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Human beings have always been captivated with the ability to precisely anticipate the future, to shape it towards a more favorable outcome. With the appearance of massive amount of data, in addition to traditional business intelligence solutions, enterprises have currently started evaluating predictive health care analytics. Predictive analytics is a sub practice of data science which processes earlier data to determine patterns, trends and estimate future outcomes. This shift from business intelligence solutions to predictive healthcare analytics has opened a whole new market for recognized enterprises.
There are some key advantages of predictive analytics in healthcare domain and stay into the process of predictive modelling
Major Advantages of Predictive analytics in healthcare:
Healthcare analytics software has been raking a huge expenditure. Predictive analytics can help in cost reduction by allowing a patient centric model, to increase care delivery and patient well-being. Hospitals can deliver health prognosis to patients to define wellness programs. Also using patient historical data, hospitals can create predictive analytics reports for insurance companies, to allow them to provide the most appropriate plans for patients, which eventually results into cost reductions.
Improving chronic disease management
Based on the health records, Predictive analytics can perceive if a patient is prone to any chronic diseases, and suggest proactive measures to increase the individual wellness accordingly.
Predicting outbreak of disease
Based on the historic data and in association with the weather ailments, government bodies can forecast if a particular strain of flu is about to break. This can aid the state bodies in better planning by broadcasting preventive measures to the public and stocking enough medicines.
Implementing predictive analytics
We need to define models to procedure raw healthcare data and exploit patterns that may be create in historical and transactional data. This will help in identifying risks and chances. Models capture relationships among many issues to allow assessment of risk or potential related with a particular set of conditions, guiding decision creation for candidate transactions. Hence predictive modelling is significant in predictive analytics
- Recognize the problem which we want to address in Predictive analytics
- Discover raw healthcare data and identify the predictors, which are variables that can be restrained for individuals or other entities, to predict required analytics
- Collect data rendering to the predictors identified and convert it into models by designing algorithms
- Validate and verify the models and tweak them for better correctness
- Implement designed model to form predictive analytics