BACKGROUND
Hospitalizations account for almost one-third of $4.1 trillion healthcare cost in the US. A substantial portion of these hospitalizations are readmissions, which led to Hospital Readmissions Reduction Program (HRRP) in 2012.15 HRRP reduces payments to hospitals with excess readmissions. In 2018, more than $700 million was withheld; this is expected to exceed $1 billion by the year 2022.1 More importantly, there is nothing more physically and emotionally taxing for readmitted patients, demoralizing hospital physicians, nurses, and administrators.
OBJECTIVE
Given this high uncertainty of home recovery, intelligent monitoring is needed to predict the outcome of discharged patients to reduce readmissions. Therefore, we developed a remote, low-cost, cloud-based machine learning (ML) platform to enable precision health monitoring, which may fundamentally alter the delivery of home healthcare.
METHODS
Our platform consists of wearable, iPhone-synced sensors connected to our cloud-based ML interface to analyze physical activity remotely and predict clinical outcomes. This system was deployed in skilled nursing facilities where we collected over 17,000 person-day data over 2 years, generating a solid training database. We employed these data to train our XGBoost-based ML environment to conduct a clinical trial, “Activity Assessment of Patients Discharged from Hospital (ACT-I Trial, Stanford University Institutional Review Board Approval #53805),” to test the hypothesis that a comprehensive profile of physical activity will predict clinical outcome.
RESULTS
We achieved precise prediction of the patients’ clinical outcomes with a sensitivity of 87%, specificity of 79%, and accuracy of 85%.
CONCLUSIONS
We present AiCare’s comprehensive technology solution, consisting of wearable sensors, Bluetooth low energy (BLE)-enabled iOS infrastructure, ML algorithm to implement artificial intelligence, and API-enabled web technology, to measure the daily activities of patients. In this study, remote data collection, robust XGBoost AI analysis and reliable prediction of clinical outcome are reported.