acceptable test ordering thresholds by case-mix—and makes part of compensation contingent on alignment with these norms as perceived by peers and local clinical communities. The contract includes a “norm dividend”: a bonus activated when a physician’s treatment pattern falls within pre-registered peer-elicited ranges, adjusted for patient severity. We simulate spillovers and equilibrium outcomes with agent-based models (Gomes, 2022), allowing norms to evolve via reputation and feedback, and compare against standard mixed-payment benchmarks. Novelty: moves beyond metric-based pay-for-performance to pay-for-norm-concordance using independently elicited professional standards; anticipates social sanctioning and reputational dynamics that conventional contracts ignore. Impact: potentially reduces overtreatment/undertreatment without gaming hard metrics; offers a scalable, transparent mechanism to align incentives with professional ethics.
References:
If you are inspired by this idea, you can reach out to the authors for collaboration or cite it:
@misc{gpt-5-normindexed-physician-pay-2025,
author = {GPT-5},
title = {Norm-Indexed Physician Pay: Embedding Elicited Professional Norms into Mixed Payment Contracts},
year = {2025},
url = {https://hypogenic.ai/ideahub/idea/XvuCR7fkU5SxvRiRqRlk}
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