How Appropriate Is All This Data Sharing? Building Consensus Around What We Need to Know About Shared Electronic Health Records in Extended Circles of Care
Bibliographic record
Abstract
BACKGROUND: The bulk of healthcare spending is on individuals who have complex needs related to age, income, chronic disease and mental illness. Care involves many different professions, and interoperable electronic health records (EHRs) are increasingly essential. OBJECTIVES: The objective of this paper is to describe the use of a nominal group technique (NGT) to develop a stakeholder-centred research agenda for clinical interoperability in extended circles of care that include social supports. METHODS: We held a day-long meeting with 30 stakeholders, including primary care providers, social supports, patient representatives, health region managers, technology experts, health organizations and experts in privacy, law and ethics. Participants considered, "What research needs to be done to better understand how EHRs should be shared across large healthcare teams that include social supports?" Following sensitizing presentations from researchers and participants, we used an NGT to generate and rank research questions on a 9-point Likert scale. We retained research questions that had a mean score of at least 6.5/9 by at least 70% of the participants over two rounds of consensus-building. RESULTS: Participants identified and ranked 57 research questions. Five items achieved consensus, related to 1) the impact of information sharing on care team outcomes, 2) data quality/accuracy, 3) cost/benefit, 4) what processes use what data and 5) regulation/legislation. CONCLUSION: Healthcare reforms are increasingly focused on systems that integrate and coordinate multidisciplinary care, facilitated by EHRs. Research prioritization will ensure common concerns and barriers are addressed and resolved.
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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 | 0.375 | 0.384 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.017 | 0.035 |
| Scholarly communication | 0.015 | 0.031 |
| Open science | 0.007 | 0.029 |
| Research integrity | 0.007 | 0.008 |
| 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".