Building of a Learning Health System surrounding Hospital Discharge: A toolbox for Sustainable Metrics from Implementation to Evaluation and Emulation
Notice bibliographique
Résumé
Background: Integrated healthcare delivery systems which meaningfully address patientsneeds as they transition between acute care and home/community supports can achieve the quintuple aims, leading to improved experience and health outcomes. Minimum datasets rooted in high quality, cross-sectoral and patient-centered outcomes can help direct continuous improvement and ensure sustainability of these integrated care systems. Since 209, in Toronto, Ontario, University Health Network (UHN) Integrated Care (IC) program has been enrolling and linking patients and their caregivers, post-discharge, to home and community supports through one point of contact (i.e., an IC lead) and 24/7 phone support line. As the program expands within and beyond our hospital, our aim was to create a feasible, standardized minimum dataset that addresses all ten Ontario quality standards and Alberta Home to Hospital to Home guidelines on care transitions and the quintuple aims to help inform learning health systems. ApproachOur primary objective was to create and test the feasibility of a minimum dataset (MDS) that could be used for continuous program evaluation. The construction of the MDS involved a mixed methods approach that incorporated chart-level and program-specific data and qualitative interviews with patients and providers. Our secondary objective was to test this MDS as a learning health system for all patients enrolled in the program during the first 4 years of implementation. Through the use of chart, program-specific and hospital level data collection data was harnessed for 3075 patients enrolled in the program between June , 209 and May 30, 2023. Stakeholders including patient and caregiver partners, institutional and program leaders, and provincial policy leads helped inform the selection, use, and dissemination of the metrics for continuous program refinement and sustainability of ongoing program evaluation. ImplicationsNotable strengths that served as accelerants for the program evaluation included harnessing hospital-level chart data, homecare and program specific data through shared data records. Low response rate (%) to CIHI Canadian Patient Experience- Inpatient Care survey led the team to use a modified patient experience survey along with qualitative interviews. Site-specific data needed further linkage to provincial administrative data to allow for comparison with controls, and to fully evaluate impact beyond the institution where the program was implemented. Additionally, low or incomplete response on language, gender, race and income equity and diversity measures when admitted to hospital led to manual chart review for ascertainment. Moving forward, the scalability of health equity and patient experience data along with greater information sharing across sites and teams must be addressed. Use of AI and machine learning for extrapolating chart level sociodemographic data may help capture health equity data. Systems that meaningfully engage with patients, caregivers, hospital, community and policy stakeholders to harness linkages between patient and corporate values are an essential component to building a Learning Health System, and play a significant role in building sustainable and prospective program evaluation for integrated care models surrounding hospital admissions.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
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 | 0,229 | 0,254 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,002 |
| Bibliométrie | 0,010 | 0,010 |
| Études des sciences et des technologies | 0,007 | 0,010 |
| Communication savante | 0,017 | 0,021 |
| Science ouverte | 0,007 | 0,024 |
| Intégrité de la recherche | 0,003 | 0,006 |
| 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 ».