Prescriptive analytics doesn’t just show us what can happen, and the likelihoods of such scenarios. It takes it a massive leap further, beyond knowing all of the factors involved. It knows the factors and the coefficient weightings of those factors. Much like the “solver function” in excel, it allows us to specify which outcomes we desire and then goes the extra step to prescribe the actions we should take now to achieve those desired outcomes in the future. This is what truly delivers on the promise of Artificial Intelligence. Prescriptive Analytics is the 3rd phase of Analytics. The first two phases are Descriptive Analytics and Predictive Analytics. As a quick background Descriptive analytics focuses on the reporting of past events which have already occurred, thus ‘describing’ those events using the data and the metrics that have been collected. The vast majority of data analytics today are descriptive in nature.

However, as of the past few years there’s been a major paradigm shift towards forward looking prediction as more organizations heavily invest in it, and more importantly the desire for Predictive Analytics; which not only shows what can happen, but the likelihoods of such scenarios as well. This is incredibly powerful, especially when we begin to think about the future potential with healthcare and what it means for our own well-being and lifespan longevity. But that’s just predictive analytics. Prescriptive analytics is far more powerful as I described above, and the promises that prescriptive analytics hold, take the benefits of predictive analytics to a whole new level.

Prescriptive Analytics: a ‘Natural Fit’ for the Next Step of Data Analytics in Healthcare

As natural planners and insecure animals who hate the idea of uncertainty its natural that we’d be keenly interested in the promise of predictive and prescriptive analytics. We human beings always want to know what is ahead and we believe that the past it the best predictor of the future. In fact, the mathematical concepts and fields of Probability and Statistics have largely shown this to be true -at least on a long-term or macro scale. Meaning the larger the population or the bigger the data sets, the more accurate that a sample of it will be for the purposes of prediction. So in the age of big data and digital everything, we’ve done an incredible job at collecting data on nearly everything. I’m sure it comes as no surprise that health insurance analytics is a rapidly accelerating sector within healthcare data analytics. As long as the data isn’t used against us, we’re perfectly happy, but I’ll leave the data privacy topic for another article.

The beauty is that nearly everything is now measurable so we’ve truly enabled ourselves to start analyzing this data in incredibly powerful and meaningful ways. Its fitting that the data for which we’ve collected about ourselves and our environments now enables us to truly improve our health, well-being, and quality of life. Which brings me to big data analytics in healthcare and the promises that predictive modeling and prescriptive analytics hold for our future.

Why Data Science and Analytics in Healthcare are Vital to Advancements

Enter the rise of Healthcare Data Analytics and proliferation of Prescriptive Analytics in Healthcare. I’m sure as many of you are intently reading this very article, your smart phone, smart watch, fitbit, or other intelligent device is now enabled with highly sensitive bio-metric sensors that are actively collecting detailed data about your health and well-being. The great news is that this data is for your future benefit, as well as your fellow humans. These devices collect signals from heart-rate to foot-steps to hours of sleep and more. The culmination of this data in the analytics world of data science are coming together with the world of Healthcare that allows both sets of practitioners to work together in tandem in new and incredible ways. Doctors and Data Scientists alike can work together to make us live longer happier lives -even as short as 10 years ago, not many anticipated Doctors turning to us data geeks for answers. Regardless, prescriptive analytics in healthcare allows both practitioners to build models that solve medical problems that were once totally impossible. Everything from detecting arrhythmia signals for heart-attacks to precursor signals or tissue scanning densities for cancer. The data is being collected, and it is this data and how we use it, specifically through prescriptive analytics and the prescriptive analytics data science MODELS that we build, that hold the keys to future treatments and cures.

The Biggest Question: Will Healthcare Professionals Push Hard for a ‘Model’ Future?

The biggest question, is whether enough Healthcare Professionals are making the necessary investments in digital transformation and partnering with Data Science and Analytics companies to leverage their services to bring the potential for rapid advances in prescriptive analytics to reality. It’s incumbent on the healthcare and medical communities to be forward thinking enough to leverage the data science analytics experts in market today to take the steps necessary for the medical advancements they seek.

What’s been proven in the past century is that the benefits of specialization of industry far outweigh trying to be a jack of all trades. Medical professionals need to bring their domain knowledge and expertise in medicine and invite the data science experts to the table to bring their measurement expertise. Together in partnership, as a team, the things that can be accomplished are limitless.