185 Making the mandatory meaningful – leveraging regulatory requirements to achieve system improvement
Bibliographic record
Abstract
Background Mandatory regulatory/College obligations can seem burdensome and feel like they pull you away from meaningful work. The College of Physicians and Surgeons Ontario (CPSO) in Canada has a QI partnership program for hospital-based physicians. This program makes QI activities a mandatory requirement of the regulatory college. One must complete a project to maintain their licensure. Hospital departments can sign up for a group QI project which is often less burdensome to physicians than an individual project. This also allows the hospital to align a project with a larger quality goal. In Ontario, Canada, three separate hospitals undertook this College program to improve psychiatric care.Case Studies Improving Communication at DischargeWaypoint Centre for Mental Health Care is a 315 bed psychiatric hospital located in Central Ontario. Transitions in care and the discharge summary were identified as the priority focus based on incident reviews. Twenty-five physicians participated in completing 5–10 self-directed chart reviews with peer feedback, auditing more than 100 records and identifying areas for improvement related to timeliness, distribution and formatting of discharge summaries. The results of this work informed the development of a dashboard to track discharge summary completion and display timely feedback to physicians. The work supported the business case for the redevelopment of the discharge summary in alignment with feedback from the QI program and our community providers. This discharge summary format has been adopted at two additional mental health facilities.Suicide Risk AssessmentMount Sinai Hospital is a general hospital with 400 beds in Toronto, Canada. Chart reviews demonstrated wide variation in the documentation of suicide risk assessment and intervention across psychiatric inpatient and outpatient programs. Leveraging the CPSO partnership program, we introduced the Columbia Suicide Severity Rating Scale (CSSRS) to standardize suicide risk assessment and recruited 25 physicians to participate. A pilot implementation undertaken on one inpatient service between October 2023 to October 2024 yielded an increase in use of the CSSRS Screener from 43/268 (16.0%, pre-intervention) to 180/497 (36.2%, post-intervention). To spread from this pilot, in September 2024, an electronic version of the CSSRS was incorporated into the electronic patient record for outpatients. Between September and December 2024, the CSSRS Screener was completed for 321/448 (71.6%) of newly referred patients. Individualized audit and feedback to physicians will be used to sustain these gains and further enhance standardization to improve patient safety.Measurement Based CareThe Department of Psychiatry at Sunnybrook Health Sciences Centre, a 1325-bed general hospital in Toronto, Canada, aimed to address low utilization of Measurement-Based Care (MBC) among psychiatrists. At baseline, only 18% used scales to inform treatment decisions. The Department launched several iterative interventions including use of personalized monthly audit-feedback reports, individual coaching sessions, and opportunities for self-reflection. After 1 year, results demonstrated an increase in sustained MBC adherence from 25% to 57.5%. Some early-adopters maintained adherence above 70%. Barriers to adoption included practitioner skepticism regarding accuracy and utility of measures compared to clinical judgment, administrative workflow challenges and lack of an integrated EMR. Enablers included administrative support and offering patients the opportunity to discuss their improvements. This project highlights the need for stakeholder engagement, ongoing audit and feedback, and addressing administrative burden to sustain MBC implementation and inform future scale and spread of MBC in mental health settings more broadly.Conclusion Participation in the regulatory college’s quality improvement partnership has been an effective lever to enhance physician engagement in adopting best practices in psychiatry including: improving the timeliness and quality of discharge communications, adopting a standardized approach to suicide risk assessment, and using measurement based care. Each project highlights the need for stakeholder engagement, audit and feedback, and addressing system level issues to facilitate clinician behaviour change.
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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 | 0.024 | 0.090 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 0.004 |
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".