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When you generally think about words like data, analytics, and analysis it is very easy to visualize big computers, engineers, or even bankers- you wouldn’t imagine a doctor. Sorry if the previous statement sounded a little presumptuous to me, but it needed to be used as it would set a good opening for this blog. Today as a part of analytics in real life we will attempt to take a closer look at analytics from a medical perspective.

The use of data in medical research is probably the most critical function in analytics. This ranges from establishing what are acceptable ranges for key blood parameters to identify the pattern in the spread of viruses- a recent conversation about the R-value of Covid is one such example. The collection of data and its use in medical science can be the subject of many exciting blogs. So let’s look at some examples of what are some of the most common data models used in medical science:

Predictive analytics

Probably one of the most important models in the medical field, predictive analytics based on the electronic health record system can be used from clinical prediction to treatment outcomes. The clinical prediction uses a large set of data to predict what conditions the patient is likely to develop based on multiple sets of data and regression. Predictive analytics is also used for understanding disease progression and comorbidities. This article is quite a great read on this subject. This article though lengthy is a great read on the subject of the history of predictive analytics in the medical field, give it a read it is quite interesting.

The next element of analytics that is becoming critical to medicine is the rise of AI. We have to talk about AI, just cause we love it so much. The foundation of AI in medical science was laid by Robert Ledley and Lee Lusted in 1959 when they published a paper that outlined the importance of the reasoning process in medical diagnosis. They also outlined the importance of electronic machines in processing all available datasets in the diagnosis process. Ledley and Less used a lot of mathematical models in their analysis ranging from Bayes theorem to symbolic logic. We highly encourage you to read this article that outlines quite a few critical moments of AI in the medical field.

With the giant leaps in the medical data tracking and analysis systems, the governments and other administrative functions will be able to track prescription patterns, forecast potential substance abuse, predict potential side effects of new medication, and a lot more. Across the world, medical records tracking is being implemented and the focus will be on ensuring all the prescriptions and records are tracked, additionally, you will not have to read handwritten prescriptions- which is the biggest benefit- because who can really read them. Sorry, this was too tempting to not take a shot at.

So next time you think about analytics, also envision doctors and medical professionals scanning a large amount of data for the betterment of our race.

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