Benefits of predictive analytics in healthcare

As healthcare moves toward value-based payments and accountable care, providers need better tools for population health and risk management. The ability to prevent unnecessary hospitalizations is a major piece of the puzzle. Doing this well means proactively identifying the highest-risk patients and prioritizing them for care coordination and targeted interventions.

Predictive analytics has long held the promise of solving this problem. Powered by vast quantities of high-fidelity clinical and claims data, predictive analytics can identify high-risk patients with greater speed and accuracy than ever before. Here are some of the key benefits of Predictive Analytics:

1. Increased Efficiency in Hospital Management 

Predictive analytics allows hospitals, insurance companies, and patients to work together to process claims and avoid problems. Delays in claim processing and approval can be reduced to help patients obtain treatment more promptly. By automating tedious operations, healthcare facilities may provide a stress-free work environment, allowing personnel to focus on providing better and more efficient customer service.

2. Cost savings

Predictive analytics allows for earlier and more successful medical interventions, as well as more efficient healthcare administration and operations management, which reduces costs for both patients and healthcare providers.

3. Early Diagnosis

In this domain, predictive analytics is already working miracles. By offering therapy early, the condition can be treated before it threatens the patient's long-term health. This is especially valuable in determining which cancer patients have a greater chance of recovery and how they might be assisted in overcoming the terrible disease.

4. Personalized Treatment

Hospitals can create precise models to reduce mortality and provide patients with the appropriate treatment. Doctors are discovering how easy it is to use predictive analytics to give high-quality treatment to every patient. Doctors can decide if a certain prescription would work for the patient based on their history, or whether they can create a unique combination of therapies tailored to the patient's individual needs.

5. Predicting the Risk of adverse events

Researchers and scientists can forecast outbreaks and the spread of infectious illnesses using historical and real-time data. This can assist governments in taking suitable and required actions to manage the epidemic and reduce societal fatalities.

6. Analyzing and Controlling the Deterioration of Patients’ Health

Machine learning algorithms have made it possible to forecast patient outcomes using health graphs based on information gathered about the person. Though medical personnel are aware that surgery or a sophisticated medical process may jeopardize a patient's life, the precise amount of risk may be assessed via predictive analytics, resulting in early intervention.

7. Remote Healthcare

Predictive analytics is not exclusive to the healthcare setting. It can be utilized to give continuous healthcare services to people who are unable to leave their homes. Many at-risk patients live at home rather than in hospitals.

The article has been written by Valli Bollavaram, Senior Vice President, IT, Optum Global Solutions (India) Private Limited