Healthcare

High Value AI Use Cases In Healthcare

• Reduce Readmissions

  Patient readmission results in significant costs for payers, hospitals, and patients. Using DataRobot’s enterprise AI 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 operations must be improved. Using an enterprise AI platform to create models that more accurately predict which patients need and do not need intensive care and to anticipate optimum staffing levels represents an enormous cost saving opportunity for hospitals.

• 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 predict their behavioral drivers helps create the right intervention plan to increase medication adherence.

• Identify Fraudulent Payment Activity

  Healthcare fraud can be costly, but it is also too expensive and inefficient to investigate every suspicious claim. Using DataRobot’s enterprise AI platform, organizations build accurate predictive models to identify and prioritize the most likely fraudulent activity, allowing for more effective deployment of resources to investigate, 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 their 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 patients are costly for providers and payers. can also be costly for patients because skipping medical appointments can lead to untreated medical conditions and adverse health outcomes. DataRobot can help predict which patients are most likely to be 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.