Medplum

So today we’re chatting about Medplum and how it was used at your previous company. Before we jump into that, could you give a brief overview of the company and your role there?

It’s a telehealth company that owns multiple brands focusing on various health conditions. People would sign up for our services based on their specific health needs. We would then guide them through the process, starting from initial patient engagement to clinical management of a large number of patients. We also owned automated and high-volume pharmacies that were integrated into our services. We had different brands for different health conditions, some of which were quite complex. Our mental health brand, for example, provided not only medication but also psychiatric help, following a comprehensive care model.

In terms of my role, I was primarily responsible for the clinical care aspect as a principal engineer. I identified appropriate technical solutions for the business problems raised by our product management team. One of the challenges I faced was how to unify patient data across multiple brands, especially as we acquired different companies. We had to integrate legacy systems from two startups that had been operating for over five years. My main focus was finding the best technical solution to establish a unified view and streamline workflows for our patients.

What drove the need for a tool like Medplum?

The business problem at hand was how to merge a substantial system that already had its own patient view and data model with our existing systems. These systems held patient data in different ways and had varying structures. We aimed to find a solution to unify this data so that different views were not required. Our goal was for providers to access patient information without having to navigate multiple systems with inconsistent data. Additionally, this unification would enable us to analyze aggregated information across all patients, allowing us to identify correlations between specific problems and treatments. Ultimately, the objective was to achieve both data unification and operational unification.