App Platform Data AI: Maximising HealthTech Value in the next 2 years

Nelson Advisors
Aug 18
19 min read

Updated: Aug 19

App Platform Data AI: Maximising HealthTech Value in the next 2 years

1. The Current HealthTech Landscape: A Confluence of Momentum and Opportunity

The healthcare industry is in the midst of a profound transformation, driven by the convergence of digital platforms, a growing abundance of health data, and the analytical power of artificial intelligence (AI). This shift is not merely an incremental improvement but a fundamental re-architecture of care delivery, moving toward models that are more proactive, personalised, and efficient. The market for these technologies is expanding at an unprecedented rate, creating a clear and immediate imperative for organisations to define and execute a strategy for value maximisation.

An analysis of the market's current trajectory reveals a robust and accelerating growth. The global mHealth apps market, which includes a wide range of mobile health applications, was valued at USD $36.68 billion in 2024 and is projected to reach USD $88.70 billion by 2032, exhibiting a compound annual growth rate (CAGR) of 11.8% from 2025 to 2032. This expansion is propelled by rising consumer demand for accurate health monitoring solutions and the increasing prevalence of chronic conditions linked to sedentary lifestyles.Meanwhile, the broader digital health market demonstrates an even more resilient and rapid ascent. Valued at USD $180.2 billion in 2023, the market is projected to grow at a substantial CAGR of 25.0% to reach USD $549.7 billion by 2028. This expansion is significantly driven by the widespread penetration of smartphones, tablets, and other mobile platforms, which serve as the conduits for digital health services. The growth was further accelerated by the COVID-19 pandemic, which spurred high adoption rates for virtual care solutions and shifted both consumer and provider attitudes toward remote health services. North America stands out as a clear market leader, commanding the largest share in both the mHealth apps market (30.56% in 2024) and the overall digital health sector. This regional dominance is attributed to a favourable regulatory environment, the early adoption of advanced technologies, and a high concentration of major HealthTech companies.

A granular examination of the market dynamics reveals a critical strategic nuance. The difference in growth rates between the mHealth apps market (11.8% CAGR) and the broader digital health market (25.0% CAGR) indicates a key strategic evolution. The most significant value is not being created solely in consumer-facing wellness applications, but in the enterprise-level, behind-the-scenes digital health platforms. The higher CAGR for the broader digital health market highlights that the most impactful investment and value creation are occurring in complex areas such as telehealth, remote patient monitoring systems, and software that streamlines clinical and administrative workflows. This suggests that the strategic imperative is to build platforms that serve the entire healthcare ecosystem, including providers, payers, and patients, rather than focusing on a single consumer touchpoint. For organisations aiming to maximise value, the most fruitful path involves developing solutions that integrate deeply into the professional healthcare infrastructure, enabling a more cohesive and data-driven approach to care delivery.

The app platform is emerging as the foundational element upon which this new healthcare ecosystem is built. It is no longer a simple application but a strategic asset that serves as the primary interface for patient and provider engagement, the conduit for collecting real-time health data and the delivery mechanism for AI-powered services. The very performance of this platform is a business-critical concern that extends beyond mere user experience to directly impact clinical outcomes. A glitchy application, a delayed response, or a frozen dashboard can cause serious delays in treatment, a missed medication dose, or a failure to sync a critical biometric reading, each of which can interfere directly with patient care.

A 2024 study published in the Journal of Medical Internet Research found that poor technical performance was a top reason for patients abandoning mobile health apps, which in turn meant their care providers no longer received important real-time data. This disruption can result in a delay of treatment adjustments and potentially poorer health outcomes. This relationship between technical reliability and clinical safety is a defining and distinguishing characteristic of the HealthTech sector. It highlights that in this field, technical debt is not merely a financial liability but a potential clinical and ethical risk, underscoring the necessity of a "performance-first" strategy as a matter of patient safety, not just competitive advantage.

2. Defining and Measuring Value in a Digital-First Ecosystem

Assessing the value of HealthTech solutions requires moving beyond traditional financial metrics to a more comprehensive, multi-dimensional framework. Value in this sector is not a single number but a composite of four critical dimensions: financial return, user satisfaction, clinical effectiveness, and operational performance. This framework provides a pragmatic and holistic method for quantifying success and justifying investments in a complex and rapidly evolving market.

2.1. A Multi-Dimensional Value Framework

The financial dimension remains a crucial component of the value equation. While it can be challenging to measure the return on investment (ROI) for emerging technologies due to a lack of unbiased data, organizations can identify potential financial improvements and track them over time. AI-driven administrative automation, for example, has the potential to save billions of dollars annually by automating tasks such as appointment scheduling, billing, and claims processing. The reduction of hospital readmission rates through patient monitoring solutions is another clear example of direct financial value. New revenue streams can be unlocked through subscription-based models for chronic condition management or by monetizing digital services and data-driven partnerships. For investors and executives, a clear proof of financial ROI is often a prerequisite for a sustained investment.

User satisfaction and experience are equally vital, as perceived value directly influences utilisation. This applies to both patients and healthcare professionals. Effective human-centred design is essential for ensuring an optimal experience, but ongoing measurement through consistent surveys is required to validate that expectations are being met. High patient satisfaction scores are directly correlated with improved clinical outcomes and are a powerful indicator of the program’s efficacy. For instance, hospitals with "excellent" patient ratings have achieved significantly higher net margins than those with "low" ratings, demonstrating a clear link between patient experience and business success. A fast and stable application, as previously noted, is not just about convenience; it is fundamental to user engagement, and a poor experience can lead to negative reviews, reduced trust, and user drop-offs, which in turn impacts the entire business model.

Clinical effectiveness is arguably the most significant driver of value in HealthTech. This dimension measures the demonstrated improvement in patient health outcomes. While often challenging to measure and reliant on time and volume, showcasing a higher quality of care is the most powerful justification for a program's investment. Success stories, such as Atrium Health's Hospital at Home program, which improved patient outcomes and reduced hospital costs, or Ochsner Health's Connected Maternity Online Monitoring program, which advanced maternal health outcomes, provide compelling evidence of clinical value. AI powered predictive analytics that enable proactive care and earlier disease detection are shifting the paradigm from reactive to preventive medicine, which not only improves patient health but also reduces costs associated with long-term treatments and hospitalisations.

Finally, operational performance measures the improvements in efficiency and productivity across the healthcare ecosystem. This includes metrics such as reduced administrative overhead, faster time to diagnosis, and the ability to provide a higher level of care to a broader population with the same number of resources. AI’s ability to automate administrative tasks is a key component of this dimension, with estimates suggesting that up to 45% of these tasks could be automated, leading to billions of dollars in annual savings. By streamlining processes and reducing manual effort, HealthTech platforms enable clinicians to focus on direct patient care, which enhances overall productivity and job satisfaction.

2.2. Quantifying Success: KPIs for Digital Health Platforms

To assess the value of a digital health platform, organisations must adopt a set of Key Performance Indicators (KPIs) that align with this multi-dimensional framework. This approach provides a clear and actionable method for benchmarking initiatives, tracking improvements, and demonstrating success to stakeholders.

The effectiveness of a digital health platform is fundamentally tied to its technical reliability and performance. This goes far beyond simple user frustration to directly impact clinical safety and business viability. When a platform is glitchy, unreliable, or slow, it can lead to direct disruptions in care. A patient using a remote monitoring app for a chronic condition, such as diabetes, may experience repeated crashes or syncing failures.Over time, this unreliability erodes their trust in the technology and they may abandon the app entirely. When this happens, their healthcare provider loses access to important real-time data, which can result in delays in treatment adjustments and, ultimately, lead to poorer health outcomes.

This illustrates that a loss of trust from technical issues can create a negative feedback loop: an unreliable app causes patients to disengage, which leads to a loss of critical data for providers, and this in turn can harm patient care and make the system less effective. Organisations must recognise that a robust, high-performing technical foundation is not merely a competitive advantage but a foundational requirement for patient safety and business viability in the HealthTech sector.

3. The Core Drivers of Value: App, Data, and AI

The synergy between a well-designed app platform, high-quality data, and the analytical power of AI is the primary engine for creating value in HealthTech. The platform serves as the delivery mechanism for AI-powered services, which are fueled by the continuous stream of data from users and integrated systems. This combination enables a wide range of use cases that are transforming the healthcare ecosystem.

3.1. The AI-Powered Platform: Use Cases for the Next 24 Months

3.1.1. Enhancing Clinical Care

The application of AI in clinical care is rapidly evolving, with a focus on improving diagnostic accuracy and personalising treatment. AI-driven diagnostic tools, powered by machine learning algorithms, can process vast amounts of medical data to detect patterns that may elude human physicians. This is particularly evident in medical imaging, where AI can analyse X-rays, CT scans, and MRIs to identify anomalies with remarkable accuracy. For example, Omron received FDA authorisation for its home blood pressure monitors equipped with an AI-driven algorithm that analyses pressure pulse waves to enhance the early detection of atrial fibrillation (AFib).

Similarly, companies like Cleerly use FDA-cleared machine learning algorithms to generate a 3D model of a patient's coronary arteries, helping to identify and quantify plaque to support diagnosis and personalised treatment. Qure.ai leverages AI and deep learning to automate the interpretation of radiology exams, enabling faster diagnosis and treatment. This capability to analyse data more quickly and accurately than human experts, combined with human oversight, has the potential to speed up diagnosis and improve patient outcomes. Beyond diagnostics, AI excels in predictive analytics, which allows healthcare providers to identify high-risk patients and intervene proactively. By analysing a patient’s medical history, lifestyle, and genetic factors, AI can forecast the likelihood of certain conditions and recommend preventive measures, representing a foundational shift from reactive to preventive medicine.

3.1.2. Transforming Patient Engagement and Experience

App platforms with integrated AI are revolutionizing the patient experience by enabling continuous and personalized care outside of the traditional clinical setting. Generative AI is being used in remote patient monitoring (RPM) to analyse real-time data from wearable devices and sensors, predict potential health risks, and customise treatment plans. This allows healthcare providers to intervene in a timely manner, reduce unnecessary hospital visits, and enhance overall patient outcomes. AI-powered virtual assistants and chatbots can automate a range of patient-facing tasks, such as providing medication reminders, delivering tailored health tips, and answering common questions.

This not only empowers patients with self-management tools but also reduces the workload for healthcare providers, allowing them to focus on more complex clinical interactions. The use of digital tools and AI-driven insights fosters more meaningful connections with patients by breaking down physical access barriers and building trust through genuine patient stories. The ability to offer continuous, personalised care through these platforms is a key value proposition that attracts and retains tech-savvy, health-conscious patients.

3.1.3. Streamlining Operational and Administrative Efficiency

The administrative overhead in healthcare is a significant cost driver, but AI can automate a wide range of administrative and operational tasks. A study by McKinsey & Company suggests that AI could automate up to 45% of administrative tasks in healthcare, freeing up USD $150 billion in annual costs. AI-powered algorithms can streamline appointment scheduling, billing, medical coding, and insurance claims processing, reducing the need for manual labor and minimising errors and fraud. Companies like Pieces use generative AI to draft, chart, and summarise clinical notes for doctors and nurses, freeing up valuable time for patient-facing work.

Similarly, Microsoft’s Dragon Copilot is an AI healthcare tool that can listen to clinical consultations and automatically create notes. AI also improves data management by handling massive volumes of information and breaking down data silos, connecting disparate data points in minutes, a task that once took years. This improves the speed and quality of decision-making for healthcare providers and contributes to more efficient daily operations.

The following table summarises key AI applications and their value propositions.

Application Area	Specific Use Cases	Value Proposition	Company/Case Study Examples
Clinical Care	- Diagnostic Imaging - Predictive Analytics - Precision Medicine	- Improved diagnostic accuracy and speed - Proactive, preventive care - Personalized, evidence-based treatment plans - Reduced risk of errors	- Cleerly: Non-invasive atherosclerosis measurement	- Qure.ai: Automated radiology exam interpretation	- AstraZeneca: Early detection of over 1,000 diseases	- Omron: AI-driven AFib detection	- Insitro: AI-driven drug discovery
Patient Engagement	- Remote Patient Monitoring - AI Chatbots & Virtual Assistants - Medication Adherence Tracking	- Continuous, personalized care - Improved patient access and empowerment - Increased adherence and engagement - Reduced need for in-person visits	- Ochsner Health:Connected Maternity Online Monitoring	- Huma: Reduced patient readmission rates	- Omada Health: Chronic care management with coaching & connected devices	- SnapLogic/GenAI App Builder: Automated medication management
Operational Efficiency	- Administrative Automation - Clinical Documentation - Resource Optimization	- Significant cost savings - Reduced staff workload - Streamlined workflows - Faster data access & improved accuracy	- Pieces:Generative AI for drafting clinical notes	- Microsoft Dragon Copilot: AI for clinical documentation	- Google: Suite of AI models for administrative burdens	- Elea: Reduced testing and diagnosis times

4. Overcoming the Obstacles: Challenges Impeding Value Maximisation

Despite the immense potential, the path to maximising value in HealthTech is fraught with significant and interconnected challenges. These are not isolated technical or ethical issues, but a cascading set of risks that can impede adoption, erode trust, and compromise clinical outcomes.

4.1. Data Fragmentation and Interoperability

A foundational barrier to the full potential of AI is the deeply fragmented nature of healthcare data. Medical data exists in various formats, from doctor's notes and X-rays to lab results and wearable device records and is often siloed across disparate systems, making it difficult to exchange and use for AI training.

Technical obstacles include the widespread use of proprietary Electronic Health Record (EHR) systems that do not communicate well with each other, hindering seamless data exchange across departments and even within the same hospital. On-premises data storage further complicates matters, presenting significant scalability and integration challenges when attempting to connect with external systems and third-party platforms.

Organizational barriers are equally formidable. These include resistance to change, a lack of investment in training, and hierarchical structures that impede cross-departmental collaboration. A major bottleneck is the absence of universal data standards. Even with progress being made, many healthcare providers use customised systems with non-standardised formats, making it difficult to translate and share data effectively. Although federal initiatives such as the CMS Interoperability Framework and the promotion of FHIR APIs are beginning to address these issues, the problem remains a primary hurdle to leveraging data for AI-driven insights.

4.2. Navigating the Regulatory and Ethical Maze

4.2.1. Data Privacy and Security

The use of large datasets for training and operating AI models introduces significant privacy risks. Regulations like the Health Insurance Portability and Accountability Act (HIPAA) in the U.S. and the General Data Protection Regulation (GDPR) in Europe set high standards for protecting patient data. However, new risks emerge with AI, including the potential for "anonymous" data to be re-identified by cross-referencing with other sources.

There is also the risk of "scope creep" where data collected for one purpose may be used for another without clear consent, and the potential for breaches to occur at an unprecedented scale across multiple connected systems. To mitigate these risks, organisations must implement robust safeguards such as advanced encryption protocols, data anonymisation, and comprehensive risk management strategies.Collaborating with technology partners who are willing to sign Business Associate Agreements (BAAs) and adhere to government-grade security standards is essential.

4.2.2. Algorithmic Bias and Fairness

A critical ethical challenge in HealthTech AI is the potential for algorithmic bias. If AI systems are trained on non-representative datasets, they can unintentionally perpetuate or amplify existing healthcare inequities.This can lead to misdiagnosis or suboptimal care for marginalised populations. For instance, a widely used commercial algorithm that was designed to predict healthcare costs rather than illness severity was found to systematically underestimate the care needs of Black patients, as less money is historically spent on them for similar conditions.

Similarly, AI models for detecting skin cancer, which were trained predominantly on images of light-skinned individuals, are significantly less accurate when applied to patients with darker skin tones.This demonstrates that AI models are only as unbiased as the data they learn from. Solutions require a multi-disciplinary approach that includes inclusive data collection, continuous monitoring of AI outputs, and the involvement of representatives from underrepresented populations in the development process.

4.2.3. Trust and Transparency

Overcoming patient and professional skepticism is paramount to the successful adoption of HealthTech AI. A significant portion of patients feel uncomfortable if a doctor relies on AI, and many believe it could worsen their treatment. A major source of this distrust is the "black box" problem, where the decision-making processes of deep-learning systems are opaque and difficult to interpret, raising concerns about accountability and liability when an error occurs. To build trust, organisations must clearly communicate how AI assists in treatment and emphasise that it is a tool to augment, not replace, human expertise.

Transparent policies regarding how patient data is used, clear consent processes, and a commitment to fairness and ethical standards are essential for fostering confidence among all stakeholders.

The three primary challenges, data fragmentation, ethical concerns, and trust deficits, are deeply intertwined. The lack of standardised and interoperable data makes it impossible to build large, diverse datasets. This directly leads to algorithmic bias because AI models, lacking representative data, inevitably learn and amplify existing biases present in the limited data they can access.

The manifestation of this bias in real-world scenarios, such as misdiagnoses or disparate treatment outcomes, then erodes trust among both patients and clinicians. This creates a negative feedback loop: fragmented data leads to bias, which leads to a loss of trust, which in turn acts as a barrier to collecting the new, more diverse data needed to fix the original problem. A strategic solution cannot be a simple technical fix; it must combine technical interoperability solutions, robust data governance, and transparent, collaborative development processes to rebuild trust and create a more equitable and effective system.

5. Strategic Pillars for Maximising Value in the Next 2 Years

Successfully navigating the complexities of HealthTech requires a proactive and multi-faceted strategy. The following pillars provide a definitive roadmap for organizations to not only overcome obstacles but also to transform their digital health initiatives into a source of sustainable value.

5.1. The Business of Digital Health: Monetisation and Partnerships

A clear understanding of business models and revenue streams is essential for long-term sustainability. The research highlights two primary models: Healthcare SaaS and Tech-Enabled Services. Healthcare SaaS, similar to traditional cloud software, offers highly recurring revenue, but the total addressable market (TAM) can be smaller, leading to slower sales cycles and lower growth rates compared to the broader tech industry. In contrast, Tech-Enabled Services, which deliver care or navigation support to patients, often have a significantly larger TAM and higher growth rates because they directly address the complex problem of care delivery. They can operate on a fee-for-service, fee-for-value, or subscription basis, providing a flexible and scalable approach.

Monetisation strategies extend beyond these core models. Organisations can generate revenue by offering tiered subscription plans with fixed monthly fees, providing flexible care options and recurring revenue streams. Reimbursement opportunities for services like telehealth and remote patient monitoring are also expanding, with both public and private insurers offering pathways for coverage.

Data itself can become a monetisable asset; by offering embedded analytics and custom reporting services to outside providers or insurers, organisations can create new revenue sources based on their data assets. Strategic partnerships are another key pillar, as they facilitate data-driven research and open new market and revenue opportunities.

5.2. The Foundation of Interoperability and Data Governance

To unlock the full potential of AI, organizations must view interoperability as a core business differentiator, not merely a regulatory or technical hurdle. By strategically investing in interoperability, a company can transform its fragmented data from a liability into a valuable, monetizable asset. This is achieved by moving data out of silos and into a structured, usable format that fuels AI-driven insights.

A robust interoperability strategy requires a multi-pronged approach:

Adopting Data Standards: Leveraging standards like FHIR APIs and USCDI ensures that data can be exchanged in compatible formats across different systems and organisations. These initiatives provide a foundation for scalable data sharing and a more cohesive healthcare ecosystem.
Implementing Semantic Interoperability: This goes beyond simple data transfer to ensure that the data exchanged between systems retains its meaning, regardless of the source. This is achieved by using standardized terminologies like LOINC for lab results and SNOMED CT for clinical findings, ensuring that AI models can interpret data consistently across systems and deliver accurate insights.
Establishing Robust Data Governance: A formal, organisation wide framework is essential for managing the entire data lifecycle, from collection to secure destruction. This framework should include clear policies for data accuracy, completeness, and consistency, as well as defined roles and responsibilities for data stewards and trustees. Prioritising critical areas first, such as patient demographics and medication lists, allows for a strategic and scalable approach to governance.

5.3. The Human-AI Partnership: Evolving the Roles of Professionals

The future of healthcare is a collaborative partnership between human expertise and AI-powered tools. AI is not designed to replace clinicians but to augment their capabilities, addressing a looming global health worker shortage. AI-powered clinical decision support systems can provide nurses and doctors with valuable insights and evidence-based recommendations, helping them make more informed decisions with higher precision.These systems can also automate administrative tasks, prioritise patient needs, and facilitate seamless communication within a healthcare team, freeing up professionals to focus on direct patient care and improve job satisfaction.

To ensure the success of this partnership, strategic initiatives must include cross-functional collaboration from the earliest stages of development, bringing together clinicians, AI scientists, and legal experts. Ongoing training and educational initiatives are crucial to help professionals understand how AI functions, how to interpret its outputs, and how to effectively integrate it into their daily workflows. By proactively fostering a culture of trust and transparency, organisations can ensure that the technology is seen as an ally that enhances care, not an obstacle to it.

6. Case Studies in Value Maximisation: Lessons from the Vanguard

Real-world examples demonstrate how a strategic approach to HealthTech, underpinned by the principles of data, platforms, and AI, can lead to measurable and transformative outcomes. These case studies provide valuable lessons for organizations seeking to maximize value in the coming years.

6.1. Atrium Health: The Hospital at Home Model

Atrium Health’s innovative Hospital at Home (AH-HaH) program has successfully transformed healthcare delivery by leveraging a strategic partnership with a technology company.The program provides quality care through a combination of in-person and virtual consultations, remote patient monitoring kits, and seamless integration with electronic health records. This approach demonstrates how a digital platform can extend the reach of a hospital beyond its physical walls. The program's success is highlighted by its ability to improve patient outcomes and significantly reduce hospital costs, validating the clinical and financial value of a digitally enabled care model.

6.2. Ochsner Health: Advancing Maternal Care

Ochsner Health launched the Connected Maternity Online Monitoring (MOM) program to advance maternal health care in Louisiana and Mississippi. This program uses digital tools to remotely monitor pregnant patients, reducing the need for in-person visits and enhancing both patient experience and operational efficiency. The initiative showcases how a targeted, technology-enabled program can address a specific clinical need and lead to measurable improvements in health outcomes. It underscores the power of a platform-based approach to deliver personalised, proactive care for key populations, serving as a model for other organizations seeking to leverage digital tools to improve specialized care delivery.

6.3. Omron: AI and Hardware Convergence

Omron’s strategic moves in 2024 demonstrate a clear vision for maximising HealthTech value through the convergence of hardware, data, and AI.The company received FDA De Novo authorisation for its home blood pressure monitors with an AI-driven atrial fibrillation (AFib) detection algorithm. This innovation enhances the diagnostic capability of a traditional medical device, transforming it into an intelligent tool for proactive health management.

Simultaneously, Omron's acquisition of Luscii, a European remote patient monitoring service provider, solidified its presence in digital health and expanded its offerings to include care plans for over 150 diseases. These actions illustrate a powerful strategic approach: instead of relying solely on hardware sales, Omron is integrating AI to enhance its products and acquiring digital services to become a holistic HealthTech player. This validates the importance of a synergistic strategy that combines technological innovation with strategic partnerships and acquisitions to create an integrated ecosystem of care.

7. Conclusion & The Way Forward: A Proactive Roadmap

The evidence presented throughout this report makes a compelling case: maximizing HealthTech value in the next two years requires a holistic strategy centred on the seamless synergy of app platforms, high-quality data, and AI. The market is ripe for growth, but success will not be granted to those who simply adopt technology. It will be earned by those who build a connected ecosystem that not only improves patient care and operational efficiency but also proactively addresses the systemic challenges of data fragmentation, ethics, and trust.

The most significant opportunities for value creation lie in the enterprise-level digital health solutions that enable a human-AI partnership. By focusing on administrative automation, enhancing clinical decision-making, and transforming patient engagement through remote monitoring, organisations can generate substantial financial returns while delivering a higher quality of care. This will also help to address the looming health worker shortage by freeing clinicians from administrative burdens and allowing them to focus on direct patient care.

To capture this value and emerge as a leader, organisations must follow a definitive two-year roadmap:

Year 1: Foundation Building

The first year must be dedicated to establishing a robust and scalable foundation. This begins with a comprehensive operational audit to identify key data silos and high-impact areas for transformation, such as those with the highest costs or lowest patient satisfaction scores. A multidisciplinary data governance committee should be established with defined roles for data stewards and trustees, tasked with creating clear policies for data quality, access controls, and security. Concurrently, the organisation should prioritise the adoption of standardised terminologies and data exchange protocols like FHIR APIs and USCDI to begin the crucial process of building a semantically interoperable data ecosystem.

Year 2: Value Creation and Scaling

With a strong data foundation in place, the second year should focus on scaling AI-powered solutions. The strategy should pivot from a technical implementation to a value-driven one, targeting a few high-value use cases that directly address core pain points and demonstrate clear, measurable ROI across all four value dimensions: Financial, User Satisfaction, Clinical Effectiveness, and Operational Performance. This includes implementing solutions for administrative automation, remote patient monitoring, and AI-assisted diagnostics. Furthermore, organisations must invest in a culture of trust by ensuring transparent AI policies and providing ongoing training to professionals to facilitate the human-AI partnership.

The window for strategic action is now. The organisations that prioritise the convergence of platforms, data, and AI, while proactively addressing the interconnected challenges of interoperability, ethics, and trust, will be the ones to define the future of healthcare, a future that is more predictive, preventive and personalised for all.

Nelson Advisors > Healthcare Technology M&A

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