@Article{info:doi/10.2196/10045,
author="Palacholla, Ramya Sita
and Fischer, Nils C
and Agboola, Stephen
and Nikolova-Simons, Mariana
and Odametey, Sharon
and Golas, Sara Bersche
and op den Buijs, Jorn
and Schertzer, Linda
and Kvedar, Joseph
and Jethwani, Kamal",
title="Evaluating the Impact of a Web-Based Risk Assessment System (CareSage) and Tailored Interventions on Health Care Utilization: Protocol for a Randomized Controlled Trial",
journal="JMIR Res Protoc",
year="2018",
month="May",
day="09",
volume="7",
number="5",
pages="e10045",
keywords="decision support techniques, algorithm, multiple chronic diseases, risk assessment, health services for the aged, emergency responders, hospitalization, patient readmission",
abstract="Background: Soaring health care costs and a rapidly aging population, with multiple comorbidities, necessitates the development of innovative strategies to deliver high-quality, value-based care. Objective: The goal of this study is to evaluate the impact of a risk assessment system (CareSage) and targeted interventions on health care utilization. Methods: This is a two-arm randomized controlled trial recruiting 370 participants from a pool of high-risk patients receiving care at a home health agency. CareSage is a risk assessment system that utilizes both real-time data collected via a Personal Emergency Response Service and historical patient data collected from the electronic medical records. All patients will first be observed for 3 months (observation period) to allow the CareSage algorithm to calibrate based on patient data. During the next 6 months (intervention period), CareSage will use a predictive algorithm to classify patients in the intervention group as ``high'' or ``low'' risk for emergency transport every 30 days. All patients flagged as ``high risk'' by CareSage will receive nurse triage calls to assess their needs and personalized interventions including patient education, home visits, and tele-monitoring. The primary outcome is the number of 180-day emergency department visits. Secondary outcomes include the number of 90-day emergency department visits, total medical expenses, 180-day mortality rates, time to first readmission, total number of readmissions and avoidable readmissions, 30-, 90-, and 180-day readmission rates, as well as cost of intervention per patient. The two study groups will be compared using the Student t test (two-tailed) for normally distributed and Mann Whitney U test for skewed continuous variables, respectively. The chi-square test will be used for categorical variables. Time to event (readmission) and 180-day mortality between the two study groups will be compared by using the Kaplan-Meier survival plots and the log-rank test. Cox proportional hazard regression will be used to compute hazard ratio and compare outcomes between the two groups. Results: We are actively enrolling participants and the study is expected to be completed by end of 2018; results are expected to be published in early 2019. Conclusions: Innovative solutions for identifying high-risk patients and personalizing interventions based on individual risk and needs may help facilitate the delivery of value-based care, improve long-term patient health outcomes and decrease health care costs. Trial Registration: ClinicalTrials.gov NCT03126565; https://clinicaltrials.gov/ct2/show/NCT03126565 (Archived by WebCite at http://www.webcitation.org/6ymDuAwQA). ",
issn="1929-0748",
doi="10.2196/10045",
url="http://www.researchprotocols.org/2018/5/e10045/",
url="https://doi.org/10.2196/10045",
url="http://www.ncbi.nlm.nih.gov/pubmed/29743156"
}