Metrics that matter: Performance tools to improve prescribing and reporting at Princess Margaret (PM) Cancer Centre.
Bibliographic record
Abstract
614 Background: Ordering anti-cancer systemic therapy (ST) with Systemic Treatment Computerized Prescriber Order Entry (STCPOE) is critical for safe prescribing. STCPOE enables secondary data capture for organizational reporting, reimbursement, and insights into prescribing patterns. In 2022, PM implemented a new Electronic Health Record (EHR) with STCPOE system. Post Go-Live, reduced clinician use of STCPOE for take home cancer drug (THCD) prescribing and 19% decrease in provincial quality-based procedure (QBP) funding volumes for THCD was noted compared to the prior fiscal year (FY). A quality improvement project was launched to promote appropriate STCPOE use for prescribing, improve data quality, and recover funding. Methods: A working group with data scientists, clinicians, executives & EHR specialists, conducted a root-cause analysis (RCA) using 5 Whys methodology to understand key gaps. Over one year, education and change management initiatives were developed, guided by Plan-Do-Study-Act methodology. Clinician education focused on STCPOE for safe prescribing, use of real-time data for reporting, and funding impact of inaccurate clinician-entered data. Executive and clinician buy-in was prioritized to develop individual and disease-site level performance report cards with data insights on prescribing practices outside STCPOE and issues with data fields affecting funding. Performance report cards were co-designed with end-users, for leaders and clinicians. These were shared monthly with leadership, along with individual feedback loop on ideas to improve STCPOE use. Key evaluation metrics included rates of ordering outside of ST-CPOE and monthly QBP funded volumes. Results: RCA revealed high volumes of THCD ordered outside STCPOE and while intravenous (IV) ST was ordered using STCPOE, there were inaccuracies in clinician-entered data fields required to trigger funding affecting data quality & volume capture, leading to funding loss. The following results were seen since the launch of interventions in September 2024 to March 2025: 6% decrease in ordering THCD prescriptions outside of STCPOE; 8% decrease in clinicians selecting inappropriate data elements in IV ST; 9% increase in reported funding volume for THCD prescriptions compared to prior FY (adjusted 3% natural patient volume growth); and 2 medium-scale informatics projects to improve STCPOE usability & efficient prescribing completed. Conclusions: The use of a performance improvement approach improved STCPOE adherence for THCD, safe medication prescribing, and accuracy of secondary data capture for funding. Drivers for success included early executive sponsorship, clinician-engagement, & education with data-driven performance report cards and feedback loops to sustain behavior change. This initiative underlines the importance of funding models & safe prescribing as motivators for change.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.050 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".