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

• Published in: Journal of the American Geriatrics Society (JAGS) Vol. 71; no. 10; pp. 3229 - 3236
• Main Authors: Bélanger, Emmanuelle, Rosendaal, Nicole, Gutman, Roee, Lake, Derek, Santostefano, Christopher M, Meyers, David J, Gozalo, Pedro L
• Format: Journal Article
• Language: English
• Published: United States Wiley Subscription Services, Inc 01.10.2023
• Subjects: Aged; Algorithms; Alzheimer Disease - diagnosis; Alzheimer Disease - epidemiology; Alzheimer's disease; Dementia; Dementia disorders; Diagnosis; Elder care; Hospitalization; Humans; Mathematical models; Medical diagnosis; Medicare; Medicare Part C; Neurodegenerative diseases; Older people; Patient Discharge; Prediction models; Regression analysis; Retrospective Studies; Statistical analysis; United States; United States; home healthcare; Alzheimer's disease diagnosis; measurement validity
• ISSN: 0002-8614; 1532-5415; 1532-5415
• DOI: 10.1111/jgs.18487

Home 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). We 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. The 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. The 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.