AI Applications in Pharma Today

Imagine you are in the year 2037 and anything you want to do with AI is possible. How would you use that to really redefine how you interact with your customers? What would be your first projects on your ‘to do’ list? Interestingly, when I ask my pharma clients that question, some of the things they come up with actually are already not only possible, but we are already doing them with clients today.

Do you know the breadth of areas and things that can be done now? I thought this may be an interesting blog to get people thinking about their own projects and what is already possible.

In pharma R&D and clinical trials, the uses being applied now are things like:
    •    discovering new uses for existing drugs
    •    identifying cancer in tissue slides better than humans
    •    analyzing DNS code to identify genetic conditions
    •    identifying numerous conditions (e.g. diabetic retinopathy) from images
    •    faster clinical trial recruitment by understanding why people sign up to participate or decline to participate
    •    analyzing patient record data to match patients with appropriate clinical trials
    •    identifying diagnoses before physicians
    •    automating clinical trial data collection using IOT and AI
    •    integrating clinical trial platforms managing all aspects of trials and connecting the separate systems such as EDC, IRT, safety, CTMS etc so that they all speak to each other and cross analyzed data is automated
    •    automating clinical trial data analysis
    •    faster automated clinical trial report writing (create reports 90% complete in less than an hour saving humans 4-6 weeks and internal resources round 250 hours)
    •    predicting medical events in patients before they happen (e.g. hypoglycemia, relapse in cancer)
In pharma marketing the uses are also wide such as:
    •    precision targeting (to an individual) and segmentation
    •    understanding market structure – the what’s as well as the why’s and the dynamic changes as they occur
    •    brand positioning and messaging optimization
    •    resource allocation and optimization
    •    predicting physicians switching brands in advance and how to retain them
    •    recommending to sales reps which message to give a specific physician at that point in time
    •    identifying which physicians will never respond to a sales rep
    •    predicting which physicians would be more influenced by eDetailing rather than in person detailing, or what combination of these will be optimal for individual physicians
    •    predicting which patients will cease to take their medicines in advance, and why, and how to retain them
    •    personalizing customer interactions by serving up the next best content in the next best channel to move customers along their buyer decision journey to our brands,
    •    creating content marketing that is having more impact than human written content
    •    Utilizing IOT data to enrich data sets – especially in diabetes and respiratory areas and also in injectables

Conclusion

Like all technology and applications, we are advancing rapidly in this field and as soon as someone thinks of something, the Eularis team of data scientists can figure out how to do it. Think laterally about your challenges and if you want to see if it is already possible, speak with us. www.eularis.com/contact

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To learn more about how Eularis can help you find the best solutions to the challenges faced by healthcare teams, please drop us a note or email the author at abates@eularis.com.

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