81 The Use of Online Care-Maps for Children with Medical Complexity
Bibliographic record
Abstract
Abstract Primary Subject area Complex Care Background Children with medical complexity (CMC) are a highly medicalized population of children who require specialized care across various settings including the hospital, home and community, making care coordination challenging. Care-maps, a visual representation of the people and places involved in a patient’s care, are one such tool to facilitate care coordination (Figure 1). To date, care-maps have not yet been used in a clinical environment, examined in real time or used via a standardized approach. Objectives The aims of our study were to develop a shareable standardized online tool that supports the parental creation of a care-map, and to assess the utility of care-maps in clinical care from a parent, health care provider (HCP), and community perspective. Design/Methods Parents of CMC were invited to use a standardized online care platform called Connecting2gether for 6-months and create online care-maps that could be shared with their HCPs and other community members (i.e., teachers, secondary caregivers). Demographics and internet usage surveys were completed at baseline and an acceptability survey was completed at 6-months. Surveys were analyzed using descriptive methods and care-maps were analyzed via descriptive visual analysis. Results Thirty-seven parents enrolled on the platform and 25 (70%) created a care-map and used it for the duration of the study. Of the 25, 14 (66%) went back and made revisions and 17 (80%) reported using it in clinic, home or school. Visual analysis demonstrated 11 categories (bubbles) that were commonly included. All care-maps included a Medical Team, School/Daycare and Family and Friends category, which automatically populated. The majority of care-maps included a central child bubble with the child’s photo (92%), and Community Medical Services (i.e. rehab centers) (60%). Less frequent categories included Home Care (28%), Goals (16%), and 12% included What I Like, Funding, and Community/Foundation individual bubbles. Some parents reported initial uncertainty, but at end-of-study, some reported care-maps as the most useful feature of the platform. Fifty seven percent (12/23) of HCPs viewed the created care-map and only 20% used it in the child’s care. The majority (83%) of HCPs specifically valued seeing the big picture of the child’s care, found it easy to navigate and the detail it provided. Conclusion The ability of care-maps to illustrate the intricate web of medical and non-medical care supporting CMCs in their daily life provides insight and value for parents, HCPs and non-HCPs. Care-maps were found to be valuable from the perspective of HCPs. Parents reported initial uncertainty, highlighting the importance of the HCP promoting the use of care-maps with their patients and families.
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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 | 0.003 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".