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Unique and proprietary healthcare biomarker datasets are crucial for developing advanced AI and ML models that can discover new biomarkers, enhance diagnostics and create personalised treatment plans. These datasets provide a competitive advantage by offering specific, high-quality data that public datasets often lack, allowing for more precise and effective model training.Why Proprietary Datasets are a Game Changer While public datasets like those from the Alzheimer's Disease Neuroimaging Initiative (ADNI) and The Cancer Genome Atlas (TCGA) are valuable for foundational research, proprietary datasets offer a distinct advantage. These unique data sets are often gathered through specific clinical trials, collaborations, or advanced lab testing, providing a more detailed and controlled view of a particular disease or patient population.Key characteristics of these valuable datasets include:Multimodal data: Integrating various data types like genomics (e.g., DNA, RNA), proteomics (e.g., proteins, peptides), metabolomics (e.g., metabolites), and medical imaging (e.g., MRI, CT scans) provides a holistic view of a patient's health.Longitudinal data: Datasets that track patients over extended periods offer insights into disease progression and treatment response, which is crucial for developing prognostic and predictive biomarkers.High-quality and curated data: Unlike raw data, which can be messy and inconsistent, proprietary datasets are often meticulously cleaned, harmonized, and annotated. This "AI-ready" data is essential for training robust and accurate models.Unique patient populations: These datasets can focus on rare diseases, specific demographics, or patients with particular genetic profiles, enabling the development of highly specialized AI models.Applications in Healthcare AIUsing unique biomarker datasets, AI and ML algorithms can unlock new possibilities in several healthcare domains.Biomarker Discovery: AI models can analyze complex, high-dimensional data to identify novel biomarkers that might be undetectable to human researchers. For example, machine learning algorithms can analyze patterns in wearable device data to detect early signs of cardiovascular problems.Personalised Medicine: By integrating genomic, clinical, and environmental data, AI can create a comprehensive patient profile. This allows for the selection of the most effective therapy and dosage for an individual, minimizing the "trial-and-error" approach to treatment.Diagnostics and Prognostics: AI-powered models trained on specific biomarker data can improve the accuracy of disease diagnosis and predict a patient's likely outcome. This helps clinicians make more informed decisions and plan timely interventions.Drug Discovery and Clinical Trials: AI can use biomarker data to simulate drug interactions, identify suitable patient cohorts for clinical trials, and predict the potential success of new drug candidates. This streamlines the drug development process, making it faster and more cost-effective.Email <a href="mailto:lloyd@nelsonadvisors.co.uk">lloyd@nelsonadvisors.co.uk</a> to discuss further.

Healthcare biomarker datasets enhancing AI models and ML algorithms

The intersection of private equity continuation funds and the healthcare technology sector is a particularly dynamic trend in 2025. This area is seeing significant activity driven by both the unique characteristics of health tech and the broader market forces shaping the private equity landscape.Here are the key trends for private equity continuation funds in healthcare technology in 2025:1. A Solution for Extended Value Creation in Complex MarketsHealthcare technology assets often require longer investment horizons than typical private equity investments. This is due to the time it takes to navigate:Regulatory Hurdles: Gaining approvals from bodies like the FDA and ensuring compliance with regulations such as HIPAA is a lengthy and complex process.Reimbursement Models: Establishing and scaling a profitable business model often depends on securing favorable reimbursement from private insurers and government programs (e.g., Medicare and Medicaid), which can take several years.Implementation and Adoption Cycles: Selling to large hospital systems or healthcare providers involves long sales cycles and slow integration processes.Continuation funds are a perfect fit for this scenario. They allow a GP to "hold and grow" a high-performing health tech company beyond the typical 10-year fund life. This gives the GP and the management team the necessary time to mature the company, secure regulatory approvals, and achieve a critical mass of adoption, ultimately positioning it for a more lucrative exit.2. Focus on "Crown Jewel" Digital Health AssetsPrivate equity firms are not using continuation funds to exit underperforming health tech companies. Instead, the trend is to move "crown jewel" assets into a new vehicle. These are companies with:Strong, recurring revenue models: Particularly those with a Software-as-a-Service (SaaS) component.Proven clinical efficacy: The technology has demonstrated a positive impact on patient outcomes or operational efficiency.Scalable platforms: The technology can be easily expanded to new markets or customer segments.Substantial growth potential: The company is well-positioned to capitalize on mega-trends like aging populations, telehealth, and value-based care.The continuation fund provides a fresh capital injection to fund strategic initiatives, such as tuck-in acquisitions to expand a company's offerings or to accelerate international expansion, further enhancing its value.3. AI and Data Analytics as Key DriversThe use of artificial intelligence (AI) and data analytics is revolutionizing healthcare, and this is a significant driver of continuation fund activity. PE firms are using these vehicles to back health tech companies that are:Developing AI-powered solutions: These could be for drug discovery, diagnostic tools, or administrative workflow automation.Leveraging large datasets: Companies that can effectively use patient data to improve care delivery or personalize treatments are highly sought after.Paving the way for new reimbursement pathways: Proposed legislation, such as the "Health Tech Investment Act," which aims to create a stable Medicare payment pathway for algorithm-based services, is de-risking investment in this space and making these companies more attractive for long-term holds. 4. Increased Scrutiny and Evolving GovernanceAs private equity's role in healthcare becomes more prominent, so does the scrutiny from regulators and the public. This is a crucial factor shaping continuation fund deals in 2025.State-level oversight: Several states are passing or considering legislation to regulate PE deals in healthcare, particularly those involving a "Management Services Organization" (MSO) model. This adds a layer of complexity and risk that needs to be carefully managed in a continuation fund's structure.Antitrust and fraud enforcement: Federal agencies are ramping up their enforcement actions against anti-competitive practices and healthcare fraud. GPs must ensure their health tech portfolio companies have robust compliance programs to mitigate these risks.Transparency and fairness: The established trend of using independent third-party valuations and advisory committees to ensure fair pricing and a smooth process for LPs cashing out is even more critical in the sensitive healthcare sector.In summary, continuation funds in healthcare technology are a powerful tool for private equity in 2025. They provide a mechanism to maximize the value of high-potential assets in a sector characterized by long development cycles and complex regulatory landscapes, while also addressing the growing need for liquidity and distributions to LPs. The focus remains on quality assets, particularly those leveraging AI and data, but the transactions are increasingly structured with heightened attention to governance and regulatory compliance.

Private Equity continuation funds trend in Healthcare Technology in 2025

Nelson Advisors: One of Europe's Leading Healthcare Technology Mergers and Acquisitions, Partnerships and Investment Advisory Firms

Nelson Advisors partners Lloyd Price and Paul Hemings interviewed by Mergermarket for their 'AI fuels European medtech M&amp;A despite regulatory uncertainty' story (<a href="https://mergermarket.ionanalytics.com/content/1004331406?source=news" data-mce-href="https://mergermarket.ionanalytics.com/content/1004331406?source=news">https://mergermarket.ionanalytics.com/content/1004331406?source=news</a>) Europe’s medtech M&amp;A activity is making a comeback as buyers clamour for artificial intelligence (AI) assets amid regulatory fragmentation and uncertainty.“With AI as the enabler, there’s a feeling companies don’t want to get left behind. You can’t sit on your laurels,” Lloyd Price, partner at the European healthtech M&amp;A advisor Nelson Advisors, told Mergermarket.One crucial consideration is the EU’s recent Medical Device Regulation and In Vitro Diagnostic Regulation, Paul Hemings, also partner at NelsonAdvisors, said. These measures have tightened requirements for clinical evidence, documentation, and post-market surveillance compared to before, often making development more costly and slower, he said.Despite the challenges, European medtech is attractive for M&amp;A as healthcare is seen as more resilient against macroeconomic headwinds compared to other industries, Price said.Sector end customers, which include insurers and public health systems, are generally reliable to work with, and M&amp;A is often the best way to enter the rigid world of procurement of equipment by national healthcare services, Price added.Cardiovascular disease therapies, robotics and AI drive dealmakingOne of the top killers in the world, cardiovascular disease is one of the hottest therapeutic areas for medtech M&amp;A, Price said, adding that wearables and remote monitoring devices are booming with an increasing focus on prevention.Another high-interest area is the use of robotics in pharmacies and remote surgery, Price said.The pipeline of expected deals in this space includes UK remote surgery developer CMR Surgical, which has reportedly engaged advisers to manage a potential sale of the business.The main buyers in Europe’s medtech scene include strategics such as Medtronic, Johnson &amp; Johnson, and Philips, which aim to diversify their core revenue streams, Hemings said. Private equity (PE) firms active in the space include Gilde Healthcare and Apposite Capital, he added.AI-powered startups have also become a big focus for investors, accounting for 65% of total equity funding in 1H25, even though only 40% of global healthcare startups currently deploy AI, according to Hemings.Strategics, including imaging specialists Siemens and Philips, have an appetite for AI to enhance image analysis and clinical workflows, Price said. Other buyers are tempted by the potential of AI to speed up the processing of notes and recordings in a clinical environment, he said.One recent example of the increasing AI focus is the buyout of the UK ophthalmology company Optegra by the French eyewear company EssilorLuxottica in May this year as part of a mission to power its procedures with AI, Hemings said.Meanwhile, PE buyers have previously aimed to merge companies into AI-based platforms or acquire next- generation software-as-a-service (SaaS) platforms, Hemings said.String of pearls approach amid tougher regulationsLarge buyers in medtech are increasingly branching out from big deals with late-stage companies to a series of smaller acquisitions in a “string of pearls” strategy, Hemings said.Examples include the US giant Johnson &amp; Johnson MedTech’s acquisitions of Abiomed (US), Laminar (US), Shockwave Medical (US) and V-Wave (Israel) in 2022, 2023, 2024 and 2024, respectively, according to a report by Nelson Advisors.This trend reflects a desire to avoid the risks of a huge buyout, including the costs of integration, rising staffing costs, uncertainties from fluctuating tariff policies, and regulatory fragmentation between different geographies, Price said.While the EU is attempting to streamline its regulatory and reimbursement processes, this has not yet been achieved and overseas players are thinking twice about operating in Europe, Pashazadeh said.However, these changes also drive M&amp;A demand for targets with expertise and a proven track record in navigating Europe’s regulatory environment, Price said.“At the end of the day, we're not really in the technology business; we're really in the business of behaviour change and trust,” he said.by Jonathan Smith with analytics by Kunal Samnani, Mergermarket<a href="https://info.mergermarket.com">https://info.mergermarket.com﻿</a>

Nelson Advisors partners interviewed by Mergermarket - 'AI fuels European MedTech M&A despite regulatory uncertainty'

When venture capitalists (VCs) evaluate healthcare startups, the concept of Total Addressable Market (TAM) is crucial, but it's often viewed through a specific lens tailored to the complexities of the healthcare industry. It's not just about a large number; it's about a "VC-backable" TAM, meaning it's big enough to generate the outsized returns VCs seek and the startup has a credible path to capturing a significant portion of it.Here's a breakdown of how VCs approach TAM in healthcare:1. The "Why" Behind TAM for VCs:Scalability and Growth Potential: VCs invest for exponential growth. A large TAM indicates that a company has the potential to scale significantly and achieve substantial revenue, providing a strong return on their investment.De-risking the Investment: A well-researched TAM demonstrates that the founders understand their market, the problem they're solving, and the potential demand for their solution. It reduces the perceived risk.Exit Opportunities: A large and growing market makes a company more attractive for future acquisition by larger players or for an IPO, which are typical VC exit strategies. VCs want to see a path to a liquidity event.Strategic Fit: TAM helps VCs assess if the startup fits within their fund's investment thesis and focus areas (e.g., digital health, biotech, medical devices, health tech platforms). 2. Key Considerations for a VC-Backable TAM in Healthcare:Beyond the "Billion Dollar" Number: While a "$1B+ TAM" is often cited as a general rule of thumb, VCs are less interested in a loosely calculated, massive number and more in a credible and actionable TAM.The "TAM, SAM, SOM" Framework is Critical:TAM (Total Addressable Market): The absolute maximum revenue opportunity if your product or service captured 100% of the entire market demand. For healthcare, this might be the total spend on a certain condition, a specific type of medical procedure, or the entire budget of a relevant healthcare segment (e.g., all hospitals in the US).SAM (Serviceable Addressable Market): The portion of the TAM that your product/service can realistically serve given its specific features, target geography, regulatory approvals, and business model. This is where healthcare's nuances become very important (e.g., does your solution apply to all hospitals or just large academic medical centers? Is it reimbursed in all states/countries?).SOM (Serviceable Obtainable Market): The portion of the SAM that your company can realistically capture in the short to medium term (e.g., 3-5 years) given your resources, go-to-market strategy, and competitive landscape.6 This is essentially your realistic revenue projection.VCs scrutinize the logic and assumptions behind the progression from TAM to SAM to SOM.Healthcare-Specific Nuances for TAM Calculation:Stakeholder Complexity: Healthcare is a multi-stakeholder ecosystem (patients, providers, payers, pharmaceutical companies, device manufacturers, regulators).7 A healthcare TAM needs to clearly define who pays, who benefits, and how value flows. Simply multiplying "number of patients with X condition" by "average cost of treatment" is often too simplistic.Reimbursement Models: This is paramount. Is your solution reimbursed by insurance? If so, what are the codes? What's the typical reimbursement rate? This significantly impacts the actual revenue opportunity. A large "clinical need" doesn't automatically translate to a large "market" if there's no clear path to payment.Regulatory Pathways: FDA, CE Mark, etc., introduce significant time and cost. The TAM must account for these hurdles and the time it takes to gain market access. Behavioural Change &amp; Adoption: Healthcare is notoriously slow to adopt new technologies.8 VCs want to see how you'll overcome inertia and drive adoption among busy clinicians or entrenched systems.Fragmented Market: Many healthcare markets are highly fragmented.9 While this can present opportunities for consolidation, it also impacts go-to-market strategies and the speed of market penetration.Data Availability and Quality: Accurate healthcare market data can be challenging to obtain. VCs look for startups that have leveraged credible sources (e.g., government statistics, industry reports from reputable firms like Definitive Healthcare, IQVIA, Gartner, etc., or even proprietary research). Methods for Calculating TAM (and how VCs prefer them):Bottom-Up (Preferred): This involves estimating the number of potential customers (e.g., hospitals, clinics, specific patient populations) and multiplying by your average revenue per customer (or annual contract value - ACV).10 This is generally seen as more credible because it's grounded in specific, realistic assumptions about your product/service and pricing.Top-Down: Starting with broad industry reports and then narrowing down to your specific segment. While useful for initial context, VCs will want to see it validated by a bottom-up approach.Value Theory: Estimating TAM based on the value your product provides to customers and what they would be willing to pay.11 This is more abstract but can be powerful for truly innovative solutions creating new markets.3. What VCs Look For in a TAM Presentation:Clarity and Specificity: No vague generalizations. Define your target customer precisely.Data-Backed Assumptions: Every number should have a credible source or a logical, explainable assumption behind it. Transparency about assumptions is key.Logical Progression: A clear, defensible narrative from the broad TAM down to your achievable SOM.Understanding of Market Dynamics: Demonstrate knowledge of competitors, market trends, regulatory landscape, and customer pain points.Realistic Projections: VCs are wary of "hockey stick" growth projections without a strong foundation.12The SOM should align with your team, resources, and go-to-market strategy.Defendability: How will you capture and maintain market share? What are your competitive advantages?In essence, a VC-backable TAM in healthcare isn't just about size; it's about demonstrating a deep understanding of the market, a credible strategy to penetrate it, and a clear path to generating significant, defensible revenue within that market.

How Venture Capitalists approach Total Addressable Market models in Healthcare Technology

The integration of Artificial Intelligence (AI) in healthcare has made Return on Investment (ROI) a critical factor in the relationship between health systems and vendors. While AI holds immense potential to improve patient outcomes and operational efficiency, healthcare organizations are under pressure to justify these significant investments with clear, measurable returns.Here's a breakdown of why ROI has become so important and the key considerations for both health systems and vendors:The Growing Importance of ROI Financial Pressures: Healthcare systems operate on tight margins and are constantly looking for ways to cut costs and increase revenue. AI solutions often come with substantial upfront costs for software, hardware, and integration. As such, health systems need to see a clear business case and financial return to justify the expenditure.Demonstrating Value: Unlike some other industries, the value of AI in healthcare isn't always purely financial. It can be measured in improved patient outcomes, better clinician satisfaction, or increased operational efficiency. However, a traditional ROI model focused solely on cost savings may not capture this full value. This has led to the need for a more holistic approach that considers both tangible and intangible benefits.Vendor Accountability: Health systems are becoming more discerning in their vendor selection. They expect vendors to provide a clear and evidence-based framework for how their AI solution will deliver value. Vague claims of "improved efficiency" are no longer sufficient; vendors must be able to demonstrate a clear path to ROI with specific, measurable metrics. The "Return on Influence" (ROI): A new concept of "Return on Influence" is emerging, which expands the definition of ROI beyond just financial gains. It focuses on the non-monetary impacts of AI that shape stakeholder behavior and system outcomes. This includes things like improved patient retention, reduced clinician burnout, and increased trust in the technology. Key Metrics for Measuring ROITo effectively measure the ROI of AI in healthcare, both health systems and vendors need to move beyond simple cost-benefit analyses. A comprehensive approach includes a mix of financial, operational, and clinical metrics.Financial Metrics:Cost Savings: Reductions in administrative costs (e.g., automated scheduling, billing, and documentation), reduced readmission rates, and optimized resource allocation. Revenue Growth: Increased revenue from improved risk-adjustment coding, better patient retention, and more efficient patient throughput. Total Cost of Ownership: A full accounting of all costs, including initial acquisition, implementation, staff training, and ongoing maintenance. Operational Metrics:Efficiency and Productivity: Metrics like average handle time for patient inquiries, time saved on administrative tasks (e.g., using AI scribes), and optimized use of operating rooms and other assets. Workflow Integration: How seamlessly the AI solution fits into existing clinical and operational workflows, and the rate of adoption by staff. Clinical Metrics:Patient Outcomes: Improvements in diagnostic accuracy, reduced readmission rates, and better patient adherence to treatment plans. Patient and Staff Satisfaction: Measures of patient satisfaction with care and staff satisfaction with their work environment (e.g., reduced burnout). Challenges in Measuring ROI for AI Despite the clear need for ROI, there are significant challenges in measuring it accurately:Delayed and Complex Returns: The benefits of AI, especially for long-term population health initiatives, can take years to materialize. This conflicts with the short-term ROI expectations often held by healthcare leaders. Attribution Issues: It can be difficult to isolate the impact of a single AI tool in a complex healthcare system with many interacting variables. If patient outcomes improve, it's hard to definitively say whether it was due to the AI tool, a new medication, or other factors. Data Quality and Integration: AI systems rely on high-quality, integrated data. Many healthcare organizations still have fragmented data across incompatible systems, which can make it difficult to establish a baseline and measure improvements. Intangible Benefits: Quantifying the value of things like improved patient trust or reduced clinician burnout is a challenge, yet these are crucial components of a successful AI implementation. In conclusion, the focus on ROI is transforming the relationship between health systems and AI vendors. It is shifting the conversation from a focus on the technology's potential to a data-driven discussion about measurable value. This dynamic is pushing both parties to collaborate more closely, define clear success metrics upfront, and build a strong business case for every AI investment.

Nelson Advisors

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Healthcare biomarker datasets enhancing AI models and ML ...

Private Equity continuation funds trend in Healthcare Tec...

Nelson Advisors: One of Europe's Leading Healthcare Techn...

Nelson Advisors partners interviewed by Mergermarket - 'A...

How Venture Capitalists approach Total Addressable Market...

The integration of AI in healthcare has made ROI a critic...