Leveraging electronic medical record (EMR) changes and virtual care towards improving tobacco screening and referral rates.
Bibliographic record
Abstract
426 Background: Continued smoking after a cancer diagnosis is associated with poorer outcomes. Ontario Regional Cancer Centres are required to screen all new cancer patients for tobacco use and offer referrals for cessation support to patients using tobacco. At Princess Margaret Cancer Centre (Toronto, Canada), we leveraged an EMR change to Epic along with uptake in virtual care strategies from COVID-19, to improve both tobacco screening and referral rates for new patients. Methods: Following the EMR transition to Epic in June 2022, we piloted EMR and virtual care strategies from Jan 2023 to Mar 2024 to improve both tobacco screening and referral rates. Due to precautions during COVID-19, screening and referrals were completed using a hybrid pre-visit electronic tobacco use assessment questionnaire and in clinic paper-based referral system. Our interventions were co-developed by a team of hospital leadership, IT team members and clinicians. These included: 1) Collation of tobacco assessment data sources on Epic (Jan 2023); 2) Creation of an Epic referral inbox to track referrals (Aug 2023); 3) Assignment of electronic pre-visit tobacco assessments questionnaires to correct visit types (Sep 2023); 4) Creation of a half-day virtual nursing clinic to offer referrals for cessation support to patients initially declining support or those who were not offered using monthly reports from Epic (Nov 2023). Screening and referral rates were tracked monthly and quarterly during this period. Results: From Jan 2023 to March 2024, 15,063 new patients presented to our cancer centre. Baseline screening and referral rates before COVID-19 were 47% and 16%, respectively. Prior to this QI initiative, screening and referral rates were 18% and 1%, respectively. Post implementation, screening and referral rates improved to 42% and 25% (Table); rates of current tobacco use remained consistent from 12-16%. Collation of tobacco use assessments data on Epic had the greatest impact on improving screening rates from 18% to 39% (p<0.001), while the virtual nursing clinic helped to improve referral rates from 3% to 25% (p<0.001), with the greatest referral rate in Jan 2024 at 36%. Our virtual nursing clinic was feasible, where among patients contacted who initially declined or were not offered cessation support, 90% were contacted within 1-3 phone calls. Among patients contacted, 20% accepted a referral, while 41% of patients had already quit smoking by the time of contact. Conclusions: Both EMR and virtual care strategies helped to successfully improve both tobacco screening and referral rates for smoking cessation support; with the greatest impact from the collation of EMR data sources on tobacco use and development of a virtual nurse led clinic. Quarterly tobacco screening and referral rates. Jan-Mar 2023 Apr-Jun 2023 Jul-Sep 2023 Oct-Dec 2023 Jan-Mar 2024 Screening 33% 38% 39% 41% 42% Referrals 0% 1% 3% 20% 25%
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".