Predictive Modeling for Improving Healthcare Using IoT: Role of Predictive Models in Healthcare Using IoT

Predictive Modeling for Improving Healthcare Using IoT: Role of Predictive Models in Healthcare Using IoT

Jayanthi Jagannathan (Sona College of Technology, India) and Udaykumar U. (Sona College of Technology, India)
DOI: 10.4018/978-1-7998-1090-2.ch015

Abstract

The chapter covers the challenges faced in real-world healthcare services such as operating room bottlenecks, upcoming newborn medicines, managing datasets, and sources. It includes future directions that address practitioner difficulties. When IoT is merged with predictive techniques, it improves the medical service performance rate tremendously. Finally, the chapter covers the case studies and the tools that are in use to motivate the researchers to contribute to this domain.
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Literature Review

Operating Room Bottlenecks

The University of Chicago Medical Center (UCMC) (2017) used predictive analytics to tackle the problem of operating room delays. Such delays are aggravating for clinicians, patients, and families and they are wasteful since ORs are expensive to run. But delays are hard to prevent, with so many individuals and teams working on each surgical case. When one procedure ends, there is a sequence of certain tasks that must be completed before the next surgery can start.

UCMC combined real-time data with a complex-event processing algorithm to improve workflows, create notifications, and streamline the handoffs from one team to the next for each step of the OR process. The effort decreased turnover time 15% to 20% (four minutes per room), which was expected to save the hospital up to $600,000 annually. The new system also increased visibility into what was causing each delay and how to intervene in real time to get things back on track.

Newborn Antibiotics

Kaiser Permanente (2015) led the development of a risk calculator that has reduced the use of antibiotics in newborns. Antibiotics are necessary for a small percentage of newborns who are at risk for early onset neonatal sepsis, an infection that can lead to meningitis or death. Researchers developed a risk prediction model after drawing data from the EHRs of about 600,000 babies and their mothers. The approach better targets newborns who are at the highest risk for sepsis without exposing those at low risk to antibiotics. The effort safely reduced antibiotic use by nearly 50% in newborns delivered at Kaiser’s Northern California birthing centers in 2015. It also allowed mothers and babies to stay together in the first few days. Kaiser makes the risk calculator available online.

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