Prediction using a randomized evaluation of data collection integrated through connected technologies (PREDICT): Design and rationale of a randomized trial of patients discharged from the hospital to home.
Adult
Data Collection
/ methods
Humans
Medication Adherence
/ statistics & numerical data
Models, Statistical
Monitoring, Ambulatory
/ methods
Patient Discharge
/ statistics & numerical data
Patient Readmission
/ statistics & numerical data
Randomized Controlled Trials as Topic
Smartphone
Wearable Electronic Devices
Health behaviors
Hospital readmission
Physical activity
Prediction models
Smartphones
Wearable devices
Journal
Contemporary clinical trials
ISSN: 1559-2030
Titre abrégé: Contemp Clin Trials
Pays: United States
ID NLM: 101242342
Informations de publication
Date de publication:
08 2019
08 2019
Historique:
received:
20
02
2019
revised:
19
06
2019
accepted:
27
06
2019
pubmed:
3
7
2019
medline:
3
10
2020
entrez:
3
7
2019
Statut:
ppublish
Résumé
Hospital readmission prediction models often perform poorly. A critical limitation is that they use data collected up until the time of discharge but do not leverage information on patient behaviors at home after discharge. PREDICT is a two-arm, randomized trial comparing ways to use remotely-monitored patient activity levels after hospital discharge to improve hospital readmission prediction models. Patients are randomly assigned to use a wearable device or smartphone application to track physical activity data. The study collects also validated assessments on patient characteristics as well as disparate data on credit scores and medication adherence. Patients are followed for 6 months. We evaluate whether these data sources can improve prediction compared to standard modelling approaches. The PREDICT Trial tests a novel method of remotely-monitoring patient behaviors after hospital discharge. Findings from the trial could inform new ways to improve the identification of patients at high-risk for hospital readmission. Clinicaltrials.gov Identifier: NCT02983812.
Sections du résumé
BACKGROUND
Hospital readmission prediction models often perform poorly. A critical limitation is that they use data collected up until the time of discharge but do not leverage information on patient behaviors at home after discharge.
METHODS
PREDICT is a two-arm, randomized trial comparing ways to use remotely-monitored patient activity levels after hospital discharge to improve hospital readmission prediction models. Patients are randomly assigned to use a wearable device or smartphone application to track physical activity data. The study collects also validated assessments on patient characteristics as well as disparate data on credit scores and medication adherence. Patients are followed for 6 months. We evaluate whether these data sources can improve prediction compared to standard modelling approaches.
CONCLUSION
The PREDICT Trial tests a novel method of remotely-monitoring patient behaviors after hospital discharge. Findings from the trial could inform new ways to improve the identification of patients at high-risk for hospital readmission.
TRIAL REGISTRATION
Clinicaltrials.gov Identifier: NCT02983812.
Identifiants
pubmed: 31265915
pii: S1551-7144(19)30127-2
doi: 10.1016/j.cct.2019.06.018
pii:
doi:
Banques de données
ClinicalTrials.gov
['NCT02983812']
Types de publication
Clinical Trial Protocol
Journal Article
Research Support, Non-U.S. Gov't
Research Support, U.S. Gov't, Non-P.H.S.
Langues
eng
Sous-ensembles de citation
IM
Pagination
53-56Informations de copyright
Published by Elsevier Inc.