How Market Structure and Technology Are Evolving

Talking Trends

The future of market structure depends on deliberate planning and industry-wide collaboration to ensure that every step forward enhances, not disrupts, the quality of our markets. Chuck Mack, SVP and Head of Strategic Operations and Public Policy at Nasdaq, and Frank La Salla, CEO of DTCC, join Nasdaq TradeTalks to discuss how market structure and technology are evolving, built on resilience, transparency, and investor trust. 

Mack and La Salla explain that the goal is to reduce risk and improve resiliency by creating efficiencies, such as embracing standards and retiring proprietary processes, improving the client experience, expanding capabilities through technology, driving capital and liquidity optimization for clients, and building operational resilience.

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In your recent TradeTalks interview, you spoke about how AI is prompting the healthcare industry to rearchitect their entire tech stacks. Can you elaborate on how value is being delivered all the way through the stack?

The traditional healthcare tech stack, often built around Electronic Health Records (EHRs) and disparate systems, is being fundamentally rearchitected to accommodate AI. This shift is driven by the need to deliver value across the entire healthcare ecosystem. Value is being delivered in several key ways:

• Data Foundation: The foundation of the new tech stack is a unified, interoperable data platform. AI thrives on data, and healthcare organizations are breaking down data silos to create comprehensive, multi-modal data sets that include everything from clinical notes and imaging to genomic data and patient-generated information from wearables. This creates a single source of truth that AI models can leverage for insights.
• Operational Efficiency: AI is automating administrative and operational tasks, from patient scheduling and prior authorizations to medical coding and billing. This frees up healthcare professionals to focus on direct patient care, improving efficiency, and reducing burnout.
• Clinical Augmentation: At the clinical level, AI acts as an assistant to healthcare providers. It analyzes medical images to detect anomalies, assists in making more accurate diagnoses, and helps create personalized treatment plans by sifting through vast amounts of medical literature and patient data.
• Patient Engagement: AI-powered chatbots and virtual assistants are providing 24/7 support to patients, answering questions, managing appointments, and providing personalized health advice. This improves patient experience and access to care.
• Precision Medicine and Drug Discovery: AI is accelerating the pace of innovation in drug discovery and precision medicine. It can rapidly screen millions of potential drug candidates, simulate their interactions, and identify patient cohorts that will respond best to specific treatments.

How do you think it will evolve in the next five to 10 years?

In the next five to 10 years, AI in healthcare will move beyond being a novelty to becoming an integral part of the healthcare system. Here's how it is likely to evolve:

• Widespread Integration: AI will be seamlessly integrated into existing clinical workflows and devices. It will be the "co-pilot" for physicians, from the moment a patient's data is entered into an EHR to the point of diagnosis and treatment.
• Predictive and Proactive Care: The focus will shift from reactive to predictive healthcare. AI will analyze population health data and individual risk factors to identify and intervene in a person's health before a disease manifests. This will lead to a significant increase in preventative care and chronic disease management.
• Hyper-Personalization: AI will enable a new level of personalized medicine. Beyond just genomics, it will analyze a person's lifestyle, environmental factors, and real-time data from wearables to provide a holistic view of their health and deliver highly tailored interventions.
• Regulatory Maturation: As AI becomes more embedded in healthcare, regulators will establish more clear and specific frameworks for its use, particularly for "locked vs. adaptive" AI models. This will provide the necessary guardrails for ethical and safe innovation.
• Ecosystem Collaboration: We will see a greater collaboration between technology companies, healthcare providers, and pharmaceutical firms. This will lead to the development of new business models that are centered on value-based care and improving health outcomes.

You also highlighted how AI has been accelerating the pace of innovation within healthcare. How should regulators approach AI regulation without hindering innovation?

Regulators face a delicate balancing act: Ensuring patient safety and data privacy while not stifling the incredible potential of AI to revolutionize healthcare. A few key principles are emerging, including:

• Outcome-Based Regulation: Rather than trying to regulate the algorithms themselves, regulators can focus on the outcomes they produce. This approach, similar to the European Union's AI Act, categorizes AI by risk level and applies more stringent rules to high-risk applications like medical diagnostics.
• Transparency and Explainability: Regulations should require that AI models are transparent, and their outputs are explainable. Clinicians and patients need to understand how an AI system arrived at its recommendation to build trust and ensure accountability.
• Data Governance and Bias Mitigation: Regulations must address data governance, requiring robust security measures and protocols for bias detection and mitigation. This is crucial for ensuring that AI models are fair and don't perpetuate or amplify existing health disparities.
• Agile and Adaptive Frameworks: Traditional regulatory processes are often slow. Regulators, such as the FDA, are exploring more agile frameworks that allow for the continuous improvement of AI models while maintaining oversight. An example of this is the "predetermined change control plan" which allows a manufacturer to pre-define the ways an AI model can evolve without needing a full re-authorization.

What can healthcare companies do to prepare for the next wave of AI innovation?   

To prepare for the next wave of AI innovation, healthcare companies should take a proactive approach:

• Invest in a Data Strategy: Start by building a robust data foundation. This involves creating a comprehensive data strategy, investing in interoperability, and establishing strong data governance and quality control processes.
• Foster a Culture of Innovation: Healthcare organizations need to move beyond a conservative mindset and foster a culture that embraces experimentation with new technologies. This means creating dedicated innovation teams and providing clinicians with the training and support they need to use new tools effectively.
• Strategic Partnerships: Companies should not try to build everything in-house. Strategic partnerships with technology companies, research institutions, and startups can provide access to cutting-edge AI expertise and solutions.
• Focus on Workforce Transformation: The healthcare workforce will need new skills. Companies should invest in training and upskilling programs to ensure their staff can work effectively with AI-powered tools and understand how to interpret their outputs.

You noted that we’re still early on in fourth industrial revolution. Do you have any unique predictions or analysis on the future landscape of healthcare?

While it's early in the "fourth industrial revolution," AI's trajectory suggests some unique predictions for the future of healthcare:

• The "Digital Twin" of the Patient: AI will be used to create a "digital twin" of a patient—a sophisticated, real-time computational model of their physiology. This twin will be used to simulate the effects of different treatments and lifestyle changes, enabling highly personalized and precise care.

• Democratization of Medical Expertise: AI will democratize access to medical knowledge, especially in underserved areas. AI-powered diagnostic tools and clinical assistants will empower primary care physicians and even community health workers to perform tasks that previously required a specialist, bridging the gap in healthcare access.

• AI as a "Health System Manager": Beyond individual patient care, AI will become a powerful tool for managing entire health systems. It will optimize hospital operations, predict disease outbreaks, and manage public health crises by analyzing data from various sources.

• The Rise of "Behavioral AI": AI will increasingly be used to understand and influence human behavior. It will help patients adhere to treatment plans, manage chronic conditions, and adopt healthier lifestyles through personalized nudges and digital coaching.

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