In a world where one tweet can change the future course of a product, three in-pharmacists explain how AI and NLP can help manage the ever-growing amount of relevant data.

Jeff Rowe


One of the interesting things about AI in health care is how it can be applied to all aspects of health care and services and has an impact on how participants view and pursue their business.


For example, a recent PharmaExec article by three industry veterans looks at the question of how AI, as well as Natural Language Processing (NLP), should be viewed by pharmaceutical Medical Affairs teams, or those individuals whose job it is to ensure patient health- to be at the forefront of pharmacy marketing decisions, while also achieving “an important goal of informing clinical practice patterns in order to ultimately improve patient outcomes.”


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Specifically, they ask, should medical media teams use AI and NLP as support tools or as tools to maximize their companies’ profits over rivals? The answer, they say, is “both.”


In their view, the real strategy is to think about this in terms of return on investment (ROI). Where can you spend a little money and get the most out of your money by taking out machine learning and AI? ”


In response, they point to three important examples of how AI and NLP can be used throughout the pharmacy.


First, there are requests for medical information from clients.


“The longer it takes to answer a question or request information, the more expensive the request,” they note. “Add to this the complex growth of information requests due to the ever-growing questionnaire forums (phone, email, SMS, and Tweets at the end) and the range of spoken languages ​​offered, and you need to control its running costs. acceleration over time. Given the strict regulatory requirements adjacent to MedInfo, it is not possible or appropriate to remove people from this loop. However, if you can get people to work well, you can keep costs down. A powerful MedInfo AI program built into your company’s workflow can achieve this. ”


Next, there is the Medical Science Liaison (MSL) data section. In their view, the MSL “role has the potential to dramatically and positively affect the market perception of the drug through the information collected. . . (but). (t) oo often, data is lost in a company CRM system. . . Successfully extracting this data requires a Natural Language Processing (NLP) engine embedded in the MSL-covered environment. ”







Finally, there are what they call “content meta tagging.” At large pharmaceutical companies, they point out that, “the more information you collect, the more likely you are to be fired and sponsored by the collecting team. This particular issue is a mixture of AI and human knowledge, where you can design and build economics and branding schemes that apply to a wide range of activities within medical and beyond. These taxonomies integrated with NLP and AI can access and enhance content in the CRM system to allow companies to get as much value as possible from the data they collect. ”


In short, they say, at a time when increasing information dissemination means more data to medical media teams to manage, “the clever use of machine learning and AI can save money and time. . . and, in some cases, may be more effective in protecting the source of revenue. ”

Complete AI Guide and Medical Information

There is a lot of talk these days about artificial intelligence (AI), machine learning (ML) and natural language processing (NLP). What do these words mean? What is the difference? And more importantly what do they mean by medical / scientific information? How will they influence Medical Affairs organizations?


In this final AI guide and medical information, you will learn:


Differences between AI, ML, and NLP


What this means for analyzing medical data


How AI can help medical media make better decisions


The downsides of AI


An example of law-based AI


Suppose we want to create an application using AI to help people choose their right clothing size. The user enters his or her height and weight and the app makes a suggestion of what size to buy.



With AI based on the rule, we can set a set of rules for the app to follow in order to make recommendations (e.g., if it is more than 5’6 ”and 180 pounds, buy Larger size. See Figure 2). The user can enter his or her height and weight and based on the rules set, the app can spit a recommendation:

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Figure 2: An example of a legal AI. Making predictions based on the decision tree. Based on length and weight, the model recommends the size of the clothes for the user.


Law-based AI is good for most programs, but what if you do not have a set of rules? Or is it more challenging to create a set of rules? This is where the ML comes in.


AI vs. Machine learning (ML)

In contrast to standard AI based on standard rules, ML allows a computer to analyze a set of data, learn to identify patterns and associate these patterns with a specific result, and then make a recommendation.


“In the end, machine learning is just a symbol of something, which takes your definition of something and tells you which label to find.” – Google’s Cassie Kozyrkov


Instead of having engineers create a set of rules to follow, you give computer examples and say go and test the rules yourself. This is a major problem with computer science because it means that people no longer have to build a huge set of complex rules for computers to follow. Now, we can deal with problems that we could have solved in the past by giving computers examples of what we want as a result.

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