HealthTech and MedTech Business Model Transition from SaaS to AGaaS by 2028

Lloyd Price
10 minutes ago
13 min read

The global healthtech and medtech sectors are currently navigating a profound structural realignment, characterised by the obsolescence of traditional Software-as-a-Service (SaaS) models and the rapid ascent of Agent-as-a-Service (AGaaS) architectures.

This transition, accelerated by the "Anthropic Effect" and the subsequent market correction of early 2026, represents more than a technological upgrade; it signifies a fundamental shift in the definition of value within the healthcare ecosystem. As the industry moves toward 2028, the prevailing focus has pivoted from the provision of digital tools to the delivery of autonomous, clinical-grade outcomes. This evolution is rooted in the integration of Large Action Models (LAMs) and agentic AI, which possess the autonomy to reason, plan, and execute complex medical and administrative workflows with minimal human intervention.

The 2026 Inflection Point: The Collapse of Tool-Based Valuation

The transition to agentic platforms was catalyzed by a significant technological shock in early 2026, often identified by industry analysts as the "Claude Cowork Event". This event fundamentally disrupted the traditional SaaS business model by demonstrating that agentic AI could automate high-level cognitive tasks previously reserved for human professionals, thereby rendering seat-based licensing metrics largely obsolete.

Historically, healthtech providers derived value from selling access to interfaces, CRMs, analytics dashboards and EHR overlays. However, the 2026 paradigm shifted the "center of gravity" toward delivering finalized outcomes, such as a resolved insurance claim, a completed radiology report, or a successfully managed patient discharge.

This shift triggered a 30% decline in the North American Tech Software Index by February 2026, as investors realised that traditional software "wrappers" lacked the proprietary data moats necessary to withstand competition from AI-native startups. The valuation of healthtech firms began to decouple from general speculative tech, with a new emphasis placed on "clinical-grade" reliability and deep workflow integration.

The "Rule of 40," which measures the sum of revenue growth and free cash flow margin, became the definitive metric for late-stage funding, with the "HealthTech 2.0" cohort reaching an average score of 65, significantly outperforming broader cloud indices.

Metric	Traditional SaaS (Pre-2026)	Agent-as-a-Service (2026-2028)
Primary Value Unit	Software License (Seat/User)	Resolved Outcome / Task Completion
Pricing Philosophy	Input-based (Usage/Access)	Performance-based (ROI/Clinical Gain)
System Autonomy	Low (Reactive/Rule-based)	High (Proactive/Reasoning-based)
Implementation Focus	Tool Adoption and Training	Workflow Integration and "Fit"
Valuation Moat	Code and UI/UX	Proprietary Data and Regulatory Clearance

The economic implications of this transition are extensive. By converting traditional labor costs into software investments, the Total Addressable Market (TAM) for healthtech has expanded significantly. However, this expansion is constrained by the "compute plateau," where the rising cost of processing power must remain below the marginal cost of human labor for the substitution to remain economically viable.

Technical Foundations: From Generative LLMs to Large Action Models (LAMs)

The architectural shift from SaaS to AGaaS is underpinned by the transition from Large Language Models (LLMs) to Large Action Models (LAMs). While LLMs excel at language comprehension and generation, tasks such as summarising a medical history or drafting a patient message, they are inherently passive. In contrast, LAMs are designed to interpret intent and execute multi-step plans across disparate software environments.

The Architectural Role of Agentic AI

Agentic AI in healthcare operates through a multi-layered framework that facilitates autonomous decision-making. The Perception Layer ingests heterogeneous, multimodal data, ranging from real-time wearable sensor streams to longitudinal EHR records and unifies them through a shared memory architecture. This is followed by the Orchestration Layer, which manages specialized agents to ensure efficient task allocation, such as separating the analysis of diagnostic images from the administrative task of scheduling a follow-up appointment.

The functional mechanism of these systems involves four interrelated phases: Perception, Reasoning/Planning, Action, and Learning. Unlike traditional Robotic Process Automation (RPA), which relies on static, deterministic scripts, agentic systems utilise reinforcement learning to adapt to dynamic clinical environments. This allows the AI to prioritize high-risk patients, adjust personalised treatment plans autonomously, and recognise patterns across medical data without explicit rules for every scenario.

Feature	Large Language Models (LLMs)	Large Action Models (LAMs)
Core Function	Text synthesis and comprehension	Goal execution and task completion
Reasoning	Single-step / Pattern-based	Multi-step / Strategic planning
Integration	Chat interfaces / Knowledge bases	APIs / Robotics / Workflow tools
Healthcare Use Case	Drafting SOAP notes	Adjusting insulin pumps / Automated billing
Resource Demand	High (Compute-heavy)	Optimized for specific task efficiency

The synergy between LLMs and LAMs is what enables the AGaaS model to function effectively. In a clinical setting, an LLM provides the linguistic fluency to communicate with a patient, while a LAM acts as the "muscle" that updates the EHR, orders necessary lab tests, and alerts the specialist. This reduces the cognitive load on clinicians and transitions software from being a "tool" to being a "coworker".

Commercialization Strategy: The Shift from Launch to Fit

As the medtech market becomes more constrained and value-driven, commercialisation strategies are undergoing a fundamental reconfiguration. In 2026, the traditional model of treating product adoption as a "moment in time" (the launch) is being replaced by a focus on "fit". Winning medtech firms are those whose solutions integrate seamlessly into existing care pathways and deliver standardised operational returns across multiple sites.

The Rigor of Value Committees and Procurement

Success in the 2026-2028 period requires medtech teams to move away from static launch plans toward continuously evolving integration strategies shaped by real-world usage data and implementation friction. Value committees and procurement teams have raised the bar for early traction, demanding clinical proof, economic value, and implementation reality be presented as a consistent story. The window for proving momentum has shortened and firms are now backing fewer, larger bets in high-growth therapeutic areas such as pulsed field ablation, structural heart disease, and neuro-modulation.

This "fit-first" approach necessitates a deep understanding of the care pathway, knowing who interacts with the patient, where decisions are made, and how workflows truly change on a day-to-day basis. Medtech companies are increasingly required to generate real-world evidence (RWE) to demonstrate not only clinical effectiveness but also productivity gains that satisfy the financial discipline of hospital C-suites.

Commercial Focus	Traditional Medtech Strategy	2026-2028 Strategic Pivot
Adoption Metric	Initial Surge / Key Account Wins	Sustained Use / Pathway Integration
Evidence Requirement	Regulatory Clearance (FDA/CE)	Value Committee / Coding Alignment
Portfolio Management	Broad Product Lines	High-Growth / High-Margin Focus
Commercial Model	Capital Equipment Sales	Recurring Value / Outcome Models
Team Structure	Sales-Centric	Integrated (Clinical/Technical/Economic)

Furthermore, the migration of procedures from traditional hospital settings to Ambulatory Surgery Centers (ASCs) is reshaping commercial targets. The 2026 CMS Hospital Outpatient Prospective Payment System final rule added over 500 procedures to the ASC Covered Procedures List, including complex cardiac catheter ablation and spine procedures. This shift demands that medtech providers offer technologies that enable high-acuity care to be performed safely and efficiently in these lower-cost, high-throughput settings.

Outcome-Based Pricing: Aligning Incentives in the AGaaS Era

The transition to AGaaS is perhaps most visible in the evolution of pricing models. By 2026, AI customer service and clinical support pricing have shifted dramatically toward outcome-based structures, directly linking payment to measurable business value. This shift addresses the demand for clear ROI and aligns vendor incentives with the objectives of healthcare payers and providers.

Primary Outcome-Based Models in Healthcare AI

Several distinct models have emerged to replace the traditional per-seat or per-interaction structures. These models require robust data analytics and a transparent agreement on baseline metrics.

Deflection Rate Pricing: Compensation is linked to the percentage of tasks (e.g., patient inquiries, prior authorisations) successfully resolved by the AI without human intervention.
Clinical Improvement Pricing: Payment is tied to measurable increases in patient outcomes, such as reduced 30-day readmission rates or improved sepsis detection times.
Resolution Time Reduction: Pricing is structured around the AI’s ability to decrease average handling or processing times, thereby increasing provider throughput.
Revenue Cycle Uplift: Relevant for RCM (Revenue Cycle Management), this model links payment to increased sales volume, coding accuracy, or higher conversion rates in patient enrollment.

Pricing Model	Clinical/Operational KPI	Case Study / Data Point
Readmission Reduction	30-day Heart Failure Readmission	Decline from 27.9% to 23.9%
Sepsis Alerting	Mortality Rate / Length of Stay	39.5% mortality reduction
Administrative Efficiency	Prior Auth Processing Time	Reduced from weeks to minutes
Patient Experience	Interaction Abandonment Rate	85% reduction (Sutter Health)
RCM Optimization	Reimbursement per Clinician	$13,000 increase (St. Luke's)

The implementation of these models requires a collaborative partnership where both parties invest in tracking mechanisms and data transparency. Organisations are encouraged to pilot these models with precisely defined success criteria before broader deployment, ensuring that performance metrics are optimised within their specific clinical or regional context.

Clinical and Administrative Transformation: Case Studies in AGaaS

The practical application of agentic AI is already yielding significant dividends across various healthcare functions. The transition from "testing" to "implementation" is characterised by the themes of scale, value and trust.

Clinical Documentation and Ambient Scribes

One of the most immediate benefits of agentic systems is the reduction of administrative burden on clinicians. Ambient listening tools are being used to create pre-visit patient histories and transcribe clinical notes, saving an average of 20% of documentation time.

Medical LLMs tailored to healthcare are summarising patient visits into structured SOAP notes, reducing after-hours charting time by up to 50%. These systems go beyond transcription by extracting structured data from free-text narratives, which is crucial for quality reporting and accurate reimbursement.

Emergency Triage and Radiology

At institutions like Northwestern Medicine, in-house agentic systems are drafting radiology reports in real-time. These reports are 95% complete and automatically flag life-threatening findings, allowing radiologists to deliver faster, more consistent reporting and enabling earlier detection in emergency settings. Similarly, agentic platforms in the ER have been shown to prioritise cases automatically based on medical history and symptoms, reducing patient wait times by as much as 40%.

Predictive Health and Value-Based Care

Predictive health tools are beginning to replace reactive care models. AI agents that analyse genomics, wearable sensor data and social determinants of health can predict the onset of major diseases, such as Alzheimer's or kidney disease, up to two years earlier than traditional methods with 80% accuracy.

This capability makes value-based care (VBC) more economically viable, as early intervention can occur at one-tenth the cost of acute treatment. Under favorable implementation conditions, AI-enabled VBC has been shown to reduce episode costs by approximately 20% in cases like congestive heart failure.

Administrative Overhead and Prior Authorization

The administrative side of healthcare is perhaps the most ripe for agentic disruption. Autonomous agents are handling prior authorizations, claims, and scheduling 24/7, reducing administrative overhead by 50% and shortening seven-day processing cycles to seven hours. Highmark Health, for instance, uses an AI agent with ambient listening to submit prior authorisation requests in real-time, drastically reducing the friction in care delivery.

Regulatory Evolution: Navigating the AI as a Medical Device (AIaMD) Landscape

As agentic systems move from pilot projects to infrastructural elements, global regulators are racing to update their frameworks. The transition necessitates a shift from regulating "static" software to managing "adaptive" AI throughout its lifecycle.

The FDA’s Risk-Based Approach and "Elsa"

The U.S. FDA remains the primary regulator for AI-enabled devices, with over 1,250 authorized devices as of July 2025.The agency has grounded its oversight in the Total Product Life Cycle (TPLC) approach, assessing devices through design, deployment, and postmarket monitoring. A key development in 2025 was the FDA’s internal deployment of agentic AI capabilities, including the "Elsa" chatbot powered by Anthropic's Claude, to help staff streamline pre-market reviews and post-market surveillance.

The FDA is also focusing on Predetermined Change Control Plans (PCCPs), which allow manufacturers to outline future modifications to AI/ML software without requiring a new 510(k) submission for every update. This is essential for agentic systems that continuously learn and adapt from real-world data.

The UK's AI Airlock and MHRA Change Programme

Post-Brexit, the UK is reassessing its regulatory framework and is likely to introduce a new regime in 2026. The MHRA’s "Software and AI as a Medical Device Change Programme" sets out clear requirements for patient safety and innovation.Central to this effort is the "AI Airlock," a regulatory sandbox that allows developers to trial AIaMD products in a supervised environment. The first pilot phase, which included radiology report generation and oncology pathway support, was successful enough to warrant a second phase for 2025-2026.

Furthermore, to facilitate small-firm innovation, the MHRA has waived fees from January 2026 for micro and small UK firms participating in pilot schemes. The UK also became the first country to join the HealthAI Global Regulatory Network, committed to shaping international standards for responsible AI.

The EU Transparency Shift: MDR and EUDAMED

In the European Union, the regulatory landscape for 2026 is defined by the full implementation of the Medical Device Regulation (MDR) and the upcoming mandatory functionality of the European Database on Medical Devices (EUDAMED).

Starting May 28th, 2026, four key EUDAMED modules will become mandatory, requiring companies to ensure device data is complete and validated. This transparency shift provides buyers with clearer visibility into device status but also increases the risk of litigation from competitors.

Regulatory Body	Key 2026-2027 Milestone	Strategic Implication for AGaaS
FDA (USA)	Final Guidance on PCCPs	Allows for "Adaptive" AI updates without re-filing
MHRA (UK)	AI Airlock Phase 2 Results	Shapes future rules for "safe-to-fail" AI trials
EU (EUDAMED)	Mandatory Modules (May 2026)	Heightened transparency and data validation needs
NICE (UK)	Updated Evidence Standards	Direct requirements for adaptive AI algorithms
FDA (Internal)	Agentic AI "Elsa" Deployment	Streamlined regulatory reviews and surveillance

The Payer Perspective: From Administrative Cost to "Partner for Life"

Payers are currently reimagining their role in the healthcare ecosystem, leveraging AI to evolve into true partners for their members. The goal for many forward-thinking payers is to cut administrative costs by half while doubling the level of service provided.

The Four Phases of Payer Transformation

The reduction of the administrative cost curve is projected to occur in four distinct phases:

Baseline: Addressing the high fixed and variable costs associated with fragmented systems and manual, fax-heavy processes.
Productivity: Streamlining workflows through centralised enrolment and digital sales management.
Adoption: Implementing AI-native platforms and predictive care models to replace manual tasks, shifting variable costs to lower fixed costs.
Maturation: Retiring legacy systems and achieving an end-state where administrative costs are less dependent on scale and more driven by AI-enabled efficiency.

By reaching the maturation phase, payers can use AI to anticipate member needs and guide them through care pathways seamlessly. This includes offering transparent, nearly instantaneous transactions and predictive modeling to help members manage chronic conditions effectively.

Workforce Implications: Empowering the Human-Centric Model

A critical theme for the 2026-2028 period is the empowerment of the healthcare workforce. Technology is increasingly viewed as a tool to support human expertise rather than replace it. In the nursing sector, which faces persistent shortages and burnout, the adoption of generative AI and ambient listening tools is critical for repositioning nursing as a dynamic, technology-supported profession.

Cultural Shifts in Tech Adoption

Successful health systems are those that involve their clinicians in the rollout and evaluation of AI tools. This ensures that the use cases directly support daily workflows and are not viewed as top-down mandates from leadership. As AI agents handle repetitive tasks, healthcare professionals are freed to focus on more complex, high-value activities that require empathy and strategic decision-making.

Furthermore, the emergence of verifiable digital credentials and decentralised identifiers (DIDs) is reimagining clinician mobility. These standards allow for the validation of practitioner expertise across regions, supporting a borderless digital health workforce and facilitating safer AI model training.

Financial Sustainability and Portfolio Re-construction

Health systems are entering 2026 at a pivotal inflection point, with rising medical costs (averaging 7% annually) and capital constraints pushing many toward more volatile financial environments. EBITDA growth is projected to remain steady at 5% through 2027 before accelerating to 10% by 2029 as the full benefits of AI-driven transformation are realized.

High-Growth Segments and M&A Dynamics

Medtech companies are responding by reallocating resources to high-growth market segments through strategic M&A and divestitures. Portfolio reconstruction is increasingly centered on therapeutic areas that align with evolving patient needs and technological capabilities.

Pulsed Field Ablation (PFA): A rapidly growing segment in cardiology.
Structural Heart Disease: A major focus for medtech investment.
Neuromodulation: High-growth therapeutic area with substantial upside.
Health System Tech (HST): Expected to be the fastest-growing healthcare segment as software platforms enable greater efficiency.

Financial Metric	2024-2027 Forecast	2027-2029 Forecast
Healthcare EBITDA Growth	5% annually	10% annually
Medical Cost Inflation	~7% annually	Variable
Pharmacy Spending Growth	~8-9% annually	Driven by GLP-1s/Injectables
HST Segment Growth	Leading Category	Driven by AI/Interoperability
Digital Health Market Size	~$300 Bn (by 2026)	Continued Expansion

The shift toward these high-margin, high-growth segments is often paired with parallel cost-optimisation programs to fund the required capital investments. This involves streamlining operations, consolidating facilities, and reducing overhead to meet shareholder expectations for sustainable growth.

The Convergence of 6G, Robotics and Agentic AI

Looking toward 2027 and 2028, the integration of agentic AI with high-speed, low-latency 6G communication is poised to enable advanced remote care models. Architecture supported by 6G will facilitate coordinated task execution in complex workflows, including remote robotic surgery with enhanced precision and reliability.

Agentic AI’s ability to proactively monitor patient data streams from wearable devices and synchronise them with EHRs in real-time will allow for the autonomous adjustment of personalised treatment plans.

This "closed-loop" system, where the AI perceives, reasons, and acts, represents the pinnacle of the AGaaS model, transforming healthcare from a series of episodic interactions into a continuous, data-enabled journey.

Ethical Governance and Cybersecurity

This increased autonomy brings new challenges in ethical governance, system robustness and security. Multi-agent systems face expanded cybersecurity vulnerabilities that require adaptive security measures, such as zero-trust frameworks and real-time threat detection powered by AI. Regulators and healthcare organisations must also resolve the legal complexities regarding liability for autonomous decision-making.

Conclusion: The Strategic Agenda for 2028

The next 24 months will favour healthcare organisations that can successfully pivot from "tool-based" SaaS to "outcome-based" AGaaS models. This transition is not optional; it is a structural necessity driven by financial pressure, clinician burnout and the rapid maturation of agentic intelligence.

By 2028, the winners in the healthtech and medtech space will be those that have:

Consolidated Technology Portfolios: Prioritizing a small number of priority platforms that deliver the greatest impact and exponential value to care workflows.
Embedded AI into Real-World Workflows: Moving beyond experimental pilots to solutions that are "clinically-grade" and seamlessly integrated into the day-to-day work of providers.
Adopted Outcome-Based Economic Models: Aligning their revenue strategies with the measurable value they provide to patients and payers.
Navigated the Regulatory Shift: Proactively engaging with adaptive AI frameworks like PCCPs and sandboxes like the AI Airlock to ensure continuous innovation.

As the industry moves from the concepts of the "Future of Health" to its execution, the payoff for stakeholders who can align incentives, trust and interoperability around this proactive, agentic model will be substantial.

The technology is now ready; the focus must now turn to the deliberate and responsible integration of these systems into the fabric of human care.

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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