The healthcare industry has massive amounts of data available in health records, clinical trials, and billings and claims processing systems; and yet, the industry still struggles to unlock value in this data to drive better patient outcomes and comply with healthcare regulations.

Learn how automated machine learning is transforming healthcare.

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Automated machine learning is helping transform the billions of data points collected in electronic health records, clinical trials, and billings and claims processing into predictions that drive down costs, improve operations, and ultimately, save lives.

Transform operations through machine learning

Whether through cost minimization or revenue maximization, all facets of the healthcare industry — payers, providers, and pharmas — have the opportunity to greatly transform and improve their operations through machine learning. Here are some of the ways that the healthcare industry is already applying automated machine learning:


For consumer-facing teams, maximizing revenue is all about preventing churn. DataRobot’s machine learning platform helps payers automatically build accurate predictive models to identify customers likely to leave a payer, in addition to uncovering insights on pricing and service quality that help extend the lifetime value of customers.

By using risk stratification to prioritize the management of at-risk patients and prevent adverse outcomes, payers also have the opportunity to not only improve quality of care, but also dramatically reduce costs for all involved. Machine learning can help them create a system that better utilizes limited resources to process claims and handle fraud, minimizing overall costs.


Improving patient outcomes whenever possible is the best way for providers to increase revenues while also reducing costs. For example, DataRobot quickly builds models that predict which patients are at a higher risk of contracting a healthcare-associated infection (HAI), allowing doctors to take proactive action while also significantly reducing costs.

The high rate of hospital readmissions is one of the biggest cost burdens on the healthcare industry. Another way for providers to minimize costs is through improving operational efficiencies. Models that provide insights on how to staff efficiently or manage medical inventory create improved efficiency that substantially reduces cost burdens.


Experts predict machine learning in pharma is expected to generate up to $100 billion in value annually by optimizing innovation and decision-making. Its potential applications in early-stage drug discovery — from initial screening of drug compounds to predicted success rate based on biological factors — could fundamentally change the life sciences research industry.

DataRobot helps pharma companies by automatically building highly accurate models that optimize the design of clinical trials, shorten the approval process, and greatly reduce pharma innovation costs. The pharma industry is rife with inefficiencies that could be solved through machine learning.

DataRobot Can Help You With:

Reduced readmissions

When patients are readmitted into hospitals after having just completed a treatment stay, the costs incurred by both the hospital and the patient are significant. USing DataRobot’s automated machine learning platform to predict and prevent hospital readmissions leads to the more efficient use of scarce hospital resources while improving the overall quality of care that patients receive.

ICU utilization predictions

High costs and periodic scarcity of critical care resources are two key reasons why ICU utilization must be improved. Using automated machine learning to accurately predict which patients truly need intensive care - and which ones for whom ICU admission might not be necessary -- represents enormous cost savings for hospitals


During hospital stays, patients are more susceptible to bloodstream infections, which is very costly because this often leads to hospital readmissions. Use DataRobot to predict which patients are more likely to contract sepsis or CLABSI and automatically trigger doctors to intervene by running additional diagnostics and testing.

Medication adherence

Patients with chronic diseases who don’t consistently take their medications lead to more than $100 billion in preventable costs annually. Using DataRobot to create models that identify the patients less likely to adhere to prescribed drug regimens, and predict the behavioral drivers, helps create the right intervention plan to decrease medication non-adherence.

Healthcare hot spotting

Five percent of the United States population accounts for nearly 50% of total healthcare costs in the country. Healthcare hot spotting - segmenting big data sets to strategically target different pockets of need - reveals extreme patterns in defined regions of the healthcare system. With DataRobot, healthcare hot spotting can be both more efficient and more accurate.

Payer fraud

Fraudulent claims are costly, but it is too expensive and inefficient to investigate every claim. Using DataRobot’s machine learning platform, organizations automatically build accurate predictive models to identify and prioritize likely fraudulent activity, allowing for more effective deployment of resources and optimizing customer satisfaction.

Nathan Patrick Taylor
Nathan Patrick Taylor
VP - Analytics Strategy, Symphony Post Acute Network
In this webinar recording, Nathan from Symphony Post Acute Network discusses how DataRobot is transforming data science for challenges like hospital readmissions and patient falls. Find out why Nathan says, “DataRobot’s platform makes my work exciting, my job fun, and the results more accurate and timely -- it’s almost like magic!”

What our Customers Say

  • "DataRobot's platform makes my work exciting, my job fun, and the results more accurate and timely -- it's almost like magic!"

    Omair Tariq
    Omair Tariq

    Data Analyst, Symphony Post Acute Network

  • "We know part of the science and the heavy lifting are intrinsic to the DataRobot technology. Prior to working with DataRobot, the modeling process was more hands-on. Now, the platform has optimized and automated many of the steps, while still leaving us in full control. Without DataRobot, we would need to add two full-time staffers to replace what DataRobot delivers."


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