Predictive Analytics Platform for Targeted Healthcare Bonus Payments
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Solution Overview
Problem
Existing financial incentive programs in healthcare are inefficient due to uniformly distributed bonus payments across large patient populations, leading to small incremental payments per patient and reduced incentives for healthcare professionals to participate.
Innovation Solution
A system that uses predictive analytics to identify a subset of patients most likely to benefit from targeted interventions, allowing for customized bonus payments that maximize economic gains while minimizing program costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If bonus payments are uniformly distributed across large patient populations, then program coverage is maximized, but incremental payment per patient becomes too small to effectively incentivize healthcare professionals
Solution Approach 1:
The patent segments the patient population into high-risk and low-risk categories based on predictive analytics. By dividing patients into distinct risk segments, the system can allocate bonus payments more effectively - focusing resources on high-risk patients where interventions are most needed, thereby maintaining broad coverage while improving per-patient incentive effectiveness.
Solution Approach 2:
The patent applies local quality by tailoring bonus payment amounts and intervention strategies to specific patient risk profiles. High-risk patients receive targeted interventions with appropriate bonus incentives, while low-risk patients receive standard care. This localized approach ensures that bonus payments are meaningful and effective for each patient segment rather than applying a uniform distribution.
2Ease of operation
If predictive analytics are used to identify high-risk patients for targeted interventions, then incentive effectiveness is improved, but system complexity increases
Solution Approach 1:
The patent implements preliminary action by pre-calculating risk scores and identifying high-risk patients before the bonus payment period begins. The predictive analytics model is trained in advance on historical data to automatically segment patients and determine intervention priorities. This preliminary processing reduces the complexity of real-time decision-making and allows the system to operate more efficiently once implemented.
Data Source
AI summary
The technology described herein relates to using predictions about patients' future health care utilization and/or outcomes (e.g., patients' expected future adherence to medication regimens) and the expected economic benefits of targeted improvements in the same utilization and/or outcomes (e.g., reduced likelihood of hospitalization attributable to more consistent medication use) to implement more effective and efficient health care improvement programs. The technology described here computes which subset of patients should be included in a value-based health care provider payment scheme and what the specific bonus payment amounts should be such that expected benefits from better patient outcomes, once realized, are greater than the expected costs of the payment scheme itself.