My path to co-founding Ferrum Health started when I realized our health system faced some significant challenges that would require a variety of skills and backgrounds to solve. Developing software solutions for real-world problems has been an interest of mine ever since I got my hands on a QBASIC compiler in the ’90s. While problem domains and technologies have varied throughout the years, a constant thread has been to identify and realize appropriate software solutions.
As a computer and software engineer by training, I recognized early on that solving hard problems for the enterprise has a multiplicative effect, enabling downstream organizations to be more efficient and focused on developing their own unique skill sets. Joining Cisco, I gained early experience with the complications and complexities when addressing enterprise needs. Nuances arise when balancing user experience and explainability when relating data from disparate standards-based systems, in real-time, across corporate campuses. Building on that, I then spent several years building scalable, zero-downtime, shared video storage clusters at Harmonic. Relying on an Ethernet-based backplane stressed the importance of simplicity for the adoption, deployment, and integration of complex clusters into heterogeneous data centers environments.
During this same time, it was hard to ignore the strides being made in modern technologies, such as machine learning and artificial intelligence (AI), and the disconnect between their adoption in consumer and enterprise users. Critical industries, such as healthcare, were almost ignoring these modern software solutions. As I looked across healthcare and asked myself why, the answer was clear – it was too risky.
Brainstorming with Pelu, using his healthcare industry knowledge and my enterprise software experience, it became obvious to me that there was a growing disconnect between the greater healthcare industry and the cloud-first hyperfocus of machine learning and AI developers. Health IT is, understandably, prioritizing risk management and patient privacy, while machine learning algorithms took a diametrically opposed approach of pooling resources and Big Data, due to their cloud-first methods. My discussions with Pelu focused on these divergent directions and what could we do to align the stakeholders and improve patient care.
My history with delivering these mission-critical compute clusters on bare metal and familiarity with complex data center networking illuminated an opportunity to bridge this gap between health IT and the machine learning ecosystem. Paired with Pelu’s experience in navigating the healthcare enterprise, our skill sets complimented each other to result in an AI application layer for healthcare. Our vision is to enable health systems to easily deploy AI applications across different service lines, improving patient care while minimizing risk.
Stay tuned, this is just the start of the journey!