The Strategic Divergence and Convergence of C2B2C and B2B2C Business Models in HealthTech: A Data Ownership Analysis

Nelson Advisors
Sep 27
17 min read

HealthTech C2B2C Business Models Explored

Executive Summary: The Strategic Divergence of C2B2C and B2B2C in HealthTech

The digital health ecosystem is witnessing the commercial maturation of intermediated business models, yet a critical distinction must be drawn between distribution channels and value origination. The business-to-business-to-consumer (B2B2C) model, characterised by partnerships between HealthTech companies and enterprise payers (employers, insurers), has become the default go-to-market strategy, primarily due to its ability to scale adoption and mitigate high customer acquisition costs (CAC).

In contrast, the Consumer-to-Business-to-Consumer (C2B2C) model operates on the principle that the consumer (C1) is the source of the primary transactional asset: Patient-Generated Health Data (PGHD).While current HealthTech success stories are financially structured as B2B2C, their long-term strategic value is derived from this underlying C1-to-B data flow. The challenge inherent in C2B2C, unlike models in physical goods like automotive or electronics, is that the asset (health data) lacks an established, universally accepted valuation mechanism and carries immense regulatory risk.

Expert analysis concludes that pure C2B2C is currently subsumed within the B2B2C distribution channel. Strategic investment should prioritise platforms that combine the scale and reliability of B2B2C revenue streams (enterprise contracts) with C2B2C architectural principles, such as verifiable, decentralised data governance and explicit consent management. This fusion is necessary to future-proof operations against evolving data ownership regulations and maximise the monetisation potential of patient outcomes data.

Foundational Definitions and Market Context

Delineating C2B2C, B2B2C, and D2C in HealthTech

Understanding the landscape of digital health requires a precise definition of the primary operational models, which are differentiated by who pays, who benefits, and, crucially, who owns the data.

The Direct-to-Consumer (D2C) model involves HealthTech companies selling subscriptions or services directly to individual patients or consumers. Classic examples include consumer wellness applications like Peloton or the Calm app. Historically, D2C models were highly utilised by early care delivery startups but eventually faced market saturation in digital marketing channels, leading to high and unsustainable Customer Acquisition Costs (CACs).

The Business-to-Business (B2B) model involves companies selling products or software used exclusively by staff or organisations, such as providers or insurers. An example is a company that sells specialised hospital billing systems or claims-processing software to insurers, where the end user is internal staff rather than the patient.

The B2B2C (The Dominant HealthTech Model) structure is defined by the HealthTech startup (B1) partnering with an intermediary organisation (B2), typically an employer, a self-insured health plan, or an insurer, to deliver services to the ultimate user (C). The B2 entity is the paying customer, providing the service as part of health coverage or workplace perks. Headspace Care, which offers mindfulness programs through corporate wellness benefits, and Maven Clinic, which distributes its FemTech solutions through employer contracts, are prominent examples.

The C2B2C (The Data-Driven Model) structure, by definition, places the Consumer (C1) as the originator of the core asset. In established non-healthcare markets like electronics, the business (B) acts as a trusted intermediary, providing a margin by valuing and transacting a physical asset (C1 to C2). In HealthTech, the asset is the sensitive, regulated patient-generated health data (PGHD). The consumer provides this data (C1-to-B), which the business aggregates and licenses to a receiving party (C2/B2), such as a researcher, provider, or payer.

An examination of the market reveals a paradoxical situation: while firms like Maven Clinic achieve strong financial success, reporting $268 Million in Annual Recurring Revenue (ARR) with 98% client retention through B2B2C contracts, the long-term, scalable strategic value is rooted in the continuous collection and analysis of usage data and clinical outcomes generated by the C1-to-B flow.Therefore, the commercial model is B2B2C, but the data engine is fundamentally C2B, creating a necessary convergence point between distribution and data architecture.

Why B2B2C Became the Default Go-to-Market Strategy

The prevailing B2B2C model is a direct consequence of addressing the systemic failures and limitations inherent in D2C models. The primary driver for the shift was the struggle of D2C startups with high and unsustainable CACs stemming from saturated digital channels.

B2B2C allows companies to acquire members at lower costs and position themselves for value-based arrangements. This model leverages the vast distribution potential of employer-sponsored coverage, which reaches approximately 153 Million people in the US. Furthermore, a large portion of this market is self-funded (63% of covered workers in 2024, including 79% at large firms), creating a substantial, accessible channel for digital health solutions offered as benefits.

The adoption hurdle associated with consumers being cautious about sharing personal medical data with new digital health tools is significantly mitigated in the B2B2C context. Employees often already trust their employer or payer, reducing the barrier of paying out-of-pocket and increasing adoption rates. Startups gain immediate access to a ready user base, valuable usage data, and reliable revenue streams, ensuring a more sustainable and scalable business structure than the D2C approach.

The Unique Value Chain of C2B2C: Consumer Data as the Origin of Transactional Value

The theoretical C2B2C model hinges on the premise that consumer data is the core transactional asset. The foundation for this model is the demonstrated willingness of consumers to share their health information. Survey data indicates that 63% of consumers are quite willing to share their personal health data if the value proposition is clear, such as ensuring their medical care is the highest quality possible.

This willingness is being accelerated by regulatory and technological forces that are increasingly requiring data to be released from closed, centralised ecosystems. This shift transfers control to the consumer, allowing them to choose with whom and when they share their information.This regulatory impetus directly enables the C1-to-B link, transforming patient data from a static record managed by institutions into a dynamic, transferable asset.

However, a notable gap exists between the conceptual C2B2C model and current operational reality: while C2B2C platforms are centred on C1 data ownership, successful B2B2C models primarily collect this C1 data and monetise the aggregated results for the B2 partner’s benefit (eg, improved workforce health or reduced long-term costs). The overarching challenge for pure C2B2C evolution is creating mechanisms that translate C1 control and contribution into direct economic value for the individual, beyond merely providing better quality care.

The Dominant Paradigm: Analysing the B2B2C Model

Stakeholder Value Proposition Analysis

The success of the B2B2C model lies in its ability to deliver synergistic value to all three key stakeholders: the HealthTech startup (B1), the employer/payer (B2), and the end-user (C).

For Startups (B1), the primary benefit is the combination of scale and financial stability. B1 gains immediate access to a ready user base and reliable revenue streams. This minimises the high customer acquisition costs inherent in D2C and provides large volumes of valuable real-world usage data needed to test features and validate efficacy.

For Employers/Payers (B2), the value proposition centers on risk management and human capital strategy. These partnerships demonstrably improve workforce health, reduce long-term healthcare costs and enhance employee satisfaction. For large corporations like AT&T, extending benefits such as those provided by Maven Clinic to 125,000 employees serves as both a talent strategy and a market advantage, aiding retention and culture.

For End Users (C), the model provides access to specialised, comprehensive virtual care solutions, such as femtech services across fertility, maternity, and menopause, often at no direct cost to the individual, delivered as a workplace perk. This access provides benefits like personalised care navigation and unlimited telehealth visits, removing financial and logistical barriers common in traditional care settings.

Revenue Streams and Financial Metrics

The financial engine of the B2B2C model is driven largely by enterprise contracts, offering predictability that supports high growth valuations.

The most common and strategically preferred revenue model is Subscription or Per Member Per Month (PMPM) billing. This model generates predictable Annual Recurring Revenue (ARR), which is foundational for valuation and stable growth. Maven Clinic exemplifies this stability, achieving high client retention rates (98%) and significant financial scaling.

A secondary, but emerging, model is Usage-Based Billing, seen in startups offering AI medical scribe services or platforms that charge based on the volume of data processed, such as documents stored or care directives generated. While usage-based billing appeals to consumers who prioritise transparency, it introduces forecasting difficulties for the business due to its lack of predictability compared to flat subscription fees.

Furthermore, B2B2C companies are increasingly monetising Clinical Outcomes Data. Companies extract clinical data showing measurable impact, such as faster time to conception for fertility patients and use this validated data to negotiate contracts with employers or fertility benefit companies.This approach transforms the cost of the service into an investment, as B2 partners have a direct vested interest in adopting solutions that help reduce overall treatment costs, such as decreasing the need for multiple expensive IVF cycles. This conversion of C1-generated outcomes data into B2 cost savings forms the critical financial link between the consumer's activity and the business's profitability.

Critical Challenges in B2B2C Adoption: Enrolment Marketing and Engagement Hurdles

Despite the high-value enterprise contracts, the B2B2C model is not immune to consumer-facing challenges. Securing the enterprise contract is only the first hurdle; the second is achieving high enrolment and engagement among the "covered lives". This transition creates an Enrolment Chasm where the burden shifts from traditional D2C marketing spend to specialised enrolment marketing and patient acquisition strategies.

Crucial to overcoming this is seamless Integration Necessity. For enterprise adoption to be streamlined, the digital health solution must integrate cleanly with existing HR platforms and benefits portals, such as Workday or ADP. This infrastructural integration is essential for verifying eligibility and streamlining the user onboarding process.

Finally, the Messaging Duality required for B2B2C creates complex communications challenges. The company must simultaneously craft messaging that appeals to HR leaders, focused on outcomes, quantifiable ROI and cost reduction, while addressing busy employees with clear, tangible value from day one and firm assurances of privacy by design.

The table below provides a comparative overview of the core digital health business models, highlighting how C2B2C is strategically positioned as a value originator, contrasting with B2B2C’s strength as a distribution mechanism.

Comparison of Core Digital Health Business Models

Model	Primary Payer	Primary User/Beneficiary	Core Value Origin	Key Scalability Challenge
Direct-to-Consumer (D2C)	Consumer (Individual)	Consumer	Service Access, Personal Wellness	High Customer Acquisition Cost (CAC)
Business-to-Business (B2B)	Enterprise (Hospital, Insurer)	Enterprise Staff/Internal Systems	Efficiency, Claims Processing	Lack of Direct User Engagement
B2B2C (Employer-Driven)	Enterprise (Employer, Payer)	Employee/Member	Distribution Scale, Reduced Adoption Barriers	Enrollment and Ongoing Engagement
C2B2C (Data-Driven)	Intermediary Business (B) or Researcher	C2 (Recipient) / Researcher (B2)	Patient-Generated Health Data (PGHD)	Trust, Regulatory Compliance, Interoperability

The Evolution to Patient-Centric Value: C2B2C Architecture

PGHD and the Consumer Data Explosion: Shifting Data Ownership

Historically, the healthcare industry has been characterised by data-centric accumulation, where patient health information was fragmented and isolated within the centralised, siloed systems of individual health institutions. A patient might hold disparate records in Hospital A and Hospital B, with no immediate means for a provider in Hospital C to access both, limiting transparency and quality of care.

The current industry transformation emphasises a shift toward patient centric personalised care, leveraging advanced data analysis and Artificial Intelligence (AI) to transform large datasets into actionable, tailored insights. This movement recognises that maximising the potential of data requires prioritising the patient's needs. Empowering patients with 24/7 access to their records facilitates continuous monitoring, simplifies chronic disease management, reduces medical errors associated with paper records, and enables patients to make more informed decisions about their conditions. This control is the vital mechanism allowing C1 to initiate the C2B transaction.

The Role of Decentralisation (Web3 and Blockchain) in Empowering C1

For the C2B2C model to achieve its promise of patient-controlled data, new architectural foundations are necessary. The emerging era of Web3, the decentralised web, offers a transformative approach, particularly in how patient data are governed, accessed, and valued.

Decentralisation, often facilitated by blockchain technology, is positioned as the architectural solution to enhance patient data access, control, privacy, and value. This technology directly addresses the problems of siloed data storage and centralised points of failure, which are susceptible to breaches. Platforms based on this concept, such as MediLinker, have been developed to provide decentralised health information management, improving interoperability and data protection.

The deployment of decentralised technology serves as the trust enabler required for sensitive health transactions. In a C2B2C model, the intermediary business (B) must provide an ironclad guarantee that it handles the sensitive asset (PGHD) with integrity. Decentralization offers an immutable, verifiable mechanism for demonstrating data control and explicit consent to the consumer (C1). This fundamental technological shift redefines the role of the "B" entity from a mere centralised data collector to a compliant governance and transaction layer, solving the trust deficit inherent in traditional centralised data systems.

Monetisation Strategies for Patient-Generated Data

Monetisation of PGHD within the intermediated health ecosystem generally follows two pathways. The current dominant approach is the B2B Data Licensing Model, where the HealthTech platform (B) aggregates, anonymises, and analyses clinical outcomes and usage data, licensing these valuable insights to external partners (B2). These B2 partners include pharmaceutical researchers, payers, and employers seeking to validate health intervention effectiveness or identify new risk cohorts. This strategy allows the business to extract value from C1's data on a large scale while shielding the enterprise client from the complexity of individual consent management.

The anticipated Future C1-to-C2/B2 Payment Models involve mechanisms that directly compensate C1 for their data contribution. This model is underpinned by regulatory shifts, such as the proposed European Health Data Space, which introduces permit-based systems through Health Data Access Bodies (HDABs) allowing for the secondary use of data for research, algorithm training, and evaluation. These systems could eventually allow usage-based payment models where the consumer (C1) is rewarded directly or indirectly for granting permission for their anonymised data to be used by researchers (C2/B2), fully realising the economic potential of the C2B2C framework.

Technology, Interoperability and Infrastructure Requirements

Mandatory Integration with Enterprise Ecosystems

The operational success of scaling digital health solutions relies on mandatory integration across multiple enterprise systems. For the dominant B2B2C channel, seamless integration with human resources (HR) systems, such as Workday and ADP, is non-negotiable for streamlining member acquisition, ensuring eligibility validation, and simplifying the onboarding journey.

Beyond HR integration, there is a powerful strategic necessity for integrating with Payer-Provider Collaboration platforms. Collaborative relationships between payers and providers significantly enhance efficiency and effectiveness in the healthcare system. Utilising a joint payer-provider platform facilitates real-time data sharing, reduces administrative "red tape" associated with specialist referrals, and enables both parties to prioritise preventative care initiatives.

This convergence creates a major opportunity for C2B2C models. These joint payer-provider platforms form a compliant, high-value "B" layer. A C2B solution can integrate PGHD (C1) directly into these joint decision-making platforms (P-P) for optimised care delivery (C2/B2), effectively creating a C2B2P-P model that leverages existing enterprise infrastructure for maximized clinical and financial impact. Rewarding quality treatment over high service volumes becomes possible when data flows are streamlined and aligned between these parties.

FHIR Adoption: Opportunities for Seamless Data Exchange and Implementation Challenges

Fast Healthcare Interoperability Resources (FHIR) stands as the essential standard for achieving seamless data exchange in modern HealthTech. FHIR adoption ensures cleaner data flows, interoperability with wearables, Electronic Health Records (EHRs), and third-party services, allowing the C2B solution to function effectively within diverse and fragmented digital ecosystems. Tighter integration with existing HR stacks is also supported by standards like FHIR, enhancing personalisation powered by machine learning (ML).

However, the transition to FHIR is fraught with implementation challenges:

Steep Learning Curve: FHIR's resource-based model requires significant technical expertise and development time, challenging teams without specialised prior experience.
Increased Development Costs: The complexity mandates sophisticated development and testing procedures, leading to increased costs and longer implementation timelines.
Interoperability and Data Mapping Friction: Despite its purpose, FHIR's resource model can still introduce interoperability challenges, as different systems may interpret resources inconsistently. The complex structure requires extensive, time-consuming, and error-prone data mapping and transformation.

Building Scalable, Secure and Patient-Centric Digital Platforms

The technology infrastructure must be designed for both security and massive scale. Scalable architectures are mandatory to handle potential sudden spikes in usage typical of the B2B2C mass adoption channel.Furthermore, a mobile-first, accessible design and intuitive onboarding process are fundamental product instincts necessary to drive high adoption and engagement.

Security is the foundational layer upon which trust and compliance are built. Secure cloud infrastructure that meets stringent regulatory requirements like HIPAA and GDPR is essential, mandating strong encryption, precise access controls, and immutable audit logs to protect sensitive health data.

While patient-facing platforms offer advantages, such as monitoring and controlling patient records and 24/7 access to information, they also introduce specific disadvantages. Patients may experience

information overload and struggle to interpret or use the vast amount of clinical data provided. Moreover, for patients who lack access to technology or are uncomfortable with the internet, the digital divide can limit access. Most significantly, if proper security measures are not meticulously maintained, patient portals that store sensitive information pose a constant risk of data breaches and security concerns.

Technical Interoperability Requirements for Platform Success

Category	Specific Requirement/Standard	Strategic Purpose	Associated Challenge
Data Exchange	FHIR (Fast Healthcare Interoperability Resources)	Standardised, real-time data flows between systems (EHRs, Payers)	Steep learning curve, increased development complexity, data mapping
Enterprise Integration	API integration with HR systems (e.g., Workday, ADP)	Critical for seamless B2B2C adoption, onboarding, and eligibility validation	Ensuring long-term maintenance and compliance of third-party APIs
Patient Control	Blockchain/Decentralised Architecture	Enhancing patient data ownership, privacy, and verifiable consent for C1-to-B trust	Nascent technology, scaling hurdles, regulatory uncertainty in monetisation
Data Analytics	Machine Learning (ML), Visualization Tools (Looker, Tableau)	Transforming raw C1 usage data into actionable outcomes (ROI) for B2 (employers)	Ensuring data privacy is maintained during ML processing and reporting

Regulatory and Ethical Imperatives for Data-Driven Models

Global Compliance Requirements: Detailed Comparison of HIPAA vs. GDPR

Scaling an intermediated HealthTech model requires navigating complex, and often contradictory, global compliance frameworks. The two most critical are the U.S. Health Insurance Portability and Accountability Act (HIPAA) and the European Union’s General Data Protection Regulation (GDPR).

HIPAA is specific to the U.S., focusing solely on the privacy and security of Protected Health Information (PHI). It traditionally allows for implied consent for standard healthcare operations.

GDPR, in contrast, applies globally to any organisation handling the personal data of EU residents. Its scope is significantly broader, encompassing all personal data, including health information, names, and IP addresses.GDPR mandates explicit, granular consent for data use, emphasises data minimisation, and requires breach notification within 72 hours.

Crucially, for any platform aiming for global scalability, GDPR effectively establishes the minimum standard for privacy and consent. The penalties for non-compliance are severe up to €20 million or 4% of global annual revenue dramatically outweighing HIPAA’s capped fine structure ($1.5 million per violation per year). This financial exposure forces digital health companies to adopt a "privacy by design" approach globally, ensuring stricter privacy controls from the earliest stages of product development.

Achieving Legal Patient Consent Management (The Single Source of Truth)

The C2B2C model fundamentally relies on the C1 entity willingly and knowingly authorizing the transaction of their data. This necessitates an evolution beyond traditional implied consent systems toward mandatory explicit consent, particularly in light of GDPR's stringent requirements.

To manage these complex permissions across multiple stakeholders, platforms, and geographies, sophisticated Consent Management Platforms (CMPs) are required. Tools that connect consent preferences across third-party suppliers, channels, and stakeholders, creating a real-time, "single source of truth" for patient preferences, are essential for maintaining adherence to regulations like HIPAA and GDPR.

Furthermore, regulatory bodies are actively creating mechanisms to standardise the secondary use of health data. Initiatives like the European Health Data Space, operating via new Health Data Access Bodies (HDABs), aim to create a legitimate, permit-based system for sharing health data for purposes such as scientific research, training AI algorithms, and evaluating platform efficacy. This regulatory standardisation is vital for professionalising the C2B data market.

Mitigating Data Risks and Liability in Decentralised Systems

Even with advanced security, centralised patient data systems carry inherent risks, including the vulnerability of data breaches. Decentralisation aims to mitigate this by eliminating centralised points of failure through blockchain technology.

However, empowering C1 with greater access introduces new forms of liability. Increased patient access to complex or fragmented health information can lead to unnecessary patient concerns regarding their diagnosis or treatment plan, and platforms may face malpractice liability concerns related to data accuracy, interpretation of records, or remote monitoring functionality.

To ensure trust, privacy by design must be a non-negotiable core principle of the product architecture.This entails the mandatory use of robust security features, including encryption, rigorous access controls, and auditable logging, that comply with global data protection mandates.

Comparative Analysis of Core Regulatory Frameworks

Feature	HIPAA (USA)	GDPR (EU/UK)	Implication for C2B2C HealthTech
Scope of Data	Protected Health Information (PHI)	All Personal Data (including health data, IP addresses)	Global platforms must adopt the broader, more restrictive GDPR scope.
Consent Standard	Implied consent often sufficient for care operations	Requires Explicit, Granular Consent for processing and secondary use	C2B link (data acquisition) demands a robust CMP ensuring explicit C1 authorization.
Breach Notification	Generally 60 days	Mandated notification within 72 hours	Stricter operational urgency and liability exposure under GDPR.
Maximum Penalties	Up to $1.5M per violation per year (Capped)	Up to €20M or 4% of Global Annual Revenue (Uncapped based on revenue)	Non-compliance risk is exponentially higher for globally scaling enterprises.

Strategic Outlook and Recommendations

Competitive Strategy: Leveraging C2B2C Data Assets to Gain B2B2C Traction

The strategic path for digital health ventures involves accepting the B2B2C model as the necessary distribution channel while simultaneously developing the underlying C2B model as the source of sustainable competitive advantage. Success is measured by the ability to generate and leverage clinical outcomes data derived from patient usage (C1).

Startups must utilise the high volume of C1 usage data to prove measurable impact and ROI to B2 partners (employers/insurers). High engagement data, such as Maven Clinic’s 95% enrolment rate at Snap Inc. and high average provider visits per member, serves as the primary sales pitch, validating the efficacy and uptake of the solution.The most successful strategy is not to compete with the dominant B2B2C channels, but to integrate C2B architectural principles, focused on verifiable consent and data control, into the B2B2C platform, enhancing compliance credibility and delivering superior outcomes reporting to the enterprise client.

Future Trends: Payer-Provider Collaboration and Joint Platforms

The industry trend toward value-based care is driving increasingly strong Payer-Provider Collaboration.Joint payer-provider platforms that facilitate real-time data sharing and align incentives away from high service volumes toward quality outcomes are crucial for preventative care and systemic efficiency.

This dynamic suggests that the most effective and efficient future models will align the C2B data origin with this joint enterprise infrastructure. This model, potentially described as C2B2P-P (Consumer -> HealthTech Business -> Payer/Provider Joint Platform), leverages the scale and compliant nature of the Payer-Provider environment for seamless clinical integration and decision support. By channeling PGHD through this robust intermediary layer, companies can achieve both market scale and deep clinical impact, accelerating the shift toward personalized medicine driven by real-world evidence.

Actionable Recommendations for Investment and Platform Development

Based on the analysis of regulatory risks, market adoption dynamics, and technological requirements, the following strategic actions are recommended for investing in or developing HealthTech platforms:

Prioritise Trust Infrastructure and Consent Management: Investment must focus heavily on developing or adopting robust, auditable Consent Management Platforms (CMPs). These systems must function as the single source of truth for granular patient preferences (C1), mitigating the existential regulatory risk posed by GDPR and HIPAA non-compliance. This infrastructure is the foundation of the C2B relationship.
Mandate a Structured FHIR Maturity Roadmap: While FHIR implementation presents technical complexity (steep learning curve, high cost) , it is essential for long-term survival. Development resources should adopt a phased approach, prioritising FHIR capabilities necessary for high-value data exchange with payers and providers, which is crucial for achieving alignment in value-based care and facilitating the C2B2P-P model.
Pilot Decentralised Governance Models: Allocate research and development capital to pilot decentralised architectures (e.g., blockchain/Web3). This investment should focus specifically on managing patient consent, data access permissions, and immutable audit trails, rather than attempting full EHR replacement. This positions the company as a leader in patient-centric control, establishing a significant long-term competitive moat built on verifiable trust.
Enforce Enrolment and Engagement Expertise: Recognising that B2B2C success hinges on consumer adoption, companies must require and invest in specialised enrolment marketing expertise.The technical excellence of the solution is rendered irrelevant if the target user base (C) is not effectively reached, acquired, and engaged through seamless integration with enterprise systems.

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