In the modern landscape of American healthcare, a silent, high-stakes conflict is unfolding behind the screens of administrative offices and insurance headquarters. It is a war of algorithms: on one side, payers deploy AI-driven utilization management tools to automate prior authorizations and minimize costs; on the other, providers utilize AI-powered appeals software to push back against denials and streamline reimbursement.
This digital "arms race" is, according to industry leaders, a futile exercise that serves only to inflate the administrative burden on a system already struggling under the weight of excessive costs. Ashis Barad, the Chief Digital and Information Officer at the Hospital for Special Surgery (HSS), argues that the industry is trapped in a "finite game"—a short-sighted scramble for tactical advantage—when it should be pursuing an "infinite game" of long-term sustainability and patient-centric value.
A Career Shaped by Both Sides of the Aisle
To understand the absurdity of the current technological divide, one must look at the career of Dr. Ashis Barad. His professional trajectory has provided him with a rare, panoramic view of the friction between those who provide care and those who fund it.
Barad began his medical journey as a practicing physician at Sutter Health, an experience that introduced him to the clinical realities of patient care. He later transitioned to Baylor Scott & White Health, where he took a leadership role in championing digital transformation, seeking to bridge the gap between clinical intent and administrative execution.
His perspective shifted significantly in 2022 when he stepped into the role of Chief Digital and Information Officer at Allegheny Health Network and its parent company, Highmark Health. This tenure offered him an "under-the-hood" look at how payers build, deploy, and scale AI internally. Having navigated the labyrinthine processes of both sides, Barad returned to the provider fold two years ago at HSS, where he now serves as CDIO.
"We’re playing a finite game when there’s an infinite game to be played," Barad remarked in a recent interview. "If we play the finite game of today, we’re going to continue on the inflationary route. So we have to figure out how to work together to figure out what the deflationary path for AI is, because that’s what we owe the people, honestly."
The Chronology of the Algorithmic Divide
The current impasse is the result of years of escalating administrative friction:
- Pre-2010s: Prior authorization was largely a manual, phone-and-fax-heavy process, characterized by high friction but relatively low technological sophistication.
- 2015–2020: Payers began adopting rudimentary algorithmic tools to flag high-cost procedures, leading to a surge in initial denials. Providers responded by increasing staffing for authorization departments.
- 2020–2023: The "AI Boom" accelerated the deployment of predictive modeling. Payers began using sophisticated machine learning to deny claims based on historical patterns. Providers, in turn, began deploying generative AI and automation tools to draft and file high-volume appeals.
- 2024–Present: The industry has hit a stalemate. The cost of maintaining these opposing AI stacks is now a significant, hidden contributor to healthcare inflation, as both sides essentially spend billions to circumvent the other’s gatekeeping.
Supporting Data: The Power of Clinical Intelligence
The solution, according to Barad, is not to build better defensive AI, but to pool data to create better clinical intelligence. HSS serves as the perfect case study for this potential. As a world-leading orthopedic institution, HSS performs more than 40,000 orthopedic surgeries annually—more than double the volume of any other U.S. hospital.
HSS has successfully developed a structured, proprietary repository that connects complex imaging data directly to long-term surgical outcomes. This is the "holy grail" of healthcare data, and it is something insurers—who typically rely on broad, surface-level claims data—lack entirely.
"Payers typically only have access to basic claims data—infection or readmission rates after a first surgery, for instance—not the outcomes data for patients on their third, fourth or fifth procedure," Barad noted. In complex cases, which define the patient population at HSS, this data gap results in blanket authorization rules that are often clinically inappropriate.
When Barad recently presented this data-linking concept to a major insurance company, the response was telling. "They were like, ‘We would salivate over it,’" he said. The potential for these models to move from "blanket" approvals to "personalized" care pathways is immense, yet the infrastructure for such collaboration remains largely unbuilt.
Official Responses and Strategic Implications
The implications of this data-sharing model are profound, particularly regarding the practice of "gold carding"—the policy of exempting high-performing providers from prior authorization. Currently, gold carding is often a blunt instrument, determined by rigid flowcharts and arbitrary cost thresholds that fail to account for the nuance of individual patient health.
Barad envisions a future where authorization is "baked into" the care pathway. By leveraging shared data, authorization would no longer be a reactive, post-hoc hurdle, but a proactive element of the clinical plan, tailored to the specific risk profile of the patient.
However, moving toward this future requires a fundamental shift in the payer-provider relationship.
Key Implications for the Industry:
- The Shift from Administrative to Clinical AI: The industry must pivot from using AI to "fight" for payment to using AI to "validate" the efficacy of care pathways.
- Standardization of Data: Collaboration is impossible without interoperability. Payers and providers must agree on data standards that allow for the exchange of clinical outcomes without compromising patient privacy.
- The End of the "Arms Race": As long as the primary incentive for AI investment is to outmaneuver the other party, the system will remain inflationary. ROI must be redefined as "cost reduction for the patient," not "revenue protection for the institution."
Conclusion: A Path Toward Deflationary AI
The trajectory of American healthcare is currently defined by a zero-sum mentality. For every dollar spent by a payer to automate a denial, a provider spends a dollar to automate an appeal, effectively canceling out any efficiency gains.
Barad’s call to action is a sobering reminder of the ethical imperative of digital transformation. If healthcare organizations continue to use AI as a weapon, they will only accelerate the financial strain on the patients they serve. By pivoting toward a model of data transparency and shared clinical goals, providers and payers have the opportunity to transform AI from an instrument of conflict into a tool of genuine care coordination.
The "infinite game" of healthcare is not about winning the next round of authorizations; it is about building a system that delivers better outcomes at a lower cost. Until stakeholders acknowledge that their fates are inextricably linked, the algorithmic arms race will continue to extract a heavy toll from the U.S. healthcare economy. The technology to fix this already exists—what remains to be seen is whether the industry has the leadership to set aside its adversarial past in favor of a collaborative future.
