Bridging the Healthcare IT Innovation Gap

Late last year, I co-founded Ravenpath Consulting, a firm focused on practical healthcare IT strategy, data integration, and AI readiness. As part of that work, I’ve been publishing a series of essays under the Ravenpath banner that reflect recurring patterns I’ve seen in healthcare technology delivery over the past two decades.

One of those pieces, Bridging the Healthcare IT Innovation Gap with AI and Data, addresses a growing disconnect: organizations invest heavily in AI narratives while underinvesting in the data foundations, interoperability, and operational discipline required to make those tools work in practice.

In consulting work, this gap shows up in familiar ways. AI initiatives stall because data remains fragmented across EHRs, claims systems, and vendor silos. Integration is treated as plumbing rather than strategy, and governance arrives late, if at all. The result is technically impressive pilots that never survive contact with real clinical or operational workflows.
The Ravenpath article focuses less on AI hype and more on the conditions that actually enable innovation: clean data contracts, realistic integration patterns, clear ownership, and delivery models aligned with how care is delivered and paid for. None of this is glamorous, but all of it is necessary.

The full article was published on the Ravenpath site and can be read here:
https://www.ravenpath.ai/post/bridging-the-healthcare-it-innovation-gap-with-ai-and-data

I’m linking to it here to situate it within my broader body of work. Whether writing independently or under the Ravenpath banner, the theme is consistent: AI amplifies whatever foundations already exist. When those foundations are weak, the outcomes are predictable.
Future entries here may reference additional pieces from this Ravenpath series, always with the same intent: to connect applied consulting work with the longer arc of thinking behind it.