Organizations throughout the health ecosystem have opted for AI. The emotion is justified. Implementing these technologies can save a lot of time and money to do many wonderful things.
Unfortunately, however, the implementation of AI can waste a lot of time and money to do many stupid things.
One of the worst things that any organization can do to its data architecture are automatic processes to improve the wrong problem. Not only does it waste time and resources, but it increases swelling and strengthening distractions and unnecessary obstacles to function and progress. It is sure to say that the thesis that aggravates the night effects are already quite familiar to anyone who has worked with you, payers, suppliers, pharmaceuticals, biotechnology. . . No one is immune.
The use of silly AI could make this state of things with worms easily, a crime effort towards the dazzling functions that no one needs and the expensive characteristics that no one uses. So, although it may seem contradictory, when it comes to an effective or AI use, the real shoulder does not begin with AI.
You have to start identifying the problem you are trying to solve.
Change in perspective
Back at the University, I studied Civil Engineering, where the “first thought of the principles” of Aristotle is the canon to generate efficient processes and optimal results. The approach implies breaking complicated projects in basic fundamental elements and only then reensate the issue to achieve its goal. And having a goal is key.
In terms of civil engineering, why would an extent steel suspension bridge on a perfectly functional road stretch erected? Even if it is the strongest and printing bridge ever built, no one benefits from using it, so it has no purpose.
In the real world, each organization has computer systems and data management. AI is a new and impressive capacity organizations naturally because joining these systems. But regardless of capacity, it has to offer real world benefits to be of any value.
Therefore, it must begin with an appropriate definition of the problem aligned with the desired result. It can then systematically address the relevant components and the real process involved. And cannot weigh it with all the old processes that it implies due to the requirements and limits of adjusted technology. Question everything. The legend of the Grace Hopper computer science once said that the dangerous phrase is “we have always done it that way”, and it is worth noting that I was talking about data processing when he said it.
Challenge each assumption and preconception, eliminate anything unnecessary, deploy everything to its form and function for its purpose. This ensures that you understand the real needs to address a real problem. That should dictate the data strategy in the future, and that focuses AI’s integration in value delivery.
First principles in life sciences to use
Generative-Ai related to language and text is currently one of the most mature forms of technology (and I am not talking about chatbots). To illustrate the intelligent use, let’s concentrate on Healthcare’s life sciences sector for the first principles of thought of examples in the integration of the problem solving.
Consider a pharmaceutical company or medical technology devices and how they build a manufacturing process for a new medicine or medical device. This process needs a design for physical manufacturing and material management, as well as to meet the regulatory requirements for each aspect of production. This guides that establish the real manufacturing site from individual equipment tests, to equipment sections, to facilitate continuous tests of the entire installation. This process is called “implementation, qualification and verification”, and can involve hundreds of thousands of documentation pages. In terms of lay people, the level of documentation represses tons of work.
The role of documentation is incredible because it validates all tests and supplies an understanding based on the science that the process works correctly, the materials are occurring properly and that everything will approve the inspection for the distribution of the market.
The approval of the FDA is the prize, the laborious process of appropriate documentation is required to achieve it.
Therefore, a clear engineering objective for the diving value of AI integration in this context would be to automate the production of launch, qualification and verification documentation that meets the FDA standards. The data that translate all aspects of the construction and test procedures, together with data that detail the minutiae of all the requirements of the FDA of Varouse for each aspect of that process, can be a liver to feed a large language model (LLM) and a generative docataticate that are collected and produced. That would save innumerable hours of human work!
In addition to that, the depth of experience and institutional knowledge of an organization on the commercial processes involved in the manufacture of pharmaceutical or medical devices can also be this model to further refine the perspective of management and development of sophistication. Obviously, humans will still have to review the documentation, but the difference is who (or rather, what) is the documentation in a consistent and precise way and how long and effort it is saved. The point is that the integration of AI focuses on addressing the “correct” problem, the documentation load, where it offers a practical and significantly valuable improvement.
If that sounds a bit esoteric, how about the tools of AI to provide processes prior to the selection for clinical trials in a way that adjusts to existing patient cases review operations in medical practices. This type of capacity is incredible to, for example, rural doctors who can support several 1000 patients about 1000 miles and simply do not have informed research and sensitive to the time of avoidable human resources.
The correct AI model applied to that problem expresses its ability to match patients with the potential treatments of life salvation in a faster and effective way. That can save real lives, and represent exactly how we want these new technologies to save time and money and do wonderful things.
The only “trick” required for the integration of truly successful, in life sciences or in any other facet of the health industry, is the clarity of purpose. The thought of the first principles is an excellent way to ensure that their effort and investment are aligned with the desired results and real value.
Photo: Overearth, Getty Images
Chris Puuri, vice president, global head of Health Sciences and Life Sciences in Hakkōda, uses his intimate understanding of IT of health and regulatory challenges to solve problems in data and exclusive analysis of medical care. With approximately 18 years of experience as data architect for voltage organizations of medical, pharmaceutical systems, payers and biotechnology companies, Chris has built, integrated and launched data solutions for some of the largest medical care organizations in the country.
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