Some Examples and Use Cases
The above was a general overview of the value of predictive analytics in healthcare. Now, let us turn to some specific use cases to clarify the issue.
To begin with, the more data there is, the better predictive analytics will work. This observation is also accurate in healthcare. Institutions and agencies should collect data from every possible source - whether from state-run or private hospitals.
Hospitals can pool and unify data across departments for better insights into a patient’s medical condition. Novel ways of treatment can emerge, breaking away from specialized data that may miss the bigger picture.
Outside factors known as social determinants can be fed into predictive models for comprehensive healthcare. Such data include community behaviour, specific locations, and income and education levels.
Social determinants can also be incorporated into models to create probabilities of patients skipping appointments or not taking medicines in a prescribed manner. These can lead to specific actions that conserve resources and improve health outcomes.
That apart, predictive analytics can be valuable at granular and individual levels. For example, data from bio-sensors can predict the best ways to set up ICUs. Equipment malfunctions can be anticipated, and the field of genetics can help in natal care.
Carly Fiorina, the former CEO of Hewlett Packard, once said: “The goal is to turn data into information, and information into insight.” In future, the increasing availability of healthcare and medicinal data will enable predictive analytics to come up with even more precise, life-saving insights.