The American healthcare system is a quagmire. It is a disconnected, inefficient network where spending consumes a staggering 18% of the national GDP yet remains dangerously misaligned with actual patient outcomes. After 17 years of direct industry experience, healthcare data expert Lincoln Haycock admits that the cost curve simply continues to rise.
In a recent conversation on the Go-to-Market podcast, Amy Osmond Cook, Co-Founder & Chief Marketing Officer at Fullcast, sat down with Lincoln Haycock, Chief Analytics Officer at Castell. Together, they dissected the underlying systemic issues driving modern medicine and explored why massive technological investments are failing to deliver proportional value.
Here is Haycock’s strategic framework for moving beyond expensive, siloed technology. It presents a comprehensive healthcare analytics strategy designed to tackle the root causes of inefficiency. By realigning financial incentives, enforcing radical data interoperability, and empowering consumers to co-author their health outcomes, healthcare organizations can finally drive down system costs and improve patient care.
Why Today’s Tech Isn’t Fixing Healthcare’s Value Problem
Technology has added significant cost to the healthcare system without delivering a proportional improvement in patient outcomes. Before any healthcare analytics strategy can succeed, leaders must first confront an uncomfortable truth: technology alone cannot fix a fundamentally broken system. The massive investments poured into healthcare innovation over the past two decades have not translated into better, more affordable care for patients.
Acknowledging the Disconnect When Rising Costs Don’t Lead to Better Outcomes
Lincoln Haycock brings a rare level of candor to this discussion. After nearly two decades working to improve healthcare through data and analytics, he admits to “some soul searching” about the industry’s lack of progress.
“I was recently shopping for health insurance on the marketplace here in Utah and was frankly disgusted by how expensive it is. I’ve been at this for 17 years, been doing what I think is some pretty innovative things, working with great technology that in small pockets has done some amazing things, but at large, we’re still paying more for healthcare in this country than we ever have.”
The numbers are stark. Healthcare spending now consumes 18% of GDP, yet life expectancy has plateaued and objective health outcomes are not improving. As Haycock puts it, “No one in this country would agree that we are getting the value commensurate with what we’re spending on healthcare.”
This disconnect reveals a critical insight: technology has often added cost without delivering proportional value. The system itself contains fundamental challenges that no amount of AI or innovation will solve on its own.
Moving Beyond “Profit Extraction” The Mandate to Actually Reduce System Costs
For Haycock, healthcare analytics must prioritize reducing systemic costs over finding new ways to extract profit. He argues that the guiding principle for AI leaders should be leveraging technology to drive down expenses rather than simply carving out more profitable niches.
This mandate is gaining traction at the highest levels. CMS leadership is actively seeking partners committed to genuine cost reduction. “You listen to Chris talk and that’s what he is talking about,” Haycock noted. “He’s saying, we want innovators, we want partners, and we want people who are committed to doing that.”
This is a fundamental change in thinking that must happen before any data strategy can work. As healthcare organizations adopt this cost-takeout mindset, their go-to-market and marketing approaches must also evolve to reflect a new healthcare marketing strategy aligned with these priorities.
A Practical Framework for Data-Driven Healthcare
A successful data strategy is built on three actions: enforcing data interoperability, powering value-based payment models, and deploying AI to eliminate waste. With the right mindset established, healthcare organizations can build a new data strategy around these key actions.
Pillar 1: Enforce Radical Interoperability to Break Down Data Silos
Haycock expresses genuine excitement about the administration’s push to enforce interoperability rules for both payers and providers. “The way that the administration will require payers to share data, the way that providers have been required to, but I think we’ll be more operationally effective at sharing data. I think that holds promise.”
The historical problem is not capability but execution. EMRs have long possessed the technical ability to share data, but operational and incentive barriers prevent it from happening. “For years, regulations have required EMRs to support the capability to share data, but when you’re actually asking them for an extract, there’s no one at the clinic who knows how that works,” Haycock explained. “And it’s not very seamless to get meaningful, actionable data out of those systems.”
A true healthcare analytics strategy requires building data pipelines that unify EMR, payer, and cost data to create a unified view of patient care. The challenge of unifying disparate data sources is not unique to clinical settings; as organizations like Sonic Healthcare have shown, creating a unified data view is critical for running an effective organization across industries.
As Adam Cornwell noted on The Go-to-Market Podcast, the goal is to “bring it together so they can look at their patients and their outcomes holistically in a single place.”
Pillar 2: Use Analytics to Power Value-Based Care and New Payment Models
Value-based care changes the core of how healthcare gets paid for, moving from incentivizing procedures to incentivizing outcomes. Haycock points to Accountable Care Organizations as a key construct where “you have who would otherwise be competitors sharing data with one another. And there are incentives to get the patient to the lowest cost care setting.”
However, Haycock brings healthy skepticism born from two decades of slow progress. “We’ve been on this value-based care journey for 20 years and there’s been isolated successes. We haven’t seen the industry adopt it wholesale, and we haven’t seen a tapering of the growth in the cost curve associated with that.”
Still, he sees promise in CMS’s new 10-year pilot with the ACO REACH model. “They just kicked off a 10-year pilot with their access model to pay for outcomes, and that’s innovative.”
The strategy here is using predictive analytics to identify which patients would benefit most from proactive outreach, making these models financially viable at scale.
Pillar 3: Deploy AI to Eliminate Administrative Waste, Not Just Add Expense
AI can help reduce the administrative waste that drives significant healthcare costs. However, Haycock draws a clear distinction between potential and current reality. “Do I think AI has the opportunity to take out some of the administrative waste that we know exists in healthcare? I do. So far though, I’ve only seen it add a lot of cost.”
An effective strategy uses predictive models to target interventions where they will have the most impact, rather than applying expensive outreach across an entire population. “If you look at the expense to provide that proactive outreach to patients, you can’t do that across the entire population. It’s too expensive, and so it’s very targeted,” Haycock explained. “We have predictive models. Who needs this the most? Who would benefit from it the most? And those are the people who receive the outreach.”
To implement this effectively, leaders must understand the key differences between AI vs. machine learning vs. predictive analytics and choose the right tool for each specific challenge.
Engaging the Patient The Final Piece of the Puzzle
Effective analytics must include patients as active partners, providing them the tools to co-author their own health outcomes.
Empowering Patients Through Preventative Health
Haycock holds strong convictions about the role of individual responsibility in healthcare outcomes. “I’m a huge fan of diet as medicine,” he shared. “There’s so much evidence supporting the connection between diet and health outcomes.”
He frames consumer behavior as a critical, often overlooked variable in the healthcare cost equation. Drawing from personal experience, Haycock noted, “I have a family history of heart disease. My grandfather had open heart surgery. My dad had a stent put in relatively young in life.” He believes analytics and engagement tools can foster prevention for conditions like diabetes and heart disease, giving patients control over their health.
Setting Realistic Expectations Navigating a System in Transition
Haycock’s advice for patients navigating the current system is refreshingly honest. “For the average consumer, I just think the expected experience is one of frustration, delay, and lack of transparency. And I think if you expect something different, you’re setting yourself up for just unneeded frustration.”
The takeaway is strategic patience: while leaders work to fix the system, consumers must advocate for themselves. “Just know that there are really smart people, really ambitious people who are out there trying to make it better,” Haycock added. “There’s a lot of investment going into this space.”
How to Stay Ahead in a Rapidly Evolving Industry
Effective leaders in healthcare analytics must commit to structured learning and cultivate a professional network to navigate constant change. Navigating healthcare’s complexity requires leaders who can adapt continuously to technological and systemic change.
Embrace Structured Learning to Master Complexity
Haycock has developed a deliberate approach to staying current in a field where AI capabilities evolve daily. “I’ve actually enrolled in a Chief AI Officer program through the University of Chicago Booth School of Business to give me some structure,” he explained.
He contrasts this with less formal methods he has heard from peers. “A lot of people I’ve talked to said the way I keep up on it is I watch YouTube videos at night. I don’t have the patience to watch YouTube videos like that all of the time.” This commitment to structured, rigorous learning over surface-level trends demonstrates the depth required to lead effectively in healthcare analytics.
Cultivate Humility, Curiosity, and Community
Haycock distills his principles for professional longevity into a few core tenets. First is humility and curiosity: “Staying humble and curious, like we know that we must always be learning. The job that I’ve had didn’t exist when I graduated from college, and the job I’ll have tomorrow doesn’t exist today.”
Second is the power of community. “Talking to people is amazing,” he shared. “I use my personal network, people I go to church with, they’re happy to talk about how they’re using AI. And then colleagues that I have from school. And then Silicon Slopes is a great community as well.”
This mindset is essential for any leader implementing data-driven strategies in complex industries.
Final Thoughts
Real progress in healthcare analytics isn’t about adding expensive, trendy tech to a bloated system. Haycock believes success means using data to align incentives, remove barriers, and empower all stakeholders.
Haycock’s vision offers a path out of the quagmire. This vision requires strategic discipline: cutting costs over chasing profits, while balancing tech innovation with human behavioral change.
The three pillars of radical interoperability, value-based payment models, and targeted AI deployment provide a practical framework. Yet the foundation beneath them all is a leadership mindset rooted in curiosity, community, and continuous learning. The healthcare system will not transform overnight. But for organizations ready to build the smart, data-driven backbone their most critical initiatives require, the work can begin today.
Learn how leading organizations are doing exactly that with Fullcast Revenue Intelligence.
