First Principles on Healthcare: AI Integration and Life Sciences

Organizations across the healthcare ecosystem have been shining in AI. Excitement is reasonable. Implementing these technologies can save a lot of time and money to do a lot of wonderful things.

Sadly, implementing AI can easily waste a lot of time and money doing a lot of stupid things.

One of the worst things any organization can do with its data architecture is to automate processes to improve the problem of errors. It not only wastes time and resources, it also increases inflation and consolidates unnecessary interference and obstacles to function and progress. It is safe to say that these compound adverse effects are already very familiar to anyone who has worked in healthcare, such as payers, providers, pharmaceuticals, biotechnology. . . No one is immune.

Stupid AI usage can easily make this worse, misleading the effort of fantasy features, no one needs and expensive features, no one uses. So while it seems counterintuitive, you shouldn't actually start with AI when it comes to using AI effectively.

You have to start by determining the problem you want to solve.

Transfer perspective

Back at university, I studied civil engineering, and Aristotle’s “first principle thinking” was designed to produce efficient processes and optimal results. This approach involves breaking complex problems into basic basic elements and then reassembling them to achieve your goals. Achieving goals is key.

In civil engineering terms, why do you want to build an expensive steel suspension bridge on a functionally perfect highway? Even if it is the strongest and most impressive bridge ever, no one has benefited from using it, so it has no purpose.

In the real world, every organization has a computing and data management system. AI is a powerful and impressive new capability organization that naturally wants to incorporate into these systems. But no matter what capacity it is, it must provide real-world benefits to have any value.

Therefore, you must start with the appropriate definition of the problem that matches the desired result. You can then systematically solve the relevant components and the actual processes involved. And, due to past technical requirements and limitations, you can't weigh it with all the old processes. Question everything. Computer science legend Grace Hopper once said the most dangerous phrase is “We always do this in this way”, and it is worth noting that she is talking about data processing when she says this.

Challenge every assumption and pre-select, eliminate anything unnecessary, and strip everything from its best form and function. This ensures that you understand the actual necessities to solve the real problem. This should determine future data strategies and focus AI integration on delivering value.

The first principle of life sciences AI use

Language and text-related generation ai are currently one of the more mature forms of the technology (I'm not talking about chatbots). To illustrate smart use, let’s get into the first principle thinking examples in the field of life sciences in healthcare to solve problem-solving solutions.

Consider pharmaceutical or medical technology companies and how they build manufacturing processes for new drugs or medical devices. This process requires design for physical manufacturing and material management, as well as meeting regulatory requirements in all aspects of production. This guides the establishment of actual manufacturing sites, from testing individual equipment to testing parts of the test equipment, to facilitating ongoing testing throughout the facility. The process is called “debugging, qualification and verification” and it can involve hundreds of thousands of pages of documentation. In layman's terms, the document level represents a lot of work.

The role of the document is very important because it validates all tests and provides a scientific understanding that the process works properly, that the material is being produced properly, and that all of this can be inspected and approved by the FDA for market allocation.

FDA approval is a prize and requires proper documentation to achieve it.

Therefore, in this case, the clear engineering goal of derive value from AI integration will be to automate review and debugging of correct formats, qualify and verify the production of documents, and comply with FDA standards. Data can be used to transform all aspects of the building and testing procedures, as well as details of all the various FDA requirements of the process, and can be utilized to feed large language models (LLMs) and generated AI-Engine to ensure that appropriate documents are automatically and correctly collected and produced continuously. This can save countless manual working hours!

Most importantly, the organization’s expertise and institutional knowledge of the business processes for pharmaceutical or medical device manufacturing can also be ingested in the model to further improve the complexity of document management and development, thereby enhancing the competitive advantage from a financial perspective. Obviously, humans still have to review documents, but the difference is who (or rather, what) is consistently preparing documents and how much time and effort is saved. The key is that AI integration focuses on solving the “right” problem (document burden), where it provides practical and valuable improvements.

If this sounds a bit esoteric, how AI tools provide a screening process for clinical trials to fit the existing patient case review operations in physician practice. This type of capability is very helpful for rural clinicians who may support several 1,000 patients within a few short 1,000 miles and have no available human resources to provide informed and time-sensitive research.

The right AI model suitable for this problem can exponentially enhance its ability to match patients, resulting in faster and more efficient treatments to feed lives. This can actually save lives, which completely represents what we hope these new technologies save time and money and do amazing things.

The only “skill” required for a truly successful AI integration (in any other aspect of the life sciences or healthcare industry) is the goal. First principle thinking is an excellent way to ensure that your efforts and investments are actually aligned with the desired outcomes and produce real value.

Photo: Cowhide, Getty Images

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