Healthcare

For the healthcare industry, the quest to deliver optimal care faces a number of challenges. From controlling costs to delivering the best outcomes for consumers, the industry is under increasing pressure to deliver better results with greater accuracy. AI allows healthcare organizations to transform billions of data points into insights and predictions that drive down costs, improve quality of care, and, ultimately, save lives.

See how healthcare companies are using AI.

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AI in Healthcare

Healthcare organizations have more information about consumers than ever before. The question becomes how to intelligently use all of that data so that it leads to better health outcomes. Enter DataRobot’s Enterprise AI platform. Our mission for healthcare organizations is to use AI to improve operations, lower costs, and deliver the best care for patients. DataRobot can work with you to turn the troves of data found in electronic medical records, diagnostic data, and medical claims information into cutting edge insights and predictions that optimize business processes across your organization.

Payers

Be a leader in CAHPS, HEDIS and Medicare Star quality ratings with superior analytics
Determine which members are at risk for leaving the health plan
Improve risk adjustment and capture the best target opportunities
Flag potential fraudulent claims
Build precise financial, actuarial, and underwriting models for cost of care, IBNR, MLR, large claims forecasting, and premium pricing models
Use analytics to understand member hospital inpatient length of stay and risk for readmission

Providers

Build more precise patient readmission risk models
Optimize your revenue cycle management and revenue prediction
Accurately forecast staffing needs
Actively manage your patient population health and accurately stratify your patient population risk
Use analytics to understand patient length of stay and patients at risk for hospital-acquired conditions
Be a leader in value-based care

Healthcare Vendors

Leverage precision analytics to optimize your patient marketing campaigns, messaging, and call center operations
Increase renewals and reduce customer turnover while actively managing your sales force effectiveness
Forecast product sales more accurately
Build more effective patient/customer messaging
Optimize your target marketing
Be a leader in supply and demand chain planning with exceptional analytics

Payers
- Be a leader in CAHPS, HEDIS and Medicare Star quality ratings with superior analytics
- Determine which members are at risk for leaving the health plan
- Improve risk adjustment and capture the best target opportunities
- Flag potential fraudulent claims
- Build precise financial, actuarial, and underwriting models for cost of care, IBNR, MLR, large claims forecasting, and premium pricing models
- Use analytics to understand member hospital inpatient length of stay and risk for readmission
Providers
- Build more precise patient readmission risk models
- Optimize your revenue cycle management and revenue prediction
- Accurately forecast staffing needs
- Actively manage your patient population health and accurately stratify your patient population risk
- Use analytics to understand patient length of stay and patients at risk for hospital-acquired conditions
- Be a leader in value-based care
Healthcare Vendors
- Leverage precision analytics to optimize your patient marketing campaigns, messaging, and call center operations
- Increase renewals and reduce customer turnover while actively managing your sales force effectiveness
- Forecast product sales more accurately
- Build more effective patient/customer messaging
- Optimize your target marketing
- Be a leader in supply and demand chain planning with exceptional analytics

High Value Use Cases In Healthcare

The healthcare industry has a multitude of AI and machine learning applications it can use to improve the quality of care, reduce costs, and streamline operations. Payers, providers, healthcare vendors (i.e., medical device/supplies companies, pharmacies, MSOs, dental, vision, and public sector) can use actionable insights from automated machine learning to reduce costs while maximizing revenue, improving patient/member outcomes, and optimizing operations throughout the industry. Healthcare organizations that adapt their businesses to take full advantage of AI and machine learning will dominate their markets.
Check out all Healthcare use cases

Reduce Readmissions

Patient readmission leads to significant costs for the payer, hospital, and patient alike. Using DataRobot’s automated machine learning platform to predict and prevent hospital readmissions leads to more efficient use of scarce hospital resources while improving the overall quality of care that patients receive.
Accurate Predictions About ICU and ED Utilization

High costs and periodic scarcity of critical care resources are two key reasons why ICU utilization must be improved. Using automated machine learning to more accurately predict which patients need and do not need intensive care represents enormous cost savings for hospitals. This also assists hospitals in anticipating staffing needs for these units.
Determine which patients or members are less likely to adhere to prescribed drug regimens

Patients or members with chronic diseases who do not consistently take their medications lead to more than $100 billion in preventable costs annually. Using DataRobot to create models that identify the patients or members who are less likely to adhere to prescribed drug regimens and to predict the behavioral drivers for those patients or members helps create the right intervention plan to increase medication adherence.
Identify Fraudulent Payment Activity

Fraudulent claims are costly, but it is too expensive and inefficient to investigate every claim. Using DataRobot’s automated machine learning platform, organizations build accurate predictive models to identify and prioritize likely fraudulent activity, allowing for more effective deployment of resources and optimization of customer satisfaction.
Flag members or patients who are at risk for churn

Payers and providers lose money when members decline renewal with the health plan or when patients do not return to their facilities. Payers and providers can use DataRobot to incorporate patient retention risks into their organization’s workflow, leading to reductions in non-renewals and patients not returning for further treatments.
Alert providers to patients at risk for hospital-acquired conditions

Patients are more susceptible to bloodstream infections during hospital stays. This is a costly outcome that often leads to hospital readmissions. DataRobot can predict which patients are more likely to contract sepsis or CLABSI and automatically alert doctors to run additional diagnostics and testing.
Predict patient or member length of stay

One of the primary predictors of cost is length of stay. Longer stays not only place patients at higher risk of hospital-acquired conditions, but they also constrain hospital bed availability and physician time. DataRobot can help providers and payers predict the length of an inpatient stay, which creates greater scheduling flexibility, reduces costs, enables targeted interventions for at-risk patients, and helps organizations transform to value-based care.
Predict No-Shows

No-show appointments are costly for providers and payers, but they can also be costly for patients because skipping medical appointments can lead to untreated medical conditions and adverse health outcomes. DataRobot can help predict these no-shows, equipping decision-makers with the information they need to rearrange schedules, craft interventions, and proactively engage patients. DataRobot can also aid in determining the probability of non-adherence and whether patients will respond to outreach efforts.

DataRobot Can Help:

Chief Medical Informatics Officers

Let DataRobot suggest the best model for each situation, saving your team time and effort trying and comparing every model. Use automated machine learning to build many models at the same time it took to build one, increasing precision with more model granularity.
Chief Analytics Officers

With automated machine learning, you acquire the productivity of a large data science team from a small one. Let DataRobot find the best models for you and use DataRobot’s simple deployment options to get them to market faster.
Business and Department Heads

Tap into the expertise within the data that your healthcare organization already has. Enable business analysts and data analysts without formal data science training to build and use sophisticated models.
Chief Digital Officers

Get models into production faster using DataRobot’s low-risk model deployment options, including code generation, deployment to Spark, and API-based deployment capability.
Chief Information Officers

The bottleneck in many healthcare organizations is no longer a lack of data, but rather plenty of data and not enough analytics staff to turn the data into insight. Democratize data science with DataRobot and watch the performance of your healthcare organization take off as the data reveals opportunities and improvements.

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!”

Nathan Patrick Taylor

CIO, Symphony Post Acute Network

We are using DataRobot to make some pretty huge decisions at Steward Health Care. It’s very much a part of our growth strategy. It’s uncharted territory for healthcare.

Erin Sullivan
Executive Director, Steward Health Care
Explainability is key in healthcare, and DataRobot's platform makes it easy for our clinicians to understand DataRobot's predictions and take action on those insights.

Nathan Patrick Taylor
CIO, Symphony Post Acute Network
We see DataRobot as a competitive advantage in healthcare. How else are you going to improve quality rates, or drive down medical costs, or reduce member churn, if not AI? It's an obvious choice.

Steve Prewitt
Chief Analytics Officer, HealthFirst