A qualitative analysis of information sharing for children with medical complexity within and across health care organizations
Bibliographic record
Abstract
BACKGROUND: Children with medical complexity (CMC) are characterized by substantial family-identified service needs, chronic and severe conditions, functional limitations, and high health care use. Information exchange is critically important in high quality care of complex patients at high risk for poor care coordination. Written care plans for CMC are an excellent test case for how well information sharing is currently occurring. The purpose of this study was to identify the barriers to and facilitators of information sharing for CMC across providers, care settings, and families. METHODS: A qualitative study design with data analysis informed by a grounded theory approach was utilized. Two independent coders conducted secondary analysis of interviews with parents of CMC and health care professionals involved in the care of CMC, collected from two studies of healthcare service delivery for this population. Additional interviews were conducted with privacy officers of associated organizations to supplement these data. Emerging themes related to barriers and facilitators to information sharing were identified by the two coders and the research team, and a theory of facilitators and barriers to information exchange evolved. RESULTS: Barriers to information sharing were related to one of three major themes; 1) the lack of an integrated, accessible, secure platform on which summative health care information is stored, 2) fragmentation of the current health system, and 3) the lack of consistent policies, standards, and organizational priorities across organizations for information sharing. Facilitators of information sharing were related to improving accessibility to a common document, expanding the use of technology, and improving upon a structured communication plan. CONCLUSIONS: Findings informed a model of how various barriers to information sharing interact to prevent optimal information sharing both within and across organizations and how the use of technology to improve communication and access to information can act as a solution.
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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 | 0.017 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.015 | 0.011 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".