How is AI used in healthcare?

Using AI, healthcare organizations can develop and deploy breakthrough preventative treatments, improve medical procedures, and even design new pharmaceutical solutions. According to one global study, 78 percent of businesses, including the healthcare industry, use AI in at least one business unit.

What are examples of artificial intelligence in healthcare?

Three significant areas of impact for AI in healthcare: Social Determinants of Health (SDOH): Predict disease outbreak and spread Staffing and retention: Predict which members (in any capacity, including staff) are at risk of churn and identify opportunities for retention Disrupted care: Identify patients in need of preventative care

What are the upcoming tools of artificial intelligence in healthcare?

Trends in the healthcare industry show AI being leveraged in the following areas: Enhanced Operations Clinical decision support Predictive and prescriptive medicine Funding of care (including payers) Population health

What are the Benefits of AI in Healthcare?

With trusted, explainable AI, healthcare providers can deliver high-impact business results that unify human intuition and machine intelligence to empower confident decision-making. As security, regulatory, and operational challenges keep on growing, stronger insights into data are critical for healthcare professionals to deliver quality patient care.

AI Cloud for Healthcare

AI Cloud for Healthcare has the power to unlock true value from healthcare system data to optimize patient care, accelerate research in disease prevention and treatment, and accurately forecast staffing and operational needs, while optimizing payer operations — all which saves lives and improves the quality of care for all patients, regardless of socioeconomic status.

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AI in Healthcare

The ability to access and understand patient data and improve health outcomes is stronger than ever before. Stronger data-driven insights are critical for healthcare organizations across the board to confidently respond to the changing dynamics of the social determinants of health, staffing and operations, and disrupted care. AI Cloud for Healthcare gives the ability to harness data to optimize patient care, accelerate research in disease prevention and treatment, accurately forecast staffing and operational needs, optimize financial performance, and enable equitable service provision. The power of machine learning can transform consumer health.

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Our Healthcare Customers are Shaping the Future

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AI Use Cases in Healthcare

In the wake of the global pandemic, supply chain disruptions, and tense economic environments around the world, the healthcare industry is facing unprecedented demand for patient and consumer health services alongside historically complex challenges. The healthcare industry needs to find new ways to address critical needs.

Improve Medication Adherence

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

Predict Patient Propensity to Have Certain Diseases

Supplement the existing medical diagnostic process to identify high risk patients that may be overlooked.

Predict Which Patients Will Admit

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

Predict Side Effects

Predict side effects to control drug reactions.

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 Outpatient Appointment No Shows

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

Predict Equipment Failure

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

Improve Medical Representatives Performance (i.e. Sellers)

Improve medical rep performance by personalizing approach.

Reduce 30-Day Readmissions Rate

Proactively reduce 30-day readmissions rate by predicting in advance which patients are likely to readmit and understanding the top reasons why

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.

Forecast Patient Volume to Improve Staffing

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

Predict Member or Employer Disenrollment

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

Predict Patient Nonadherence / Leakage in Provider Facilities

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

Improve Patient Engagement with Case Managers

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

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 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.

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.

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.

Predict Opioid Abuse

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

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 Hospital Acquired Conditions

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

Predict Churn for Clinical Trials

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

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