The healthcare industry across the globe is currently going through a lot of stress.

The recent outbreak of COVID–19, coupled with the growing cases of chronic diseases, is adding a burden on an already overburdened healthcare system.

Hospitals are scurrying to accommodate patients who need prioritized care. There is a massive shortage of beds and resources. There is a shortage of masks and other protective gear required by healthcare teams.

So, how can healthcare companies cope up with this stress?

How can they ensure that every patient receives customized treatment despite the pandemic outbreak? What can they do to utilize the existing resources optimally?

One way to work around these issues is by investing in prescriptive analytics. It helps healthcare companies to optimize their services.

What Is Prescriptive Analytics?

Prescriptive analytics is an area of data analytics that focuses on prescribing possible actions and solutions for a problem. It uses modeling, data mining, and artificial intelligence to evaluate historical data and real-time data to make future predictions. It gives the healthcare companies multiple ‘what if’ options, which they can compare to find the best possible solution for the patient.

How Can Prescriptive Analytics Benefit Healthcare Companies?

To understand how prescriptive analytics can help healthcare companies, let’s take an example of patients with diabetes that IBM’s Product Marketing Manager, Sajan Kuttappa, gave in his blog. A health insurance company might find out from its data that a significant number of diabetics are prone to diabetic retinopathy. They can use prescriptive analytics to analyze if there will be an increase in ophthalmology claims in the next year. This will help them to determine if they should keep the average ophthalmology reimbursement rates the same, increase, or decrease it by next year. It enables them to make more informed decisions.

Another advantage of prescriptive analytics is that it prepares the healthcare companies for future and unforeseen events. For example, let’s take a hypothetical situation of COVID-19. The hospitals know from historical and real-time data people with pre-existing diseases and old-aged patients are more susceptible to infections. This will enable the hospitals to provide topmost care to the vulnerable category of patients. It will also help the hospitals to trace the doctors and nurses who provide care to the patients and ensure that they follow the guidelines laid down by the Government and the World Health Organization (WHO) to avoid getting infected or spreading infection. It helps healthcare companies to mitigate further risks.

The benefit of prescriptive analytics is that it goes a step ahead of the predictive model that hospitals usually use. If predictive analytics helps a healthcare company to forecast future outcomes, prescriptive analytics nudges it to take action on those findings. It gives the healthcare company the power to influence the results. Dr. John Frownfelter calls prescriptive analytics the future of healthcare. He says that creating a risk model of a patient is simpler, and it might not add much value to an experienced doctor who can already predict what can happen to the patient next. Instead, he suggests using prescriptive analytics. With prescriptive analytics, doctors can understand the patient holistically, find out the associated risks, and then determine what interventions could help in the patient’s recovery. Prescriptive analytics removes all the guesswork from the decision-making process and optimizes it to provide more improved care to its patients.

Let’s look at the Dijon University Hospital Centre (CHU Dijon) in France that Sajan mentioned in his blog. Their hospitals were distributed in various places. So, they faced a tough time during intra-hospital patient transport. It was important to get the logistics right. A single skip or delay could lead to disastrous circumstances for the patient. The hospital used an optimization model on their transport data that keeps changing. This helped them to manage and execute multiple daily transport requests in real-time. They were able to improve their punctuality by 25% and reduce the patient wait time too.

Conclusion

Prescriptive analytics facilitates hospitals in cutting costs, delivering better services, and improving transparency in their day-to-day functioning.

Prescriptive analytics can also benefit health insurers and pharmaceutical companies. Insurance companies can use it to provide pricing and premium information to their clients, while pharmaceutical companies can use it to accelerate the speed of developing drugs and getting faster approvals.

The onus lies on these healthcare companies on how they leverage it to improve their services and offerings. In the end, it is all about delivering the best possible care to patients.