Integrating shared decision making into primary care: Lessons learned from a multi-centre feasibility randomized controlled trial (Preprint)
Bibliographic record
Abstract
BACKGROUND We previously developed MyDiabetesPlan, an evidence-based, online, interactive patient decision-aid to facilitate patient-centred, diabetes-specific goal-setting and action-planning, using shared decision making (SDM) with interprofessional (IP) healthcare teams. OBJECTIVE The aim of this study is to assess the feasibility of (1) integrating MyDiabetesPlan into routine workflows in IP primary care clinics, and (2) conducting a cluster randomized controlled trial (RCT). METHODS We conducted a pilot cluster-RCT in 10 IP primary care clinics with patients living with diabetes and 2+ other comorbidities; half of the clinics were assigned to the MyDiabetesPlan intervention and the remainder were assigned to usual care. For Objective 1, we used RCT conduct logs and financial account summaries to assess recruitment, retention metrics, and resource use. For Objective 2, we used RCT conduct logs and website usage logs to assess intervention fidelity and resource usage. We used audiotapes of clinical encounters in the intervention groups to identify barriers and facilitators to integration of MyDiabetesPlan into clinical care across the IP team. RESULTS Objective 1: 1597 potentially eligible patients were identified through electronic medical record-based searches, of which 1113 patients met eligibility criteria upon detailed chart review. A total of 425 patients were randomly selected; of these, 213 were able to participate and were allocated (intervention: n=102; control: n=111), for a recruitment rate of 50.1%. 151 patients completed the study, for a retention rate of 70.9%. A total of 5745 personnel-hours and $6104 CAD were attributed to recruitment and retention activities. Objective 2: A total of 179 appointments occurred (out of a total of 204 expected appointments - 2 per participant over the 12-month study period; 87.7%). Forty (36%), 25 (23%) and 32 (29%) patients completed MyDiabetesPlan at least twice, once, and zero times respectively. Mean time for completion of MyDiabetesPlan by the clinician and the patient during initial appointments was 37 minutes. From the clinical encounter transcripts, we identified diverse strategies used by health care providers and patients to integrate MyDiabetesPlan into the appointment, characterized by rapport-building and individualization. Barriers to use included MyDiabetesPlan-related factors (e.g. limited selection of potential diabetes management strategies), clinician-related factors (e.g. discomfort with asking certain questions), and patient-related factors (e.g. computer literacy). CONCLUSIONS We evaluated the feasibility of an IPSDM approach using decision aids to help establish treatment priorities in patients with diabetes and found that it would be feasible. A total of 151 (70.9%) patients were retained for 12 months, which required 38 personnel hours and $40.42 CAD per participant who completed the study. Lower than expected numbers of diabetes-specific appointments were observed, and only 39% of patients completed MyDiabetesPlan twice. Addressing facilitators and barriers identified in this study will improve feasibility and promote more complete and seamless integration into clinical care. CLINICALTRIAL Clinicaltrials.gov Identifier: NCT02379078 Date of Registration: February 11, 2015
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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 | 0.138 | 0.192 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.005 | 0.004 |
| 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".