Predictive analytics is the process of learning from historical data in order to make predictions about the future or any unknown. Combining genetic information with a set of historical health data that is generated and controlled by a patient, can help healthcare professionals arrive at better personalized solutions. Obviously, the benefits of using predictive analytics are better care and lower costs.
Predictive analytics is not reinventing the wheel. It’s applying what doctors have been doing all these years. What’s changed is the ability to better measure, aggregate and makes sense of previously hard-to-obtain or non-existent behavioural, psychosocial and biometric data.
After the successful completion of the human genome project, there has been so much hope and excitement around the ability to personalize medicine to an individual’s genomic data. It has helped revolutionize the diagnosis, prevention, and treatment of most, if not all human diseases. For health care, predictive analytics will enable the best decisions to be made, allowing for care to be personalized to each individual.
Big data and algorithm production has reignited interest and excitement around predictive analytics. There has been an explosion of health care data, with new technologies to sequence our DNA, collect continuous monitoring data and patient-reported social media data, the amount of healthcare data is expected to grow massively. Interest with the promise that predictive analytics can help deliver better care while reducing costs, has been increasing.
Predictive analytics can be used to improve the certainty of a prediction. Personalized care can emerge from high confidence algorithms that can predict actionable interventions that improve long-term health outcomes. There’s a massive opportunity for predictive analytics to improve care and dramatically reduce waste in the healthcare system, addressing systematic issues in over-treatment, care delivery, and care co-ordination. The entire healthcare industry could benefit from the usage and adoption of predictive analytics.