Big data analytics transforms the operating room
Company Size
1,000+
Region
- America
Country
- United States
Product
- Statistica
Tech Stack
- Predictive Analytics
- Big Data Analytics
- Electronic Medical Records (EMRs)
Implementation Scale
- Enterprise-wide Deployment
Impact Metrics
- Cost Savings
- Customer Satisfaction
- Productivity Improvements
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
About The 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.
The 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.
The 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.
Operational Impact
Quantitative Benefit
Case Study missing?
Start adding your own!
Register with your work email and create a new case study profile for your business.
Related Case Studies.
Case Study
Hospital Inventory Management
The hospital supply chain team is responsible for ensuring that the right medical supplies are readily available to clinicians when and where needed, and to do so in the most efficient manner possible. However, many of the systems and processes in use at the cancer center for supply chain management were not best suited to support these goals. Barcoding technology, a commonly used method for inventory management of medical supplies, is labor intensive, time consuming, does not provide real-time visibility into inventory levels and can be prone to error. Consequently, the lack of accurate and real-time visibility into inventory levels across multiple supply rooms in multiple hospital facilities creates additional inefficiency in the system causing over-ordering, hoarding, and wasted supplies. Other sources of waste and cost were also identified as candidates for improvement. Existing systems and processes did not provide adequate security for high-cost inventory within the hospital, which was another driver of cost. A lack of visibility into expiration dates for supplies resulted in supplies being wasted due to past expiry dates. Storage of supplies was also a key consideration given the location of the cancer center’s facilities in a dense urban setting, where space is always at a premium. In order to address the challenges outlined above, the hospital sought a solution that would provide real-time inventory information with high levels of accuracy, reduce the level of manual effort required and enable data driven decision making to ensure that the right supplies were readily available to clinicians in the right location at the right time.
Case Study
Gas Pipeline Monitoring System for Hospitals
This system integrator focuses on providing centralized gas pipeline monitoring systems for hospitals. The service they provide makes it possible for hospitals to reduce both maintenance and labor costs. Since hospitals may not have an existing network suitable for this type of system, GPRS communication provides an easy and ready-to-use solution for remote, distributed monitoring systems System Requirements - GPRS communication - Seamless connection with SCADA software - Simple, front-end control capability - Expandable I/O channels - Combine AI, DI, and DO channels
Case Study
Driving Digital Transformations for Vitro Diagnostic Medical Devices
Diagnostic devices play a vital role in helping to improve healthcare delivery. In fact, an estimated 60 percent of the world’s medical decisions are made with support from in vitrodiagnostics (IVD) solutions, such as those provided by Roche Diagnostics, an industry leader. As the demand for medical diagnostic services grows rapidly in hospitals and clinics across China, so does the market for IVD solutions. In addition, the typically high cost of these diagnostic devices means that comprehensive post-sales services are needed. Wanteed to improve three portions of thr IVD:1. Remotely monitor and manage IVD devices as fixed assets.2. Optimizing device availability with predictive maintenance.3. Recommending the best IVD solution for a customer’s needs.
Case Study
HaemoCloud Global Blood Management System
1) Deliver a connected digital product system to protect and increase the differentiated value of Haemonetics blood and plasma solutions. 2) Improve patient outcomes by increasing the efficiency of blood supply flows. 3) Navigate and satisfy a complex web of global regulatory compliance requirements. 4) Reduce costly and labor-intensive maintenance procedures.
Case Study
Harnessing real-time data to give a holistic picture of patient health
Every day, vast quantities of data are collected about patients as they pass through health service organizations—from operational data such as treatment history and medications to physiological data captured by medical devices. The insights hidden within this treasure trove of data can be used to support more personalized treatments, more accurate diagnosis and more advanced preparative care. But since the information is generated faster than most organizations can consume it, unlocking the power of this big data can be a struggle. This type of predictive approach not only improves patient care—it also helps to reduce costs, because in the healthcare industry, prevention is almost always more cost-effective than treatment. However, collecting, analyzing and presenting these data-streams in a way that clinicians can easily understand can pose a significant technical challenge.
