Teaching Today in the Practice Setting of the Future: Implementing Innovations in Graduate Medical Education
Bibliographic record
Abstract
PROBLEM: Implementing an innovation, such as offering new types of patient-physician encounters through the patient-centered medical home (PCMH) model while maintaining Accreditation Council for Graduate Medical Education (ACGME) accreditation standards (e.g., patient encounter minimums for trainees), is challenging. APPROACH: In 2009, the Group Health Family Medicine Residency (GHFMR) received an ACGME Program Experimentation and Innovation Project (PEIP) exception that redefined the minimum Family Medicine Resident Review Committee requirement to 1,400 face-to-face visits and 250 electronic visits (1 electronic visit defined as 3 secure message or telephone encounters). The authors report GHFMR residents' continuity clinic encounters, specifically volume, from 2006 through 2013 via pre- and post-PCMH implementation. They discuss the implications for leaders of high-performing practices who desire to innovate while maintaining accreditation. OUTCOMES: Post-PCMH residents had 20% more overall patient contact. The largest change in care delivery method included a large increase in secure messages between patients and residents. Pre-PCMH residents had more face-to-face encounters; however, post-PCMH residents had more contact for all types of patient care encounters (face-to-face, secure messaging, and telephone) per hour of clinic time. NEXT STEPS: The ACGME PEIP exception, allowing the incorporation of the PCMH, facilitated an increase in patient access and immersed residents in primary care innovation (namely, practicing in a PCMH model during graduate medical education training). The next steps are to assess the effect of the PCMH on resident learning and clinical outcomes and to continue residents' access to training that keeps pace with today's health care delivery needs.
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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 | 0.046 | 0.090 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.010 |
| Scholarly communication | 0.011 | 0.015 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.007 | 0.011 |
| Insufficient payload (model declined to judge) | 0.005 | 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".