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Validating and Expanding Claims-Based Algorithms of Frailty and Functional Disability for Value-Based Care and Payment

Validate and Expand Claims-Based Algorithms, Identifying Patients with Frailty and Functional Disabilities across Payer and Patient Populations
  • Office of the Assistant Secretary for Planning and Evaluation (ASPE)
  • Agency for Healthcare Research and Quality (AHRQ)
  • Centers for Disease Control and Prevention (CDC)
  • Centers for Medicare and Medicaid Services (CMS)
Start Date
  • ASPE - 5/10/2019
  • AHRQ - 5/28/2019
  • CDC - 6/18/2019
  • CMS - TBD
  • Use of Clinical Data for Research
  • Linking of Clinical and Other Data for Research
  • Use of Enhanced Publicly-Funded Data Systems for Research


STATUS: Active Project


Patient function - both physical and cognitive - are important outcomes assessed by patient-centered outcomes (PCOR) researchers. Older adults who are frail and persons with functional impairment (such as, but not limited to, difficulties with mobility, or cognitive impairment that result in limitations in a person’s ability to perform activities of daily living) are at increased risk for poor health outcomes. Frailty increases a person’s risk for functional impairment and cognitive decline. In addition to medical comorbid conditions, identifying frailty and functional disability plays an important role in informing clinical care, risk-adjustment of patient-centered outcomes research studies, as well as for evaluating performance and payments in value-based care programs. Improved data on frail persons with or at risk for functional impairment will help with case identification for research studies and public health surveillance, and enable researchers to evaluate clinical and health systems interventions for these vulnerable, high-need patients.

Providing publicly available data on frailty and functional risk generated from claims and/or EHR-based algorithms will help support patient-centered outcomes research, for case identification or for more robust risk-adjustment, as well as to guide and assess the quality of care in value-based care. Validation of these algorithms to identify risk of functional impairment using claims and/or existing EHR data across payers would ensure broader use and acceptance for use in care and payment programs and in research studies.


The project objectives of this multi-agency project are to build data capacity to identify frailty to support robust patient-centered outcomes research and use in value-based care and payment through the following tasks:

  1. Develop and validate a claims-based frailty algorithm using Medicare claims, to be made available to the public through the Chronic Condition Warehouse (CCW).
  2. Evaluation of the claims-based frailty algorithm applied to EHR data and tested in one or more health systems across payers.
  3. Compile an EHR guidance report with sharing learnings from the EHR Learning Network and project partners on collecting and extracting information on patients’ frailty and functional status from the EHR.

Project Partners:

ASPE contracted with the RAND Corporation to develop and evaluate existing claims-based algorithms of frailty and functional disabilities for potential inclusion in the Chronic Condition Warehouse (CCW) using Medicare fee-for-service (FFS) data.

The Agency for Healthcare Research and Quality (AHRQ) supported this project by testing the claims-based algorithm using electronic health record (EHR) data across health systems. The application of the claims-based algorithm with EHR data will support potential use in clinical care by health systems as well as help researchers conduct studies of patients with frailty and functional disabilities across patient and payer populations.


  1. Development and evaluation of claims-based frailty algorithm
    The RAND Corporation reviewed existing claims algorithms to predict frailty and functional impairment and attempted to improve upon previous algorithms using a novel source of data - post-acute care assessment data. The project developed new claims-based algorithms to predict two functional impairment outcomes: memory limitations and activity/mobility limitations using post-acute care assessment data. Several model specifications were tested and the new algorithm was compared to existing frailty algorithms developed by Kim et al. and Faurot et al that were developed based on the deficit accumulation approach to frailty first proposed by Kenneth Rockwood. The performance of the two best models were also stratified by subgroups of interest.

    Overall, the evaluation found that the Claims-based Frailty Index (CFI) developed and validated by Dae Kim was the best at predicting claims-based outcomes of hospitalizations, nursing facility stays, and days at home, assessed across most measures of model fit and subgroups.

    Based on the project findings in the final report, ASPE recommends using the Claims-based Frailty Index (CFI) to generate frailty scores using Medicare data for inclusion in the Chronic Condition Warehouse.
  2. Evaluation of claims-based frailty algorithm using EHR data across health systems
    Agency for Healthcare Research and Quality (AHRQ) contracted with Johns Hopkins University Center for Population Health Information Technology (JHU-CPHIT) to evaluate the claims-based frailty algorithm using EHR data across health systems with varying degrees of network open/closed-ness.

    The final report is on AHRQ’s website at:

    SQL and SAS code used for this project is available in Github.
  3. EHR data to identify frailty - implementation guidance report with sharing learnings from the EHR Learning Network
    RAND identified use cases on identifying frailty using EHR data in health systems in the US and examples from other countries, which demonstrate applications in both primary and specialist care. The final EHR implementation guide summarizes the learnings from the EHR Learning Network and the identified use cases.

Project Publications

Peer-reviewed journal articles describe the development and evaluation of the claims-based frailty algorithms:

  • Heins, S. E., Agniel, D., Mann, J., & Sorbero, M. E. (2023). Development and Validation of Algorithms to Predict Activity, Mobility, and Memory Limitations Using Medicare Claims and Post-Acute Care Assessments. Journal of Applied Gerontology, 42(7), 1651-1661.
  • Heins, S. E., Agniel, D., Mann, J., & Sorbero, M. E. (in press). Comparative Performance of Three Claims-Based Frailty Measures among Medicare Beneficiaries. Journal of Applied Gerontology.


  • Kim, Dae Hyun, Elisabetta Patorno, Ajinkya Pawar, Hemin Lee, Sebastian Schneeweiss, and Robert J Glynn, "Measuring frailty in administrative claims data: comparative performance of four claims-based frailty measures in the US medicare data," The Journals of Gerontology: Series A, Vol. 75, No. 6, 2020, pp. 1120-1125.
  • Kim, Dae Hyun, Sebastian Schneeweiss, Robert J Glynn, Lewis A Lipsitz, Kenneth Rockwood, and Jerry Avorn, "Measuring frailty in Medicare data: development and validation of a claims-based frailty index," The Journals of Gerontology: Series A, Vol. 73, No. 7, 2018, pp. 980-987.
  • Faurot, Keturah R, Michele Jonsson Funk, Virginia Pate, M Alan Brookhart, Amanda Patrick, Laura C Hanson, Wendy Camelo Castillo, and Til Stürmer, "Using claims data to predict dependency in activities of daily living as a proxy for frailty," Pharmacoepidemiology and drug safety, Vol. 24, No. 1, 2015, pp. 59-66.
  • Rockwood K, Mitnitski A. Frailty defined by deficit accumulation and geriatric medicine defined by frailty. Clin Geriatr Med. 2011 Feb;27(1):17-26.