The Evaluation of Telehealth's Impact on Medicare Annual Wellness Visits and Dementia Diagnosis
Bibliographic record
Abstract
BACKGROUND: Telehealth has emerged as a pivotal tool in modern healthcare and holds particular promise for early screening and diagnosis of cognitive impairment and dementia, which are critical for timely interventions. A key initiative in this area is the digitalized Montreal Cognitive Assessment, a sensitive screening tool for detecting executive dysfunction. MoCA can be integrated into annual wellness visits (AWVs), which are free benefits that screen for cognitive impairment and develop personalized prevention plans among Medicare beneficiaries in the U.S. Telehealth has the potential to improve access to AWVs, streamline cognitive assessments, and subsequently facilitate earlier dementia diagnosis. However, barriers to telehealth adoption remain unclear for vulnerable populations, such as older adults with risk factors for Alzheimer's Disease and Related Dementias. This study aims to evaluate the impact of telehealth on AWV uptake and the modality of AWV completion (in-person vs. telehealth). METHOD: We created a longitudinal cohort using 2020-2022 Medicare Advantage (MA) and traditional fee-for-service (FFS) data. We applied probit regression models to examine the relationship between telehealth adoption and AWV uptake. An instrumental variable (IV) design was used to address potential endogeneity. Geographic clusters of internet connectivity (e.g., broadband) and county-level telehealth adoption rates among providers served as IVs to predict telehealth use. We included beneficiaries aged 65 and older who were continuously enrolled in either MA or traditional FFS plans. RESULT: We found a significant increase in the likelihood of AWV uptake among telehealth adopters by 10.2 percentage points (p <0.001) than non-adopters of telehealth in traditional FFS Medicare plans. We will examine the pattern among MA beneficiaries. We expect the effect will be more pronounced for MA beneficiaries than those in traditional FFS plans. This difference may reflect MA's greater ability to manage health expenditures and provide additional resources, such as internet access and incentives for preventative care, that improve AWVs via telehealth. CONCLUSION: Our study demonstrates the role of telehealth in increasing AWV uptake and facilitating cognitive assessments. Telehealth has the potential to bridge gaps in cognitive screening and enhance early detection of MCI and ADRD, supporting timely diagnosis and care planning.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".