Utilizing information technology to improve transition of care from hospital to home
Notice bibliographique
Résumé
Background and objective: Failure to appropriately plan for a safe and effective transition to the next level of care leads to a greater use of hospital and emergency services, often measured by rates of readmission. A large academic medical center located in Houston, Texas, USA consistently achieves an overall University Health System Consortium (UHC) ranking for most benchmarks in the top decile (90th percentile) except for 30-day all-cause readmission rate, which ranks in the bottom decile. The objective of the study was to implement changes in Midas+, the system used by Houston Methodist Hospital for quality and case management activities, select a formal transition of care plan and implement the process on a pilot unit to reduce the 30-day readmission rate and improve the discharge planning process. Methods: Setting: Cardiovascular Intermediate Care Unit (CVIMU), a 30-bed cardiovascular surgery unit within an academic medical center in Houston Texas. The project intervention included the addition of a readmission risk screen in the Hospital Case Management (HCM) and intervention screens based on the Coleman Model in the Community Case Management (CCM) module of Midas+. The clinical improvement involved three components spanning from hospital to home: (1) Screening patients for readmission risk upon admission and assigning those identified as high-risk for readmission (with a planned discharge to home) to a Transition Coach, (2) A visit by the Transition Coach during the patient’s hospital stay to assess the patient and provide coaching, and (3) Providing five follow-up phone calls from the Transition Coach post-discharge. Results: The system changes in Midas+ were implemented and were effective in tracking the interventions. Of the 258 patients admitted from July through August 2014, 226 or 87.5% of the patients were screened using the readmission risk assessment tool. Of the patients’ screened, 49 were considered high risk with 26 discharged home, 22 or 45.8% discharged to another level of care and one patient expired. During the pilot, of 19 patients were followed by a transition coach only one patient readmitted to the hospital. Conclusions: The project demonstrated that utilization of a computer system to record the readmission risk screen, track the assessment of the pillars (medication management, continued care, red flags to report, and personal health record) over the six time intervals of the pilot transition program was effective in tracking the intervention. The data collected through information technology was easily retrieved for tracking progress and evaluation. The outcome of this pilot has shown that a well-defined transition of care program may decrease the 30-day readmission rate.
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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 | 0,004 | 0,015 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,003 | 0,002 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,000 |
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 ».