Effective personal medicine
Patient Wellness and Prevention of Chronic Diseases
Accounts Receivable and Denials Management in Revenue Cycle Management
Controlling patient deterioration
During their stay at the hospital, patients face various threats, including the development of sepsis, acquisition of infection, or sudden downturn due to the existing clinical conditions.
Equipped with predictive analytics, doctors can figure out possible deterioration based on the changes in a patient’s vitals. Most importantly, they can do that before the symptoms clearly manifest themselves.
Potential in precision medicine
Evidently, healthcare institutions have access to heaps of data that can be used to uncover patients who have had similar responses to specific medications.
Only machine learning-based analytics can help uncover such insights, because the data sets we’re talking about are really huge, and include a lot of cluttered, unstructured data, including age, gender, location, and other relevant healthcare data.
Predictive analytics can, therefore, lead to improved precision medicine outcomes and make it easier for doctors to customize medical treatments, practices, and products to the use case at hand.
Cost reductions from eliminating waste and fraud
According to a NCSL podcast released in Dec 2017, healthcare fraud costs insurers anywhere between $70 billion to $234 billion each year.
By analyzing the heaps of patient data, payers are building or have built predictive models to prevent insurance fraud before payouts. This could include duplicate claims, doctors prescribing high rates of tests, or medically unnecessary treatments.