Implementing Measurement-Based Care (MBC) for Mental Health in a Paediatric Hospital Setting
Notice bibliographique
Résumé
Introduction: Measurement-Based Care (MBC) is the routine, systematic use of validated measures (e.g., patient-reported outcome measures [PROMs]) before or during clinical encounters to inform treatment decision-making. A large body of evidence, including randomized controlled trials, suggest that MBC outperforms usual care in response to treatment, remission rates, time to response and remission for individuals with depression, anxiety and other mental health conditions. MBC is a standard of care for most chronic conditions (e.g., diabetes, hypertension, asthma), however, it is underutilized in pediatric mental healthcare, despite well documented benefits and feasibility. As part of the hospital’s broader mandate to better integrate physical and mental healthcare, the MBC implementation team set out to develop an MBC system for evaluation of mental health concerns across the hospital with the goal to facilitate monitoring and response of mental health metrics at the patient, provider, organizational, and system levels. Methods: An environmental scan was conducted to understand the priority areas for health systems’ improvement. Stakeholders engaged in the process were physician leaders, clinical operational directors, clinical staff, Youth and Family Advisory Panel members across hospital departments. The environmental scan and follow-up focus groups (n=57) highlighted the need to prioritize implementation of standardized mental assessment tools hospital-wide for patients at each health system encounter as part of a vision to deliver holistic, integrated care and improve quality of patient care. Early identification of mental health needs of patients across the hospital and the importance of establishing standardized metrics for common mental health conditions were additional key priorities. Hospital surveys highlighted anxiety and depression as the top mental health concerns across the hospital. An extensive review was conducted to identify validated tools available and to understand implementation best practices. Learnings: Learnings from our review pointed to some key factors for successful implementation. These included the need for tools that are brief and easy to use, are validated and sensitive to change, are user friendly and visually appealing for the pediatric population and are integrated into the electronic health record with appropriate decision support tools to enable real time review. The need for extensive patient and provider education about the value of MBC and its implementation was also a key learning. Work in progress & next steps: Work is underway to map out clinical workflows, including ensuring a timely response to results, addressing implementation challenges such as virtual vs. in-person tool completion, and managing accessibility of sensitive data to the patient or their proxy in the electronic health record. Workflows will take an integrated, holistic care approach, whereby, a patient’s care team will action appropriate interventions based on a standardized algorithm that engages an interdisciplinary care team. Voxe, an external, patient-friendly platform to administer the two chosen assessment tools will be trialed. Discussions are underway with Information Management and Technology teams to integrate Voxe and clinical workflows in the hospital’s electronic health record. Evaluation of implementation of MBC in clinical settings will consider both process and outcome measures such as uptake, timely receipt of services, and clinical response.
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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 | 0,033 | 0,061 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,002 | 0,003 |
| Études des sciences et des technologies | 0,003 | 0,002 |
| Communication savante | 0,003 | 0,003 |
| Science ouverte | 0,003 | 0,010 |
| Intégrité de la recherche | 0,001 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 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 ».