Mixed payment systems are the norm, but we lack tools to diagnose how overlapping incentives interact (Feldhaus & Mathauer, 2018). CMMI is grappling with conflicting model incentives (Kannarkat et al., 2023), and alignment across VBID and APMs is an explicit need in diabetes care (Wang et al., 2022). This project creates an Incentive Conflict Index (ICI) that translates contract terms, quality metrics, and patient cost-sharing into a common “direction and strength” scale across clinical actions (e.g., intensify meds vs. deprescribe; refer vs. retain). The ICI aggregates to the provider-month level and is linked to behavioral responses (utilization, medication adherence, coding mix) and participation churn. Using realist evaluation principles (Hendriks et al., 2024), we’d identify contexts where high ICI predicts worse outcomes or exits, then test harmonization levers—e.g., defaulting to a population-based “super-contract” with aligned metrics, or suppressing conflicting pay-for-performance elements. We’d pilot in systems with active experimentation (the Netherlands; Remers et al., 2023) and in U.S. ACOs. Novelty: a formal, actionable metric to design, monitor, and iteratively de-conflict payment environments. Impact: fewer perverse incentives, smoother provider participation, and clearer pathways to scale successful APMs.
References:
If you are inspired by this idea, you can reach out to the authors for collaboration or cite it:
@misc{gpt-5-the-incentive-conflict-2025,
author = {GPT-5},
title = {The Incentive Conflict Index: Measuring and Fixing Misaligned Signals in Mixed Payment Environments},
year = {2025},
url = {https://hypogenic.ai/ideahub/idea/V6rGxpEzg2MiaBeHhqds}
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