A core skill in process automation, honed in early robotics and enterprise integration, can be the unifying driver of a career. This focus provides a consistent framework for innovation and problem-solving, even when pivoting into a complex new domain like healthcare data.
A visionary founder must be willing to shelve their ultimate, long-term product vision if the market isn't ready. The pragmatic approach is to pivot to an immediate, tangible customer problem. This builds a foundational business and necessary ecosystem trust, paving the way to realize the grander vision in the future.
A company can build a significant competitive advantage in healthcare by deliberately *not* touching or seeing Protected Health Information (PHI). Focusing exclusively on metadata reduces regulatory overhead and security risks, allowing the business to solve the critical problem of data orchestration and intelligence, a layer often neglected by data aggregators.
True entrepreneurship often stems from a 'compulsion' to solve a problem, rather than a conscious decision to adopt a job title. This internal drive is what fuels founders through the difficult decisions, particularly when forced to choose between short-term financial engineering and long-term adherence to a mission of creating real value.
Instead of building AI models, a company can create immense value by being 'AI adjacent'. The strategy is to focus on enabling good AI by solving the foundational 'garbage in, garbage out' problem. Providing high-quality, complete, and well-understood data is a critical and defensible niche in the AI value chain.
The majority of what payers identify as 'care gaps' are actually 'data gaps'—a lack of information leads to an assumption of missing care. By solving the data acquisition problem first, organizations can distinguish between the two. This dramatically shrinks the problem set, focusing expensive outreach efforts only on patients with true care needs.