Proprietary Health Data is the new M&A Currency

Nelson Advisors
10 hours ago
14 min read

The Data Primacy Shift: Proprietary Health Datasets as the Sovereign Currency in Healthcare M&A

The global healthcare ecosystem is currently traversing a structural inflection point where the traditional metrics of enterprise value, physical infrastructure, patient volume, and legacy software interfaces, are being systematically superseded by the strategic accumulation and utilisation of proprietary health data. In the current mergers and acquisitions landscape of 2024 and 2025, data has transitioned from a passive byproduct of clinical operations into a primary sovereign currency.

This shift is predicated on the realisation that generic public data is fundamentally insufficient for the development of high-performance healthcare artificial intelligence tools. Such generic datasets frequently lack the clinical context, longitudinal depth and rigorous outcome labeling required to move AI from impressive laboratory demonstrations to reliable performance in real-world clinical settings.

Consequently, the market is witnessing a profound "flight to quality," where buyers prioritise assets capable of medical record unification and structured extraction, recognising that a company possessing unified, AI-ready datasets is inherently more valuable than one with a sophisticated user interface but brittle underlying information architecture.

The Structural Imperative for High-Fidelity Clinical Data

The urgency driving the valuation of proprietary datasets is rooted in the deepening crisis of healthcare productivity, often characterised by the divergence between spending and outcomes. Global healthcare systems are approaching a structural breaking point where expenditure continues to outpace clinical efficacy, and innovation productivity in the pharmaceutical sector is in a state of precipitous decline.

This phenomenon, colloquially known as Eroom’s Law, the inverse of Moore’s Law, illustrates that the number of FDA-approved drugs produced per one billion dollars in R&D spending has collapsed from more than 65 in 1955 to a mere 0.5 in 2024.

This deterioration reflects a broken economic model characterized by astronomical upfront costs and diminishing returns. Investors and strategic buyers increasingly believe that the integration of digitalisation, advanced data models and proprietary AI can reverse this trend, reshaping drug development and unlocking unprecedented operational efficiencies.

Innovation Metric	1955 Status	2024 Status
FDA-approved drugs per $1B R&D	> 65	~0.5
Known Diseases vs. Available Cures	18,000 Known	3,900 Cured
US Healthcare Spending (% of GDP)	< 6%	18%
Medical Data Structure	Analog/Narrative	80% Unstructured Digital

This structural demand for data is further amplified by the performance of the health technology sector in the public and private markets. After a two-year drought, the IPO window reopened in 2024 and 2025 with the emergence of "Health Tech 2.0" companies. Unlike the unprofitable, hype-driven businesses of the 2020-2021 cycle, this new cohort is characterised by robust unit economics and mission-critical data assets.

Companies like Waystar and Tempus, followed by Hinge Health and Caris Life Sciences, have demonstrated that the public markets respond favourably to businesses that leverage data to drive growth and margin expansion simultaneously.

For example, Hinge Health, which entered the public market in May 2025 at a $2.6 Billion valuation, showcased a "Rule of 40" performance of 98%, a metric that measures the sum of annualised revenue growth and free cash flow margin.

Defining the Five Traits of Proprietary Data Value

The strategic value of proprietary health data is derived from five specific traits that distinguish it from the commoditised information available in the public domain. These traits, exclusivity, scale, quality, depth and usability, collectively determine the "defensibility" of a data asset in an AI-driven market. Proprietary health data encompasses information that a company has collected, organised and can lawfully utilise in ways that competitors cannot easily match, including patient outcomes, imaging libraries, claims patterns, remote monitoring signals and real-world evidence (RWE) tied to treatment response.

Exclusivity and the Creation of Strategic Moats

Exclusivity is the primary driver of the "data moat." In an era where generic large language models are widely accessible, the ability to control unique, hard-to-copy datasets provides a significant competitive advantage. This exclusivity is often found in specialised clinical domains, such as diagnostic imaging or rare disease registries, where the cost of data acquisition is prohibitive.

For instance, Stanford University’s Center for Artificial Intelligence in Medical Imaging curated a repository of over 223,000 unique pairs of radiology reports and chest X-rays; such datasets are licensed at substantial annual fees, reflecting their scarcity. In the automotive and media sectors, companies like BMW and Reddit have similarly moved to monetise their unique data streams, charging significant premiums for API access to training data for AI models.

In healthcare, this translates to pharmaceutical companies aggressively acquiring "Data & Evidence" platforms to accelerate clinical trials, representing a shift from general digital health toward a high-value "TechBio" paradigm.

Scale as a Prerequisite for Model Robustness

Scale is a necessary, though not sufficient, condition for high-value health data. AI models, particularly those based on deep learning and neural architectures, require vast quantities of information to identify the subtle patterns and correlations that govern clinical outcomes.

However, the 2025 M&A market has seen a shift from "volume for volume’s sake" to "volume of relevant data." While 80% of medical data remains unstructured and untapped, the most sought-after assets are those that have aggregated information across diverse patient populations to ensure that AI models are generalisable and free from local biases.

The "Industrialisation" of health AI in 2026 implies a move away from fragmented pilots toward enterprise-wide solutions that can only be supported by large-scale, unified datasets.

Quality and Clinical Trustworthiness

The quality of a dataset, often defined by its cleanliness, accuracy and clinical fidelity is the trait that determines whether an AI model can be trusted in a high-stakes medical environment. High-quality data must be "hallucination-free" and clinically specific.

In the M&A context, quality is verified through rigorous data governance and provenance tracking.If the source data is biased or riddled with artifacts, the resulting AI replicas can amplify inequities and create self-reinforcing feedback loops that degrade trust. Consequently, datasets that have undergone structured extraction and are validated against rigorous quality frameworks, such as Flatiron Health's "VALID" framework, command premium valuations.

Depth and the Longitudinal Patient Journey

Longitudinal depth is perhaps the most transformative trait of proprietary data, as it allows for the examination of temporal patterns rather than static snapshots. Traditional healthcare has frequently relied on isolated data points, a single high blood sugar reading or one-time imaging, which can be misleading when viewed out of context.

Proprietary datasets that track a patient’s journey over years or decades enable the discovery of predictive biomarkers and the refinement of clinical guidelines. This is particularly critical in chronic disease research, where conditions like cardiovascular disease and diabetes evolve dynamically. By showing the chronological order of risk factors and outcomes, longitudinal studies make it possible to distinguish causes from consequences, a foundational step in personalised medicine.

Usability and the Activation of Data Assets

Usability refers to the ease with which data can be integrated into clinical products and decision tools. A company that has already solved the challenges of medical record unification and structured extraction is far more attractive to buyers than one with a flashy interface but weak underlying data.

Usability is often facilitated by standardisation to protocols such as HL7 FHIR and SMART on FHIR, ensuring seamless connectivity with existing health IT ecosystems. Companies like Reducto and Abridge focus on the "last mile" of data performance, turning complex documents, clinical notes, claims and regulatory forms, into structured, citation-grounded data that can power advanced AI features and production-ready pipelines.

M&A Market Dynamics: The 2024-2025 Resurgence

The M&A landscape for healthcare technology has experienced a notable acceleration in 2024 and 2025, driven by a clear thesis that AI drives both growth and margin expansion. Global healthcare private equity deal value reached a record $190 Billion in 2025, a spike driven by large-scale transactions exceeding $1 Billion.

Investors have increasingly focused on areas such as analytics, workforce optimisation, and platform solutions, with health IT deal value in the provider segment doubling in a single year to an estimated $32 Billion.

Valuation Multiples and the Flight to Quality

As of December 2025, the market has stabilised into a "flight to quality" environment, where valuations are heavily bifurcated based on sub-sector and profitability profiles. Premium AI and data assets, particularly those with proprietary algorithms and clean datasets for drug discovery or imaging, command revenue multiples of 6.0x to 8.0x+.

In contrast, unprofitable or early-stage startups with high burn rates are seeing significant valuation compression, trading at multiples of 3.0x to 4.0x.

HealthTech Sub-Sector	EV/Revenue Multiple (Dec 2025)	Growth Profile
Premium AI & Data	6.0x – 8.0x+	High; Defensible moats and proprietary moats.
Value-Based Care	5.5x – 7.0x	Moderate; High ROI for payers and risk models.
Hybrid Telehealth	5.0x – 7.0x	Mature; Integrated virtual and in-person care.
General SaaS	4.0x – 6.0x	Average; Growing with standard retention.
Unprofitable/High Burn	3.0x – 4.0x	Low; Seeing compression or distressed exits.

The average revenue multiple for AI M&A deals across all sectors in 2025 reached 25.8x, reflecting the extreme premium placed on high-growth companies that prioritize expansion over immediate profitability. However, for mature, profitable software firms with EBITDA margins exceeding 20%, multiples typically range from 10x to 14x EBITDA. These "Rule of 40" companies remain the most sought-after targets for strategic acquirers and private equity firms looking for stability and cash flow.

Strategic Buyers and the Shift to "TechBio"

Pharmaceutical companies have emerged as aggressive acquirers of data and evidence platforms, seeking to transition from traditional "Digital Health" to more specialised "TechBio" capabilities. These acquisitions are often designed to speed up clinical trials and drug discovery by integrating proprietary datasets into the R&D workflow.

For instance, MSD's £7.5 billion acquisition of Verona Pharma and Novartis's acquisition of Avidity Biosciences reflect a strategic move toward securing novel mechanisms and platform technologies that address unmet medical needs.

Private equity activity has also surged, with firms driving platform strategies and multiple arbitrage through "buy-and-build" models in specialties like behavioral health and ophthalmology. In 2025, approximately 75% of the top ten transactions were private equity deals, particularly in revenue-cycle management and back-office platforms where data-driven tools offer immediate operational intelligence and revenue integrity.

The Role of AI in Financial Due Diligence

AI is not only a target of M&A but also a fundamental catalyst for the financial due diligence process itself. It allows investors to analyze complex datasets, uncover hidden risks, and identify opportunities for value creation that traditional manual reviews might miss.

By analysing 100% of claim and denial data, including digital data transmission files like 837 and 835 logs, AI can pinpoint systematic inefficiencies and detect coding misalignments that cause significant revenue leakage.

Financial Lever	Traditional Analysis Impact	AI-Powered Due Diligence Impact
Revenue Integrity	3-5% patient charges written off.	Recovers 20-30% of write-offs (0.5% revenue).
Denial Rate	Limited sample review.	Reduces denial rate by ≥1% via predictive analysis.
EBITDA Quality	Subjective forecasting.	0.3-0.4% EBITDA improvement from denial reduction.
Decision Precision	Historical benchmarks.	Real-time patterns and hidden margin trends.

This advanced financial decision-making provides a roadmap for leaders to build a defensible, differentiated strategy. It turns "hidden inefficiencies into value-creation opportunities" by identifying problem areas before and after the deal closes. Investors prioritise targets with specialised expertise in diagnostics, clinical trial acceleration, or patient engagement, provided those targets can deliver scalable and reliable AI solutions.

Technical Foundations: The Challenge of EHR Unification

A critical barrier to creating AI-ready datasets is the fragmentation and "messiness" of electronic health record (EHR) data. Health data is often scattered across multiple source systems, with some large health systems managing more than ten different EHRs from nearly twenty disparate vendors. The historical purpose of EHR software was clinical documentation and billing, not secondary research or AI training, which has led to data that is "messy, incomplete, and heterogeneous".

The Extraction and Preparation Workflow

The process of transforming raw EHR data into a structured format involves an Extract, Transform, and Load (ETL) process. Standards like the Observational Medical Outcomes Partnership Common Data Model (OMOP CDM) have been developed to facilitate terminology consistency for research purposes. However, over 40 distinct challenges have been identified during the data extraction and preparation stages, categorised into cohort definition, outcome definition, feature engineering, and data cleaning.

Data Challenge	Frequency of Occurrence	Remedy/Remediation
Unstructured Text	High (80% of data)	NLP and Reasoning LLMs (e.g., o1-mini).
Alphanumerical in Numerical	High	Regex-based data cleaning.
Inconsistent Timestamps	High	SQL-based date/time functions.
Scattered Records	High	Application rationalization and integration.
Legacy System Knowledge	Increasing Risk	Experienced data management partners.

Breakthroughs in Medical Document Parsing

The "last mile" of performance in healthcare AI depends on the ability to parse complex medical documents with extreme accuracy. Companies like Reducto have developed agentic OCR and HIPAA-compliant pipelines that can recover text and structure from low-quality scans and faxes while maintaining context.

This is essential for processing physician notes, pathology reports, and consent documents that were previously "underutilised" due to the labor-intensive nature of manual extraction. In clinical trials, the use of reasoning-based LLMs like OpenAI's o1-mini has shown promise in extracting structured details from unstructured dossiers, revealing insights into events like heart failure hospitalisations that were previously hidden in narrative formats.

Longitudinal Modeling: Capturing the Temporal Patient Journey

The transition from cross-sectional snapshots to longitudinal analysis is essential for truly personalized medicine. Cross-sectional studies provide valuable snapshots but miss the dynamic nature of disease progression, while longitudinal studies collect data from the same individuals over time, revealing patterns that transform clinical understanding.

Deep Learning for Temporal Sequences

Recurrent Neural Networks (RNNs), specifically Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) models, are particularly effective for modeling longitudinal patient trajectories. Their "contextual memory" allows them to span time and handle sequential dependencies in clinical data, such as lab values, vital signs, and administered treatments. In multi centre clinical cohorts, these temporal architectures have demonstrated significant performance gains in predicting adverse outcomes like mortality and readmission compared to traditional models like the Cox proportional hazards model, which often fail to exploit time-dependent trends.

Model Architecture	Core Advantage	Primary Healthcare Task
LSTM / GRU	Handles sequential/time-variant dependencies.	Risk stratification for readmission/mortality.
Transformer	Identifies important segments in sequences.	Multi-modal clinical journey modeling.
BiLSTM + Attention	Accesses past and future context at each step.	Real-time forecasting of clinical deterioration.
TCN (ConvNet)	Efficient training on long-term dependencies.	Disease classification and temporal patterns.

The longitudinal modeling of serial biomarkers in blood has been shown to outperform single-threshold methods in cancer screening, highlighting the necessity of capturing the disease’s evolution. However, significant challenges remain, including participant attrition, comorbidity confounding, and the high technological infrastructure demands of secure, longitudinal data storage.

The Strategic Moat: Federated Learning vs. Data Centralisation

As the value of proprietary datasets increases, the difficulty of centralizing sensitive medical data due to privacy regulations and "data silos" has led to the rise of Federated Learning (FL). FL is a distributed machine learning framework that allows institutions to collaboratively train models without ever sharing the raw, patient-level data.

Architecture and Advantages of Federated Learning

In a federated model, the training occurs locally on each institution’s data (e.g., within a hospital’s own server), and only the encrypted model updates, such as gradients or weights are sent to a central coordinator.

This approach fundamentally addresses the "High Wall of Data Privacy" by keeping raw data local and secure. It enables multiple organisations to build stronger, more generalisable models across diverse populations and different medical scanners, which is particularly valuable for rare disease research where no single institution has sufficient data.

Feature	Centralized Data	Federated Learning
Data Location	Aggregated in a single data center.	Decentralized at the source (devices/hospitals).
Privacy Risk	High; concentrated data increases attack surface.	Low; raw data never leaves the institution.
Regulatory (GDPR)	Difficult; requires complex data transfers.	Easier; complies with data sovereignty rules.
Communication	High bandwidth for data transfers.	High overhead for update synchronization.
Scalability	Limited by centralization costs.	High; works across edge and diverse institutions.

The Economic Impact of Federated Learning

The adoption of federated learning is transforming the valuation of data silos. Instead of a single company needing to own all the data to build a dominant model, FL allows for "collaborative modeling" without sharing customer-level identifiers.

The global federated learning in healthcare market is projected to reach $141 Million by 2034, with applications ranging from drug discovery to remote patient monitoring. Major technology players like NVIDIA, Microsoft, and Google are already providing the infrastructure for these FL systems, facilitating breakthroughs in cancer diagnosis and COVID-19 detection.

Case Studies in Data-Driven Valuation: Flatiron and Truveta

The most prominent market leaders are those that have successfully built large-scale, longitudinal, and unified datasets that solve the evidence gaps for the life sciences industry.

Flatiron Health: The "Panoramic" Oncology Benchmark

Flatiron Health, an affiliate of the Roche Group, has redefined oncology research through its "Panoramic" datasets. These datasets unlock Flatiron’s entire patient network, leveraging AI and large language models to extract and validate clinical data from over five million patient records, representing 1.5 Billion data points.

Their new hematology datasets represent a six-fold increase in cohort sizes compared to prior collections, capturing critical details like measurable residual disease (MRD) testing and CAR-T therapy utilisaation. Flatiron’s ability to deliver global real-world data, spanning the US, UK, and Germany allows researchers to analyse outcomes and treatment patterns across markets using a common data model, a level of interoperability previously unavailable in the oncology space.

Truveta: The Multi-Modal Representative Dataset

Truveta, a collaboration between 30 major health systems representing over 120 Million patients, focuses on creating the most representative and complete patient journey data available. Their dataset links EHR data, including clinical notes and images, with closed claims from 200 Million patients.

The "Truveta Genome Project" is a groundbreaking effort to sequence the exomes of ten million volunteers, combining genotypic and phenotypic information at ten times the scale of previous efforts. By using the "Truveta Language Model" a multi-modal AI, to normalise billions of data points, they enable biopharma and academic researchers to develop AI for drug discovery and value-based care optimisation.

Regulatory Safeguards and the Public Interest

The use of proprietary health data is strictly governed by legal and ethical frameworks designed to protect patient privacy and ensure the "public good." In the UK, the NHS and other health care organisations are committed to the principle that they "do not sell data" for profit, but rather operate on a "cost recovery basis" for research and planning purposes.

The European Health Data Space (EHDS)

The EHDS is the most significant structural driver for health technology investment in 2026, mandating that data holders make electronic health data available for secondary use. This has effectively created a new asset class: "Curated Clinical Data."

Startups that provide the "picks and shovels" for this economy, anonymisation engines, synthetic data generators and federated learning platforms, are commanding premium valuations as they enable the "industrialisation" of health data while respecting sovereignty.

Synthetic Data: The Fidelity Debate

Synthetic healthcare data mimics the statistical properties of real data while protecting individual identities, offering a solution to privacy risks and legal constraints. However, synthetic data faces criticism for its "Foundational Pitfalls," including the tendency to mimic the center of a distribution and miss rare "edge cases" or temporal nuances. In high-stakes healthcare, speed without "provenance", the ability to tie a record to a clinician, timestamp, or EHR source, is a liability. Consequently, regulatory bodies like the FDA heavily favour high-quality RWE for drug and device submissions, requiring detailed justification if synthetic data is used.

Data Type	Validation Level	Audit Readiness	Regulatory Standing
Real-World Evidence	High fidelity; captures rare events.	Excellent; tied to source/clinician.	Gold standard for FDA/CMS.
Synthetic Data	Struggles with edge cases/nuance.	Poor; no end-to-end chain of custody.	Requires detailed justification.
De-identified EHR	Good; reflects real practice.	Moderate; depends on tokenization.	Widely used for research.

Conclusion: Data as the Determinative Competitive Moat

The current era of healthcare M&A is defined by the transition of proprietary data from a supporting asset to the central source of competitive advantage. The structural decline in pharmaceutical productivity and the unsustainable rise in global healthcare spending have made the "AI-ready" dataset a strategic necessity. As buyers look beyond "flashy interfaces," they are placing their bets on companies that have mastered the technical and regulatory complexities of medical record unification and structured extraction.

The value of these assets is underpinned by the five pillars of exclusivity, scale, quality, depth, and usability. While federated learning and synthetic data offer new pathways for collaboration and privacy, the primacy of high-fidelity, longitudinal real-world evidence remains unchallenged for clinical validation and regulatory approval.

In the "decisive decade" ahead, the successful integration of data assets into the healthcare value chain will determine the winners in a market that has moved from "growth at all costs" to a rigorous "outcomes-plus-durability" paradigm. For strategic acquirers and financial sponsors alike, the ability to identify, value, and monetize proprietary health data is no longer merely a part of the M&A toolkit. It is the very engine of the new healthcare economy.

Nelson Advisors > European MedTech and HealthTech Investment Banking

Nelson Advisors specialise in Mergers and Acquisitions, Partnerships and Investments for Digital Health, HealthTech, Health IT, Consumer HealthTech, Healthcare Cybersecurity, Healthcare AI companies. www.nelsonadvisors.co.uk

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