Predictive & Prescriptive Analytics in Healthcare: Promise and Potential

From preventing disease outbreaks to maintaining operational efficiency, data analytics is breaking new ground in the healthcare sector. Read on to learn how prescriptive and predictive analytics are transforming the healthcare industry worldwide.


The global healthcare industry is undergoing a paradigm shift as it moves from a volume-based payment model to a value-based model. Rising demands from patients for high-quality services, new market entrants, and innovative approaches to healthcare delivery are forcing providers to change the way they operate. There is a pressing need for providers to deliver personalized patient experiences and deepen their own understanding of population health to better identify and respond to patterns. Both of these objectives can only be met by applying advanced analytics to providers’ data.


Healthcare data in perspective

Before discussing the role of data analytics in the healthcare sector, let’s first understand what healthcare data is and how it’s being collected and analyzed. 

Every time a patient visits a medical facility, valuable data on the patient’s health, processes, medical procedures, and health data is gathered, stored, and analyzed.

This information, gathered through health information systems (HIS) and other tools used by doctors, insurance companies, and government organizations, is broken down into datasets that can be analyzed.

Some of the most common tools to collect, store, and analyze data include electronic health records (EHRs), electronic prescription services (E-prescribing), personal health records (PHRs), patient portals, and healthcare apps on smartphones.

The growing importance of data analytics in the Healthcare Sector

We can collect all the patient data we want, but it doesn't do any good if we are unable to comprehend it. 

With the majority of healthcare data being collected digitally, there are troves of real-time data to be analyzed every second. These data sets are so complicated that traditional software will be inefficient. This is where data analytics comes into the picture. 

Data analytics allows medical professionals to assess their patients’ needs, improve care quality, provide faster and more accurate diagnoses, and predict large-scale healthcare trends. The ability to quickly gather and analyze accurate data helps providers make more informed decisions. At the business level, using data analytics can help healthcare organizations lower costs, simplify operations, and save time and resources.

With the healthcare industry reeling under the COVID-19 pandemic, data analytics plays an even crucial role in keeping the outbreak under control. It is not just doctors but researchers and governments that are also turning to data analytics to predict surges, allocate resources, improve health outcomes and take preventive measures.

There’s a catch here! 

Not every healthcare-related puzzle can be solved using the same data analysis. Different problems require different methods. There are three primary analytical methods: retrospective or descriptive analytics, predictive analytics, and prescriptive or prospective analytics.

Building a Case for Predictive and Prescriptive Analytics

Both disciplines, predictive and prescriptive, are a step up the data analytics ladder for the healthcare industry. Both give insight and foresight to improve decision-making. 

Predictive analytics tells providers what to do. It allows them to learn from historical trends and predict future outcomes. For instance, predictive analytics can tell a provider the expected denial rate associated with specific claims. This arm of data analytics helps with the identification of patients in various disease categories and stages. Patients with chronic diseases, such as diabetes or heart disease, can be identified and monitored to prevent the development of other comorbidities. 

Prescriptive analytics tells providers how to do it. It assists them in determining the best course of action from the data gathered from descriptive and predictive analytics. For instance, prescriptive analytics will tell a provider how to bring down the number of claim denials by implementing a solution that will prevent claim denials from occurring in the future. By reducing the claim denial rate, the overall financial health of the medical facility will improve. 

Here’s an example:

An insurance company performs a retrospective analysis of claims data for the previous year and finds that a significant percentage of their diabetic population suffers from retinopathy.


Using predictive analytics, the insurance company estimates the probability of an increase in ophthalmology claims during the next year.

Using prescriptive analytics, the company calculates the cost impact by increasing and decreasing the average reimbursement rates for the next year and chooses a course of action.

Making data analytics work for you

At the individual level, data analytics can help healthcare providers deliver the proper care to the right patient at the right time. At the business level, it can enable healthcare organizations to identify and understand trends at a larger scale, leading to improved population health. When used correctly, not only can data analytics improve care quality, it can also reduce costs. For example, predicting a patient’s length of stay and readmission rates can enable hospital authorities to reduce operational expenses. 

The enormous potential of data analytics includes identifying high-risk patients for chronic diseases, developing proven best practices, and predicting obstacles to treatment compliance. 

Predictive and prescriptive analytics take this advancement one step further by providing a plan of action. Predictive analytics can shed light on the dark alley, and prescriptive analytics can reveal the stepping-stones to map out the course of action. These methods empower healthcare providers to make well-defined, split-second decisions, which is of utmost importance in the healthcare industry. 

According to research, the global predictive and prescriptive market is projected to reach US$28.71 billion by 2026. The reason for skyrocketing growth is because these methods have the potential to analyze, sort, and learn from troves of unstructured data much more effectively and quickly than any human mind ever can.

Conclusion

Data analytics is opening up new horizons for the healthcare sector. From improving medical outcomes to delivering personalized care, data analytics offers an excellent opportunity to healthcare providers to improve both the quality and speed of services. Cutting costs, providing real-time care, and reducing medication errors are all part of the many advantages that data analytics brings to the table, and these capabilities will only grow stronger with time




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