Executive Overview
The modern American healthcare ecosystem is locked in an escalating technological arms race. On one side, health insurance payers deploy increasingly sophisticated artificial intelligence and machine learning models designed to automate, scrutinize, and systematically deny prior authorizations. On the other side, healthcare providers fight back with their own fleets of automated algorithms, optimized to generate rapid-fire appeals, bypass administrative bottlenecks, and force reimbursement.
This digital proxy war has turned prior authorization—once intended as a utilization-management safeguard—into a high-stakes battle of attrition. Yet, according to industry leaders who have witnessed the conflict from both the provider and payer perspectives, this back-and-forth cycle of mutual out-automation is a catastrophic misallocation of capital. Rather than reining in runaway expenditures, it merely injects a costly new layer of technological overhead into a healthcare system already buckling under administrative bloat.
Enter Ashis Barad, Chief Digital and Information Officer at the renowned Hospital for Special Surgery (HSS). Having navigated the highest echelons of clinical practice, health system leadership, and payer operations, Barad offers a uniquely comprehensive vantage point. His thesis is simple yet radical: providers and payers are locked in a finite game with a zero-sum outcome, when they should instead be playing an infinite game focused on systemic cost deflation.
To achieve this, Barad argues that the industry must abandon the algorithmic arms race. Instead of investing billions in tools meant to outmaneuver one another, payers and providers must forge unprecedented data-sharing partnerships. By pooling deep, granular clinical registries with vast actuarial and claims databases, the healthcare industry can transition away from blunt, retrospective administrative hurdles and toward proactive, personalized care pathways. This in-depth feature explores the roots of the provider-payer algorithmic divide, evaluates the structural limitations of current utilization management, and highlights a roadmap toward data-driven collaboration that could fundamentally rewrite the economics of modern medicine.
Detailed Chronology: A Career Forged on Both Sides of the Aisle
To understand why Ashis Barad’s perspective carries such weight across the healthcare landscape, one must examine the distinct trajectory of his career. Barad’s professional evolution mirrors the shifting intersections of clinical medicine, digital transformation, and healthcare financing over the past two decades.
The Clinical Foundation: Sutter Health and Baylor Scott & White Health
Barad’s journey began at the bedside. As a practicing physician trained within major delivery systems, he experienced firsthand the administrative friction that frustrates clinicians and alienates patients. Early in his tenure at Sutter Health, Barad recognized that the analog workflows governing clinical documentation, diagnostic ordering, and patient handoffs were fundamentally broken.
This realization propelled him toward digital health leadership. When he transitioned to Baylor Scott & White Health—one of the largest not-for-profit healthcare systems in Texas—Barad took on leadership responsibilities aimed at modernizing the organization’s digital architecture. During this phase, he spearheaded initiatives to streamline clinical workflows, integrate electronic health records (EHRs), and introduce early iterations of digital tools meant to alleviate administrative burnout among physicians. However, even as digital tools improved internal efficiency, the friction between care delivery networks and external insurance gatekeepers remained a stubborn bottleneck.
Crossing the Aisle: Highmark Health and Allegheny Health Network
In 2022, Barad made a pivotal career pivot that provided him with a rare vantage point: he crossed the aisle from the provider world into the integrated payer-provider space. Accepting the role of Chief Digital and Information Officer at Allegheny Health Network (AHN) and its parent company, Highmark Health, Barad was thrust directly into the operational heart of a major health insurance enterprise.
Operating within an integrated delivery and financing system (IDFS) meant Barad was no longer just fighting the insurance apparatus; he was helping to build it. At Highmark and AHN, he gained a behind-the-scenes look at how national and regional payers conceptualize, construct, and deploy artificial intelligence internally. He observed how predictive algorithms are utilized to flag utilization anomalies, automate claims adjudication, and establish tight thresholds for prior authorization.
This experience proved transformative. While many provider executives view payers as monolithic adversaries acting in bad faith, Barad’s time inside Highmark revealed the structural motivations driving insurer behavior. Payers were not merely malicious actors trying to withhold care; they were data-starved entities attempting to manage escalating medical inflation using blunt, legacy tools built on insufficient datasets.
Returning to the Provider Side: Hospital for Special Surgery (HSS)
Armed with an insider’s understanding of payer algorithms, Barad returned firmly to the provider side two years ago when he was appointed Chief Digital and Information Officer at the Hospital for Special Surgery (HSS) in New York. HSS is globally recognized as a premier orthopedic powerhouse, consistently ranked as the top orthopedic hospital in the world.
At HSS, Barad oversees a digital strategy tailored to a hyper-specialized, high-volume surgical environment. Rather than managing the broad-spectrum primary care needs of an entire regional population, HSS focuses intensely on musculoskeletal health, performing tens of thousands of complex orthopedic interventions annually.
Sitting in the CDIO chair at HSS, Barad quickly realized that the escalating war of algorithms between insurers and health systems was reaching a boiling point. Rather than leaning into the arms race by building proprietary AI tools designed purely to beat payer algorithms at their own game, Barad began advocating for a paradigm shift. He recognized that HSS possessed something extraordinarily valuable—deep, longitudinal, structured clinical data—that payers desperately needed, setting the stage for a new model of collaborative healthcare economics.
Supporting Context & Metrics: The Anatomy of the Algorithmic Standoff
The conflict between healthcare providers and payers is fueled by structural imbalances in data access, administrative waste, and misaligned economic incentives. To grasp the urgency of Barad’s warnings, one must examine the metrics defining today’s administrative burden.
The Prior Authorization Burden
According to data compiled by the American Medical Association (AMA), prior authorization remains one of the most significant sources of clinician burnout and operational inefficiency in the United States.
- Delays in Care: Over 90% of physicians report that prior authorization causes delays in necessary care, with a significant percentage noting that these delays occasionally lead to serious adverse medical events.
- Administrative Waste: Practices spend an average of 14 hours per week—nearly two full business days—processing prior authorizations. This administrative overhead consumes billions of dollars annually, draining resources that could otherwise be allocated to direct patient care.
- The AI Escalation: In response to mounting physician complaints, insurance companies have increasingly turned to artificial intelligence to scale up their prior authorization processing capacity. These algorithms can screen millions of claims and requests daily. However, critics note that these models frequently rely on rigid, automated rejection thresholds that force providers to flood the system with time-consuming appeals.
The Data Deficit: Payers vs. Providers
At the core of the current impasse is a profound asymmetry in the types of data available to each stakeholder:
| Stakeholder | Primary Data Assets | Blind Spots & Limitations |
|---|---|---|
| Payers (Insurers) | Broad claims data, broad population demographics, cost-per-episode benchmarks, first-line readmission rates. | Lack of granular clinical registries, sparse visibility into multi-stage surgical outcomes, limited understanding of nuanced patient histories beyond primary diagnostic codes. |
| Providers (Health Systems) | High-resolution electronic health records (EHRs), surgical videos, imaging datasets, patient-reported outcome measures (PROMs), longitudinal tracking of complex revision surgeries. | Fragmented view across regional insurance networks, limited actuarial modeling infrastructure, restricted visibility into total cost of care outside their specific delivery network. |
This data divide explains why payer utilization management often feels frustratingly crude to clinicians. Payers evaluate procedures based on broad actuarial risk tables and generalized clinical guidelines because they lack the high-fidelity clinical context possessed by elite specialty institutions.
The HSS Clinical Repository: A Case Study in Granular Data
The Hospital for Special Surgery offers a striking contrast to the data-starved models typically used in utilization management. HSS performs more than 40,000 orthopedic surgeries every year—more than double the volume of any other hospital in the United States.
Crucially, HSS has invested heavily in building a structured, institutional data repository that connects high-resolution imaging data directly to long-term surgical outcomes and patient-reported measures. While a standard insurance claims database might record that a patient underwent a knee replacement and returned six months later for a follow-up, the HSS repository tracks biomechanical indicators, implant performance, patient functional recovery scores, and complication trajectories across multiple revisions.
This depth of data is precisely what is missing from mainstream utilization management. When Barad recently met with the actuarial teams of a major national insurance carrier and presented the concept of integrating HSS’s imaging and outcomes data into their models, the response was immediate. As Barad recalled, insurer executives admitted that their analytical teams would "salivate" over access to such granular, longitudinal datasets.
Official Statements and Industry Insights: Playing the Infinite Game
To shift the narrative away from perpetual algorithmic warfare, industry leaders are increasingly invoking game theory to describe the state of modern healthcare IT. In his public remarks, Ashis Barad has drawn a sharp distinction between finite and infinite games.
"We’re playing a finite game when there’s an infinite game to be played," Barad declared during a recent industry address. "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."
Deconstructing the "Finite Game"
In a finite game, players are known, rules are fixed, and there is a clear winning and losing condition. In the context of healthcare AI, the finite game is the ongoing battle of automated attrition:
- Payers deploy AI to catch and deny questionable prior authorization requests.
- Providers deploy AI to automatically generate appeals, compile clinical justification packets, and overturn denials.
- Both sides sink immense capital into software development, licensing fees, and administrative overhead.
Barad warns that this dynamic is inherently inflationary. Every dollar spent by an insurer on automated denial engines, and every dollar spent by a health system on automated appeal generators, represents administrative waste that ultimately drives up total healthcare expenditures. Neither side wins in the long run; instead, the system absorbs the cost, passing the financial burden onto employers, patients, and taxpayers through rising premiums and out-of-pocket expenses.
Transitioning to the "Infinite Game"
An infinite game, by contrast, features no fixed endpoint. The objective is not to defeat the opponent, but to perpetuate the system, foster continuous improvement, and drive down long-term costs. For Barad, the infinite game in healthcare technology is systemic cost deflation driven by collaborative intelligence.
By shifting focus away from administrative combat and toward shared data infrastructure, payers and providers can transform the prior authorization process from a adversarial gatekeeping mechanism into an integrated, evidence-based care pathway.
Future Outlook: Reimagining Gold Carding and Authorization Pathways
If the current trajectory of AI-driven administrative warfare is unsustainable, what does a collaborative, data-driven future look like? Barad and other forward-thinking digital health leaders point toward a fundamental reinvention of utilization management tools, starting with the modernization of gold carding.
The Flaws of Legacy Gold Carding
Today, gold carding is often implemented as a blunt instrument. State legislatures and private payers have increasingly introduced gold carding laws designed to exempt high-performing clinicians and health systems from prior authorization requirements. However, current criteria are typically based on rigid flowcharts, historical approval rates, or broad cost thresholds rather than true clinical excellence or patient outcomes.
Under legacy systems, a provider might be granted a gold card simply because 95% of their prior authorization requests were eventually approved over a calendar year. This approach fails to account for the actual quality of care delivered, the long-term durability of surgical interventions, or the avoidance of costly downstream complications.
Personalized, Pathway-Integrated Authorization
Barad envisions a next-generation model of gold carding powered by collaborative AI and deep data integration between payers and elite providers:
- Dynamic, Outcome-Based Tiering: Instead of relying on static approval thresholds, gold carding status would be dynamically determined using longitudinal outcome data. High-performing institutions like HSS, which can empirically prove superior long-term surgical outcomes and lower complication rates, would automatically secure streamlined authorization pathways.
- Pathways Over Permissions: In an optimized digital ecosystem, prior authorization would not be a retrospective hurdle requested after a clinical decision has been made. Instead, authorization would be natively baked into personalized care pathways at the point of care.
- Transparent Risk-Sharing: When payers and providers share access to advanced clinical registries, clinical decision support tools can automatically align patient needs with evidence-based guidelines agreed upon by both entities. The need for manual reviews, appeals, and administrative friction diminishes naturally because the underlying clinical validity of the procedure is supported by shared data.
The Road Ahead: Overcoming Implementation Barriers
Realizing this collaborative vision will not be easy. Significant hurdles remain:
- Data Privacy and Governance: Merging provider clinical registries with payer claims data requires robust cybersecurity, patient consent frameworks, and strict adherence to HIPAA and emerging privacy regulations.
- Competitive Animosity: Decades of mistrust between insurance companies and health delivery networks have fostered an insular corporate culture where data sharing is viewed with suspicion.
- Technical Interoperability: Bridging disparate EHR architectures with legacy payer mainframe systems demands standardized APIs (such as HL7 FHIR standards) and a unified commitment to technical modernization.
Despite these challenges, the economic and operational pressures facing the U.S. healthcare system are making the status quo untenable. As medical inflation continues to outpace broader economic growth, the cost of maintaining an algorithmic war zone will become too heavy to bear.
By shifting from an adversarial mindset to a collaborative one, payers and providers have a historic opportunity to harness artificial intelligence not as a weapon of administrative exclusion, but as an engine of clinical excellence and systemic cost deflation. As Ashis Barad has argued, moving beyond the finite game of mutual out-automation is not just a strategic option—it is an ethical imperative owed to the patients and communities relying on the healthcare system every single day.