Implementing predictive analytics in medicine
Hearing the term ‘analytics’ in the Philosophy department usually refers to the application of logical techniques to attain conceptual clarity, while coming across it in Mathematics or Informatics departments generally refers to mining through data in order to create a logical consensus.
At the end of the day, however, all disciplines implementing an analytic process do so from the same point of departure: creating precise conclusive information.
Humanity, then, is no stranger to streamlining multiple disciplines by using the analytic process, so why not extend its reach to make accurate medical predictions, as well? Predictive analytics (PA) uses technology and statistical methods to search through massive amounts of information, analysing it to predict outcomes for individual patients ("Seven Ways Predictive Analytics Can Improve Healthcare").
The evolution of medicine has largely been catalysed by its investment in technology and digitalised systems, but will, arguably, always require a human element to adapt medical methodologies to unique situations. Doctors are skilled practitioners that utilise past experience and knowledge to make situation-appropriate decisions when dealing with unique medical cases, but can this process be digitalised?
Applying a tried-and-tested predictive analytic solution to handling vast amounts of medical data in order to make correct diagnoses can be a seamless solution to handling the increasing amount of medical information that practitioners must acquire. However, this process can cause practitioners the discomfort of abandoning the instincts they’ve relied on in making crucial decisions. This should not be the case, as such processes should never dictate practitioners’ judgements, but rather facilitate their diagnoses and make sure that they have a wellspring of accurate information to draw from.
Using predictive analytics can improve all fields of the medical industry, according to Linda A. Winters-Miner, PhD
· It can aid in increasing the accuracy of diagnoses,
· It can help develop preventive medicine and public health through its ability to produce individually-tailored health predictions and solutions,
· It can provide stakeholders within the medical industry with predictions concerning insurance cost and other expenses, as well as increase the effectiveness of the medicine production industry.
Although streamlining the medical industry would be the main focus of predictive analytics, perhaps the largest benefit of its implementation would be its aiding in the creation of a global health conscious culture, where individuals can be aware of what specific health issues they are prone to and what lifestyle changes could best prevent such health issues.