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Redefining medical affairs: the journey to digital health and medical AI

Foto del escritor: Manuel CossioManuel Cossio

Actualizado: 4 jul 2023

Due mainly to the COVID-19 pandemic, migration to digital health has been promoted rapidly in order to provide care to patients without risk of contagion in health centers. This has brought, as a positive consequence, the acceleration of medical AI ventures and the explosion of ideas in the field. During my work as an executive medical affairs consultant, I had the possibility to work on several projects involving digital health for global pharma. In the following paragraphs, I will explain and debate a little about some key aspects to transform classical medical affairs into a digital and medical AI modernized approach.



Credit: intellectsoft

Invest your time into generating good knowledge graphs


Day by day, huge amounts of data are generated. The better we analyze it, the better the decisions we can make. Therefore, we need to invest as much time as we can in generating the best knowledge graphs. A knowledge graph is a representation of different concepts and their relationships. These representations allow us to generate summaries of information in an effective and fast way transforming complex processes into understandable ones. Thanks to automatic algorithms, today we can generate knowledge graphs of very high complexity in real-time where we can include data from clinical publications, molecules, biomarkers, social media and all types of information that is public and does not violate privacy laws. Knowing exactly what is happening out there is crucial to produce the best actions towards that reality.


Make your best to avoid bias: choose your data carefully


When we choose datasets to train algorithms on, the algorithm learns what the data shows. Therefore, if our data is biased, the algorithm will make decisions in the future with the same bias. Cases of biased algorithms have been reported in countless AI applications. Specifically in medical AI, there are thousands of reported cases with gender and race imbalance coupled also with socioeconomic status and location. This has resulted in algorithms that made inaccurate decisions because the data was not correct. Now, how to fix this problem? Firstly, performing deep data analysis of the datasets we receive and if we detect biases, proceed to discard them. Secondly, when we design the studies that will provide us with the data, use patient recruitment processes that include everyone.


Don't be afraid to learn


YouTube is the greatest place to pick something and start learning. There are also other more complex platforms with better-organized content, but if you want to choose a topic and get to it, this is the best place to start. Within this video page, we can find the function of molecules, surgical procedures, the diagnosis of multiple pathologies, the operation of medical devices, with the best of all, everything step by step. Of course, it will be very difficult for us to put into practice the assembly of an MRI machine. However, regarding medical AI we can find many videos where we can practice artificial vision in oncology or neurology, decision trees with clinical datasets and even clustering with results of clinical trials. Additionally, we will not have to buy absolutely anything or download any software. We will be able to do many of the practices from online environments such as Google Colab.


Empower project awareness


One of the best lessons learned from the COVID-19 pandemic was that everything, absolutely everything can be done remotely and digitally. Since relentlessly this has already come into our lives to stay, let's take advantage of this situation. How to do it? Generating short digital events where we tell the public in general terms the projects we are working on. This will make many employees from different areas provide their feedback from the side of professional interest, camaraderie, or simply the desire to participate in something different. Remember that the more feedback we have on a project, the greater the chances that it will be successful. We never know where the best idea can come from that is able to change the forecast of a nascent project making it successful and useful for the organization.


Think in-house


Every company is different. Each organizational structure is peculiar and the processes that emerge from it are inseparable from the structure. Therefore, it is important to begin to lose the fear of assembling a work team that develops within the company digital health projects and solutions for medical AI. Many companies turn to solutions already designed and try to request an adaptation to use them in their projects. This can be thought of as a short-term strategy. However, having an in-house team that learns the company's distinctive signature is crucial. That team could progressively produce taylor-made solutions from within transforming tremendously the corporate landscape. In addition, this will not only create a specific solution to the problem that we ask at a time but also innovative ideas for other fields that have never been thought of before.


Remember: you are more than just your product


The old pharmaceutical industry was always focused on sales and product-centricity. The new industry is the one that focuses on the purpose for which it works and on the patients it helps. It is no longer just a product. The new industry is an experience for patients and doctors and, when they come into contact something changes for the better. Therefore, let's think beyond the products and let's not let our minds get stuck in just that. Let's get to know the lives of our patients, our doctors and put ourselves in their shoes. What makes a difference to them on a day-to-day basis? When we find out, let's get down to it. A community well-nurtured and well-cared is a community that has the tools to put patient care first. Let's generate that and what a better tool than AI?


It's crucial to invest time to define your digital signature.

What makes you unique and unrepeatable will be exactly what others will see in the distance. Therefore, it is important to think about the points made earlier and try to take small steps towards change. Digital health is here to stay, so the faster health companies adopt it, the better answers they will be able to give to society and patients.








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