Related Experiment Video
Updated: Jun 13, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Use of Claims Data to Screen for Functional Limitations Among Medicare Beneficiaries
Megan Mathews1, Steven C Martino2, Denis Agniel1
1RAND, Santa Monica, California.
Importance:
The only functional impairment data available for all Medicare beneficiaries identify those who qualified for Medicare before age 65 years due to work-related disability. This administrative measure omits impairments arising after age 64 years or affecting nonwork-related activities. Surveys may offer more accurate assessments, but survey data on functional limitations are not routinely available. Claims data may provide a practical, scalable alternative.
Objective:
To describe the development of a survey-based index of functional limitations and a new claims-based model for predicting limitations.
Design, Setting, And Participants:
Data from respondents to the 2024 Medicare fee-for-service (FFS) Consumer Assessment of Healthcare Providers and Systems (CAHPS) survey were used to create the FFS CAHPS functional limitations index (FCFLI), a measure of the extent to which functional limitations affect overall health and the benchmark for the study's claims-based measure. The FCFLI scores of respondents were linked to Medicare FFS claims from April 2023 to March 2024 to develop a claims-based predictive model (FCFLI-claims). The final model was applied to all FFS beneficiaries enrolled in Medicare Parts A and B during the same period.
Main Outcomes And Measures:
The FCFLI was derived from beneficiaries' self-rated health and ability to perform 6 basic activities of daily living and 1 instrumental activities of daily living (collectively, I/ADLs). Nine sets of claims-based indicators of functional impairment were used to predict FCFLI scores.
Results:
Data were collected from 67 596 respondents (53.4% female; mean [SD] age, 75 [8] years). In the FCFLI model, all I/ADL indicators were negatively associated with self-rated health, with the largest effect estimates for difficulty walking (-13.9 points; 95% CI, -14.6 to -13.2 points) and running errands (-11.7 points; 95% CI, -12.7 to -10.8 points). In the FCFLI-claims model, 14 claims indicators had relative importance values that were 5% or greater of the maximum value. The model reliably identified beneficiaries with functional limitations (80.4% positive predictive value). Applied to the 2024 Medicare FFS population, approximately 12% were identified as likely to have functional limitations. Nearly 63% (approximately 2 450 000) of those identified by the algorithm were not identified by Centers for Medicare & Medicaid Services administrative measure of disability, which focuses on limitations that develop before age 65 years.
Conclusions And Relevance:
In this cross-sectional study, FCFLI-claims identified beneficiaries likely to have functional limitations who could be prioritized for a survey assessment to verify the algorithm's results. Integrating this information with entitlement data could enhance Centers for Medicare & Medicaid Services' monitoring of functional status, especially among age-eligible beneficiaries.
Related Concept Videos
Kaplan-Meier Approach
Documentation in Long-Term and Home Healthcare Setting
Long-Term Care Facilities
Methods of Documentation VI: Case Management Model
For example, a patient with a chronic illness...
Data Validation
Nursing assessment guides are generally based on holistic models rather than medical...

