Patient-Derived Design Principles for Technology-Enabled Healing at Home Following Hospital Discharge: Mixed Methods Study
Bibliographic record
Abstract
BACKGROUND: As more patients transition from hospital to home for post-acute care, a growing interest exists in leveraging technology to support recovery, yet limited understanding exists on how to design these tools to align with patient and caregiver needs and preferences. OBJECTIVE: To explore the perceptions, attitudes, and beliefs of recently discharged patients in order to develop user-centered design principles for digital tools that support safe and effective transitions from hospital to home. METHODS: A vignette-based, mixed-methods survey grounded in the Technology Acceptance Model (TAM) to explore patient perceptions of digital tools supporting post-discharge care. A random sample of 1,000 recently discharged adult patients received a survey featuring validated vignettes and TAM-informed questions, with both quantitative and qualitative items. Open-ended responses were analyzed using Grounded Theory to derive design principles that inform the development and implementation of patient-centered digital health tools. Quantitative items were descriptive in nature and are summarized as count (n) and frequency (%). RESULTS: Of the 967 eligible patients contacted, 116 completed the survey (12.0% response rate), with respondents having a median age of 71 years, high rates of chronic illness, and access to smartphones (84.5%) and in-home internet (95.7%). Qualitative analysis revealed six key themes-connection to care, technical ease-of-use, solution usability, human connection, cost, and privacy-informing three patient-centered design principles focused on user experience, affordability, and transparent communication to guide future technology-supported hospital discharge interventions. Respondents reported the following factors as highly important: reassurance that a care team member would reach out if something seemed wrong (92.2%, n=107/116), responsiveness to patient need (81.9%, n=95/116), ability to see their own data (81.9%, n=95/116), and out of pocket cost (81.0%, n=94/116). Less important factors included duration of device use (19.0%, n=22/116) and battery life (18.0%, n=21/116). CONCLUSIONS: Grounded in patient perspectives, this study identified the three core design principles of User Experience and Accessibility, Cost and Privacy, and Communication and Transparency that should guide the development and implementation of digital tools to support safe, effective, and human-centered transitions from hospital to home.
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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 | 0.055 | 0.067 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| 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".