The Return That Never Came

The most noticeable gap in a deployed system is between a working demo and an actual scaled system. Eighty-five percent of pilots in the US, no matter the industry, are failing. Not unpredictable. I was beating that drum several years ago. I said, guys, there's a limit to how far we can go if we don't pay attention to these underlying things.

The Safe Bet That Was Not

The big investments with AWS, Google, or Microsoft feel like safe bets, so they overcommit. They sign seven to eight year deals convinced they're getting solutions, and what they got is cloud computing and cloud storage. Then the parade begins, all these people knocking on your door trying to get you to give them a use case. You have no solutions. Your team is still going to have to do all the same work tomorrow that they're doing today.

I've seen that in almost every organization I've talked to in the last three years. We spend tens of millions on a multi year commitment to one of the top 3 cloud providers, for solutions, which the leadership thought they were getting. But it turns out what they got is very, very expensive cloud compute and cloud storage, and a never-ending parade of people from the company coming in and trying to get you to give them a use case because they want to do interesting work. They're not materializing the value they're supposed to be bringing. And so the pressures just keep climbing.

The Part They Never Curated

In healthcare especially, they've got so much data technical debt they cannot even begin to touch AI solutions. The technical debt is disguising and hiding the data technical debt, because they'll spend tens of millions on the tech but won't deal with the data quality. They've invested in the tech, but they've not invested in curating the data. If you're growing by acquisition, you're inheriting a matrix as ugly as anything you could imagine. Every time you acquire another company, you've got another one of those matrices to figure out how to connect. When it comes to clinical data, there's a few unicorns here and there, but there's not a large number of people who understand clinical workflow, who understand data flow, who can start to figure out how to build a model that supports all the things they want to do. The only thing they normalize is the stuff that runs the business. So why is it we can't fix the pieces that actually touch people in healthcare?

We Can Do That

Whenever you're looking at a solution, your IT guys stand up and say, we can do that, we don't need to pay all that money. You're taking valuable resources off the lights-on strategy, reducing support to your existing people, and giving half focus at best to something you may or may not deliver at scale. If I'm a CEO and my IT guy says that, I ask one question. Do you have a demo for me anytime soon? Because if you could have done this and you haven't, that's a problem. But CEOs don't land on that conclusion. They just get taken advantage of. What have you built at scale? If you built something that worked, you got lucky. If you built something that scales, you caught lightning in a bottle, because that rarely happens.

The Question Before the Signature

They didn't really think about investing in the data quality piece until they started wasting money on AI that's not getting them the results they're supposed to be getting. And they're taking a lot of heat. Some of them did ask. When I was working at Northwell, before they signed the contract, they asked me to be part of these conversations. And I just asked the basic questions. I said, I'm not asking you, I want to talk to your chief medical officer. What is in the secret sauce that helps you move faster through the process, and what is helping you to normalize data? Start talking about what ROI you're going to give me, and understand that we've grown by acquisition. I have sixty-five EHRs in the health system. What can you do so that I don't have to go and ask for another one point eight to two point five million in budget to ingest another EHR? Well, you'll still have to be doing all of that work. And I said to my colleagues, that's all I need to hear.

Start With the Risk, Find the Yes

The first thing I do is help them understand where they are, because usually the issues are not the ones they think they have. Historically, for better or worse, the big investments they make feel like safe bets, based on what the chief innovation officer or the chief information officer knows historically. In terms of the things you can safely say yes to, there aren't a lot of them, but there are some. In the revenue cycle in particular, there's places where it's all one hundred percent rules-based, and no one should be sitting there key punching all day long for things that have no variation outside the documented workflows. Those are the easier ones. Scheduling should be easier as well. Now there's technology at a place where, if you haven't spent everything you could spend, you have an opportunity to do something for a fraction of what it would have cost with the legacy approach. But a lot of them are over their heads.

Part of what I do with my readiness assessment is I keep a couple of contract law experts on standby. If I go in there and find what I'm pretty sure I'm going to find, I bring them in, analyze it, and give them a way out, tell them which contracts they have the better opportunity to deal with, whether they kill it or significantly reduce it. Because they're going to need revenue freed up in order to do the things that they have to do. The board is not going to stop demanding, and if they don't have a way to address some of this, people are going to lose their jobs who shouldn't lose their jobs.

The Know-How Walks Out

You're throwing contract dollars at something that's not building any core competencies into your DNA. Every time something gets finished, all the know-how walks out the front door, and your team can't support it. Then you end up contracting longer term for people who don't know your business and aren't going to learn it. Increasingly it's more lawyers who understand contract law and can help them untangle the bad contracts they've got in place. Rather than going in trying to sell them a piece of technology, I try to understand where they are and what their major challenges are. Most every time, they start talking about the commitments they've made and how they're in a really tough spot.

Where the Return Actually Is

They're not getting any ROI because they can't even get the implementation to the point where it's testing anything of value. They've got that black hole piece. They don't understand the data. So before you sign anything, the question is what ROI you're going to give me, and what can you do so that my team doesn't have to do all the same work tomorrow that they're doing today. If the honest answer is that they still will, then you didn't buy a solution, you bought very expensive cloud compute and cloud storage. The demo proved the model, and the rollout exposed the data.

Continue reading from Hutchins Data Strategy

The Procurement Trap → https://hutchinsdatastrategy.com/the-ai-health-pulse/the-procurement-trap

The Data Debt Under the Model → https://hutchinsdatastrategy.com/the-ai-health-pulse/the-data-debt-under-the-model

On the Signal Room podcast

The Dark Side of the $50B AI Medical Boom | Lorraine Fernandes → https://signalroompodcast.com/episodes/dark-side-ai-medical-boom

Healthcare Data Readiness and AI Adoption: Why 85% of Organizations Aren't Prepared | Ratnadeep Bhattacharjee → https://signalroompodcast.com/episodes/data-readiness-ai-adoption

The Hidden Reason Hospital AI Keeps Failing | Angel Mena, MD → https://signalroompodcast.com/episodes/hospital-ai-pilots-fail

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Tags: AI Health Pulse newsletter · healthcare AI ROI · measuring AI ROI · risky AI investment · cloud overcommitment · build versus buy · AI readiness