How Big Data Can Personalize Healthcare
By Greg Bengel, contributing writer
Researchers from Notre Dame recently developed a system that could help providers personalize healthcare, lower readmission rates, and cut costs
A recent announcement from the University of Notre Dame explains how UND researchers developed a system to utilize big data science to provide personalized healthcare. The system was developed by computer science associate professor Nitesh V. Chawla and his doctoral student Darcy A. Davis.
It is called Collaborative Assessment and Recommendation Engine (CARE) and the system uses big data science and electronic medical records to personalize disease management and patient well-being. You can read the paper from Chalwa and Davis in the Journal of General Internal Medicine.
“We believe that our work can lead to reduced re-admission rates, improved quality of care ratings and can demonstrate meaningful use, impact personal and population health, and push forward the discussion and impact on the patient-centered paradigm,” says Chawla.
CARE utilizes a filtering method that focuses on patient similarities and produces personalized disease risk profiles for individual patients. It generates predictions of diseases based on data from similar patients. “In its most conservative use, the CARE rankings can provide reminders for conditions that busy doctors may have overlooked,” Chawla is quoted as saying in the UND announcement. “Utilized to its full potential, CARE can be used to explore broader disease histories, suggest previously unconsidered concerns, and facilitate discussion about early testing and prevention, as well as wellness strategies that may ring a more familiar bell with an individual and are essentially doable.”
Chawla also talks about the timeliness of his system. With the changes in health care, meaningful use incentives, reimbursement issues, and reforms, Chawla notes that, “There is an increased focus on preventive care, well-being, and reducing re-admission rates in the hospital.” He adds, “This system can help bend the cost curve.”
Chawla’s opinion seems to be shared by experts. An article from FierceHealthIT on CARE points readers to a paper from the Institute of Medicine called “Making the Case For Continuous Learning from Routinely Collected Data.” The paper makes the case that harnessing routinely collected data can improve outcomes for patients by allowing providers to pinpoint disease risks and best treatments. The paper also says that properly utilizing the data reduces costs, as it reduces errors and unnecessary treatments.