NewHealthcare Platforms Newsletter #146 – Your Guide to Value-Based Artificial Intelligence & Medical Technology

DISCLAMIER: This newsletter contains opinions and speculations and is based solely on public information. It should not be considered medical, business or investment advice. The banner and other images included in this newsletter are AI-generated and created for illustrative purposes only unless other source is provided. All brand names, logos, and trademarks are the property of their respective owners. At the time of publication of this newsletter, the author has no business relationships, affiliations, or conflicts of interest with any of the companies mentioned except as noted. ** OPINIONS ARE PERSONAL AND NOT THOSE OF ANY AFFILIATED ORGANIZATIONS!

Hello again friends and colleagues,

In my ongoing exploration of the Value-Based MedTech (VBMT) model, we've examined how this framework helps technology companies navigate healthcare's ongoing shift toward value-based care—a transformation now encompassing over 50% of Medicare members with federal targets aiming for 100% by 2030.

In our first newsletter in this series, we delved into Pillar 1 (Patient Engagement), discussing how MedTech solutions must deeply integrate with patient experiences to drive meaningful outcomes in value-based arrangements. Next, in Pillar 2 (Provider Engagement), we explored strategies for the critical yet challenging task of integrating technology into clinicians' workflows and provider ecosystems. Most recently, in Pillar 3 (Payer Engagement), we examined how payer relationships and strategic pricing can create sustainable business models aligned with value-based incentives.

Today, we turn to Pillar 4: Data Architecture and Interoperability—the technological foundation that enables all other pillars to function effectively. As the healthcare ecosystem becomes increasingly interconnected, MedTech companies must design their solutions with data integration at the core, not as an afterthought. Without robust data architecture and interoperability capabilities, even the most innovative technologies remain isolated, their potential value unrealized in the complex value-based care landscape.

The Evolving Interoperability Landscape

Before diving into implementation strategies, it's essential to understand the current state of healthcare interoperability and the forces reshaping it.

From Information Blocking to Information Flowing

The healthcare data environment has undergone a seismic shift in recent years. Historically, health data remained locked in silos—EHR systems, departmental applications, and standalone devices each held pieces of the patient story but rarely shared them effectively. The 21st Century Cures Act and subsequent ONC regulations have fundamentally altered this landscape by prohibiting information blocking and mandating standardized APIs.

As of 2023, approximately two-thirds of U.S. hospitals reported using HL7 FHIR APIs to enable patient data access, a 12 percentage point increase from the previous year. Major EHR vendors have embraced these standards, with platforms like Epic's "Epic on FHIR" allowing any FHIR-compatible application to connect with their systems and exchange health information, including the full U.S. Core Data for Interoperability dataset.

The implications for MedTech companies are profound: the technical barriers to integration are lowering, while expectations for seamless data exchange are rising. Solutions are no longer judged solely on its core functionality but increasingly on how well it fits into the broader healthcare ecosystem.

Beyond Basic Exchange: The Path to Meaningful Integration

True interoperability extends beyond simple data exchange. The VBMT model recognizes four levels of integration maturity that MedTech companies must navigate:

• Level 1: Foundational Connectivity - At this level, systems can export and import data, but typically through manual processes or basic interfaces. While sufficient for some use cases, this approach lacks the real-time capabilities needed for many value-based care scenarios.
• Level 2: Structured Data Exchange - Here, systems exchange standardized data (like HL7 messages or CCDs) through established interfaces, enabling automated workflows. This level supports basic care coordination but may still involve batch processes rather than real-time exchange.
• Level 3: Semantic Interoperability - At this more advanced level, systems not only exchange data but understand its meaning through standardized terminologies and code sets. This enables sophisticated analytics and decision support across platforms.
• Level 4: Process Interoperability - The highest level involves systems working together seamlessly to support end-to-end clinical and administrative workflows, often through API-driven architectures that allow real-time data flow and collaborative process execution.

The VBMT model emphasizes that MedTech companies must strategically advance through these levels, building capabilities that align with their market positioning and customer needs rather than pursuing interoperability for its own sake.

Building a Value-Based Data Architecture

Creating a data architecture that supports value-based care requires addressing several critical dimensions simultaneously. See Appendix below for some of the established and evolving standards used to build VBMT Data and Interoperability foundation.

Data Quality and Normalization

In value-based care, decisions are only as good as the data that informs them. The VBMT model emphasizes systematic approaches to data quality management across several dimensions:

• Completeness - Data should include all required elements for its intended use. For example, risk stratification algorithms may fail if social determinants data is missing. MedTech platforms should implement validation rules that identify missing elements and implement strategies to address gaps.
• Accuracy - Values should correctly represent the real-world concepts they describe. This requires both technical validation (e.g., checking that values fall within expected ranges) and clinical validation (ensuring the data makes medical sense).
• Consistence - Data should be represented uniformly across sources and over time. This often requires normalization processes that map local codes to standard terminologies and harmonize units of measurement.
• Timeliness - Data must be available when needed for clinical decision-making or administrative processes. In value-based care, delayed data can mean missed intervention opportunities.

The VBMT model advocates for embedding data quality processes throughout the data lifecycle—from validation at the point of entry to ongoing monitoring and remediation of quality issues in aggregated data. 

Enabling Value Measurement Through Data

A fundamental premise of value-based care is that outcomes can be measured and improved. The data architecture must explicitly support this measurement function.

Analytics Infrastructure for Value-Based Care

The VBMT model outlines several key analytics capabilities that MedTech platforms should enable:

• Real-Time Monitoring and Alerting - Value-based care often requires immediate intervention when patients show signs of deterioration or gaps in care emerge. The data architecture should support real-time event processing and notification workflows that can trigger timely interventions.
• Population Health Analytics - Beyond individual patient care, value-based arrangements require understanding and managing population-level outcomes. This requires data architectures that can aggregate and analyze patterns across entire patient panels, identifying trends and opportunities for improvement.
• Risk Stratification and Targeting - Resources in healthcare are always limited, making it crucial to identify which patients would benefit most from intensive interventions. The data architecture should support sophisticated risk stratification algorithms that can process diverse data types to predict future utilization or complications.
• Quality Measurement - Value-based contracts typically include quality metrics that affect payment. The data architecture must capture all the elements needed to calculate these measures and provide transparent, auditable results that can withstand scrutiny from payers.

The VBMT approach recommends building analytics capabilities incrementally, starting with foundational metrics aligned with common value-based contracts and progressively adding more sophisticated capabilities as the platform matures.

Supporting Outcomes-Based Contracting

As explored in Pillar 3, many MedTech companies are moving toward outcomes-based pricing models where payment is linked to demonstrated results. This requires data architecture that can:

• Define and Capture Outcome Measures - The system must clearly define what constitutes success (improved clinical outcomes, reduced utilization, enhanced patient experience) and systematically capture the data needed to measure these outcomes.
• Establish Attribution and Causality - For outcomes-based contracts to work, there must be clarity about which patients are included in measurement and how much of any improvement can be attributed to the technology versus other factors.
• Enable Audit and Verification - Payers will require evidence that reported outcomes are accurate. The data architecture should maintain detailed audit trails and support external validation of results when needed.
• Support Financial Reconciliation - The architecture must connect clinical outcomes to financial systems for reconciliation of value-based payments, ensuring that incentives are properly calculated and distributed.

By designing their data architecture with these capabilities in mind, MedTech companies can position themselves as true risk-sharing partners rather than merely technology vendors.

The Patient as Data Steward

The VBMT model recognizes that patients are increasingly central to data flows in healthcare, both as generators of data through devices and apps and as directors of how their data is shared.

Patient-Directed Exchange

Recent regulatory changes have established the patient's right to access their health data through APIs and share it with applications of their choice. MedTech companies should embrace this shift by:

• Supporting Patient Access APIs Platforms should implement standardized mechanisms for patients to authorize access to their data, typically using OAuth 2.0 and OpenID Connect for authentication.
• Enabling Patient-Mediated Data Sharing Systems should allow patients to aggregate their data from multiple sources and selectively share it with providers, caregivers, or other services according to their preferences.
• Respecting Granular Consent Not all data should be treated equally. Platforms should support granular consent models that allow patients to share some types of data while restricting others, particularly for sensitive information.
• Providing Transparency and Control Patients should have visibility into how their data is being used and the ability to modify permissions or revoke access when desired.

This approach not only complies with evolving regulations but also builds trust with patients, which is essential for sustained engagement with health technologies.

Connecting to the Broader Health Ecosystem

The healthcare data ecosystem extends far beyond traditional clinical settings to include:

• Remote Monitoring and Wearable Devices - These generate continuous streams of patient-generated health data that must be integrated with clinical records.
• Social Service Organizations - These hold valuable information about social determinants of health that can significantly impact outcomes.
• Public Health Agencies - These collect and distribute population-level health information that can inform value-based care strategies.
• Research Networks - These may offer opportunities for MedTech companies to demonstrate value through formal studies while contributing to medical knowledge.

The VBMT model suggests that MedTech companies should design their data architecture with these connections in mind, even if all are not implemented initially. This future-proofing ensures the platform can evolve as the health data ecosystem continues to expand.

Implementation Roadmap for MedTech Companies

Translating these principles into practice requires a systematic approach. The VBMT model provides a staged implementation roadmap that allows companies to build capabilities incrementally while delivering value at each step.

Stage 1: Foundation Building

The initial focus should be on establishing the core technical infrastructure needed for data exchange:

• Standards Adoption - Implement support for key interoperability standards, particularly FHIR R4 for APIs and HL7 v2 for traditional interfaces.
• Security Framework - Develop robust security measures including authentication, authorization, encryption, and audit logging to protect sensitive health data.
• Data Model Alignment - Ensure your internal data model can map to standard terminologies and exchange formats without losing fidelity.
• Basic Integration Patterns - Implement foundational integration capabilities such as API endpoints, interface engine connections, and document exchange.

At this stage, the goal is to demonstrate basic interoperability with key systems that your customers use, establishing your solution as a viable participant in the healthcare ecosystem.

Stage 2: Expanding Connectivity

With foundations in place, the next stage focuses on broadening connections:

• EHR Integration - Develop deeper integration with major EHR systems, potentially through vendor app marketplaces or certified connections.
• Payer Connectivity - Establish data exchange with health plans to support claims-based analytics, quality reporting, and value-based contracting.
• Device Integration - Implement connections to relevant remote monitoring devices, wearables, or other data sources that complement your core solution.
• HIE Participation - Connect with health information exchanges and regional networks to access broader patient data and participate in community-wide initiatives.

This stage expands the reach of your platform, making it more valuable to customers by connecting it to their existing data flows and technology investments.

Stage 3: Enabling Advanced Value

The third stage focuses on leveraging connected data to deliver sophisticated value:

• Advanced Analytics - Implement predictive models, risk stratification, and other analytics that transform raw data into actionable insights.
• Population Health Management - Develop capabilities to manage entire patient populations, identify care gaps, and target interventions for maximum impact.
• Outcomes Measurement - Create robust mechanisms to measure and report on the outcomes that matter in value-based arrangements.
• Workflow Optimization - Use integrated data to streamline clinical and administrative workflows, reducing burden while improving decision quality.

At this stage, your platform becomes not just a connected system but a generator of value that directly supports your customers' success in value-based care arrangements.

Stage 4: Ecosystem Leadership

The final stage positions the VBMT company as a leader in the health data ecosystem:

• Innovation Partnerships - Collaborate with health systems, payers, and other technology companies to develop novel approaches to value-based care.
• Standards Advancement - Participate in standards development organizations to shape the future of healthcare interoperability.
• Research Collaboration - Partner with academic institutions to demonstrate and publish the value of your approach, building credibility with evidence.
• Market Expansion - Leverage your interoperability foundation to enter new markets or address additional use cases beyond your initial focus.

This stage is about maximizing the strategic value of your interoperability investments, using them not just for technical integration but as a platform for business growth and market leadership.

Measuring Success in Interoperability

As with all aspects of the VBMT model, interoperability efforts should be guided by clear metrics that demonstrate progress and value.

Technical Metrics

These measure the functioning of the interoperability infrastructure itself:

• Connection Volume - The number of active integrations with external systems (EHRs, devices, etc.)
• Data Throughput - The volume of data flowing through integration channels, indicating utilization
• System Performance - Response times, uptime, and other technical indicators of reliability
• Error Rates - The frequency of failed transactions or data quality issues requiring remediation

These metrics help ensure that the technical foundation is solid and capable of supporting business objectives.

Business Impact Metrics

These connect interoperability to tangible business outcomes:

• Implementation Efficiency - Time and resources required to connect new customers or partners
• Customer Retention - Whether interoperability capabilities contribute to long-term customer relationships
• Market Access - Ability to participate in preferred vendor programs or meet certification requirements
• Revenue Impact - Direct financial benefits from interoperability capabilities, such as new markets entered or premium pricing achieved

These metrics demonstrate the return on investment from interoperability initiatives, justifying continued resource allocation.

Value-Based Care Metrics

These measure how interoperability supports value-based care objectives:

• Care Coordination Effectiveness - Improvements in transitions of care, referral management, or other coordination activities
• Quality Measure Performance - Impact on standard quality measures used in value-based contracts
• Utilization Patterns - Changes in healthcare utilization (hospitalizations, ED visits, etc.) attributed to improved data flow
• Patient Outcomes - Improvements in clinical outcomes linked to better data availability and use

These metrics connect interoperability directly to the ultimate goal of improving healthcare value through better outcomes at lower cost.

Conclusion: Interoperability as Strategic Differentiator

As we've explored in this examination of Pillar 4, data architecture and interoperability are no longer merely technical considerations for MedTech companies—they are strategic imperatives that can determine success or failure in the value-based care landscape.

The VBMT model provides a comprehensive framework for approaching these challenges, emphasizing that interoperability should be:

• Standards-based to ensure compatibility with the broader healthcare ecosystem
• Scalable across different healthcare settings and organizational capabilities
• Patient-centered in its approach to data access and control
• Outcomes-focused in its support for measurement and improvement
• Strategically implemented through a staged approach that delivers value at each step

MedTech companies that embrace this framework position themselves not just as technology providers but as essential partners in their customers' value-based care journey. They overcome the traditional barriers of data silos and fragmentation to enable truly integrated care delivery that benefits providers, payers, and most importantly, patients.

In our next installment, we'll explore Pillar 5 of the VBMT model: Governance and Compliance. We'll examine how MedTech companies can navigate the complex regulatory landscape of healthcare while maintaining the agility needed for innovation in the rapidly evolving value-based care environment.

As always, my goal remains helping MedTech companies navigate the complex value-based care transformation successfully. With our new service structure offering multiple entry points—from self-directed implementation packages to comprehensive solutions—I'm committed to making the VBMT model accessible to innovators at every stage of development.

If you enjoyed today's newsletter, please Like, Comment, and Share.

See you next week,

Sam

APPNEDIX

Standards-Based Foundation

The cornerstone of effective data architecture is adherence to healthcare data standards. These standards have evolved significantly, with FHIR (Fast Healthcare Interoperability Resources) emerging as the dominant API approach alongside established standards like HL7 v2 for messaging.

A comprehensive VBMT data architecture incorporates multiple standards layers:

Content Standards These define what data means through standardized terminologies and code sets:

• Clinical terminologies (SNOMED CT, LOINC, RxNorm)
• Administrative codes (ICD-10, CPT)
• Social determinants of health vocabularies (emerging through initiatives like the Gravity Project)

Exchange Standards These define how data moves between systems:

• FHIR for API-based exchange
• HL7 v2 for traditional messaging workflows
• CCDA for document exchange
• DICOM for imaging

Security Standards These ensure data is protected during exchange:

• OAuth 2.0 and OpenID Connect for authorization
• SMART on FHIR for application security
• TLS/HTTPS for transport security

The VBMT approach recommends that MedTech companies standardize on FHIR R4 for new development while maintaining compatibility with legacy standards where needed to support existing workflows.

Integration Patterns for Different Healthcare Settings

One size does not fit all when it comes to integration approaches. The VBMT model recognizes that MedTech companies must support diverse integration patterns based on the capabilities and constraints of their healthcare partners.

Large Health Systems These organizations typically have sophisticated IT departments and enterprise integration platforms. The VBMT approach recommends:

• Supporting both FHIR APIs and traditional HL7 interfaces
• Integrating with enterprise data warehouses and analytics platforms
• Offering robust bulk data capabilities for population management
• Providing detailed technical documentation and implementation support

Small and Independent Practices These settings often have limited IT resources and rely heavily on their EHR vendor. Successful integration here requires:

• Leveraging EHR vendor app marketplaces (like Epic's App Orchard or Cerner Code)
• Offering "plug and play" implementation with minimal local configuration
• Supporting simpler integration options like Direct secure messaging
• Providing turnkey solutions that don't require dedicated IT support

Rural and Underserved Settings These environments face unique challenges, including limited connectivity and infrastructure. The VBMT approach includes:

• Supporting offline capabilities with store-and-forward synchronization
• Minimizing bandwidth requirements through edge computing
• Offering cellular connectivity options for critical functions
• Designing for resilience in intermittent connectivity environments

The VBMT model emphasizes that MedTech companies should develop flexible integration architectures that can adapt to these diverse settings rather than expecting all customers to conform to a single approach.