Cloud Care: A Feasibility Study of Cloud-Based Care Plans for Children With Medical Complexity
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Cloud-based information systems can support team-based content management of care plans for children with medical complexity (CMC), yet we have limited knowledge of the feasibility of these systems. We aimed to examine the feasibility of Cloud Care, a novel cloud-based longitudinal care plan system, among a diverse group of caregivers and health care providers caring for CMC. METHODS: We conducted a 3-year, prospective, feasibility study between May 2019 and July 2022 in which caregivers and health care providers of CMC received access to Cloud Care for 4 to 32 months. We assessed the practicality, acceptability, demand, integration, and adaptation of the system using a combination of web analytics and survey data. RESULTS: Of those invited, 29 of 43 (67%) CMC/caregivers and 459 of 462 (99.5%) providers enrolled in the study. Among enrolled participants, 90% of primary caregivers and 47% of providers accessed the system at least once (ie, adoption rate), and 59% of primary caregivers and 11% of providers edited content at least once. Of the 11 caregivers and 42 providers who accessed Cloud Care and completed a survey, over 82% of caregivers and 45% of providers perceived the system was easy to use. CONCLUSIONS: Study adoption rates highlight the desire for a curated, dynamic care plan for CMC. Engagement in collaborative management of care plan information in a cloud-based system was promising among caregivers, but low among providers. Optimizing the design, accessibility, and usability of existing information systems to create and collaboratively maintain care plans warrants continued exploration.
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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 | 0.014 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".