Abstract P1-17-17: Real-word implementation of <i>p</i>atient <i>r</i>eport <i>o</i>utcomes: Sustainability constraints and impact on patients health outcomes
Notice bibliographique
Résumé
Abstract Background: St Mary’s Hospital is an affiliated McGill Teaching University Hospital in Montreal, receiving ~700 new cancer patients yearly. In 2008, the oncology department implemented Patient Reported Outcomes (PROs) on paper. In 2014-17, electronic PROs were implemented through the Improving Patient Experience and Health in Outcomes Collaborative (iPEHOC) Implementation Project, as part of a PRO Canadian Initiative to improve the Patient Experience across the cancer journey through standardized measurement that accelerates optimal care and measures impact (health-related outcomes for patients) across Canada. Methodology: The PROs measures included the Edmonton Symptom Assessment Scale (ESASr), four additional secondary PROMs for Fatigue, Anxiety, Depression, and Pain, the Social Difficulties Inventory-21 and a single item Quality of Life. PRO data was scored in real-time; nurses and patients received a printed summary report. Nurses were provided with an algorithm (Global Response to Distress) and follow-up instructions. Monthly Distress Screening Dashboard ware generated with number of screenings, number of patients screened, symptom prevalence, secondary PROMs trigger rates, Social Difficulties triggered and clinically significant symptoms changes on the four secondary PROMs. In 2016, all patients receiving chemotherapy were assessed at each cycle of treatment (screening). After project completion, due to nursing staff shortage, the frequency of screenings was reduced and offered at pre-established time-points (assessment). Yearly Dashboard results were used to promote a discussion with clinicians and address sustainability constraints of PROs in real-word oncology practice. Results: In 2016, a total of 366 patients were screened, resulting in 1366 annual screens. In 2018, 325 patients were screened resulting in 753 annual screens. This change in practice, that culminated organically, allows for an observation analysis between screening versus assessment. Noteworthy comparison between 2018 and 2016 showed that the decrease in frequency of screenings resulted in significant increase in symptom severity in all health-related outcomes. Breast cancer represented 35% of all screens completed. We propose to present a three year retrospectively cross analyzed patient data to examine symptom change over time, frequencies of screening, the impact on patients’ health outcomes, the overlapping sustainability constraints encountered and mitigation plans. Discussion: Embedding the use of PRO into existing hospital structures requires constant review involving administrative, clinician and patient engagement. PRO data can be used to better identify the needs of a specific cancer type population and create tailored care paths. Citation Format: Ashley Kushneryk, Rosana Faria. Real-word implementation of patient report outcomes: Sustainability constraints and impact on patients health outcomes [abstract]. In: Proceedings of the 2019 San Antonio Breast Cancer Symposium; 2019 Dec 10-14; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2020;80(4 Suppl):Abstract nr P1-17-17.
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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 | 0,009 | 0,030 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,002 | 0,003 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,150 | 0,042 |
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 ».