Why Good Health Data Products Fail. And What the Evidence Says About Fixing It
You built a product that works. This guide explains why that's not enough and what implementation science says about closing the gap between a successful pilot and sustained real-world use.
If you've been in health tech long enough, you have your own version of this story. A product that cleared every technical milestone — interoperability testing, security review, clinical validation — and then stalled the moment it hit a real operational environment. The pilot went well. The expansion didn't.
The industry's explanation is always the same: the health system wasn't ready, the change management was insufficient, the champion left, the vendor oversold. These explanations aren't wrong. But they're a diagnosis that never leads to treatment.
The health data industry is misdiagnosing its biggest commercial problem. What you're calling a change management problem is actually an implementation design problem. There's a discipline that has been studying this for decades. This guide introduces it.
What you'll get:
- A plain-language introduction to implementation science and why it maps directly onto the problems health data teams deal with every day
- A breakdown of the five domains that determine whether your product survives deployment and where health tech consistently gets them wrong
- A real-world case study showing what designed implementation looks like versus the default
- Practical implications for product leaders, vendor executives, and health system buyers
- An honest look at why pharma figured this out before health tech did and what that means for where the industry is headed
This is not a whitepaper. It is a practical reference document written for the people actually doing this work.
AcademyHealth is a nonpartisan health services research organization and the host of Health Datapalooza, the nation's leading conference on health data innovation and policy.