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CHARGE Wave: Jason G. Cooper on keeping humans in-the-loop, the AI space race, and the Dewey Decimal System
- Levi Miller
- 2 days ago
- 8 min read
CHARGE had the pleasure of speaking with Jason G. Cooper this past weekend in an interview that ranged from responsible AI deployment to NASA to the historical significance of the Dewey Decimal System. Throughout his career as a Chief Technology, Analytics, and AI Officer, Jason has managed the core data and analytics systems and teams for major health payers and providers, among them Paradigm, HMS and Blue Cross Blue Shield plans.
As AI deployment proliferates across major health networks, Jason is turning his attention to thought leadership, now advising .406 Ventures, Covenant HR, and the International Institute for Analytics as an AI and data analytics expert. On his robust LinkedIn page, Jason sketches a path for responsible AI deployment in healthcare that reaps the benefits of deployment and mitigates its potential dangers. Each post, and his insightful comments in this interview, speak to Jason’s larger goal of preserving (though likely augmenting) the clinician-patient relationship, what he describes as “the basic unit of healthcare.” CHARGE thanks Jason for his thoughtful participation in this interview.
Thank you for joining us, Jason. You spent decades in analytics before AI became the industry's obsession. Looking back, what changed – and what didn't – as healthcare moved from predictive/data analytics to generative AI?
Jason: Ha - a long time, happy to answer that. First of all, I really appreciate the opportunity to chat with CHARGE. I was doing AI in healthcare back in the mid-nineties before it was really cool. A lot has changed over 30 years. We have far greater computational capabilities now, especially around unstructured data. Knowledge creation is so quick today that for a clinician to practice at the top of their license, they would have to read hours and hours a day. Clinical decision support has become a necessity.
But the bigger thing is AI governance. Robust governance is starting to address the "trust gap" between developers, providers, and patients. We need frameworks to decide where to place our bets: guardrails that aren't so bureaucratic they crush innovation, but not so light that we incur undue risk. Without trust, AI will lose every time.
On that point, you've written extensively about trust for AI deployment: between patients and providers, payers and members, providers and AI. Can you define trust and describe what it looks like? How can healthcare leaders cultivate trust as they implement AI systems?
Jason: First and foremost, trust is built on a foundation of mutual consent. Patients and professionals should consent to AI assistance, whether it's ambient listening in an EMR or a decision support system. That requires workforce development; professionals must understand why we're using AI. If it’s deployed without understanding, it immediately puts people back on their heels.
The other pillar is tool choice. First, if a good old spreadsheet will solve your problem, why use AI? You’re over-clubbing it. Second, human-in-the-loop is vital. The basic unit of healthcare is the physician-patient relationship. I’m a big believer that, in the majority of cases, AI is not mature enough to operate independently.
There has been a proliferation of no-human-in-the-loop sandboxes, like in Utah, where Legion Health and Doctronic offer diagnostic consultation and autonomous refills. You just stated that, broadly, the technology is not there yet. Are there instances where the technology is ready for autonomy?
Jason: I don't want to get my medical advice from ChatGPT or Claude. I still want a great relationship with my physician and with my GI doc. The complex things still require an interpersonal relationship.
But it's not too early in every case. Take automated pharmacy refills. If I receive an “it’s time for your refill” text, I’ll reply yes. And why not, right? I’m comfortable knowing a human isn't looping on the automatic refill. That is, until it comes time for delivery, where I still anticipate a person visually confirming the pill bottle. Can we anticipate that visual confirmation coming from machines? In our food supply chain, optical recognition filters my produce, and I feel comfortable not seeing a human filter it. We’ll get there. There are a whole host of administrative areas where we can reduce the experience burden and create efficiencies at scale.
Going back to the question of trust – when you spoke about workforce development and training, you mentioned that we need to tell healthcare workers what exactly the AI is gonna help with. There's an industry-wide fear right now that AI deployment is going to replace and degrade human skills, especially those skills that might be for fallback procedures in the case of system failures. As an AI leader, what skills do you think are becoming more important in the AI era? What traditional skills are proving sticky?
Jason: When people ask, “Is AI going to take my job?” I love to respond: “AI won't take your job, but someone who understands how to leverage AI as a tool may take your job.” To your point about system failures – whether a system is down, or we lose power, or god forbid we’re in a wartime situation – healthcare providers will always need fundamental fallback skills. We can't forget how to do what we've been doing forever without AI.
When hiring, it's not about coding skills; those can be taught. I look for three fundamental skills: 1. 1. Problem Solving: To understand the true business problem that someone’s bringing to you – and leaders rarely present their exact problem – you need to peel back multiple layers of the onion. That requires consultative capabilities and the problem solving skills to dig.
2. Creativity: You can't rely on ChatGPT for your creativity. Humans are so immensely creative. Whether I’m interviewing someone that’s doing coding or architectural work, I’m still looking for creativity in our conversations.
3. Storytelling: I don’t mean marketing – I love to say, “tortured data will confess to anything,” and I’m not interested in torturing data. I mean imbuing a call to action. If you can’t convey your great analytics work to stakeholders, you’ve wasted your time.
What is the biggest difference between how payers and providers deploy, govern, and regulate AI?
Jason: It comes down to different business models and reimbursement. Payers deal with massive claims data, but they may not see the full picture if a patient pays out-of-pocket. That’s, by the way, similar on the provider side: networks don’t always share data.
At the end of the day, each business lens is for the same purpose. For providers, it’s about patients. For payers, it’s about members. So, from a governance model perspective – how you prioritize things, the right guardrails you put in place, even acceptable use policies and things of the like – I think those actually can be very similar.
On the point of reimbursements and losing information, prior authorization has become one of the most visible applications of AI on the payer side, and one of the most politically fraught. Just last week, a House Appropriations committee halted funding for the CMS’ new WISeR model. What do you think responsible use of AI utilization management looks like? Do you think the industry has gotten it wrong in any way?
Jason: I've personally had a chronic disease for over 30 years, so I've walked every hallway of the US healthcare system. Prior authorization is broken, but not because it's bad policy to ensure care is medically necessary. It's broken because we make it painful for members and providers. We can solve it two ways:
1. Data Interoperability: In countries with a lifelong medical record number, professionals can see your longitudinal health record and know you've been on a specialty medication for 15 years. They can provide you holistic care that’s not disjointed from the cradle to the grave. In the US, if you switch employers (thus insurers), your record is lost.
2. Portable Health Records: Health records shouldn’t be proprietary to provider systems. I should be able to walk around with an encrypted smart card or thumb drive containing my verified information so that when the time comes I could simply hand a professional my trusted, verified information. US law says you can access your health record, but you have to ask for it. The default position should be that the data is ours to own 24/7 without needing to ask.
You’ve argued that leaders shouldn’t respond with denial when patients use AI. What are payers and providers learning from the reality of "Dr. GPT"?
Jason: It’s just another leg of the information curve. Wind the tape back to the fifties: we had the Dewey decimal system. Absent the internet, people walked into the library. Eventually, we went from the Dewey Decimal System to Dr. Google, and providers went, “Oy vey, patients are printing out notes!” Now we’ve moved to Dr. GPT.
I think that's a great thing. Don't you want your patients to be educated? Yes, it's fraught with potential error and what I call decision frustration. All I would ask, as a healthcare professional, is let me leverage my expertise to provide a full differential diagnosis before you demand a certain prescription. But I think in the majority of cases, all of us as patients are simply trying to arrive informed and help with admittedly, the very limited time we have face to face with our providers. And if it makes us more efficient and therefore leads to a deeper human connection, I'm all for it.
Does AI resemble previous technical revolutions, or is it unique?
Jason: I don't think it's unique. Look at the aerospace industry. For bi-planes, everything was manual. Jump to the early nineties, planes have GPS and radar. Today, planes fly-by-wire with autopilot and turbulence reduction. Look at what SpaceX accomplished with self-landing vehicles upright in a pitching ocean. All of this tech provided for safer flight and less pilot burden.
We've come a long way in aerospace in terms of efficiency, safety, and experience. I think that parallels will be drawn to healthcare for all the same reasons: efficiency, patient safety, better provider experience, and reduction in provider burnout.
We are having a particular political – oftentimes politically fraught – AI moment, especially as it pertains to integration in healthcare. Do you see an out for this? Do you think it's just gonna continue to become a potentially toxic political issue? Or will it resemble the obsession we had with the space race that tapered off over time?
Jason: Back in the mid-nineties, I did a 10-year stint in aerospace working with the International Space Station and autonomous spacecraft. In aerospace, we have robust, federally recognized standards organizations like the FAA, IEEE, ANSI, etc. I could go on and on. These standards have built a structure for aerospace development – look at what SpaceX or Blue Origin or Boeing have done from an innovation perspective.
Right now, AI in healthcare is the wild wild west. At last count, there are about 250 different laws and regulations in the US alone, managed on a state-by-state basis. This tamps down innovation because of legal risk. National enterprises don’t want to get on the wrong side of a state legislature or a regulatory body, and so they might just bow out. We need a unified federal model. I named a bunch of standards organizations that are federally recognized by both commercial aeronautic entities and the government. We all use them. That’s the framework.
Is there one starting point you're thinking or looking at that we could build around to build a clear, unambiguous, and thoughtful national framework for AI regulation?
Jason: There are two organizations to my knowledge that are down the path from a maturity perspective on this. One is called CHAI, which is the Coalition for Health AI. I’ve actually read a fair amount of the Coalition for Health AI's materials, and I think the actual concepts and the
materials are very worthwhile as a starting point. The other is HL7 which has an artificial intelligence office, and they're starting to also think about AI standards from an interoperability, and data interchange perspective. Perspectives on governance and technical application of standards are very important right now.
As an example, the National Academy of Medicine has also published a purple book, which I have no commercial interest in, called An Artificial Intelligence Code of Conduct for Health and Medicine. Healthcare leaders need to be reading this and considering it. To answer your question, I think we're still very much in a nascent stage of standardized AI governance and regulations. We're just not there yet. It's a very complex fabric in the United States at least, and it actually makes it much more difficult for organizations to do their work.
Jason, thank you so much for meeting with us.
Jason: Thank you, I appreciate it.
