Reviewing, Refining, and Validating Claims-Based Algorithms of Frailty and Functional Impairment
Final Report
ResearchPublished Nov 6, 2023
The authors reviewed claims-based algorithms. To identify individuals at greater risk of frailty, they developed new algorithms using Medicare fee-for-service claims that were validated using patient assessment data from two types of post-acute care providers: home health agencies and skilled nursing facilities. Finally, they compared the relative performance of the new and existing algorithms at predicting three claims-based outcomes.
Final Report
ResearchPublished Nov 6, 2023
Frailty is a clinical syndrome that is characterized by a constellation of symptoms, including loss of strength, low energy, and weight loss. According to research, the syndrome is associated with negative health outcomes, such as falls, disability, fractures, and increased risk of mortality. Research has also shown that frailty is associated with increased utilization and spending, independent of other medical risk factors. Identifying and quantifying frailty might be an important component of risk-adjustment for value-based payments or might help target specific interventions. Despite its importance, measuring frailty is challenging because of the lack of consistent measurement of frailty-related concepts.
The authors reviewed and refined claims-based algorithms. To identify individuals at greater risk of frailty and functional impairment, they developed new algorithms using Medicare fee-for-service (FFS) claims that were validated using patient assessment data from two types of post-acute care (PAC) providers: home health agencies (HHAs) and skilled nursing facilities (SNFs). Finally, they compared the relative performance of the new and existing algorithms at predicting three claims-based outcomes in a data set representative of all Medicare FFS beneficiaries. Overall, they found that using algorithms previously developed by Kim and colleagues and reported in a 2018 article performed best for most outcomes and subpopulations, although the new algorithms performed slightly better at predicting a nursing home stay in the following year by some metrics, particularly among PAC patients.
This work was conducted under a contract with the Office of the Assistant Secretary for Planning and Evaluation with funding by the Office of the Secretary Patient-Centered Outcomes Research Trust Fund and carried out within the Payment, Cost, and Coverage Program in RAND Health Care.
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