Predictive Analytics in Healthcare Services

The significance of predictive modeling in healthcare can easily be observed in emergency care, surgery and intensive care, where the outcome of a patient is directly related to the quick reaction and acute decision making of the care provider when or if the situation takes an unexpected turn for the worse. But not all predictive analytics in healthcare require an experienced team to maneuver into position. 

But what is predictive analytics in healthcare? Let’s look at just a few predictive analytics in healthcare examples of the specific benefits and how organizations are pulling actionable, forward-thinking insights from their ever increasing healthcare analytics data. 

In the world of population health management, predictive health and prevention are closely related when learning how to improve patient care. Predictive modeling in healthcare and prospective payment systems can help organizations identify individuals with increased risks of developing chronic conditions early in the disease’s development, helping patients avoid costly and difficult to treat health problems. Creating risk scores based on health conditions, as well as demographic factors such as Medicaid and disability status, gender, age, and whether a beneficiary lives in the community or in an institution can give healthcare data companies insight into which individuals might benefit from personalized healthcare or wellness programs to prevent problems from occurring. 

Throughout all reimbursement models, the management of high-risk patients is essential for improving quality and cost results. With the use of predictive analytics healthcare companies can proactively identify patients who are at highest risk of poor health and could benefit the most from mediation or treatment. This is one solution for improving risk management and helping providers transition to value-based care.

Patients face possible threats to their wellbeing while still in the hospital, including the development of difficult-to-treat infections, or abrupt downturns due to their existing conditions. Predictive modeling in healthcare can help providers react as quickly as possible to changes in a patient’s vitals, and make it easier to identify an upcoming deterioration of symptoms before they’re clearly apparent.

Health systems can be subjected to penalties under Medicare’s Hospital Readmissions Reduction Program (HRRP), adding financial motivation for preventing frequent returns to the inpatient environment. In addition to improving care transitions, predictive analytics in healthcare can notify providers when a patient’s risk factors denote a high probability for readmission within the 30-day time period.

Predictive health analytics tools that can identify patients with characteristics that have a high likelihood of readmission can give healthcare providers an indication of when to center assets on follow-up and how to design personalized healthcare protocols to stop frequent returns to the hospital. 

A clinician’s daily workflow can easily be thrown off due to unforeseen gaps in the daily schedule and have negative financial repercussions for the organization. Predictive analytics in healthcare can identify patients likely to miss an appointment without advanced notice. 

Electronic health record systems (EHRs) can reveal predictive health data about patients most likely to no-show. A study from Duke University found that predictive modeling in healthcare using clinic-level EHR data, could capture nearly an additional 5,000 patient no-shows per year with greater accuracy than previous attempts to forecast patient patterns. Providers can use this personalized healthcare data to send frequent reminders to patients at risk of no-showing, offer transportation or additional services to help individuals make their appointments, or suggest other times as needed.

In addition to supporting chronic disease management strategies and targeting therapies to produce better outcomes, predictive analytics in healthcare can keep patients engaged in other factors of their healthcare. Patient relationship conduct has become essential for providers, predictive medicine companies and healthcare data companies looking to advance wellness and reduce long-term costs. Predicting patient behaviors is essential in developing effective communications and compliance strategies.

Anthem has used predictive modeling in healthcare to create consumer profiles that enable them to send tailored messaging and discover what strategies are most likely to be impactful for particular patients. Providers as well are using behavioral patterns to design effective personalized health plans and keep patients involved with their financial and clinical obligations. 

The impact of predictive analytics on the healthcare industry is undeniable. And these are just a few examples of the many benefits of predictive analytics in healthcare and how organizations can reduce the risks associated with chronic diseases, reinforce population health management, inform better care decisions and improve relationships between patients and providers - contributing to better outcomes across the value-based care space.