Case Studies Big data analytics transforms the operating room

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	Big data analytics transforms the operating room

Big data analytics transforms the operating room

Technology Category	Analytics & Modeling - Big Data Analytics Analytics & Modeling - Predictive Analytics Functional Applications - Remote Monitoring & Control Systems
Applicable Industries	Healthcare & Hospitals
Applicable Functions	Business Operation Quality Assurance
Use Cases	Clinical Image Analysis Predictive Maintenance Remote Patient Monitoring
Services	Data Science Services System Integration
Challenge	Surgeons at the University of Iowa Hospitals and Clinics needed to know if patients were susceptible to infections in order to make critical treatment decisions in the operating room. Reducing the infection rate has major implications for overall patient health and cost savings. In the United States, roughly one out of every 20 patients admitted to a hospital will acquire an infection. Knowing if the patient is vulnerable can help doctors make critical decisions about treatment. According to the U.S. Centers for Disease Control and Prevention, surgical site infections are the most common, accounting for more than 30 percent of occurrences, and putting patients at risk of illness and prolonged hospitalization. Sometimes, people die. The total cost of hospital-acquired infections to the healthcare industry is estimated at $10 billion per year. Read More
About Customer	The University of Iowa Hospitals and Clinics is one of the most highly regarded medical facilities in the United States. It provides treatment for serious illnesses or injuries to people throughout the state and region. The hospital employs more than 1,400 physicians, with almost 300 of them named “Best Doctors in America” in nationwide physician surveys. It was the state’s first-ever recipient of the Magnet Award for Nursing Excellence® and the 2014 recipient of the Magnet Prize®. The hospital is also recognized as one of the nation’s “most wired” hospitals for its level of IT adoption and was the 2014 winner of the HIMSS Davies Enterprise Award® for use of the EMR. Read More
Solution	The surgical team at the University of Iowa Hospitals and Clinics harnessed the power of big data analytics, coupled with other methods, to keep patients safe and reduce surgical site infections. They deployed advanced analytics software to marshal disparate data sources and make real-time predictions about the probability of a patient developing a surgical site infection. The team connected to disparate enterprise systems and brought the data into a common data set with embedded analytical tools. This allowed them to deliver predictive analytics in a real-time environment to improve healthcare and reduce costs. The analytics tool enabled the team to store predictions in its database for future analysis and to aid in follow-up patient care. The team needed a flexible, advanced analytics platform that encompassed the entire analytics lifecycle, from data aggregation and preparation to model development and deployment. Read More Log in to view content
Contents

Technology Category

Analytics & Modeling - Big Data Analytics

Analytics & Modeling - Predictive Analytics

Functional Applications - Remote Monitoring & Control Systems

Applicable Industries

Healthcare & Hospitals

Applicable Functions

Business Operation

Quality Assurance

Use Cases

Clinical Image Analysis

Predictive Maintenance

Remote Patient Monitoring

Services

Data Science Services

System Integration

Challenge

Surgeons at the University of Iowa Hospitals and Clinics needed to know if patients were susceptible to infections in order to make critical treatment decisions in the operating room. Reducing the infection rate has major implications for overall patient health and cost savings. In the United States, roughly one out of every 20 patients admitted to a hospital will acquire an infection. Knowing if the patient is vulnerable can help doctors make critical decisions about treatment. According to the U.S. Centers for Disease Control and Prevention, surgical site infections are the most common, accounting for more than 30 percent of occurrences, and putting patients at risk of illness and prolonged hospitalization. Sometimes, people die. The total cost of hospital-acquired infections to the healthcare industry is estimated at $10 billion per year.

About Customer

The University of Iowa Hospitals and Clinics is one of the most highly regarded medical facilities in the United States. It provides treatment for serious illnesses or injuries to people throughout the state and region. The hospital employs more than 1,400 physicians, with almost 300 of them named “Best Doctors in America” in nationwide physician surveys. It was the state’s first-ever recipient of the Magnet Award for Nursing Excellence® and the 2014 recipient of the Magnet Prize®. The hospital is also recognized as one of the nation’s “most wired” hospitals for its level of IT adoption and was the 2014 winner of the HIMSS Davies Enterprise Award® for use of the EMR.

Solution

The surgical team at the University of Iowa Hospitals and Clinics harnessed the power of big data analytics, coupled with other methods, to keep patients safe and reduce surgical site infections. They deployed advanced analytics software to marshal disparate data sources and make real-time predictions about the probability of a patient developing a surgical site infection. The team connected to disparate enterprise systems and brought the data into a common data set with embedded analytical tools. This allowed them to deliver predictive analytics in a real-time environment to improve healthcare and reduce costs. The analytics tool enabled the team to store predictions in its database for future analysis and to aid in follow-up patient care. The team needed a flexible, advanced analytics platform that encompassed the entire analytics lifecycle, from data aggregation and preparation to model development and deployment.

Impact #1	The surgical team was able to reduce surgical site infections by about 58 percent.
Impact #2	They merged historical and live patient data to predict infection likelihood.
Impact #3	The team demonstrated the role previously untapped health data can play in the operating room.

Impact #1

The surgical team was able to reduce surgical site infections by about 58 percent.

Impact #2

They merged historical and live patient data to predict infection likelihood.

Impact #3

The team demonstrated the role previously untapped health data can play in the operating room.

Benefit #1	58 percent reduction in occurrence of surgical site infections.
Benefit #2	Reduced cost of patient care.

Benefit #1

58 percent reduction in occurrence of surgical site infections.

Benefit #2

Reduced cost of patient care.

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Overview

Big data analytics transforms the operating room

Operational Impact

Quantitative Benefit