Predictive Model for Long Term Care Independence
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Long term care insurance (LTCI) is expensive and open-ended, as some policy holders will need care for an extended period, posing a challenge in maintaining independence and managing costs effectively.
Innovation Solution
A method and system that utilize claim data and observation data, including medical and self-sufficiency data, to build a predictive model identifying policy holders at risk of losing independence. Interventions such as home optimization, transportation services, and caregiver support are then tailored to reduce this risk.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If long term care insurance is provided to cover nursing home care and home care costs, then policy holders receive necessary care when incapacitated, but the insurance becomes expensive and open-ended with extended care periods
Solution Approach 1:
The system performs preliminary assessments of policy holders' functional status, cognitive ability, and social support before claims occur. By identifying at-risk individuals early through predictive modeling, interventions can be implemented proactively to prevent or delay care needs, thereby reducing the duration and cost of future claims while maintaining reliable coverage when truly needed
2Measurement precision
If predictive modeling is used to identify policy holders at risk of losing independence, then targeted interventions can be implemented, but data collection and model complexity increase
Solution Approach 1:
The system segments policy holders into risk categories based on predictive modeling of functional status, cognitive ability, and social support factors. By dividing the population into stratified groups (e.g., low, medium, high risk), the system achieves precise risk measurement while managing complexity through standardized assessment protocols and targeted intervention pathways for each segment
Solution Approach 2:
The system changes key parameters related to independence risk by implementing targeted interventions. For example, improving social support networks, enhancing home environment safety, or providing caregiver training are specific parameter changes that directly address measured risk factors, thereby reducing the likelihood of care claims while maintaining manageable system complexity
Data Source
AI summary
A system for helping a particular policy holder of a long term care insurance policy stay independent during a next year includes an external data gatherer, a model builder and an intervention determiner. The external data gatherer gathers claim data, and observation data of medical data and self-sufficiency data about a plurality of policy holders. The model builder builds a predictive model from features based on the claim data and the observation data and determines which of the policy holders are unlikely to remain independent during the next year. The intervention determiner determines, for a particular policy holder, an intervention to improve a probability that the particular policy holder remain independent for another year.