Hello, Health Universe

A faster way to build and share health apps

It’s been a very long time, but I still remember writing one of my first programs in BASIC on a TRS-80. I would turn on the machine, wait for the prompt, and then type in my program by hand from the command line. I remember typing in the code for a simple expert system ELISA, and creating programs that would create banners and designs from simple lines of code. It was amazing to me that I could turn these words and lines of code into something that actually did something.

But every time I turned off the computer and turned it back on again, I would have to type my program back into the machine. I eventually got a “tape drive” — an analog tape that could be used to save a program — but it was very hard to take the ideas that I had and turn them into action without a laborious task of re-creating the code each time I wanted to use it. But the potential that computers had to change how we did things was clear to me from the moment I first started to play with my TRS-80.

I kept that interest through my studies in medical school and saw the potential with how computers could change medical research and health care delivery. And I’ve been fortunate to be able to make contributions to the use of computer technology in health care.

Fast forward to 2023, and we now have cloud storage of data, the ability to move petabytes of data around the internet, and nearly every doctor in the country using electronic records to support patient care. We have data scientists and machine learning systems that can identify patterns from data and anticipate patients who may be at risk for complications or adverse outcomes. And we have billions of patient care records being exchanged for patient care.

Using this data, we are seeing more tools used to improve care. Researchers are developing applications from this data that improves are ability to diagnose disease, plan for interventions, and manage populations at risk for complications and adverse events. Increasingly, researchers are publishing not only their findings, but they are publishing the data used in their research, and the code used to generate those insights. Transparency in research is something that has helped us understand underlying biases of algorithms, identified safety issues, and helped to engender trust in the systems used to support patient care.

But in many ways, we are still at the “TRS-80” stage of making these algorithms actionable. Even if the code is available in the open source through publications or through code repositories, it can be challenging to “type it back in” and configure your development environment to run the code effectively. This affects both researcher and clinicians — researchers are unable to get their ideas into the real world to test, and clinicians struggled to use those technology advances in their care settings.

We have tried multiple solutions to get actionable algorithms (and not just data) into care settings. Sometimes it is a walled garden of apps that work only within a particular EHR environment. In other settings, it is a bespoke integration of a specific tool within a specific environment. And in still other settings, it is a host of different apps and platforms and analytics tools that all need to be maintained and integrated in complex changing healthcare environments. And even then, what seems like simple black box algorithms to support sepsis care, can sometimes mislead clinicians about their effectiveness.

We have gotten good at moving data around, but we have not gotten good at moving algorithms and apps in a marketplace of ideas.

I think there is a better way. We’ve made remarkable progress in the open-source community in using the collective energy of the community to drive transparency, trust, and re-use of code. We’ve made it easy to compile the code into actionable knowledge that can be disseminated easily. And I think the same can — and should — be true in healthcare.

What we need is the same kind of sharing of actionable knowledge as we do of static data. No more black boxes, or walled gardens, or bespoke one-off integrations into health IT systems. We need an open-source community that is driven by a desire to connect cutting edge research with forward leaning clinician without requiring the work of system configuration, debugging, integration before it can be used.

We’ve done a good job in reducing the barriers to moving data. Now we need to reduce the barriers to moving computable knowledge in apps and machine learning algorithms.

Welcome to Health Universe!