Increasingly health tech and medical device companies are building AI/ML-driven applications. Medical devices are being “connected” to build combination products. Digital health is expanding into regulated applications that qualify as Software as a Medical Device (SaMD). Harnessing the power of AI/ML has the potential to transform health tech and medical device companies by providing better outcomes, better patient and provider satisfaction and engagement, and better insight into product performance and real world evidence.
Health tech and medical device companies starting on this journey often hire a data scientist to build a proof-of-concept model. Once the company commits to this path, we believe that it pays substantial dividends to plan and implement a scalable data science infrastructure early in the process. Unfortunately, most health tech and medical device companies and their data science hires don’t start with the cloud, DevOps, or software architecture background to do this easily. Failure to do so can lead to substantial technical debt (backtracking) later and serious costs and inefficiencies in a company’s data science mission in both the short and long term.
Like the quality assurance aphorism: “Quality is a journey not a destination”, an investment into AI/ML or even simple analytics is more than just producing a model, like some final equation to be written into code and forgotten.
Model performance can unexpectedly degrade with exposure to unplanned patient populations, shifting treatment paradigms, changed use cases, or underlying shifts in data streams spanning EHR information, patient inputs, sensors on a cell phone, etc. On the other hand, AI/ML model performance is often increased over time as a company develops a larger data set. The lifecycle of build, train, test, deploy, monitor is continuous and, if infrastructure is not well designed, a company loses time and spends more just to keep that cycle going.
There is more (there is always more) but just thinking through these features will lead to a better design.
Our view is that putting the right infrastructure in place early in the game results in:
The tech industry consensus is that a business needs 2-5 data engineers per data scientist, to maintain a deployed model and to maintain the automated infrastructure that makes your data science efforts efficient. We don’t think this is always the case, but our experience suggests that data science initiatives always require more engineering work in the long run than companies anticipate at the outset of their AI/ML initiatives.
Data engineers (or ML Engineers or Cloud Engineers) are not cheap – think at least $250,000 per year each after overhead, recruiting fees, misfires (hiring the wrong person). Design your system correctly upfront and you reduce the risk of future technical debt and will need fewer engineering resources to maintain your competitive edge.
Anything beyond very simple data analytics could benefit from many of the same processes to build and maintain as complex machine learning models so it’s worth thinking about future needs. Once there is a commitment to be a data forward company, both the amount of data and the demand for increasingly sophisticated solutions only seem to grow.
We encourage CEOs to look at the benefits of a well designed data science infrastructure early on in their AI/ML journey. We believe that investors and customers reward companies that are committed to building better products using data science and that a well-designed data science infrastructure leads to a real competitive advantage.
We build data science applications to support drug development, digital & molecular biomarker discovery, and digital health. Our team works at the intersection of biology and technology to accelerate innovation. If you have an AI/ML-related question or would like to discuss your AI strategy, we’d love to hear from you! Reach out today at email@example.com, on Twitter @mercurydatasci, or on LinkedIn.
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