User-centered design of a longitudinal care plan for children with medical complexity
Bibliographic record
Abstract
OBJECTIVE: To determine the content priorities and design preferences for a longitudinal care plan (LCP) among caregivers and healthcare providers who care for children with medical complexity (CMC) in acute care settings. MATERIALS AND METHODS: We conducted iterative one-on-one design sessions with CMC caregivers (ie, parents/legal guardians) and providers from 5 groups: complex care, primary care, subspecialists, emergency care, and care coordinators. Audio-recorded sessions included content categorization activities, drawing exercises, and scenario-based testing of an electronic LCP prototype. We applied inductive content analysis of session materials to elicit content priorities and design preferences between sessions. Analysis informed iterative prototype revisions. RESULTS: We conducted 30 design sessions (10 with caregivers, 20 with providers). Caregivers expressed high within-group variability in their content priorities compared to provider groups. Emergency providers had the most unique content priorities among clinicians. We identified 6 key design preferences: a familiar yet customizable layout, a problem-based organization schema, linked content between sections, a table layout for most sections, a balance between unstructured and structured data fields, and use of family-centered terminology. DISCUSSION: Findings from this study will inform enhancements of electronic health record-embedded LCPs and the development of new LCP tools and applications. The design preferences we identified provide a framework for optimizing integration of family and provider content priorities while maintaining a user-tailored experience. CONCLUSION: Health information platforms that incorporate these design preferences into electronic LCPs will help meet the information needs of caregivers and providers caring for CMC in acute care settings.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".