Identifying Medicare beneficiaries with Alzheimer's disease and related dementia using home health OASIS assessments

Author:

Bélanger Emmanuelle12ORCID,Rosendaal Nicole1,Gutman Roee3,Lake Derek1ORCID,Santostefano Christopher M.1,Meyers David J.12ORCID,Gozalo Pedro L.12

Affiliation:

1. Center for Gerontology and Healthcare Research Brown University School of Public Health Providence Rhode Island USA

2. Department of Health Services, Policy & Practice Brown University School of Public Health Providence Rhode Island USA

3. Department of Biostatistics Brown University School of Public Health Providence Rhode Island USA

Abstract

AbstractBackgroundHome health services are an important site of care following hospitalization among Medicare beneficiaries, providing health assessments that can be leveraged to detect diagnoses that are not available in other data sources. In this work, we aimed to develop a parsimonious and accurate algorithm using home health outcome and assessment information set (OASIS) measures to identify Medicare beneficiaries with a diagnosis of Alzheimer's disease and related dementia (ADRD).MethodsWe conducted a retrospective cohort study of Medicare beneficiaries with a complete OASIS start of care assessment in 2014, 2016, 2018, or 2019 to determine how well the items from various versions could identify those with an ADRD diagnosis by the assessment date. The prediction model was developed iteratively, comparing the performance of different models in terms of sensitivity, specificity, and accuracy of prediction, from a multivariable logistic regression model using clinically relevant variables, to regression models with all available variables and predictive modeling techniques, to estimate the best performing parsimonious model.ResultsThe most important predictors of having a diagnosis of ADRD by the start of care OASIS assessment were a prior discharge diagnosis of ADRD among those admitted from an inpatient setting, and frequently exhibiting symptoms of confusion. Results from the parsimonious model were consistent across the four annual cohorts and OASIS versions with high specificity (above 96%), but poor sensitivity (below 58%). The positive predictive value was high, over 87% across study years.ConclusionsThe proposed algorithm has high accuracy, requires a single OASIS assessment, is easy to implement without sophisticated statistical models, and can be used across four OASIS versions and in situations where claims are not available to identify individuals with a diagnosis of ADRD, including the growing population of Medicare Advantage beneficiaries.

Funder

National Institute on Aging

Publisher

Wiley

Subject

Geriatrics and Gerontology

Reference29 articles.

1. Alzheimer's Association.Alzheimer's disease facts and figures.2021.

2. Advance Care Planning Video Intervention Among Long-Stay Nursing Home Residents

3. Measuring Effects of Nondrug Interventions on Behaviors: Music & Memory Pilot Study

4. Using healthcare data in embedded pragmatic clinical trials among people living with dementia and their caregivers: state of the art;Bynum JPW;J Am Geriatr Soc,2020

5. Cognitive impairment among medicare home health patients: comparing available measures

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