A Novel Voting System for Medical Catalogues in National Health Insurance

TL;DR

The paper addresses opacity and inefficiency in updating national medical insurance catalogues by proposing a doctor-agent voting framework in which agents accrue credit points through peer evaluations and stake these points to influence catalogue decisions, with potential blockchain-based smart contracts to ensure transparency. Credit-point dynamics are formalized as $DeltaCP_i = \sum_{j,m} C_{R2A} S^A_{i_m} S^R_{j_m} + \sum_{j,k,l} C_{R2R} S^R_{ik_l} S^R_{jk_l}$, with $C_{R2A} = 1 / \sum_{j,m} |S^A_{i_m} S^R_{ji_m}|$ and $C_{R2R} = 1 / \sum_{j,k,l} |S^R_{ik_l} S^R_{jk_l}|$, ensuring losses do not exceed assets. Monte Carlo simulations (Metropolis–Ulam) assess how random variations in doctor preferences affect consensus on treatment inclusion, revealing that learning dynamics—where rewards align with outcomes—enhance agreement on beneficial, cost-effective options. The approach also contemplates patient-outcome-based incentives to further align physician voting with health benefits, and outlines blockchain-enabled implementation and pilot testing to validate scalability and fairness. Overall, the framework offers a data-driven, transparent pathway to more equitable and efficient health-insurance decision-making at scale.

Abstract

This study explores the conceptual development of a medical insurance catalogue voting system. The methodology is centred on creating a model where doctors would vote on treatment inclusions, aiming to demonstrate transparency and integrity. The results from Monte Carlo simulations suggest a robust consensus on the selection of medicines and treatments. Further theoretical investigations propose incorporating a patient outcome-based incentive mechanism. This conceptual approach could enhance decision-making in healthcare by aligning stakeholder interests with patient outcomes, aiming for an optimised, equitable insurance catalogue with potential blockchain-based smart-contracts to ensure transparency and integrity.

Figures (3)

• Figure 1: Standard interaction stage in the doctor voting system: Doctor Alice and others evaluate Doctor Bob's Action, while Bob and others evaluate Alice's Action
• Figure 2: Non-Learning Model: Changes of Credit Points over Staking Rate for Actions from doctor agents
• Figure 3: Learning Model with Consumer Selection: Changes of Credit Points over Staking Rate for Actions from doctor agents