Value of Future Adherence Score for Patient Intervention Prioritization
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Solution Overview
Problem
Conventional health care technologies fail to provide a metric for predicting patient adherence to therapy and estimating cost reduction associated with adherence, leading to inaccurate population-level estimates and lack of differentiation at the patient level, which hinders targeted intervention strategies.
Innovation Solution
The development of a Value of Future Adherence (VFA) score, calculated using patient-level data, which incorporates the probability of non-adherence, probability of conversion to adherence, and cost reduction associated with adherence, enabling healthcare providers to prioritize patients for cost-effective interventions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If conventional adherence monitoring technology is used, then patient involvement in therapy can be conveyed, but actual value associated with potential future adherence cannot be determined
Solution Approach 1:
The patent transforms adherence monitoring from simple binary tracking to a multi-parameter probabilistic model that calculates VFA scores based on multiple factors including adherence probability, conversion probability, and cost reduction estimates. This parameter transformation enables quantification of future adherence value that was previously unavailable.
Solution Approach 2:
The patent introduces an intermediary computational layer (the VFA score calculation system) that processes raw adherence data and transforms it into actionable value metrics. This intermediary system bridges the gap between observing adherence behavior and understanding its economic impact, enabling providers to prioritize interventions based on calculated value rather than raw adherence percentages alone.
2Quantity of substance
If population-level cost estimates are used, then overall cost differences can be calculated, but patient-level differentiation is lost
Solution Approach 1:
The patent segments population-level cost data into individual patient-level estimates by applying the VFA calculation framework to each patient's specific adherence profile. This segmentation allows providers to identify which individual patients represent the highest value targets for intervention, rather than treating all patients uniformly based on aggregate population statistics.
Solution Approach 2:
The patent applies local quality by tailoring cost estimates to each patient's specific characteristics, adherence history, and conversion probability. Rather than using a single population-level cost figure, the system calculates customized VFA scores that reflect the unique value potential of each patient, enabling precise targeting of adherence improvement resources.
3Productivity
If adherence monitoring is implemented without VFA scoring, then adherence levels can be tracked, but cost-effective intervention prioritization is hindered
Solution Approach 1:
The patent performs preliminary action by calculating VFA scores in advance of intervention decisions. This pre-calculation allows providers to prioritize patients before deploying intervention resources, ensuring that limited adherence improvement resources are directed toward patients with the highest predicted value rather than treating all patients equally or using intuitive but unquantified prioritization methods.
Data Source
AI summary
The present technology calculates a value of future adherence (VFA) score which is a patient-level, predicted, expected cost of conversion from non-adherence to adherence over a specified time-frame. The score consists of three general components: (1) probability of being non-adherent, (2) cost reduction associated with being adherent, and (3) probability of converting from non-adherent to adherent. These values can be combined to create an overall VFA score. A user interface is then provided which shows at least a list of patients and information related to the VFA score.