November 30, 2021
Predictive analytics is the science of predicting the future based on past trends. It is a type of advanced analytics that analyses historical and current data to predict future trends with a satisfactory level of accuracy. Several techniques and models clubbed with in-depth analysis are used to predict the likely outcomes in the immediate, near and distant future.
It not only helps businesses identify opportunities but also highlight the red flags. It can enable organizations to pre-empt challenges and customise strategies to be ahead of their competitors, meet market needs, develop the best product fit and effectively manage their employees.
Healthcare organisations are poised to benefit immensely by using predictive analytics due to the sheer nature of their business which needs them to record a lot of data on an ongoing basis. When combined with the rich customer insights that caregivers have due to their hands-on experience with customers, it can successfully predict future possibilities with a great degree of precision.
As data collection methods and tools to interpret the data keep on evolving, predictive analytics has become highly relevant in aiding caregivers to provide superior healthcare solutions while ensuring higher profits.
Predictive analytics can be used by businesses in innumerable areas. Some of them have been mentioned below:
Scanning the population for probable health risks:
Predictive analytics can help caregivers to successfully predict which set of people are prone to what kind of diseases based on past data patterns and trends.
It is useful especially to identify people who are at a higher risk of suffering from chronic diseases in the future. Thus, the onset of those diseases can be successfully avoided. This results in huge financial benefits for all the parties involved.
Predictive analytics can also be of great use during the outbreak of epidemics. It can give caregivers more time to prepare themselves and help to develop more focused strategies to deal with the outbreak.
Caregivers can use tools to study the patient parameters to pre-empt if there is a likelihood of re-admission and make corrective changes to avoid it. It would also help to ascertain the bed usage time which in turn would help scheduling of admissions and discharge of patients.
For patients, it would mean significant savings of time and money.
Data tools can monitor patients’ vitals and highlight any discrepancies in the patients’ health while they are undergoing treatment in a hospital. Caregivers can act quickly in such cases before conditions deteriorate drastically.
Tools can be used to predict the likely duration of patient stays and the facilities they may use in case of admitted patients. This helps hospitals to reduce the time the beds go empty and also channelize planned admissions to suit the hospital’s needs.
It also helps in the optimum utilization of facilities like say the operation theatre or diagnostic devices.
In case of outpatient department, predictive analytics can be used to distribute patient rush to avoid commotion and reduce the burden on the employees of managing peak times. It would also translate to lower waiting times for patients.
Historic data can also be used to determine the number and type of employees that need to be employed for each shift.
Predictive analytics can help immensely to decide on what to order, when to order, how much to order and from where to order.
By using tools to study the demographic data of a place, marketers can design marketing campaigns that have a greater probability of being successful. For instance, predictive analytics can help predict whether their campaign should be for neonatal services or aimed at the elderly.
Existing customer data can give insights into cross-selling opportunities.
Pharma companies can come up with improved medicines. Moreover, they can develop personalized medicines based on the body type and genetic build-up of patients.
Hospitals can ensure better treatments by offering personalized packages.
Predictive analytics could also be put to use to study several factors to arrive at pricing of various offerings.
Since healthcare organisations sit on huge personal data, which may be accessed from several locations, depending on the scale of operations, they are prone to cyber attacks. Predictive analytics can study patterns to identify any anamolies in data usage or any other signs that indicate that the data security has been compromised.
While we discuss the several possibilities of predictive analytics for healthcare companies, we can take a look at one of Canada’s leading providers of home and community healthcare.
This is how the organization made use of predictive analytics:
A predictive model was deployed to ascertain the ideal locations where a new branch could be opened with maximum ROI or return on investment, and long term profitability.
Here’s how the organization benefitted:
The decision making time reduced by 70%.
Marketing ROI increased by 25%.
The company was able to narrow down to 5 zip codes from a data of 900,000 zip codes.
Predicted ROI of each new branch could be easily arrived at.
A predictive model, integrated with their CRM or customer relationship management system – was used to determine the Churn Probability of a customer when a new enquiry is made.
Predictive analytics thus helps organizations to strengthen their foothold on their businesses by mitigating risks on one hand and opening up doors to newer avenues on the other; all by eliminating the uncertainties the future might hold. It translates to more ways of saving and earning money while churning out satisfied customers.