Data and Analytics have helped to make the process better. What is a process? A Process is an activity that is done from Point A to Point B. For example, you have five activities: 1, 2, 3, 4, 5, now each of these five activities constitutes a process. For doing this activity, you need metrics and that is what defines the efficiency of the process. Data and Analytics were all developed to make this process efficient. During the last 4 to 5 years, however, Data and Analytics actually are redefining the process itself. If you see all the companies that are changing their tagline, that is not just about data-driven processes, it's re-imagining the process. Thinking about them differently or changing the process to provide completely different opportunities to think about how the process should be run and how you can engage yourself with the customer.
Let's take an example in healthcare itself: I’m a physician, and when I was practicing medicine, we used to have a very popular statement saying, “If you know hypertension, you know 75% of medicine. So you can imagine hypertension is such a stupid disease; it can affect every part of the body. So if you learn hypertension, you learn everything. Now on top of that, if a person has diabetes, then you know 90% percent of medicine. So what does diabetes do? Diabetes is a problem with sugar where it relates a problems to kidney, brain, eyes, joints, skin etc., therefore, if you learn diabetes completely, you actually know 90% percent of medicine. So, we used to say that diabetes is a huge problem in our country and across the world. But because of the burden of disease, it's a huge burden for everyone.
Now, when we would look at diabetes earlier, we would have a very standard way of treating diabetes. So there was a process defined by the American Diabetes Association which said that a patient with certain parameters would be treated in a certain way. We as doctors treated diabetes when the patients came to us and then prescribed them medicine and said what all has to be done. But the control of diabetes is not only dependent upon meeting the doctor or the medicine; it is also dependent on a lot of lifestyle changes that need to be done. So if you start looking at diabetes as a disease, there are many factors that drive diabetes like sugar and a test known as HbA1c, these are the traditional methods and then there are other factors like weight, activity, diet, sleep and the work that you do.
There are so many factors which can define whether diabetes will get resolved or controlled or will move on to become a complication; if diabetes turns out to be a complication, then you can imagine that a patient lands up in the hospital and the cost increases dramatically.
Let’s extrapolate this to our population. Looking at the population, there may be about 100,000 people who have diabetes. Do we follow the same treatment methodology for all of them? No, we don't because we have to start finding out what are the specific types of diabetes and then assign a risk code to them. That's where Data Analytics is helping us. What you do is that you take all the data and identify which diabetic population of patients have a high risk of potentially having a complication and learn about a hospital that will increase the cost. You would start taking all the parameters across the population and start classifying them based on the risk; for each of the risks scored, you can have a very different way of management. Let's say if it was a very high-risk patient, we can manage them in a more personalized manner if it's a low-risk patient, maybe just doing somewhat non-personalized interventions like TV ads so sending pamphlets would be enough. Now, this is the way we define the process.
But the way the world is changing, if you look at the patients who are at a very high risk in diabetes, you have so many ways to actually engage with them. For example, I used to work in a medical management or a care management company in the United States. We used to give free Fitbit and free sensors on the shoes for such patients, which means, you now have continuous data on these patients in terms of all their parameters, activity and their diet, so you know how they are doing on their risk for diabetes and you can actually manage them in a more proactive manner through multiple channels.
The whole Data Analytics seen is changing the way we manage diabetes. Is it important? Absolutely!
Just imagine the amount of population in which you can identify the high-risk patients, and manage them effectively. What happens when you manage them effectively is that they do not have to go to the hospital and if they do not go to the hospital, the overall cost of healthcare for the population comes down. That is the outcome that Healthcare Analytics is driving. So Data and Analytics are helping us to save billions of dollars or crores of rupees in the future maybe not today but in the future. So what we could have spent today is actually cut down by 50% because of these interventions that took place. As Data Analytics goes into the future, it's going to revolutionize the way patients are managed and engage the way healthcare is administered. That is the power of Data Analytics and I think we need more and more people to get into the space of Healthcare Data Analytics, and I would thus advise you to learn more science and be a part of this revolution.