Computerized system and method for identifying members at high risk of falls and fractures

A computer-implemented system for identifying a member of a health insurance market-based member population at risk for falling within a predetermined time period, the system comprising:

• (a) one or more computing devices storing:
• (1) falls model triggers comprising
• alcohol abuse, Alzheimer's disease, blood vessel injury, cognitive dysfunction, concussion, dementia, dialysis, difficulty walking, epilepsy and convulsion, face, eye, or neck contusion, face, neck, or scalp injury, fracture, hallucinations, hip, knee or joint pain, hypotension, motor problems, muscle weakness, nerve or spinal injury, obesity, one or more previous falls, other injury, Parkinson's disease, and stroke;
• (2) falls predictors comprising injury/poisoning incidence count, a lower limb fracture, head, neck, or spine trauma incidence count, previous falls, an upper limb fracture, a neck or trunk fracture, a dislocation, a narcotic prescription, age, a hospital emergency room visit, obesity, governmental agency health score, a bone disorder, gender, race, an anti-depressant prescription, and an anti-hypertensive prescription;
• and
• (b) one or more computing devices executing instructions to:
• (1) receive member data and consumer data for the entire health insurance market-based member population, wherein said member data comprises data selected from the group consisting of:
• medical claims data and pharmacy claims data, wherein said consumer data comprises data selected from the group consisting of:
• demographic data, geographic data, and financial data;
• (2) analyze said received member data for the entire health insurance market-based member population to identify a subset of members within said health insurance market-based member population having one or more of said falls model triggers present in said member's data;
• (3) process the member data and said consumer data for said subset of members using an algorithm selected from the group consisting of:
• variable selection, principle component analysis, and clustering;
• (4) extract features from the member data for said subset of members by temporal feature extraction, wherein said falls predictors are selected to correspond with the extracted features;
• (5) provide a plurality of training conditions to a computing device that comprises a falls predictive model, said training conditions comprising an unintentional fall, a skull fracture, a neck fracture, a trunk fracture, an upper limb fracture, a lower limb fracture, and a dislocation of bones;
• (6) develop the falls predictive model using a modeling technique selected from the group consisting of:
• decision tree, logistic regression, artificial neural networks, and ensemble;
• (7) provide said member data and said consumer data for said subset of members to the computing device that comprises said falls predictive model;
• (8) receive a calculated falls risk score from the computing device that comprises said falls predictive model, wherein said falls risk score represents the likelihood that the respective member of said subset of members will visit an emergency room as a result of experiencing a fall within the predetermined time period, and wherein said calculated falls risk score is determined at least in part based on the presence or absence of each of said falls predictors in said member data for the respective member;
• (9) sort the received calculated falls risk score into one of a plurality of groups according to a severity level indicated by the received calculated falls risk score;
• (10) assign a clinical program or intervention for each of the members in said subset of members, wherein said assignment is determined based on the group into which said member's calculated falls risk score has been sorted, where said intervention is adapted to reduce the member's calculated falls risk score;
• and
• (11) enroll said member in said assigned clinical program or intervention.