Using remotely monitored patient activity patterns after hospital discharge to predict 30 day hospital readmission: a randomized trial.

ABSTRACT: Hospital readmission prediction models often perform poorly, but most only use information collected until the time of hospital discharge. In this clinical trial, we randomly assigned 500 patients discharged from hospital to home to use either a smartphone or wearable device to collect and transmit remote patient monitoring (RPM) data on activity patterns after hospital discharge. Analyses were conducted at the patient-day level using discrete-time survival analysis. Each arm was split into training and testing folds. The training set used fivefold cross-validation and then final model results are from predictions on the test set. A standard model comprised data collected up to the time of discharge including demographics, comorbidities, hospital length of stay, and vitals prior to dischar

SUBMITTER: Patel MS 

PROVIDER: S-EPMC10203290 | biostudies-literature | 2023 May

REPOSITORIES: biostudies-literature