DataRobot Case Studies Steward Health Care Leverages DataRobot’s Automated Machine Learning Platform for Predictive Analytics

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	Steward Health Care Leverages DataRobot’s Automated Machine Learning Platform for Predictive Analytics DataRobot

Steward Health Care Leverages DataRobot’s Automated Machine Learning Platform for Predictive Analytics

DataRobot

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
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. Read More
About 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. Read More
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. Read More Log in to view content
Contents

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

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.

About 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.

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.

Impact #1	Steward Health Care 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.
Impact #2	The 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.
Impact #3	Built within Steward’s PLM dashboard is an accuracy tracker, one that currently has daily models at 95% accuracy.

Impact #1

Steward Health Care 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.

Impact #2

The 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.

Impact #3

Built within Steward’s PLM dashboard is an accuracy tracker, one that currently has daily models at 95% accuracy.

Benefit #1	Just a 1% reduction in registered nurses hours paid per patient day netted $2 million in savings per year, for just eight of the 38 hospitals in Steward’s network.
Benefit #2	Reducing patient length of stay by 0.1% results in savings of over $10 million per year.
Benefit #3	A marked decrease in hospital operation disruption through proactive Reduction-in-Force (RIF).

Benefit #1

Just a 1% reduction in registered nurses hours paid per patient day netted $2 million in savings per year, for just eight of the 38 hospitals in Steward’s network.

Benefit #2

Reducing patient length of stay by 0.1% results in savings of over $10 million per year.

Benefit #3

A marked decrease in hospital operation disruption through proactive Reduction-in-Force (RIF).

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Overview

Steward Health Care Leverages DataRobot’s Automated Machine Learning Platform for Predictive Analytics

Operational Impact

Quantitative Benefit