Implementation of Evidence-Based Medicine in Primary Care Through the Use of Encounter Shared Decision Making Tools: The ShareEBM Pragmatic Trial
Bibliographic record
Abstract
ABSTRACT BACKGROUND While decision aids have been proven effective to facilitate patient-centered discussion about evidence-based health information in practice and enable shared decision making (SDM), a chasm remains between the promise and the use of these SDM tools in practice. AIMS To promote evidence-based patient-centered care in primary care by using encounter SDM tools for medication management of chronic conditions. METHODS We conducted a mixed methods study centered around a practice-based, multi-centered pragmatic randomized trial comparing active implementation (active) to passive dissemination (passive) of a web-based toolkit, ShareEBM, to facilitate the uptake in primary care of four SDM tools designed for use during clinical encounters. These tools supported collaborative decisions about medications for chronic conditions. ShareEBM included activities and tactics to increase the likelihood that encounter SDM tools will be routinized in practice. Study team members worked closely with practices in the active arm to actively integrate and promote the use of SDM tools; passive arm practices received no support from the study team. The embedded qualitative evaluation included clinician phone interviews (n=10) and site observations (n=5) for active practices, and exit focus groups for all practices (n=11). RESULTS Eleven practices and 62 clinicians participated in the study. Clinicians in the active arm used SDM tools in 621 encounters (Mean [SD]: 21 [25] encounters per clinician, range: 0-93) compared to 680 in the passive arm (Mean [SD]: 20 [40] encounters per clinician, range: 0-156, p=0.4). Six of 29 (21%) clinicians in the active arm and 14 of 33 (42%) in the passive arm did not use any tools (p=0.1). Clinicians’ views covered four major themes: general views of using encounter SDM tools, perceived impact on patients, strategies used, and how encounter SDM tools are incorporated into practice flow. CONCLUSION Neither active nor passive implementation of a toolkit improved the uptake and use of encounter SDM tools in primary care. Overcoming clinician reluctance to consider using encounter SDM tools, their seamless integration into the electronic and practice workflows, and ongoing feedback about the quality of their use during encounters appear necessary to implement their use in primary care practices.
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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 | 0.015 | 0.033 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".