Healthcare

The healthcare industry has massive amounts of data available in health records, clinical trials, billings and claims processing systems, and yet they struggle to unlock value in this data to drive better patient outcomes and comply with healthcare regulations. Automated machine learning is helping transform the massive amounts of data in electronic health records, clinical trials, and billings and claims processing into predictions that drive down costs, improve operations, and ultimately, save lives.

Key Healthcare Stats

30%
percentage of the entire world's stored data generated by the healthcare industry - Ponemon Institute
$100 billion
the annual estimated value generated by using machine learning to optimize innovation and decision-making in healthcare - McKinsey
$30 billion
estimated medical costs savings from implementing HAI prevention practices - Center for Disease Control
40-50%
productivity improvement for nurses supported by AI tools - Harvard Business Review
Payer
Provider
Pharma

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 machine learning automation 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 Prediction

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.

HAIs

During hospital stays, patients are more susceptible to bloodstream infections, which is very costly because this often leads to hospital readmissions. Using DataRobot to predict which patients are more likely to contract sepsis or CLABSI triggers doctors to intervene by running additional diagnostics and testing, reducing the likelihood of patients being readmitted.

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 who are 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 hotspotting -- segmenting big data sets to strategically target different pockets of need -- reveals extreme patterns in defined regions of the healthcare system. With DataRobot, healthcare hotspotting 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, 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. And find out why Nathan said, “DataRobot’s platform makes my work exciting, my job fun, and the results more accurate and timely — it’s almost like magic.
Read Nathan's full Data Science Superhero profile here.

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