Machine Learning and AI in Healthcare

Healthcare companies are using machine learning and AI to increase top and bottom line through gaining competitive advantages, reducing expenses, and improving efficiencies. They are optimizing all areas of their business from readmission risk and occupancy rates to marketing, in order to make data-driven decisions that lead to increased profitability.

Improve Patient Satisfaction Scores

Increase patient satisfaction scores by predicting which patients are likely to submit poor scores and the primary reasons. Design interventions to improve their satisfaction.

Predict Suicide Warning Signs

Provide a supplementary assessment that helps prevent suicides and save lives by predicting ahead of time who is likely to commit suicide.

Predict Which Patients Will Admit

Predict which patients are likely to be admitted to proactively improve their health.

Predict Outpatient Appointment No Shows

Predict in advance which patients are likely to miss their appointments to reduce clinician downtime.

Predict Side Effects

Predict side effects to control drug reactions.

UCSF: Predicting Patient Outcomes Using OR Data

UCSF-BASIC uses DataRobot and operating room data to predict the outcomes of patients with traumatic spinal cord injuries.

Improve Procurement to Fulfill Medical Resource Needs

Fulfill the medical resource needs of your clinicians while limiting on-hand inventory of non-essential items to secure levels.

Predict Overpaid Medical Claims (Fraud, Waste, Abuse)

Predict if a physician is submitting an overpaid claim based on historical data and the drugs they are prescribing.

Predict Equipment Failure

Predict equipment failure using age and equipment usage to prevent downtime.

Forecast Patient Volume to Improve Staffing

Census or patient admission forecasting helps healthcare providers optimize their staffing and resource needs.

Predict Patient Nonadherence / Leakage in Provider Facilities

Foresee which patients are likely to churn before completing their course of care.

Predict Churn for Clinical Trials

Predict churn in order to improve success rate of clinical trials.

Improve Patient Engagement with Case Managers

Discover the underlying reasons behind why patients may refuse to engage with care managers.

Improve Medical Representatives Performance (i.e. Sellers)

Improve medical rep performance by personalizing approach.

Predict Member or Employer Disenrollment

Reduce disenrollment, either on the member or employer level, by predicting ahead of time which are likely to churn.

Reduce Patient Length of Stay

Reduce patient length of stay (LOS) without sacrificing quality of care by understanding the barriers to a timely and effective discharge.

Predict Hospital Acquired Conditions

Bolster prevention procedures even by predicting which patients are likely to develop HACs.

Predict Opioid Abuse

Improve the care you offer by predicting which patients with opioid prescriptions are likely to suffer from opioid abuse.

Improve Medication Adherence

Predict in advance which patients are likely to be non-adherent to their medication to intervene and improve adherence.

Identify Members Who Will Become High Cost Claimants

Predict how likely health care members will be to go over a certain cost threshold in the next 12-month period.

Estimating Sepsis Risk

DataRobot learns from historical data to help with early identification of patients at high risk of sepsis.

Drug Delivery Optimization

DataRobot helps companies optimize supply chain and drug delivery processes, balancing timely delivery with managing costs.

Disease Propensity Predictions

DataRobot uses AI to help identify patients with a high likelihood of a particular disease, along with appropriate treatments, to improve outcomes. Supplement the existing medical diagnostic process to identify high risk patients that may be overlooked.

Modeling ICU Occupancy

Forecasting ICU occupancy means being prepared for incoming patients and not staffing empty beds.

Reduce Hospital Readmission Risk

Proactively reduce 30-day readmissions rate by predicting in advance which patients are likely to readmit and understanding the top reasons why. Proactively identifying hospital readmittance means increasing quality of care, decreasing costs, and improving the lives of patients.

Explore More Use Cases