Medicaid Education and Eligibility Planning for Caregivers: Website Usability and Validation Study
Bibliographic record
Abstract
Background: Most older Americans have not saved enough to cover long-term care costs. Medicaid-a public health care program for low-income individuals-can help Americans with qualifying care needs pay for assistance in a nursing home or for services in the home. Determining financial eligibility for Medicaid is complicated, and the application process is often managed by family caregivers with limited knowledge of Medicaid programs. Objective: A one-stop digital solution is needed to help family caregivers plan for the cost of long-term care services and learn about getting help paying for services through Medicaid. We aimed to develop a web application that (1) educates informal caregivers about Medicaid programs and eligibility criteria, (2) informs them about the cost of home and institutional care in their local area with and without Medicaid coverage, and (3) uses a custom algorithm to provide personalized financial eligibility information based on the care recipient's income, assets, and monthly spending. Methods: We first interviewed aging services providers and informal family caregivers, then developed a web application that was refined based on user experience interviews with English- and Spanish-speaking caregivers. In the final validation phase, asynchronous usability sessions were recorded with 109 informal caregivers who completed a series of tasks. Participants viewed and rated animated Medicaid "explainer" videos, input financial information to enable the custom algorithm to determine the care recipient's eligibility for Medicaid, adjusted settings on a care cost calculator to estimate the regional cost of home and institutional care services, and completed a Medicaid knowledge quiz before and after using the website. Results: After engaging with the website and watching the videos, scores on a Medicaid knowledge quiz increased by 61.2% (2-tailed t92=12.9, P<.001). Participants found it easy to enter the care recipient's financial information to determine Medicaid eligibility (out of 7; mean 5.9, SD 1.3) and perceived the care cost calculator as very helpful (out of 7; mean 6.3, SD 1.2). The website received a very high System Usability Scale rating of 88.3 out of 100 (SD 13.1). Caregivers verbalized wanting more education on complex financial concepts that impact Medicaid eligibility and asset preservation. Conclusions: A comprehensive Medicaid planning website can significantly improve caregivers' knowledge of Medicaid and provide them with a personalized roadmap for accessing care services. The custom algorithm powering the Medicaid eligibility determination could be further refined to account for state-based exceptions. This application may reduce caregiver burden and help support the long-term care planning process.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".