Improving Diagnostic Safety through Integrated Care
Bibliographic record
Abstract
Introduction: Diagnostic errors in care are a global public health challenge. Published rates of diagnostic errors range from approximately 5% in ambulatory care settings up to about 10% of in-hospital stays and it is estimated that every person in their lifetime will experience a diagnostic error. The wicked problem of diagnostic errors is multifactorial with failures occurring at every point of entry to the system. Integrated systems of care provide an important opportunity for studying and codesigning solutions to diagnostic errors and are uniquely positioned to address the gaps in access and care delivery that lead to diagnostic errors. In this workshop, we will explore the challenges of diagnostic error and the promise of diagnostic safety solutions along the nine pillars of integrated care. Audience: Patients, patient advocates, caregivers, clinicians, health systems leaders, policymakers, and payers. Learning Objectives: By the end of the workshop, participants will be able to: 1.Describe diagnostic errors within the context of an integrated healthcare system. 2.Discuss unique opportunities for integrated healthcare systems to mitigate diagnostic errors and promote diagnostic safety. 3.Align opportunities to improve diagnostic safety along each of the nine pillars of integrated care. Workshop Approach: The workshop will include a mix of short theory bursts and small working groups. Theory bursts will level-set on the current state of the science of diagnostic errors and strategies to improve diagnosis while working groups have the goal of ascribing challenges and opportunities for improving diagnostic safety along the diagnostic continuum. The workshop will employ engagement and learning strategies including interactive polls, briefing and debriefing, hands-on deliberative practice, and small group facilitation. Information co-created by workshop participants will be captured using web-enabled and live brainstorming tools. Workshop Agenda: 1.10 minutes: Introduction and review agenda; Online interactive session of who is in the room. 2.10 minutes: Theory Burst – What we know about diagnostic errors and an introduction to the diagnostic error sociotechnical system. 3.5 minutes: Introduction to the brainstorming approaches and technology 4.45 Minutes: Small Group Break Outs (each group will address one pillar in each session) a.Session 1: Pillars 1-3 b.Session 2: Pillars 4-6 c.Session 3: Pillars 7-9 5.15-Minute Debrief: Review of information generated by different small groups along the nine principles of integrated care; Opportunity for participants from other groups to contribute to the other session’s responses 6.5 minutes: wrap up and next steps Lessons Learned: Take-home messages will be summarized by expert faculty using online polling and word cloud codesign by participants and through the co-created brainstorming outcomes. Participants will be invited to contribute to a white paper from the workshop.
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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 | 0.027 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.005 | 0.026 |
| Research integrity | 0.004 | 0.010 |
| Insufficient payload (model declined to judge) | 0.030 | 0.007 |
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".