Predictive Analytics Platform for Mobility Equipment Outcomes
This technology combines a validated patient-reported outcome measure with a standardized uniform data set and an analytic algorithm that predicts how individuals with mobility impairments will fare with a given assistive device over time. By revealing which devices work best for which patients—and flagging emerging problems before they escalate—it lets providers, payers, and care organizations shift from reactive repairs to preventive, evidence-based mobility care.
Description
At its core, the platform pairs a patient-reported outcome measure assessing satisfaction in performing Mobility-Related Activities of Daily Living with a uniform data set that captures contributing factors—diagnosis, living situation, age, employment status, and transportation resources—in a consistent, objective, and analyzable format. Unlike the fragmented, subjective notes typically scattered across business and health records, this standardized collection method makes large-scale outcomes analysis possible across broad populations of mobility-device users. The platform's distinguishing feature is its structured longitudinal follow-up protocol: assessments are administered at initial device evaluation, 21 days after delivery, at 90 days, and annually thereafter. As these data accumulate in a registry, the analytic algorithm models relationships between patient characteristics, device choices, and downstream outcomes such as hospital admissions, falls, pressure injuries, loss of function, and community participation. The result is a predictive engine that both guides device selection and enables early intervention when a patient's trajectory signals risk.Applications
- Mobility assistive equipment providers seeking to differentiate their services and win payer and care-organization contracts- Orthotic and prosthetic device providers managing patients with similar longitudinal follow-up and outcomes-tracking needs
- Accountable care organizations and value-based-care programs tracking high-risk mobility-device populations
- Third-party payers and government health programs evaluating coverage policy and a patient's potential for functional improvement
- Clinical and rehabilitation centers building outcomes registries to support research, quality improvement, and reimbursement
Advantages
- Predicts which mobility devices produce the best outcomes for specific patient profiles, supporting more confident, evidence-based equipment decisions- Detects emerging problems early—before costly complications such as pressure injuries, falls, or function loss occur—enabling preventive intervention
- Standardizes the collection of common but historically subjective variables, unlocking large-scale outcomes analysis not currently possible in the field
- Generates the type of longitudinal outcomes evidence increasingly required by payers and care organizations to justify coverage and contracting
- Replaces a reactive, wait-for-the-patient-to-call service model with a proactive, contemporary preventive-care workflow
Invention Readiness
The system has been developed, validated, and deployed in real clinical and business operations, with a population-scale registry actively accumulating data and producing outcomes reports. Predictive modeling continues to mature as the dataset grows, placing the technology in an operational-demonstration-to-deployment range.IP Status
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