A Novel Approach to Care Redesign Collaboration Between Emergency and Specialty Departments: Qualitative Experience Report
Bibliographic record
Abstract
Background: Given the rising demand for emergency department (ED) services and coupled with the scarcity of specialty care availability, there is an urgency to design a system for appropriate, effective, and timely ED-to-specialty ambulatory referrals. Efficient care transitions are important to patient outcomes and experience and require cross-specialty cooperation, as care transitions affect practices and resources of individuals, departments, and institutions. Objective: Here, our objective was to (1) describe a collaboration between Stanford's Emergency Medicine and Neurology and Neurological Sciences departments aimed at designing and implementing an optimized discharge process and transition of care from ED to ambulatory neurology for follow-up care and (2) the resulting intervention from the collaboration. Methods: We describe the process for barrier identification, tools used to foster partnership and intervention ideation, and the resulting intervention. Our experience and findings are integrated into a 4-component framework for future interdepartmental collaborations: (1) cross-specialty team meetings, (2) preimplementation interviews, (3) a design thinking focus group session, and (4) small group meetings. Qualitative data included observational notes and document review from biweekly cross-specialty and small group meetings, preimplementation interviews, and design-thinking focus groups. Results: Our process included 10 cross-specialty team meetings with 8 physicians and operational representatives, 18 individual preimplementation interviews, 10 focus group participants, and 9 small group meetings. The process components fostered collaboration and teamwork among the multidisciplinary team; supported early identification of barriers and facilitators, including divergent understanding of project goals; and developed creative ideas that contributed to intervention development. The collaboration resulted in a 4-pronged multimodal intervention. Two elements focused on modifying clinical practice to better triage clinically appropriate ED referrals to ambulatory neurology: (1) optimizing management of conditions in the ED to reduce preventable referrals, and (2) increasing deferral to Primary Care clinicians to direct appropriate specialty follow-up care. Two additional structural elements sought to directly improve appropriate referral timeliness by (3) streamlining insurance authorization processes and (4) increasing neurology appointment availability. Conclusions: This cross-specialty collaboration resulted in a multimodal intervention that called for both structural and practice changes, which were novel and supported by the results of comparable interventions. Future applications of this framework can validate its utility among different collaborative groups in new 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 | 0.049 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.014 | 0.015 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.004 | 0.015 |
| Research integrity | 0.002 | 0.005 |
| 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".