Association of Dementia Severity and Caregiver Support With Early Discharge From Home Health

Differences in Home Health Services and Outcomes Between Traditional Medicare and Medicare Advantage

Importance

Private Medicare Advantage (MA) plans recently surpassed traditional Medicare (TM) in enrollment. However, MA plans are facing scrutiny for burdensome prior authorization and potential rationing of care, including home health. MA beneficiaries are less likely to receive home health, but recent evidence on differences in service intensity and outcomes among home health patients is lacking.

Objective

To examine differences in home health service intensity and patient outcomes between MA and TM.

Design, Setting, and Participants

This cross-sectional study was conducted from January 2019 to December 2022 in 102 home health locations in 19 states and included 178 195 TM and 107 102 MA patients 65 years or older with 2 or fewer 60-day home health episodes. It included a secondary analysis of standardized assessment and visit data. Inverse probability of treatment weighting regression compared service intensity and patient outcomes between MA and TM episodes, accounting for differences in demographic characteristics, medical complexity, functional and cognitive impairments, social environment, caregiver support, and local community factors. Models included office location, year, and reimbursement policy fixed effects. Data were analyzed between September 2023 and July 2024.

Exposure

TM vs MA plan.

Main Outcomes and Measures

Home health length of stay and number of visits from nursing, physical, occupational, and speech therapy, social work, and home health aides. Patient outcomes included improvement in self-care and mobility function, discharge to the community, and transfer to an inpatient facility during home health.

Results

Of 285 297 total patients, 180 283 (63.2%) were female; 586 (0.2%) were American Indian/Alaska Native, 8957 (3.1%) Asian, 28 694 (10.1%) Black, 7406 (2.6%) Hispanic, 1959 (0.7%) Native Hawaiian/Pacific Islander, 237 017 (83.1%) non-Hispanic White, and 678 (0.2%) multiracial individuals. MA patients had shorter home health length of stay by 1.62 days (95% CI, -1.82 to 1.42) and received fewer visits from all disciplines except social work. There were no differences in inpatient transfers. MA patients had 3% and 4% lower adjusted odds of improving in mobility and self-care, respectively (mobility odds ratio [OR], 0.97; 95% CI, 0.94-0.99; self-care OR, 0.96; 95% CI, 0.92-0.99). MA patients were 5% more likely to discharge to the community compared with TM (OR, 1.05; 95% CI, 1.01-1.08).

Conclusions and Relevance

The results of this cross-sectional study suggest that MA patients receive shorter and less intensive home health care vs TM patients with similar needs. Differences may be due to the administrative burden and cost-limiting incentives of MA plans. MA patients experienced slightly worse functional outcomes but were more likely to discharge to the community, which may have negative implications for MA patients, including reduced functional independence or increased caregiver burden.

HomeADScreen: Developing Alzheimer's disease and related dementia risk identification model in home healthcare

BACKGROUND

More than 50 % of patients with Alzheimer's disease and related dementia (ADRD) remain undiagnosed. This is specifically the case for home healthcare (HHC) patients.

OBJECTIVES

This study aimed at developing HomeADScreen, an ADRD risk screening model built on the combination of HHC patients' structured data and information extracted from HHC clinical notes.

METHODS

The study's sample included 15,973 HHC patients with no diagnosis of ADRD and 8,901 patients diagnosed with ADRD across four follow-up time windows. First, we applied two natural language processing methods, Word2Vec and topic modeling methods, to extract ADRD risk factors from clinical notes. Next, we built the risk identification model on the combination of the Outcome and Assessment Information Set (OASIS-structured data collected in the HHC setting) and clinical notes-risk factors across the four-time windows.

RESULTS

The top-performing machine learning algorithm attained an Area under the Curve = 0.76 for a four-year risk prediction time window. After optimizing the cut-off value for screening patients with ADRD (cut-off-value = 0.31), we achieved sensitivity = 0.75 and an F1-score = 0.63. For the first-year time window, adding clinical note-derived risk factors to OASIS data improved the overall performance of the risk identification model by 60 %. We observed a similar trend of increasing the model's overall performance across other time windows. Variables associated with increased risk of ADRD were "hearing impairment" and "impaired patient ability in the use of telephone." On the other hand, being "non-Hispanic White" and the "absence of impairment with prior daily functioning" were associated with a lower risk of ADRD.

CONCLUSION

HomeADScreen has a strong potential to be translated into clinical practice and assist HHC clinicians in assessing patients' cognitive function and referring them for further neurological assessment.