Philips Lifeline CareSage Analytics Engine: Retrospective Evaluation on Patients of Partners Healthcare at Home
Notice bibliographique
Résumé
Background: The most common cause of emergency transports/admissions in the aging population is deterioration in their health status due to multiple chronic conditions. To meet the needs of this population, healthcare systems are seeking cost-effective ways to monitor, diagnose, and treat patients, based on connected solutions that seamlessly integrate data and provide actionable insights. The Philips Lifeline’s CareSage program for elderly and frail people utilizes a Personal Emergency Response Service (PERS) to detect medical emergencies and to promote independent living. The system tracks the types and outcomes of incidents, in particular the emergency transport-related events. Their timely detection is critical in optimizing clinical and financial outcomes. Objective: The study objectives are to evaluate (1) healthcare utilization and expenditure and (2) the CareSage predictive model on patients of Partners Healthcare at Home who have been using the Philips Lifeline service. This study is unique in utilizing PERS connected technology as a source of data to identify patients at risk of emergency transports or admissions that cause high healthcare expenditure. Methods: We identified 3335 patients with in-/out-patient encounters in 5 Partners Healthcare-affiliated hospitals through cross-reference of Philips Lifeline and Partners Healthcare at Home (PLL/PHH) data. The patients’ demographics, clinical outcomes, and healthcare expenditure for fiscal years 2011-2015 were extracted from Enterprise Data Warehouse (EDW) of Partners Healthcare. The medical alert data related to PERS utilization were extracted from the Philips Lifeline database. Retrospective statistical analysis of hospital utilization and healthcare cost was performed. Further, the CareSage logistic regression model that uses only PERS data to predict emergency room (ER) transport was validated on PHH patients. A new model predicting ER admissions was developed using boosted regression trees on a combination of PERS and electronic health record (EHR) data. Model performance was evaluated by the area under the receiver operator characteristic curve (AUC) and the positive predictive value (PPV). Results: Patients in the top (5%), middle (6-50%), and bottom (51-100%) segments of the cost acuity pyramid account for 40%, 55%, and 5% of the total healthcare expenditure, respectively, and these percentages stay stable across fiscal years 2011-2015. Increasing trends in total cost and average cost per patient and per encounter were detected through 2011-2015 based on linear regression analysis. The current CareSage predictive model that identifies patients at high risk of emergency transport in the coming 30 days has AUC=0.74 on the PHH population, whilst the new model that identifies patients at high risk of emergency admission in the coming 30 days has AUC=0.82. For prediction windows of one year, the PPV in the top 5% was also good: 63% and 67% for emergency transport and admission, respectively. Conclusions: Predictive models based on PERS and EHR data can identify patients at risk of emergency transports and admissions that account for high healthcare cost. Healthcare organizations can use the outcome of the predictive models to design relevant interventions targeting their high risk patients.
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 | 0,003 | 0,012 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 ».