Patient Credit Assessment Using Segmented Custom Models
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Solution Overview
Problem
Conventional credit score models are inadequate for predicting a patient's ability to pay their healthcare bills, as they are generic and fail to account for the unique characteristics of each healthcare facility's patient population, leading to inaccurate predictions.
Innovation Solution
A method and apparatus that segment the patient population using specific variables unique to each healthcare organization, incorporating additional financial information from credit bureaus when beneficial, and employing a custom model with adjustable parameters to enhance predictiveness, along with a Bureau Selector to optimize data source accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional generic credit score models are used to predict patient payment ability, then the model is simple and easy to implement, but the prediction accuracy is insufficient because it treats all patient segments the same
Solution Approach 1:
The patent segments the patient population into distinct groups based on multiple variables including demographic information, financial data, and healthcare utilization patterns. This segmentation allows the development of customized credit score models for each patient segment, improving prediction accuracy by accounting for the unique characteristics of different patient groups rather than treating all patients the same with a generic model.
Solution Approach 2:
The patent applies local quality by creating customized credit score models tailored to specific patient segments and individual healthcare facilities. Each model incorporates variables and parameters relevant to that particular segment or facility's patient population, ensuring the assessment is locally optimized rather than universally applied, thereby enhancing predictive accuracy for each specific context.
2Measurement precision
If multiple variables and custom models are used to accurately predict patient payment behavior, then prediction accuracy improves, but the system complexity and data processing requirements increase
Solution Approach 1:
The patent creates a multi-functional assessment system that can serve multiple healthcare facilities and various patient segments through a unified platform. The system is designed to be universally applicable across different organizations while accommodating customizations for specific patient populations, allowing one system to perform multiple functions: segmenting patients, selecting appropriate models, and generating credit assessments for diverse groups.
Solution Approach 2:
The patent utilizes parameter changes by allowing the credit score models to dynamically adjust their variables and weights based on the specific patient segment being assessed. The system can modify model parameters to match the characteristics of different patient populations, enabling accurate predictions without requiring completely separate models for each segment, thus managing complexity while maintaining precision.
3Productivity
If generic credit scores are used for all patients, then the assessment process is quick and efficient, but it fails to identify patients who would benefit from financial counseling or charity programs
Solution Approach 1:
The patent implements preliminary action by performing patient segmentation and customized credit assessment before the actual billing and collection process. This advance assessment identifies patients who are unlikely to pay their bills, allowing healthcare facilities to proactively enroll them in financial counseling or charity care programs before services are rendered or bills are sent, improving both efficiency and accuracy of financial management.
Solution Approach 2:
The system incorporates feedback mechanisms that continuously refine patient segmentation and credit predictions based on actual payment outcomes. By analyzing whether patients who were predicted to not pay actually defaulted, and whether those identified for charity programs qualified, the system learns and improves its segmentation accuracy over time, balancing efficiency with increasingly precise patient classification.
Data Source
AI summary
According to one example embodiment, there is provided a method and an apparatus to evaluate the credit of a healthcare patient. The example embodiment provides methods and computer systems programmed to use multiple variables that are known about a patient prior to a service being rendered to segment the patient population into finer grained groupings. These finer grained groupings allow financial factors, such as a credit score, to be a more accurate predictor. Also, according to another example embodiment, the model is not a generic model for all patients, but the variables and their parameters are specific to a particular healthcare organization's or facility's patient population. This creates a custom model that further enhances its predictiveness.