An implementation model for managing cloud-based longitudinal care plans for children with medical complexity
Bibliographic record
Abstract
OBJECTIVE: We aimed to iteratively refine an implementation model for managing cloud-based longitudinal care plans (LCPs) for children with medical complexity (CMC). MATERIALS AND METHODS: We conducted iterative 1-on-1 design sessions with CMC caregivers (ie, parents/legal guardians) and providers between August 2017 and March 2019. During audio-recorded sessions, we asked participants to walk through role-specific scenarios of how they would create, review, and edit an LCP using a cloud-based prototype, which we concurrently developed. Between sessions, we reviewed audio recordings to identify strategies that would mitigate barriers that participants reported relating to 4 processes for managing LCPs: (1) taking ownership, (2) sharing, (3) reviewing, and (4) editing. Analysis informed iterative implementation model revisions. RESULTS: We conducted 30 design sessions, with 10 caregivers and 20 providers. Participants emphasized that cloud-based LCPs required a team of owners: the caregiver(s), a caregiver-designated clinician, and a care coordinator. Permission settings would need to include universal accessibility for emergency providers, team-level permission options, and some editing restrictions for caregivers. Notifications to review and edit the LCP should be sent to team members before and after clinic visits and after hospital encounters. Mitigating double documentation barriers would require alignment of data fields between the LCP and electronic health record to maximize interoperability. DISCUSSION: These findings provide a model for how we may leverage emerging Health Insurance Portability and Accountability Act-compliant cloud computing technologies to support families and providers in comanaging health information for CMC. CONCLUSIONS: Utilizing these management strategies when implementing cloud-based LCPs has the potential to improve team-based care across settings.
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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 | 0.047 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".