Population health management to defer long term care in the short term

A method for selecting interventions for a particular policy holder of a long term care insurance policy, the method being implemented on a computing device, the method comprising:

• receiving claim data about when previous long term care insurance policy holders made a claim for long term care and for what type of care, for a block of policy holders, selecting a portion of said block which meets a set of intervention criteria;
• generating observation data over a period of at least 6 months about an ability of said portion of policy holders to handle problems when they are arrive, said observation data comprising medical data and self-sufficiency data about home environment, financial sustainability, life engagement, and functionality;
• using said computing device, providing scores to said at least said observation data and generating features as a function of said scores;
• generating scores based on said observation data
• using said computing device, building a probability based, predictive model from features based on a combination of said claim data and said observation data, at least two of said features being related to said self-sufficiency data, to identify which of said portion of policy holders are least likely to remain independent and/or at home during a next year;
• using said computing device, determining a reducible risk feature for an identified policy holder, identified by said model, from among said features, wherein said reducible risk feature is a changed feature of said model most likely to enable said identified policy holder to remain independent and/or at home during said next year;
• having an intervention table listing a set of interventions, each intervention having an associated reducible risk feature according to research data at least about improving quality of life, at least one of said interventions being non-medical, wherein said set of interventions comprises:
• a home optimization or modification, organizing transportation services, managing loss of a caregiver, preventing caregiver burnout, educating said particular senior about how to handle his/her diseases, encouraging said particular senior to engage in social activities, and encouraging said particular senior to be physically active;
• and
• using said computing device, identifying at least one intervention from said intervention table associated with said reducible risk feature for said identified policy holder to utilize as an aid to enable said identified policy holder to remain independent and/or at home for said next year, wherein said determining comprises:
• running said model with scores of said identified policy holder to generate a starting risk level of said identified policy holder;
• if said starting risk level indicates an expectation of said identified policy holder filing a claim within said one year of performing said building, changing one of said features included in said model to generate a changed model for said identified policy holder;
• running said changed model with said scores of said identified policy holder to generate a subsequent risk level for said identified policy holder;
• if said subsequent risk level is reduced from said starting risk level, selecting said changed feature as said reducible risk feature for said identified policy holder, otherwise repeating said changing and said running.