Steward Health Care Leverages DataRobot’s Automated Machine Learning Platform for Predictive Analytics
Customer Company Size
Large Corporate
Region
- America
Country
- United States
Product
- DataRobot’s automated machine learning platform
- Proactive Labor Management (PLM) dashboard
Tech Stack
- Machine Learning
- Predictive Analytics
- Artificial Intelligence
- Microsoft Azure
Implementation Scale
- Enterprise-wide Deployment
Impact Metrics
- Cost Savings
- Productivity Improvements
Technology Category
- Analytics & Modeling - Machine Learning
- Analytics & Modeling - Predictive Analytics
Applicable Industries
- Healthcare & Hospitals
Applicable Functions
- Human Resources
- Business Operation
Use Cases
- Predictive Maintenance
- Process Control & Optimization
Services
- Data Science Services
About The Customer
Steward Health Care is the largest for-profit private hospital operator in the United States. The company operates a network of 38 hospitals across the country. Steward Health Care is committed to improving operational efficiency and reducing costs within its network. The company is constantly looking for ways to leverage the vast amount of data it collects and maintains to drive value. Steward Health Care is particularly interested in using predictive analytics, artificial intelligence (AI) and machine learning to achieve these goals. The company has a dedicated team led by Erin Sullivan, the Executive Director of Information Systems and Software Development, who is tasked with finding solutions to these challenges.
The Challenge
Steward Health Care, the largest for-profit private hospital operator in the United States, was faced with the challenge of how to use predictive analytics, artificial intelligence (AI) and machine learning to derive value from the vast amount of data they are required to collect and maintain. The primary task was to improve operational efficiency across Steward’s network of 38 hospitals, with a focus on reducing costs. The company decided to address one of the most pressing challenges facing hospital operations — staffing volume. The typical hospital staffing model is set to average census and volume, leading to inefficiencies during peaks and valleys in patient volume. This results in high expenses for on-call staff and overtime pay. Steward Health Care’s CEO, Dr. Ralph de la Torre, challenged his team to find a more proactive approach.
The Solution
Steward Health Care utilized DataRobot’s automated machine learning platform to manipulate their data, quickly build and test models from that data, and ultimately learn from the data. The project began by identifying sources of historical data from all the network’s hospitals. The more data they could feed into the model, the more they could fine-tune their predictions. Inpatient volume contributors primarily came from two main sources: the emergency department (ED) and the elective operating room (OR) schedule. The team identified other external factors that might affect volume predictions. The DataRobot automated machine learning platform helped Steward build and test new, more accurate models faster than ever before. Steward was able to quickly get 384 models working on day-specific volume and 1,152 shift-specific models into production in a dashboard built by Erin and her team. These DataRobot models are fed into Steward’s proprietary and patent-pending Proactive Labor Management (PLM) dashboard, a SaaS platform run on Microsoft Azure that is accessible to all 38 hospitals within the Steward Health Care network.
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.
