Prescription OOP Cost Modeling Under Changing Insurance Terms
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Solution Overview
Problem
Estimating patient out-of-pocket costs for prescription medications is challenging due to changing insurance terms and conditions, making it difficult for pharmacies to accurately forecast enterprise profits and losses.
Innovation Solution
A machine learning system that determines an irrevocable out-of-pocket cost for prescriptions by using trained models to consider current patient information, enterprise constraints, and predicted external and patient changes over a given time interval, ensuring the cost remains fixed regardless of insurance reimbursements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional estimation methods are used for out-of-pocket costs, then the system is simple to operate, but the measurement precision of cost estimates deteriorates due to changing insurance terms
Solution Approach 1:
The system performs preliminary actions by obtaining current patient information and predicting future changes to insurance terms and patient characteristics before the prescription is filled. This advance preparation allows the system to calculate an accurate irrevocable out-of-pocket cost that accounts for anticipated changes, resolving the contradiction between precision and complexity by preparing data in advance rather than dealing with uncertainties at the point of calculation
Solution Approach 2:
The machine learning model serves as an intermediary that processes multiple inputs including current patient information, enterprise constraints, predicted patient changes, and predicted external conditions. This intermediary component transforms complex, uncertain data into a precise irrevocable out-of-pocket cost estimate, allowing the system to achieve high measurement precision while managing complexity through a dedicated computational component
2Stability of the object's composition
If insurance terms are allowed to change between estimation and filling, then the system remains adaptable to current conditions, but the stability of out-of-pocket cost estimates deteriorates
Solution Approach 1:
The system performs preliminary actions by obtaining current patient information and predicting future changes to insurance terms and patient characteristics before the prescription is filled. This advance preparation allows the system to calculate an accurate irrevocable out-of-pocket cost that accounts for anticipated changes, resolving the contradiction between precision and complexity by preparing data in advance rather than dealing with uncertainties at the point of calculation
Solution Approach 2:
The system applies preliminary anti-action by proactively predicting and compensating for anticipated changes in insurance terms and patient characteristics. The machine learning model forecasts future states and adjusts the out-of-pocket cost calculation accordingly, preventing the instability that would otherwise result from these changes. This preemptive adjustment ensures the irrevocable cost remains stable despite subsequent changes in insurance terms
3Loss of information
If out-of-pocket costs are calculated after insurance processing, then the system follows standard workflow, but the loss of information occurs regarding accurate cost forecasting for enterprise planning
Solution Approach 1:
The system performs preliminary actions by obtaining current patient information and predicting future changes to insurance terms and patient characteristics before the prescription is filled. This advance preparation allows the system to calculate an accurate irrevocable out-of-pocket cost that accounts for anticipated changes, resolving the contradiction between precision and complexity by preparing data in advance rather than dealing with uncertainties at the point of calculation
Solution Approach 2:
The system implements feedback by using the determined irrevocable out-of-pocket cost to inform enterprise planning and decision-making processes. The machine learning model continuously learns from actual outcomes versus predicted outcomes, refining its predictions of insurance term changes and patient characteristic changes. This feedback loop prevents information loss by ensuring accurate cost forecasting information is captured and utilized for enterprise planning
Data Source
AI summary
Techniques for generating an irrevocable or guaranteed out-of-pocket (OOP) cost for a patient's prescription prior to the patient submitting the prescription for processing and filling by an enterprise include receiving a request for the OOP cost, obtaining current information corresponding to the patient, and determining the irrevocable OOP cost of the prescription based on the current patient information, one or more constraints set by the enterprise for a given time interval and associated with OOP prescription costs, and changes to characteristic(s) of the patient and/or occurrences of external condition(s) predicted to occur during the given time interval. The irrevocable OOP cost may be charged to the patient for the prescription irrespective of any amount which is provided by the patient's insurance provider to cover the cost of the prescription. The techniques may utilize a set of specially-trained irrevocable OOP cost machine learning models to determine the irrevocable OOP prescription cost.