Leveraging electronic medical record (EMR) changes and virtual care towards improving tobacco screening and referral rates.
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,023 | 0,072 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,001 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,003 | 0,002 |
| Science ouverte | 0,002 | 0,005 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».