Prescriptive Analytics

Authored by Ayesha Rajan, Research Analyst at Altheia Predictive Health

Introduction

Here at Altheia Predictive Health, we are constantly improving techniques for disease prediction and diagnosis to improve the lives of people around the world. Where we leave off, companies like IBM pick up and begin applying analytics to the next step in the disease management process which is a treatment plan. Prescriptive analytics in healthcare can refer to several different things but in this article, we will look at prescriptive analytics as it related to the study of prescribed steps for those with chronic conditions to better understand how to manage disease conditions at various stages of a given condition. This can be an extremely useful tool for doctors who are looking at a borderline case and deciding whether or not to begin medication treatments by giving them the ability to see compiled and analyzed data for similar cases.

Discussion

After being diagnosed with a disease, the next question patients and providers have is what steps can be taken to prevent progression of the disease and manage the negative effects of a disease; for some people this is achieved through diet and exercise, for others it may be a prescribed medication and it is highly dependent on several patient factors. Prescriptive analytics is rooted in how to best solve a problem – if the problem is diabetes, a prescriptive model would look at variables in a patient’s file to determine if the problem can be mitigated through diet and exercise. If not, the model would then provide a game plan for how to begin prescribing medicines in the most effective way for any given patient. In diabetes patients, insulin therapy is a common form of treatment where adjustments to a specific individual can be tricky but prescriptive analytics can use the data of similar patients to provide a tried and true treatment plan.  Prescriptive analytics can also be used in radiation therapy by weighing various favors, such as treatment location and surrounding organs, to provide a highly targeted treatment plan that treats an area with a calibrated dosage while leaving the rest of the body as unharmed as possible. This is something that we as humans can make educated guesses on but the use of prescriptive analytics is likely much more accurate which means better care for patients. 

This field of study also has an incredibly relevant application amidst the Covid-19 Pandemic. Throughout the pandemic data has been collected on patients such as their age, BMI, gender, ethnicity, presence of chronic conditions and more. All of this information combined with their response to treatment plans provides a unique opportunity to apply analytics – by looking at these sets of data together, we can use artificial intelligence to understand which patients respond better to certain treatment plans. This provides a great opportunity to improve Covid-19 survival rates and lower the number of patients suffering from long-term complications as a result of Covid-19. 

Conclusion

Prescriptive analytics in healthcare is essentially a way for analytics to troubleshoot disease conditions by providing a data-based treatment plan. By providing physicians with analytical tools, such as prescriptive analytics, we can significantly improve their success rate in treatment plans and as a result improve the quality of life for hundreds of thousands of people.