Technology > Methodology > Cogniology : HealthTech's 3 Waves and 30 Year Journey

Lloyd Price

1 day ago

Exec Summary

HealthTech’s evolution over three decades can be distilled into three distinct waves, each building on the last, progressing from raw technological innovation to systematic methodologies, and finally to a cognitive synergy between humans and machines. Here’s the 30 year journey outlined:

Wave 1: Technology (2010–2019) Laying the Digital Foundations

This wave was about introducing and scaling core technologies to digitise healthcare, making it accessible and data-rich. Key developments included:

Electronic Health Records (EHRs): Catalysed by the 2009 HITECH Act, EHR adoption soared (e.g., 80%+ of US hospitals by 2014), replacing paper with digital records.

Telemedicine: Smartphones (iPad 2010, global smartphone dominance by 2013) enabled basic virtual care, with early platforms like Teladoc gaining traction.

Wearables: Fitbit and later Apple Watch (2015) introduced consumer health tracking, birthing the Internet of Medical Things (IoMT).

Early AI: IBM Watson Health (2011) and initial machine learning applications hinted at AI’s potential in diagnostics.

Genomics: Sequencing costs dropped below $1,000 by 2014, launching precision medicine (e.g., NIPT by 2013).

Healthcare shifted from analog to digital, empowering patients and providers with tools and data. By 2019, the stage was set for deeper integration. This wave was about inventing and deploying the raw tech—hardware, software, and connectivity—that HealthTech would later refine.

Wave 2: Methodology (2020–2029) Scaling and Integrating Systems

Building on Wave 1’s tech, this wave refined methodologies to operationalise and scale solutions, driven by the COVID-19 crisis and tech maturation. Key developments included:

Telehealth Expansion: Platforms integrated synchronous/asynchronous care with cloud infrastructure, stabilising at 38x pre-COVID usage by 2021.

AI Diagnostics: Machine learning with real-time data loops enabled predictive analytics (e.g., 90% accurate ICU forecasts by 2021).

Remote Monitoring: Edge computing and IoMT scaled RPM, with 30% annual growth by 2022, managing chronic conditions remotely.

Digital Therapeutics: Evidence-based apps (e.g., Sleepio, reSET) treated mental health and chronic diseases, reducing drug reliance.

Blockchain: DLT secured data and supply chains, enhancing equity (e.g., 40% counterfeit vaccine reduction in Africa by 2023).

Agile Regulation: Sandboxes (e.g., FDA’s 2020 Digital Health Center) fast-tracked innovations like contact tracing apps.

HealthTech became proactive, accessible, and resilient, embedding tech into everyday care. By 2029, it’s a seamless, data-driven ecosystem. This wave focused on how to deploy tech, systematic processes, interoperability, and rapid iteration, turning tools into solutions.

Wave 3: Cogniology (2030–2039) Cognitive Synergy and Human-Machine Fusion

With foundations and methods in place, this wave leverages "cogniology"—the study of artificial and human cognition—to create intuitive, predictive, and symbiotic health systems. Key developments are likely to be:

Cognitive AI Partners: Neuro-inspired AI acts as lifelong health companions, interpreting emotions and needs via BCIs and multimodal data (e.g., dementia detection by 2035).

Brain-Computer Interfaces: BCIs enable thought-driven health management (e.g., 50% faster stroke rehab by 2032).

Cognitive Twins: AI simulates individual health trajectories from lifelong data, predicting risks with 95% accuracy (e.g., hypertension forecasts by 2034).

AR Therapy: Immersive environments, guided by AI, treat mental health or train surgeons (e.g., 70% PTSD reduction by 2036).

Collective Networks: Blockchain-linked cognitive data informs public health (e.g., urban fatigue solutions by 2038).

Ethical Alignment: AI aligns with human values, audited by cogniologists (e.g., pausing treatments for patient comfort by 2033).

HealthTech becomes an extension of human cognition, anticipating needs, enhancing minds, and optimising societies. By 2039, it’s a partnership between artificial and biological intelligence. This wave transcends tools and methods, focusing on thinking, how AI collaborates with human cognition to redefine health.

The 30-Year Arc: A Narrative

2010–2019 (Technology): HealthTech begins as a technological revolution, digitising records, connecting patients via mobile devices, and planting seeds with wearables, AI, and genomics. It’s the era of building the tools.

2020–2029 (Methodology): A global crisis accelerates adoption, refining these tools into scalable systems. Telehealth, AI, and IoMT integrate into a robust framework, mastered through agile methods and data-driven precision. It’s the era of perfecting the process.

2030–2039 (Cogniology): With infrastructure and methods mature, HealthTech evolves into a cognitive partnership. AI doesn’t just assist, it thinks with us, enhancing our minds and bodies in a symbiotic dance. It’s the era of merging with intelligence.

2010: EHRs take root, smartphones spark telemedicine. 2020: COVID-19 forces Telehealth and AI to scale overnight. 2030: Cognitive AI and BCIs redefine personal health management.

This 30-year journey, from Technology’s raw innovation, through Methodology’s systematic scaling, to Cogniology’s cognitive fusion, charts HealthTech’s growth from a toolset to a partner. Each wave builds on the last, culminating in a future where health isn’t just managed but co-experienced with artificial minds.

Nelson Advisors > HealthTech M&A

Nelson Advisors specialise in mergers, acquisitions and partnerships for Digital Health, HealthTech, Health IT, Healthcare Cybersecurity, Healthcare AI companies based in the UK, Europe and North America. www.nelsonadvisors.co.uk

We work with our clients to assess whether they should 'Build, Buy, Partner or Sell' in order to maximise shareholder value and investment returns. Email lloyd@nelsonadvisors.co.uk

Nelson Advisors regularly publish Healthcare Technology thought leadership articles covering market insights, trends, analysis & predictions @ https://www.healthcare.digital

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Wave 1: Technology > 2010 onwards

The initial surge of transformative health technology trends and innovations that emerged or gained significant traction started in 2010. This period marks a pivotal shift in healthcare, driven by digital advancements, policy changes and a growing focus on patient-centred care.

The "first wave" of healthtech from 2010 can be seen as the convergence of digital infrastructure, mobile technology and data-driven solutions that began reshaping healthcare delivery, accessibility, and outcomes. This wave laid the groundwork for later innovations by establishing key technologies and frameworks.

The defining elements of the first wave included:

1. Electronic Health Records (EHRs) Adoption

The early 2010s saw a massive push for EHRs, catalysed by the US Health Information Technology for Economic and Clinical Health (HITECH) Act of 2009, which took effect in 2010. This legislation incentivised healthcare providers to adopt digital records, moving away from paper-based systems.

By 2014, over 80% of U.S. hospitals had adopted EHRs, up from less than 10% in 2008. This shift digitised patient data, enabling better coordination, reducing errors, and setting the stage for data analytics in healthcare.

EHRs were foundational, without digitised records, later healthtech like AI diagnostics or telemedicine couldn’t scale effectively.

2. Telemedicine and Mobile Health (mHealth)

Telemedicine, though not new, exploded in the 2010s with improved internet connectivity and smartphone proliferation. The iPad launched in 2010, and by 2013, smartphone sales surpassed feature phones globally. Mobile health apps emerged, offering patients tools for monitoring and communication.

By 2016, over half of WHO member states had telehealth policies, and early adopters like the U.S. saw telehealth visits rise sharply (e.g., 50% increase in 2020’s first months, though growth began earlier). Patients could consult remotely, and apps tracked vitals like heart rate or glucose.

This marked the first widespread use of technology to bridge physical gaps in healthcare, making services more accessible and convenient.

3. Wearables and the Internet of Medical Things (IoMT)

Wearable devices like Fitbit (gaining traction post-2010) and later the Apple Watch (2015) introduced consumer health monitoring. The "Internet of Medical Things" connected these devices to healthcare systems, sharing real-time data.

By the mid-2010s, wearables tracked steps, sleep, and heart rate, empowering patients and feeding data to providers. IoMT enabled remote monitoring, reducing hospital visits and aiding chronic disease management.

It shifted healthtech from provider-centric to patient-centric, giving individuals tools to manage their health actively.

4. Artificial Intelligence and Machine Learning (Early Applications)

AI began infiltrating healthtech in the 2010s, with early applications in diagnostics and drug discovery. IBM’s Watson Health launched in 2011, aiming to analyse medical data, while deep learning neural networks advanced image recognition (e.g., for radiology) by mid-decade.

AI systems started outperforming humans in specific tasks, like detecting skin cancer (2017) and supported clinical decisions. Though not fully mature, these tools hinted at AI’s future potential.

This was the initial integration of AI into healthcare, proving its viability and sparking investment (e.g., $44B+ in AI/ML health startups since 2010).

5. Genomic Medicine and Precision Health

The cost of genomic sequencing plummeted in the 2010s (from $10M in 2007 to under $1,000 by 2014), driven by companies like Illumina. CRISPR-Cas9 gene editing emerged in 2012, revolutionizing genetic research.

Non-invasive prenatal testing (NIPT) became widespread by 2013, and therapies like CAR-T (FDA-approved 2017) targeted cancer at a genetic level. This personalised medicine approach began tailoring treatments to individuals.

It introduced a new paradigm, healthcare based on genetic data, paving the way for later precision health breakthroughs.

Context and Timing

The 2010s kicked off with enabling technologies (smartphones, broadband) and policies (HITECH Act) aligning. The decade saw a shift from analog to digital, with healthtech riding the broader tech boom—mobile devices, cloud computing, and data analytics.

This wave was global but heavily U.S.-centric due to policy and investment (e.g., $150M for the Patient-Centred Outcomes Research Institute in 2010). Europe and Asia followed, with telemedicine and wearables gaining traction by mid-decade.

The "first wave" of healthtech from 2010 onwards wasn’t about a single technology but a synergy of digital foundations, EHRs, telemedicine, wearables, early AI, and genomics, that transformed how healthcare was delivered and experienced. It was less about radical cures (those came later) and more about infrastructure and access, setting the stage for subsequent waves like AI-driven diagnostics or advanced gene therapies in the 2020s.

Wave 2: Methodology > 2020 onwards

The "second wave" can be defined as the period from 2020 onward, heavily influenced by the COVID-19 pandemic, which acted as a catalyst for rapid evolution in healthcare technology. A number of methodologies characterised this phase focusing on how healthtech adapted, scaled, and innovated post-2020.

The second wave of healthtech from 2020 onwards built on the digital foundations of the first wave (EHRs, telemedicine, wearables, early AI, genomics) but shifted toward more integrated, scalable, and responsive solutions. The methodologies reflected a response to the global health crisis, technological maturation and a push for equity and efficiency.

The defining elements of the second wave included:

1. Acceleration of Telehealth and Virtual Care Platforms

The pandemic necessitated a rapid scale-up of telemedicine, moving beyond the first wave’s basic adoption. Healthtech companies refined platforms to handle synchronous (real-time video/audio) and asynchronous (store-and-forward, like messaging or image sharing) care seamlessly.

Cloud-based systems and APIs enabled interoperability with EHRs, allowing providers to access patient histories during virtual visits. Companies like Teladoc and Amwell expanded server capacity and user interfaces to manage millions of users simultaneously.

Example: By 2021, telehealth usage stabilised at 38 times higher than pre-COVID levels in the US, with platforms incorporating AI triage to prioritise urgent cases.

Methodology: Synchronous and Asynchronous Integration with Scalable Infrastructure > addressed immediate access needs during lockdowns and evolved into a permanent hybrid care model, reducing physical infrastructure reliance.

2. AI-Driven Diagnostics and Predictive Analytics

AI matured from experimental (first wave) to operational, leveraging vast datasets from the pandemic. Convolutional neural networks (CNNs) and natural language processing (NLP) were deployed for diagnostics (e.g., analyzing chest X-rays for COVID-19) and predicting outbreaks or patient deterioration.

Continuous learning models ingested real-time data from wearables, EHRs, and public health reports, refining accuracy. For instance, Google’s DeepMind and startups like Aidoc used AI to flag anomalies faster than human radiologists.

Example: AI models predicted ICU demand during COVID waves with up to 90% accuracy by 2021, guiding resource allocation.

Methodology: Machine Learning with Real-Time Data Feedback Loops > shifted healthtech from reactive to proactive, enabling precision at scale and reducing clinician burnout.

3. Remote Patient Monitoring (RPM) and IoMT Expansion

The second wave saw RPM explode, with wearables (e.g., Apple Watch, Oura Ring) and medical-grade sensors (e.g., continuous glucose monitors) feeding data to edge devices for local processing, reducing latency and cloud dependency.

Interconnected IoMT ecosystems linked devices to apps and provider dashboards, using standardized protocols like Bluetooth Low Energy (BLE) and FHIR (Fast Healthcare Interoperability Resources) for data exchange.

Example: By 2022, RPM adoption grew 30% year-over-year, with devices monitoring vitals like oxygen saturation for COVID patients at home.

Methodology: Edge Computing and Sensor-Driven Ecosystems > decentralised care, keeping patients out of hospitals while maintaining oversight, critical during bed shortages.

4. Digital Therapeutics and Behavioural Health Tech

Digital therapeutics (DTx) emerged as standalone or adjunct treatments, using apps to deliver cognitive behavioural therapy (CBT), chronic disease management, or medication adherence support. These were regulated (e.g., FDA-approved Pear Therapeutics’ reSET in 2020).

Behavioural health tech surged, with platforms like Talkspace and Headspace scaling to address pandemic-induced mental health crises, using gamification and AI chatbots for engagement.

Example: DTx for insomnia (e.g., Big Health’s Sleepio) reduced reliance on sedatives, with studies showing 76% improvement in sleep metrics by 2021.

Methodology: Evidence-Based Software as a Medical Intervention > tackled non-physical health needs, filling gaps left by overwhelmed traditional systems.

5. Blockchain and Data Security for Health Equity

Blockchain was deployed to secure patient data, verify supply chains (e.g., vaccines), and enable decentralised health records. Smart contracts automated consent and data sharing, ensuring privacy in telehealth and research.

Startups like BurstIQ and MedRec used DLT to create portable, patient-owned records, addressing disparities in underserved regions where EHR access was limited.

Example: By 2023, blockchain pilots in Africa improved vaccine tracking, cutting counterfeit rates by 40%.

Methodology: Distributed Ledger Technology (DLT) for Trust and Access > enhanced trust and equity, critical as healthtech expanded globally amid misinformation and access challenges.

6. Agile Development and Regulatory Adaptation

Healthtech firms adopted agile sprints (2–4 week cycles) to roll out features fast—e.g., contact tracing apps or vaccine passport systems—while iterating based on user feedback.

Governments created regulatory sandboxes (e.g., FDA’s Digital Health Center of Excellence, 2020) to fast-track approvals without compromising safety, balancing innovation with oversight.

Example: The UK’s NHS COVID-19 app pivoted from centralised to decentralised data models in months, launching successfully in September 2020.

Agile Development and Regulatory Adaptation > enabled rapid response to evolving needs, unlike the slower pace of the first wave’s foundational tech.

Context and Evolution

Trigger: The COVID-19 pandemic (2020–2021) forced healthtech to pivot from gradual growth to urgent deployment. By 2022–2023, the focus shifted to sustainability and integration into routine care.

Scale: Global healthtech funding hit $57B in 2021 (up from $21B in 2019), reflecting investment in these methodologies.

Shift from First Wave: Where the first wave (2010–2019) built infrastructure (EHRs, basic telehealth), the second wave operationalised it with AI, IoMT, and patient empowerment, driven by crisis and tech maturity.

The "second wave" of healthtech from 2020 onwards wasn’t just about new tools but about methodologies that scaled and integrated them—telehealth became ubiquitous, AI turned predictive, and RPM decentralised care. These approaches responded to immediate needs (pandemic management) while setting a new baseline for healthcare delivery, emphasising resilience, accessibility, and data-driven precision.

Wave 3: Cogniology > 2030 onwards

The third wave of healthtech, starting around 2030 is likely to be defined by the fusion of advanced artificial intelligence with human cognition, creating a seamless, adaptive, and deeply personalised healthcare ecosystem. "Cogniology" here becomes the study and application of cognitive synergy, where AI not only mimics but enhances and collaborates with human mental processes to revolutionize health outcomes. Below are the key methodologies and innovations this wave might bring:

1. Cognitive AI as a Health Partner

AI evolves beyond tools (second wave’s diagnostics) into autonomous cognitive agents that act as lifelong health companions. These systems understand patients’ emotional, psychological, and physical states holistically, adapting in real time.

Methodology: Neuro-Inspired AI with Multimodal Cognition

AI leverages brain-computer interfaces (BCIs), advanced NLP, and emotional recognition (e.g., via facial analysis or voice tone) to interpret and respond to human cognition. Think neural networks modeled on human neocortex complexity, processing sensory, linguistic, and memory data simultaneously.

Example: By 2035, a "Cognitive Health Assistant" might detect early dementia through speech patterns, suggest personalised brain exercises, and coordinate with neurologists—all while empathising like a human caregiver.

Cogniology’s Role: Studying how AI replicates and augments human decision-making, ensuring it aligns with patient intent and ethics.

2. Brain-Computer Interfaces (BCIs) for Direct Health Management

BCIs, maturing from experimental (e.g., Neuralink’s 2020s trials), become mainstream, allowing patients to control healthtech devices or monitor brain health directly via thought.

Methodology: Closed-Loop Cognitive Feedback Systems

BCIs link neural signals to external devices (e.g., prosthetics, wearables) or internal therapies (e.g., neurostimulation for depression). AI interprets brain data, adjusts interventions, and feeds insights back to the patient’s cognitive loop.

Example: By 2032, a stroke patient might "think" to adjust a robotic exoskeleton, while AI predicts fatigue and optimises rehab, reducing recovery time by 50%.

Cogniology’s Role: Exploring the boundary between artificial and biological cognition, ensuring AI enhances rather than overrides human agency.

3. Predictive Cognitive Health Models

AI anticipates mental and physical health declines before symptoms manifest, using a lifetime of cognitive and biometric data to model individual trajectories.

Methodology: Longitudinal Cognitive Simulation

AI integrates data from birth—genomics, wearables, social interactions, even digital footprints—into a "cognitive twin" that simulates a person’s health future. Machine learning refines these models daily, predicting risks like Alzheimer’s or burnout with 95%+ accuracy.

Example: In 2034, a 40-year-old’s cognitive twin might warn of stress-induced hypertension 10 years out, triggering preventive lifestyle shifts guided by AI.

Cogniology’s Role: Defining how AI constructs and validates these cognitive models, balancing prediction with human variability.

4. Augmented Reality (AR) and Cognitive Therapy

AR merges with AI to create immersive environments for mental health treatment, physical rehab, and medical training, tailored to individual cognitive profiles.

Methodology: Immersive Cognitive Rewiring

AR systems, powered by AI, adapt scenarios in real time—e.g., calming a PTSD patient with personalised visuals or guiding a surgeon through a complex procedure with overlaid cognitive prompts. These adjust based on brainwave feedback (via BCIs or wearables).

Example: By 2036, a veteran might use AR to reprocess trauma in a safe, AI-guided simulation, reducing symptoms by 70% in weeks.

Cogniology’s Role: Studying how artificial environments influence human cognition, optimizing therapeutic outcomes.

5. Collective Cognitive Networks for Public Health

AI aggregates anonymised cognitive data across populations to address systemic health challenges, from pandemics to aging societies.

Methodology: Distributed Cognitive Intelligence

Blockchain-secured networks pool data from millions of cognitive twins, enabling AI to identify patterns (e.g., cognitive decline linked to air pollution) and recommend policy or individual interventions. Think of it as a "global brain" for health.

Example: In 2038, a network might detect a cognitive fatigue epidemic in urban workers, prompting AI-designed city planning changes.

Cogniology’s Role: Analyzing how collective artificial cognition interacts with human societies, ensuring equity and privacy.

6. Ethical Cognitive Alignment

As AI becomes a cognitive partner, healthtech prioritises aligning artificial decisions with human values, avoiding overreach or bias.

Methodology: Value-Driven AI Training

AI systems are trained on diverse ethical frameworks, using reinforcement learning to prioritize patient autonomy, cultural context, and consent. Regular audits by "cogniologists" ensure alignment.

Example: By 2033, an AI might pause a treatment plan if it detects patient discomfort, deferring to human input rather than proceeding autonomously.

Cogniology’s Role: Establishing the science of ethical cognition in AI, a new frontier as machines approach human-like reasoning.

Context and Trajectory

Why 2030?: By 2030, AI’s computational power (doubling roughly every 2 years per Moore’s Law variants), BCI advancements, and data from the second wave’s IoMT will converge, enabling this cognitive leap. The global population’s aging (e.g., 1 in 6 over 60 by 2030, per UN) will also demand smarter health solutions.

Shift from Second Wave: The second wave (2020–2029) scaled tech for access and efficiency; the third wave integrates it into human cognition, making healthtech intuitive, predictive, and symbiotic.

Cogniology’s Lens: This wave isn’t just about tech but how it thinks with us. Cogniology becomes the discipline bridging AI’s artificial mind with human experience, driving healthtech’s evolution.

Imagine 2035: Your "Cognitive Health Assistant" wakes you with a tailored meditation (via AR glasses), adjusts your diet based on last night’s brain activity (via BCI), and warns your doctor of a subtle cognitive shift—all while you feel it’s an extension of your own mind. Meanwhile, a global cognitive network tweaks public health policies based on billions of such interactions. That’s the third wave, powered by cogniology.

Nelson Advisors > HealthTech M&A

We work with our clients to assess whether they should 'Build, Buy, Partner or Sell' in order to maximise shareholder value and investment returns. Email lloyd@nelsonadvisors.co.uk

Nelson Advisors regularly publish Healthcare Technology thought leadership articles covering market insights, trends, analysis & predictions @ https://www.healthcare.digital