Predictive Analytics, it is being said, is going to redefine how healthcare is delivered. It will predict occurrences of critical illnesses and probability of readmissions in the future. Other sectors such as food and beverages, publications and entertainment have reaped benefits from using Predictive Analytics. There is no reason healthcare cannot do the same. However, the definition and scope of Predictive Analytics needs to be first understood purely in the context of healthcare. The one-size-fits-all model is not going to work. It is also important that the infrastructure for delivering analytics is provided and it is able to deliver the required information to the healthcare professionals in the right format. To deliver the right and proactive healthcare, healthcare professionals need to be given the right context and metadata. So, while Predictive Analytics is good for healthcare, it must first be customized and the right data in the right format must be delivered.
What is Predictive Analytics?
Predictive Analytics is a branch of advanced analytics that provides predictions of certain events based on historical data, data patterns and other inputs. Proactive steps can be taken to address the requirements arising out of the predictions. To make the predictions, Predictive Analytics leverage techniques used in other branches such as data mining, artificial intelligence, modelling, machine learning and statistics and integrates information technology, management and modelling business processes. The predictions can be used to identify risks and opportunities in the future. Predictive Analytics can help business organizations to achieve a lot of things. A few examples are given below:
- Identify hidden associations and patterns.
- Improve customer retention.
- Reduce risk to minimize loss and exposure.
- Improve customer satisfaction.
There are a lot of real-life examples of how business corporations have benefitted from the use of Predictive Analytics. Accenture conducted a survey to find out how different businesses have benefitted from using Predictive Analytics. Some of the findings are:
- Best Buy discovered that less than 7% of its customers contributed to 43% of its sales. It then segmented its customers logically and redesigned its stores and in-store experience to reflect with the buying habits of specific customer groups.
- Olive Garden, an American casual dining restaurant uses data to design and redesign its menu. That way, it has been able to cut down on food wastage significantly.
Predictive Analytics is being applied to a lot of domains such as healthcare, Customer Relationship Management (CRM) fraud detection and risk management.
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Predictive analytics definition in the context of healthcare industry
Theoretically, Predictive Analytics has a big role in improving healthcare. Although it is still a new entrant in healthcare management and its scope is still being worked out, Predictive Analytics can analyze historical patient data and provide predictions things like illness risks, probability score of heart, asthmatic attacks based on patient profile, probability of readmissions.
The human brain cannot deeply analyze more than 6 to 8 variables at a time to properly profile a problem. But, the algorithm of a predictive model can analyze hundreds of variables at a time to create an accurate profile of a medical problem. Based on the profile, accurate diagnosis and risk predictions, if any, can be made.
Predictive Modelling can help control costs towards medical care. In the US, 1 in 5 Medicare patients are readmitted to the hospital within 30 days of discharge which results in an expense of $17 billion a year.
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Role of Predictive Analytics in healthcare
Simply put, predictive analytics can play an important part in delivering healthcare that is based on prevention and proactive actions. Healthcare costs account for a substantial chunk of the GDP in the US. Healthcare costs in the US are 17.6% of the GDP, $600 billion more than the ideal healthcare expenditure considering the population, per capita income and economy of the US. Readmissions and critical illnesses such as heart attacks, stroke, diabetes and kidney problems account for a big chunk of the healthcare costs. A certain percentage of the hospital readmissions and critical illness recurrences could be prevented or managed better with the help of data pattern analysis and the analytics thereafter.
The case studies described below set an example of how predictive analytics could transform healthcare industry.
Case Study 1: Steadman Hawkins Clinic improving their profitability
The main objective of the Steadman Hawkins Clinic of the Carolinas was to improve profitability by optimally combining the services of available physicians with the ancillary services such as pharmacy, phlebotomist and physical therapy. The clinic wanted to optimize the availability depending on the peak demand hours or seasons so that no opportunity to earn revenue was lost. For example, asthmatic attacks could peak during the transition period between two seasons. That is when pulmonologists and other technicians are in demand the most.
Steadman Hawkins Clinic partnered with River Logic, an analytics firm that implemented predictive analytics. All data points and constraints were taken to determine the optimal way to design the clinic facility and the staff required.
Steadman Hawkins Clinic was able to increase their net profitability by $20 million a year. They were also able to improve the accuracy of their financial predictions from 30% to 32%.
Case Study 2: Unnamed clinic improving their profitability
The clinic wanted to both improve services to the patients and improve their profitability by optimally using their resources which includes staff, facilities and instruments.
The clinic collected copious data on different variables such as type of care needed by patients, staff profile and qualification, patient profile, quality of services delivered such as response time, outcome, patient experience and wait time for patients. Based on the data collected, predictive analytics was put to use. They expected concrete analytics and course of actions to put in use.
Through the clinic is still putting in use the predictive analytics, there are signs that they are on course to achieve at least 10% profitability.
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Important points to remember
It is not that implementing predictive analytics will start doing wonders right away. The results depend on the approach. First, the industry needs to determine what predictive analytics mean in its context and then specify its scope. Also, the healthcare industry needs to remember the following lessons from other industries.
- The amount of insights is not directly proportionate to the amount of data. You are not going to get more insights just by increasing data collection.
- Insights do not necessarily provide value. You need to first customize the insights in your context so that it becomes useful.
- Implementation of predictive analytics is going to be a big challenge. You need to embrace the right technologies and deliver insights to the healthcare professional in the right format.
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Predictive Analytics needs to be married with Prescriptive Analytics to deliver the right results because the industry needs not only the predictions but also the course of actions. While the concept seems to be rewarding in the end, businesses need to make the right investments and be patient with the results to reap the benefits.