Why Healthcare is Behind the Data Curve - InformationWeek

InformationWeek is part of the Informa Tech Division of Informa PLC

This site is operated by a business or businesses owned by Informa PLC and all copyright resides with them.Informa PLC's registered office is 5 Howick Place, London SW1P 1WG. Registered in England and Wales. Number 8860726.

Data Management
07:00 AM
Jeremy Achin, CEO of DataRobot
Jeremy Achin, CEO of DataRobot

Why Healthcare is Behind the Data Curve

The healthcare sector has been trailing industries such as banking and retail when it comes to adoption of data analytics, but a growing number of success stories in healthcare provide proof of concept for other organizations to get on board.

Nearly every industry is searching for ways to create value from rapidly expanding amounts of data, but few sit on as much untapped opportunity as today’s healthcare providers. Electronic health records and connected medical devices have created an unprecedented volume of data. Most of this data sits unanalyzed, leading to operational inefficiency and missed opportunities for improved patient care. However some healthcare organizations are getting value from their data by finding applications that provide quick wins, and they are using their early successes to build a data-driven culture.

Health Catalyst recently released a survey of hospital executives on the role of predictive analytics in the healthcare industry. Though 80% of the executives surveyed believe forward-looking data analytics could significantly improve the industry, only 31% of respondents have integrated these advantages in their organizations. Even worse, 19% have no plans to do so.

What explains the slow adoption of predictive analytics in healthcare? It is largely historical. In contrast to industries like banking and insurance, which treat data analysis as a core competency, medicine has long been practiced as an art. Physicians accepted that medical information was locked in hard-to-analyze handwritten notes. As a result, few healthcare providers bothered to assemble analytics teams. Many still feel unprepared to do so today.

A multitude of examples show that a successful first project can build the confidence and momentum needed to create a data-driven and highly optimized organization.

As a case in point, one large Midwestern hospital system struggled with high costs arising from patients who were discharged too early and subsequently readmitted. Doctors broadly understood the risk factors for most patients, but they sometimes missed critical information because they lacked the time to pore over every detail of every patient’s medical record.

The hospital built a predictive model that physicians now reference when making discharge decisions. The model shows both a probability of readmission and the medical or social factors driving that risk. In some cases, the model confirmed the doctor’s understanding. In other cases, it flagged issues they would have missed, leading to better medical decisions.

This hospital system noticed an immediate financial return on their analytics investment. Building on this success, they created a new model to forecast staffing needs for each day. With the new model, they reduced both costly overstaffing as well as dangerous understaffing situations. Because they already had the data, they found model building and business optimization remarkably quick.

Their growing confidence in modeling has motivated dozens of new applications. Their newest predictive modeling project will improve how they escalate care from outpatient departments to the emergency department.

Jeremy Achin, DataRobot
Jeremy Achin, DataRobot

Are these highly optimized healthcare organization the wave of the future? We think so, but we also see many providers getting derailed by focusing on the wrong applications. Organizations betting on moonshots -- like replacing doctors with technologies that aren’t ready for prime time -- may become disillusioned and miss out on clear wins that are readily available today.

Healthcare organizations are at a critical juncture. They have the data to become dramatically more efficient if they start in the right direction. As one success leads to the next, they will soon look back and wonder why they waited so long to improve their patients’ health outcomes and their bottom line.


Jeremy Achin is a data scientist turned entrepreneur. As CEO of DataRobot, Jeremy sets the direction of the company, products, and the culture. He's passionate about helping organizations become more efficient by deploying machine learning everywhere. Prior to DataRobot, he was Director of Research and Modeling at Travelers Insurance where he built predictive models for pricing, retention, conversion, elasticity, lifetime value, customer behavior, claims and much more. A data science enthusiast, Jeremy spends his spare time building predictive models, usually on the data science competition platform Kaggle.com. Jeremy studied math, physics, computer science, and statistics at University of Massachusetts, Lowell.

The InformationWeek community brings together IT practitioners and industry experts with IT advice, education, and opinions. We strive to highlight technology executives and subject matter experts and use their knowledge and experiences to help our audience of IT ... View Full Bio
We welcome your comments on this topic on our social media channels, or [contact us directly] with questions about the site.
Comment  | 
Print  | 
More Insights
Newest First  |  Oldest First  |  Threaded View
User Rank: Apprentice
2/2/2017 | 10:26:37 AM
The technology is there, it's the healthcare providers who need to understand the value, especially in the supply chain
As a healthcare supply chain specialist, I find that the biggest problem is getting the message to healthcare provider leadership that the technology is available - especially from companies that have taken the same supply chain analytics and processes from retail and are working NOW in healthcare.  

I will be happy to share specific examples of this if anyone wants to contact me directly: [email protected]
User Rank: Apprentice
1/12/2017 | 9:48:54 AM
Time for healthcare companies to invest in analytics.
Good article about the opportunity for healthcare companies to do a better job with analytics. According to the article, "...80% of the executives surveyed believe forward-looking data analytics could significantly improve the industry, only 31% of respondents have integrated these advantages in their organizations." Frankly, that seems high as the hype of analytics adoption is far outrunning reality. With so many possibilities to improve patient outcomes and reduce costs, it's time for healthcare companies to invest in data science.
COVID-19: Using Data to Map Infections, Hospital Beds, and More
Jessica Davis, Senior Editor, Enterprise Apps,  3/25/2020
Enterprise Guide to Robotic Process Automation
Cathleen Gagne, Managing Editor, InformationWeek,  3/23/2020
How Startup Innovation Can Help Enterprises Face COVID-19
Joao-Pierre S. Ruth, Senior Writer,  3/24/2020
White Papers
Register for InformationWeek Newsletters
State of the Cloud
State of the Cloud
Cloud has drastically changed how IT organizations consume and deploy services in the digital age. This research report will delve into public, private and hybrid cloud adoption trends, with a special focus on infrastructure as a service and its role in the enterprise. Find out the challenges organizations are experiencing, and the technologies and strategies they are using to manage and mitigate those challenges today.
Current Issue
IT Careers: Tech Drives Constant Change
Advances in information technology and management concepts mean that IT professionals must update their skill sets, even their career goals on an almost yearly basis. In this IT Trend Report, experts share advice on how IT pros can keep up with this every-changing job market. Read it today!
Flash Poll