Artificial Intelligence (AI) is rapidly reshaping public health — from enhancing disease surveillance and diagnostics to easing workforce burdens — but it also raises complex risks and ethical questions. In Europe and globally, public health leaders are grappling with how best to harness AI’s revolutionary potential while managing its pitfalls. After decades of experience, many recognise that AI is not a magic fix for health challenges; its value depends on thoughtful integration into health systems. This article provides an in-depth review of the current relationship between AI and public health. It examines the opportunities it offers, real-world innovations already underway, practical implementation challenges, and the risks and governance frameworks that must guide responsible use. All discussions equally consider European contexts (including emerging EU regulations) and broader global health perspectives.

TL;DR Summary

• AI’s growing role in health: Artificial intelligence is increasingly used to augment public health efforts — from automating administrative tasks to advanced disease surveillance and diagnostics — offering new ways to improve efficiency and reach.
• Tangible benefits observed: Early deployments show promising results. AI tools have reduced clinicians’ paperwork burden, flagged outbreaks days before traditional systems, and enhanced diagnosis in low-resource settings (e.g. catching 15% more TB cases via X-ray analysis).
• Innovations across sectors: NGOs, governments, and companies are all investing in AI for health. For example, PATH and others use AI in field programmes, the NHS has dozens of AI pilots improving care delivery, and pharma companies leverage AI to speed up drug and vaccine development.
• Practical hurdles remain: Successful implementation requires robust data infrastructure, interoperability, and high-quality data. Many health systems must modernise IT systems and address data silos and quality issues before AI can perform optimally.
• Human factors are critical: Integrating AI into workflows and gaining staff acceptance are significant challenges. Training health workers, providing explainable outputs, and maintaining human oversight are essential to building trust in AI-assisted care.
• Key risks to manage: AI in public health brings serious risks — privacy breaches, algorithmic bias harming disadvantaged groups, opaque “black box” decisions undermining trust, and AI-generated misinformation spreading false health advice. Over-reliance on AI without safeguards can also be dangerous.
• Ethics and governance frameworks: Clear principles and regulations are emerging to guide responsible AI use. WHO’s six ethical principles (e.g. transparency, equity, accountability) set value-based guardrails, while the EU’s AI Act will enforce strict requirements on high-risk health AI (mandating transparency, risk management, and human oversight).
• Collaboration and capacity-building: Effectively advancing AI in public health will require interdisciplinary collaboration (health experts with technologists), investment in workforce AI literacy, and inclusive approaches that involve LMICs and marginalised groups so benefits are shared widely.
• Continuous evaluation and adaptation: To ensure AI delivers on its promise, public health authorities must continually monitor outcomes, audit algorithms for bias or errors, and be ready to adjust or suspend systems if problems arise. Adaptive governance and ongoing community feedback are vital for safe, effective AI integration.
• Seizing the opportunity responsibly: When guided by ethical principles and strong oversight, AI can greatly strengthen public health, easing workforce burdens, expanding outreach, and providing data-driven insights. The next few years are crucial for implementing the policies, education, and trust-building measures that will allow AI to be a force for health equity and innovation rather than a source of new disparities or dangers.

Opportunities: Transforming Public Health with AI

AI is being deployed to alleviate several longstanding public health challenges. One significant opportunity is reducing clinician burnout and workforce shortages by automating routine tasks. For example, a 2024 survey found that 57% of physicians believe automating administrative burdens is the top opportunity for AI to ease workloads amid staff shortages. Machine learning systems can transcribe medical notes, pull up patient records, and handle scheduling or prescription refills — freeing clinicians to spend more time on patient care. Many doctors see such automation as a key to improving work efficiency and reducing stress, suggesting AI could help mitigate the healthcare burnout epidemic.

AI also offers powerful tools for disease surveillance and epidemic intelligence. Algorithms can continuously scan vast data sources — news reports, social media, travel data — to spot early signs of outbreaks far faster than traditional methods. Notably, the HealthMap and BlueDot platforms (which use natural language processing and machine learning) flagged the COVID-19 outbreak days before official alerts. By sifting through informal signals and anomalies, AI-driven systems can provide precious early warnings of emerging health threats. BlueDot’s AI surveillance tools have dramatically sped up outbreak detection, reducing manual scanning time by nearly 90% in some cases. Such early alerts enable public health agencies to mobilise quicker responses and potentially contain outbreaks before they spread.

Another area of opportunity is improving diagnostics and clinical decision support, especially in resource-constrained settings. AI image recognition has shown great promise in interpreting medical images like X-rays and retinal scans. For example, AI-based chest X-ray tools for tuberculosis (TB) are being used to help screen patients in low-resource areas that lack radiologists. A recent programme in India led by PATH found that an AI tool (qXR) boosted TB case detection by ~15.8% — identifying cases that human readers missed. Many countries are now utilising AI-assisted chest X-ray screening for TB, which can lead to earlier diagnosis and treatment in underserved communities. Beyond imaging, AI-powered diagnostic apps and chatbots can guide patients through symptom checks or flag high-risk cases for follow-up, expanding access to essential healthcare advice where clinicians are scarce.

Crucially, AI is also being enlisted to address climate-related health threats and environmental impacts on health. Public health researchers increasingly pair AI with climate data to predict disease patterns under changing environmental conditions. For instance, machine learning models can correlate weather patterns (temperature, rainfall) and even animal health data with disease outbreaks to anticipate risks in specific locations. By analysing such data, AI-driven predictive analytics can serve as early warning systems — forecasting surges in vector-borne diseases like malaria following heavy rains or heat-related illness during extreme heatwaves. This capability is ever more critical as climate change intensifies health hazards. AI can help public health officials prepare for climate-sensitive disease outbreaks, allocate resources proactively, and develop adaptation strategies to protect vulnerable populations.

Real-world Applications and Innovations

AI in public health is not just theoretical — numerous real-world initiatives by NGOs, governments, and private companies have already demonstrated its potential. Global health nonprofits and international agencies have been early adopters of AI to support their missions. For example, the Bill & Melinda Gates Foundation has invested heavily in AI-driven global health projects. In 2023, it awarded grants to nearly 50 pilot projects exploring AI solutions for health and development challenges — these range from AI-augmented diagnostic tools to data systems for disease surveillance in low-income settings.

One Gates-backed innovation is AI-assisted ultrasound: in 2020, a $44 million grant was given to develop an AI-guided portable ultrasound to improve lung disease diagnosis in low-resource countries (e.g. detecting pneumonia). Likewise, PATH and other NGOs are integrating AI into field programmes — as seen in the TB screening project, where an AI tool significantly increased case finding while illuminating practical deployment hurdles. These efforts by NGOs underscore AI’s promise to close gaps in healthcare access and quality for underserved populations.

Governments and public health agencies are also launching AI initiatives. In Europe, national health systems pilot AI to improve services and efficiency. For instance, the UK’s National Health Service (NHS) created an NHS AI Lab to fund and evaluate AI innovations in care delivery. By 2025, the NHS had over 80 AI projects live, targeting everything from optimising nurse rostering and predicting hospital bed occupancy to speeding up radiology workflows.

One NHS program provided £100+ million in awards to develop AI for earlier cancer detection, resource management, and patient safety improvements. The NHS AI Lab’s “Skunkworks” team has run short-term projects that yielded practical tools — e.g. an algorithm to streamline the placement of nurses across wards and a natural language processing engine to search health records more efficiently. Meanwhile, European public health agencies are leveraging AI for epidemiology; the European Centre for Disease Prevention and Control (ECDC) has incorporated systems like BlueDot’s AI to enhance epidemic intelligence, including monitoring outbreaks during events such as the 2020 Olympics. These government-led efforts illustrate growing public sector commitment to deploying AI for health system strengthening and emergency preparedness.

The private sector, particularly in healthcare and pharmaceuticals, is likewise driving innovation at the intersection of AI and public health. Pharmaceutical companies now routinely use AI in drug discovery and development. For example, Novartis recently struck a wide-ranging partnership (worth up to $1 billion) to use a generative AI platform for designing new protein-based therapies — aiming to accelerate the search for novel disease treatments. GSK has also embraced AI to speed up R&D: its CEO noted that AI modelling helped cut two years off an RSV vaccine trial by predicting where outbreaks would occur and optimising trial site selection. This led to the faster development of the world’s first RSV vaccine, an essential public health breakthrough.

Beyond pharma, medical technology firms are integrating AI into devices, from smart wearables that flag irregular heart rhythms to imaging systems where AI assists in analysing scans for early signs of cancer. Startups and tech companies are introducing AI-driven health apps and chatbots (such as symptom checkers and mental health conversational agents), which some health services in Europe are trialling for patient triage and support. These real-world examples underscore that AI is already deeply enmeshed in the health ecosystem — from global disease surveillance networks to hospital wards and R&D labs — delivering innovations that could improve population health outcomes.

Practicalities and Implementation Challenges

While the potential is immense, implementing AI in public health is a pragmatic challenge. Infrastructure and data interoperability are foundational hurdles. Effective AI requires robust digital infrastructure — high-quality data streams, electronic health records, and cloud computing capacity — which many health systems lack, especially in low-resource settings. Data needed for public health AI often reside in silos or incompatible formats across hospitals, labs, and agencies. Poor interoperability means AI tools struggle to aggregate and interpret information from disparate sources. Bridging these gaps will require significant investment in health information systems, common data standards, and connectivity. Encouragingly, current AI technology can assist in standardising and mapping messy health datasets to make them more usable. Nonetheless, without reliable infrastructure and data-sharing frameworks, even the best AI algorithms cannot deliver consistent results across a public health network.

A related challenge is data quality and representativeness. AI models are only as good as the data they learn from, and health data can be incomplete, biased, or unrepresentative of specific populations. Studies highlight issues like variability in how data are recorded, large amounts of unstructured text, missing information, and coverage bias (e.g. most training data coming from high-income populations).

These factors can undermine an AI system’s accuracy and value to end users. Developing good AI for health requires carefully cleaning and curating data to reflect clinical reality. For instance, algorithms trained only on European hospital data may perform poorly in rural African communities. Implementers must thus invest effort in data preparation and continuously monitor model outputs for anomalies. Establishing metadata standards, common terminologies, and data quality metrics can facilitate better AI development. Additionally, clarity on data ownership and governance is needed: questions about who “owns” health data (patients, providers, governments?) affect how data can be integrated for AI. Resolving these issues through policies and trust frameworks is key to unlocking data for public health AI while respecting privacy and rights.

Another practical consideration is integrating AI tools into healthcare workflows and gaining workforce acceptance. Introducing AI decision-support systems or automation in clinics requires adapting processes and training staff. Health workers may be understandably cautious — some lack familiarity with AI, worry about accuracy, or fear being displaced. Clear protocols are needed if an AI system’s recommendation conflicts with clinical judgment. Early experience shows that human-AI collaboration works best when AI is framed as an assistive tool rather than a professional replacement. Building trust among the workforce involves providing explainable outputs and demonstrating reliability in pilot phases. It also means training clinicians in basic AI concepts and ensuring they feel confident interpreting AI outputs.

Successful deployments (like the PATH TB screening program) emphasise that significant workflow integration and training efforts are required. In that program, implementers had to solve issues of installing the software in clinics, securing internet connectivity for the AI, and ensuring staff could effectively use the AI results within their screening workflow. Without such groundwork, even a high-performing algorithm might sit on the shelf unused. Thus, the human element is crucial: public health organisations must engage and educate their workforce, adjusting roles and processes so that AI enhances rather than disrupts care delivery. Over time, as clinicians see AI reducing drudgery (e.g. auto-filling forms) and improving outcomes, their acceptance tends to grow. Indeed, physician enthusiasm for health AI has been rising year-on-year. Patience and iterative refinement are needed to blend AI smoothly into the complex fabric of health systems.

Risks and Concerns of AI in Public Health

Despite the optimism, it is vital to acknowledge the risks and potential harms associated with AI in public health. Data privacy and security tops the list of concerns. AI systems often require large datasets of patient information, raising the stakes for protecting sensitive personal health data. Any breach or misuse of such data can erode public trust and violate individuals’ rights. There is also the risk of “function creep”, where data collected for health purposes might be used in other ways (for example, a COVID-19 contact tracing app’s data later being used for law enforcement — a scenario that drew criticism in some countries). Moreover, complex AI models could inadvertently leak private details — for instance, a model might be reverse-engineered to reveal records it was trained on. Ensuring robust cybersecurity and strict data governance is therefore paramount. Many call for comprehensive privacy safeguards and compliance with regulations like Europe’s GDPR whenever AI handles health data. Techniques such as anonymisation or synthetic data can help, but they are not foolproof (even de-identified data can sometimes be unidentified).

The bottom line: without public confidence that AI will maintain confidentiality and data security, its benefits will be lost. Public health agencies must be transparent about what data are used and how to obtain informed consent where appropriate and implement state-of-the-art security measures to prevent breaches. Privacy isn’t just a legal box to tick — it’s fundamental to preserving the trust on which public health interventions depend.

Another significant risk is algorithmic bias and the exacerbation of health inequalities. AI systems can unintentionally perpetuate or even worsen disparities if their design is not carefully managed. This was starkly illustrated by a widely used healthcare risk algorithm in the United States that was found to be racially biased. The algorithm helped determine access to extra care programs and used healthcare cost as a proxy for need. This choice systematically underestimated the needs of Black patients (who often had lower healthcare expenditures due to access barriers). As a result, many high-risk Black patients were less likely to be flagged for additional care, denying them the resources they needed. This example shows how bias in data or design can translate into inequitable outcomes: the AI effectively discriminates against a vulnerable group. Similar issues could arise in public health if an AI model is trained on predominantly male patients under-detect conditions in women or if disease surveillance AI better covers wealthier communities with more data. AI could widen gaps if not addressed, with marginalised populations benefiting the least or even being harmed.

Equity must be a central design principle to counter this: datasets should be diverse and inclusive, algorithms should be tested for bias, and bias mitigation strategies (like reweighing data or algorithmic fairness adjustments) should be applied. The WHO explicitly highlights inclusiveness and equity as core ethical principles for AI, ensuring that AI tools work for all segments of society regardless of race, gender, income, or other characteristics. Ultimately, careful governance and auditing of AI systems are needed to avoid encoding systemic biases into digital form and instead use AI to reduce health inequities (for example, by targeting interventions to underserved areas).

A further concern is the lack of transparency (“black box” issue) and its impact on trust and safety. Many AI models, especially deep learning networks, operate as complex black boxes — they do not explain their reasoning in human-understandable terms. In healthcare, this opacity is problematic. Clinicians and public health decision-makers are wary of acting based on a recommendation they don’t understand, particularly if an AI’s advice contradicts intuition or standard practice. Unexplainable AI can also undermine accountability: if an AI makes a harmful mistake, it may be unclear why it happened or who is responsible. This lack of transparency feeds directly into trust issues among professionals and the public. If people perceive AI as a mysterious, untrustworthy “magic wand” imposed on health decisions, they may reject its use. There have been cautionary tales: an AI system deployed in hospitals to predict which COVID-19 patients would need ICU care was later found to underperform because it hadn’t been adequately validated. Clinicians grew sceptical of its risk scores.

To prevent such scenarios, experts call for explainable and interpretable AI in health — algorithms that can provide reasons for their predictions or use transparent, logical rules where possible. At a minimum, users should have access to information about how an AI was developed and its known limitations. Regulatory frameworks like the EU AI Act are likely to mandate a degree of transparency for high-risk AI (including many medical applications) precisely to bolster trust and enable oversight. Building more explainability into AI models remains a technical challenge, but one that is essential for aligning with the principles of transparency and accountability in healthcare.

In the age of ChatGPT and generative AI, misinformation and “AI hallucinations” have emerged as new public health risks. Advanced chatbots can produce remarkably human-like answers to questions — but they do not guarantee factual accuracy. They can hallucinate false information, confidently output incorrect medical advice, nonexistent statistics, or even fake health news. The potential for harm is considerable if the public uses such tools for health information. There is concern that AI chatbots could magnify the health misinformation problem exponentially — for instance, by generating convincing anti-vaccine narratives or spurious cures, which then spread on social media.

In recent years, public health agencies have struggled to combat misinformation (for example, false claims about vaccines or COVID-19 treatments that undermine uptake). The rise of AI-driven content generators and deepfakes only fuels this fire. Misinformation undermines public trust and can lead people to reject proven interventions in favour of dangerous alternatives. Tackling this will require new strategies — such as watermarking AI-generated content, strengthening content moderation, and improving digital health literacy so the public can better discern credible information. On the flip side, public health communicators might also leverage AI to fight misinformation (for example, using AI to detect false rumours early or personalise accurate health messages). Regardless, the advent of easy, AI-generated disinformation is a serious risk factor that the global health community cannot ignore.

Finally, there is the risk of over-reliance and systemic dependency on AI. If health systems come to depend on AI for critical functions without adequate safeguards, any failures in the technology could have severe consequences. For example, an AI model might perform well in normal conditions but fail to generalise during an unexpected scenario. If everyone has come to rely on its output, they may miss the warning signs until too late. Moreover, heavy reliance on automation might erode human skills over time (a phenomenon observed in other industries). In healthcare, this raises concerns about “deskilling” — clinicians might lose practice in specific tasks (like reading x-rays or making complex diagnoses) if those are always handled by AI, leaving them less prepared to step in when needed.

Over-reliance can also dull vigilance: users might stop double-checking results if an algorithm usually works well so that an undetected error could propagate. The key is to maintain a human-in-the-loop approach: AI should support, not replace, human expertise. Mechanisms for human review of AI outputs and fallback plans in case of system outages are essential.

Additionally, performing regular audits and updates of AI models can prevent performance from degrading unnoticed. In summary, while AI can increase efficiency, public health systems must guard against blindly relying on algorithms. A balanced approach that values human judgment and institutional memory, alongside AI’s computational power, will be safest in the long run.

Ethical and Regulatory Frameworks

Addressing the above risks requires robust ethical guidelines and regulatory oversight for AI in health. Globally, there is growing consensus on core ethical principles that should govern AI development and use in public health. The World Health Organization’s landmark 2021 report laid out six guiding principles for ethical AI in health: (1) Protect human autonomy — humans should remain in control of health decisions, with informed consent and respect for privacy; (2) Promote human well-being and safety — AI must be safe, effective, and designed to improve health outcomes; (3) Ensure transparency, explainability and intelligibility — stakeholders should have sufficient information about how AI systems work and decisions should be traceable; (4) Foster responsibility and accountability — developers and users are accountable for AI behaviour, and mechanisms for redress must exist; (5) Ensure inclusiveness and equity — AI should benefit all groups, enhancing fairness and not amplifying disparities; and (6) Promote AI that is responsive and sustainable — meaning AI should be adaptable, monitored, and designed for long-term societal benefit.

These principles, while high-level, provide a value framework to guide everything from design choices (e.g. using diverse training data to ensure equity) to deployment (e.g. always keeping a human in the loop to protect autonomy). Public health organisations are increasingly adopting such ethical frameworks. For instance, the WHO urges that AI deployments be accompanied by community engagement, training for health workers, and continuous evaluation to ensure technologies remain aligned with the public interest. The ethos is straightforward: AI must be people-centred and uphold human rights. Ethics committees or advisory boards can help oversee AI projects, reviewing them for compliance with these principles before they scale up.

On the regulatory front, governments are now moving to establish formal rules for AI in healthcare. The European Union’s AI Act is a pioneering example of comprehensive regulation. Passed in 2024, the EU AI Act takes a risk-based approach, classifying AI systems by risk level and imposing requirements accordingly. Health-related AI is generally deemed “high-risk” under this law, given its potential impact on people’s lives and rights. High-risk AI systems (including most AI used for medical diagnostics, decision support, or resource allocation in health) will face strict obligations. These include rigorous standards for transparency, risk management, and human oversight. For instance, developers of a clinical AI tool must implement a quality management system, ensure their model is trained on appropriate data, and provide documentation detailing the AI’s function and limitations. They must also conduct risk assessments and put in place human oversight measures to prevent automation bias. Notably, the EU AI Act doesn’t just apply to creators of AI — it also holds deployers (such as hospitals or public health agencies) accountable for the safe use of AI.

Health providers must monitor AI system performance, keep logs, and retain ultimate responsibility for decisions (clinicians must have the authority to override AI recommendations if needed). These provisions aim to ensure that human accountability and patient safety remain paramount even as AI becomes embedded in care delivery. Additionally, the Act has a broad reach: any AI system impacting people in Europe must comply, even if developed elsewhere. This could set an effective global benchmark as companies worldwide adjust their practices to meet the EU’s requirements.

Other jurisdictions are also crafting guidelines. The United States, through the FDA, has been evolving its regulatory approach for AI/ML-based medical devices, focusing on premarket evaluation and the idea of “continuously learning” algorithms needing ongoing monitoring. International bodies like the WHO have issued guidance and urged governance innovation, suggesting that governments update regulations to cover AI, establish certification processes, and possibly create registries of approved AI health products. We also see emerging governance models such as algorithmic impact assessments (to evaluate a health AI system’s potential societal impact before deployment) and independent reviewers’ bias audits. In some health systems, procurement of AI now requires meeting ethical checklists or obtaining approval from institutional review boards, similar to new medical interventions.

These steps are part of building a “responsible innovation” culture around AI, encouraging experimentation and advancement, but within guardrails that protect individuals and communities. Multi-stakeholder collaboration is key here — regulators, technologists, health professionals, and patient representatives need to work together to define safe and effective AI in practice and update those definitions as the technology evolves. As one example, the NHS AI Lab in the UK partnered with regulators to create a sandbox for AI developers, guiding them on navigating regulatory pathways and using synthetic data for testing. Such efforts show that with thoughtful governance, innovation and safety can advance hand in hand.

Future Directions and Recommendations

To fully realise AI’s promise in public health while minimising its downsides, several changes and strategic efforts are needed going forward:

• Investing in data and digital infrastructure: Health systems, especially in low- and middle-income countries, need support to build the data foundations for AI. This means digitising health records, improving data quality, and ensuring platform interoperability. Governments and global donors should prioritise funding for health information systems and broadband connectivity as part of public health capacity building. Better data infrastructure not only enables AI — it strengthens health systems overall. Innovative approaches like federated learning (where AI models train on distributed data without moving it) could be scaled to allow resource-constrained regions to benefit from AI insights without breaching privacy. The goal is to create a world where data flows securely and efficiently to wherever it can improve health outcomes.
• Strengthening workforce capacity and AI literacy: As AI becomes a standard tool, public health and healthcare workers must be equipped to use and oversee it. Training programmes are needed to raise AI literacy among the health workforce, including understanding AI’s capabilities and limitations. This may involve updating medical and public health curricula to cover data science basics. Additionally, new specialist roles (such as clinical AI safety officers or epidemiologists with AI expertise) could be developed to bridge the gap between tech and health domains. Frontline staff should be engaged in co-designing AI solutions so that tools are user-friendly and address actual pain points. When health workers understand and trust AI, they can become champions for its adoption and serve as critical watchdogs who notice when something isn’t right. Fostering a culture of continuous human oversight and feedback will ensure that AI remains a servant to health professionals, not a black box dictator.
• Ensuring inclusivity and equity in AI advancement: The global health community must actively work to prevent a digital divide in AI. Much cutting-edge AI development is concentrated in wealthier countries and tech companies. Deliberate efforts are needed to include researchers and perspectives from low- and middle-income countries in AI design so that solutions address diverse needs. This could consist of research funding earmarked for LMIC-led AI projects, technology transfer programs, and south-south collaboration on AI for health. Moreover, data from underrepresented populations should be collected (with consent and protection) to improve algorithms’ relevance in those settings. By democratising AI knowledge and resources, we can avoid a scenario where only certain countries or communities benefit from AI while others are left behind or subject to unchecked harm. Equity considerations should also extend to gender, age, and other demographics — for instance, ensuring women and minority groups are included in AI development teams and that tools serve users of different languages and literacy levels. An inclusive approach will make AI tools fairer and enlarge the talent pool working on creative AI solutions for entrenched public health challenges.
• Fostering collaboration between public health and technology sectors: Effective AI in public health sits at the intersection of epidemiology, medicine, data science, and engineering. No single sector can do it alone. We need stronger partnerships: governments linking with academia and tech firms, NGOs working with startups, and international agencies convening multi-sector consortia for global health AI initiatives. Such collaboration can accelerate innovation and ensure that public health priorities guide technological development (and vice versa, that technologists are aware of on-the-ground needs). For example, a partnership between a national health ministry and AI researchers might focus on building an early warning system for malaria outbreaks, combining epidemiological expertise with cutting-edge modelling. A pharmaceutical company could also collaborate with global health organisations to use AI in vaccine R&D for diseases of poverty. These cross-sector collaborations should be underpinned by fair agreements (e.g. around data sharing or intellectual property) so that all parties benefit and trust is maintained. The complexity of health + AI demands breaking down silos. International forums and networks can play a role here, enabling countries to share best practices and lessons learned (e.g. how one country successfully regulated an AI symptom-checker or how another trained health workers on AI). Since pathogens do not respect borders, a collaborative global approach to AI-enhanced public health security is in everyone’s interest.
• Adaptive governance and continuous evaluation: As AI tools roll out, it is critical to monitor their real-world impact and be ready to adjust course. Public health authorities should implement mechanisms to continuously evaluate AI interventions — collecting data on their accuracy, outcomes, and any unintended effects. Are the predictions helping improve disease control? Is a triage algorithm safely directing patients to the right level of care? This requires establishing key performance indicators and perhaps creating independent evaluation units. When problems are identified (such as an AI starting to drift in accuracy due to changes in data), there should be processes to update or pull back the tool until fixes are in place. Regulation must also remain adaptive; rigid rules could stifle innovation or become outdated as technology advances. One idea is regulatory sandboxes where new AI solutions can be tested under supervision, allowing regulators to learn and guidelines to evolve. Governance models should be proactive yet flexible, emphasising learning and iteration. Importantly, communities and civil society should have a voice in evaluating AI in public health — their feedback on whether these tools are culturally acceptable, understandable, and improving services is invaluable. Responsible AI is not a one-time certification but an ongoing commitment to quality and ethics throughout the technology’s lifecycle.

Looking ahead, it is clear that AI will play an expanding role in public health — whether in combating the next pandemic, extending healthcare to remote villages via smart apps, or analysing big data to pinpoint disease drivers. The revolution is already underway, but its trajectory depends on our current choices. With enlightened leadership, adequate safeguards, and inclusive collaboration, AI could usher in significant public health gains — from more efficient health systems to healthier communities worldwide. However, if we ignore the risks — allowing unchecked use, widening inequities, or losing the human touch in care — the potential benefits could unravel, and public trust could be irrevocably lost. The coming years are thus pivotal. Armed with decades of hard-won experience, public health professionals have a key role in steering this journey. By insisting on evidence, equity, transparency, and community engagement, they can ensure that the AI revolution in health truly becomes a boon and not a threat. The opportunity is immense, but so is the responsibility to guide AI’s integration into public health thoughtfully and ethically.

The post AI in Public Health: Revolution, Risk and Opportunity appeared first on Medika Life.

Sometimes it’s a look.

A line.

A moment that lands deeper than diagnosis.

This brief essay is a collection of those moments.

Brief.

Unexpected.

And always real.

“The Healing Power of Touch: A Patient’s Insight”

This week, a patient shared a poignant realization that emerged after years of emotional distance from his wife.

They had grown apart, but recently discovered a shared need: the simple, profound act of touch.

He reflected on how a gentle hug or a reassuring hand on the shoulder seemed to bridge the emotional gap between them.

“I think we’re wired for this,” he mused, referencing hormones like oxytocin that respond to physical affection.

His insight aligns with scientific findings.

Oxytocin, often referred to as the “love hormone,” plays a crucial role in social bonding and emotional connection.

Studies have shown that affectionate touch can increase oxytocin levels, reduce stress, and foster feelings of trust and closeness.

In fact, research indicates that even brief moments of affectionate touch can lead to measurable increases in oxytocin, a hormone that contributes to an improved mood and reduced anxiety.

This finding underscores the biological underpinnings of our need for physical connection.

My patient’s experience serves as a reminder that sometimes, healing in relationships doesn’t require grand gestures — just a touch of understanding, quite literally.

For more reflections on connection at the edge of life, read my essay: What Dying Men Confessed When No One Was Listening.

“The Prostitute’s Pasta”

In oncology, gratitude comes in many forms — thank-you notes, quiet nods, even tears.

But sometimes, it arrives as a steaming pan of pasta.

One of our patients, an older Italian gentleman with a twinkle in his eye and impeccable taste, has taken to feeding the staff.

Not metaphorically — literally.

Lasagna, tiramisu, and even delicate cannoli are dusted with sugar like freshly fallen snow.

Today, he arrived bearing a new dish. “Pasta Puttanesca!” he announced proudly. “You know — the prostitute’s pasta.”

A pause.

Then laughter. Nurses chuckled. My medical assistant nearly dropped her stethoscope.

He winked. “They say it was made quickly, between clients.”

I’ll leave the etymology to linguists.

But I can tell you this: the olives were briny, the sauce was bold, and the gratitude was unmistakable.

This event was something else entirely in a world often defined by scans and side effects.

A recipe for connection.

Served al dente.

Note: For patient privacy, I have modified some details.

Here are my previous Clinic Notes essays:

1. Clinic Notes 5/18/2025
2. Clinic Notes 6/26/2025

Want more stories like these — plus the science behind living longer and better? I’ve distilled the most powerful lessons from oncology, aging research, and patient wisdom into my new ebook: Extending Life and Healthspan.

Practical, evidence-based, and full of humanity.

The post Clinic Notes: What My Patients Said This Week appeared first on Medika Life.

In this exclusive conversation, I join Hal Wolf, President and CEO of HIMSS, to explore HIMSS’s evolving role in fostering collaboration between hospitals, startups, and policymakers. With the health-ecosystem landscape tracking the early days of a new administration, uncertainties remain—ranging from regulatory shifts to funding allocations. Yet, as Wolf underscores, HIMSS remains steadfast in advancing health equity, supporting digital transformation, and offering actionable strategies that improve care delivery.

This conversation occurs between ViVE in Nashville and HIMSS in Las Vegas—two health gatherings that bring innovators, policymakers, and industry leaders together. While ViVE spotlights digital health startups, investment trends, and edgy sparks, HIMSS serves as the broader convening ground for professionals shaping the future of health information and technology. HIMSS is where the work happens—the “Davos” of health information.

Wolf outlines key issues such as integrating artificial intelligence in hospital systems, the role of primary care in driving better patient outcomes, and how organizations must balance innovation with financial realities. At HIMSS, essential dialogue between established institutions and emerging disruptors has never been more crucial.

Join us as we delve into the forces shaping the health system’s future—where technology, policy, and leadership converge to improve patient outcomes and system-wide efficiency.

Medika Life Editor Gil Bashe: In examining hospital systems, we focus on how information flows within our industry and who has access to it. We still have a lot to learn. These are the first few weeks of the new administration, and we don’t know how things will unfold, how the Senate Committees will approach these discussions, or how actions will be implemented. Will we rely on Executive Orders? If that’s the case, we know where to focus our attention. You’re a remarkable ambassador for the discipline and the sector, and certainly for HIMSS, a professional association, trade association, and global NGO.

People come to HIMSS with tremendous optimism, hoping to gain insights that will make them more effective. I prefer to focus on current developments and HIMSS priorities rather than just reacting to them; that’s a different conversation. I’ll also note that the administration’s conversation around health access, cost, and priorities is still in motion.

HIMSS CEO Hal Wolf: We don’t know enough to discuss new policies and their potential outcomes. One challenge will be securing funding. While we know things will be different, we don’t know where the policies will land. Still, we know that HIMSS is dedicated to its vision and mission statements as they fundamentally relate to realizing the full health potential of every human everywhere.

We are dedicated to health equity and will stay dedicated to it. Our collaboration with governments and NGOs worldwide and our fundamental principles will not change, nor should they! We’ll work with our principles within the context of whatever comes out of the White House.

We worked well with the previous Trump administration and collaborated effectively with ONC and HHS on various initiatives. We look forward to advancing the HIMSS mission within the established parameters.

Bashe: HIMSS is far more than an annual mammoth gathering; it’s a professional society that covers the full spectrum of health information and technology – from cybersecurity to economics to professional development and government policies. Unlike other popular meetings that primarily focus on networking, HIMSS is where professionals from around the globe come to set objectives, strategies, and operational priorities. It’s 30,000 feet and 3,000 in scope.

Wolf: This morning at 6:00 a.m., I had an interesting conversation with the CEO of a successful start-up that is getting distribution now and beginning to roll. At HIMSS, we see the merging of different worlds.

We have our core population, core members of the health ecosystem– hospitals, clinics, health operations, nurse practitioners, CMIOS, CIOS, physician leaders, and administrators. This group represents a significant portion of the HIMSS membership, which includes over 120,000 members.  

On the other hand, we have the entire global app ecosystem that drives innovation and introduces new ideas. As you know, many of these ideas and innovations are driven by personal experiences. A family member encountered a situation, and they tried to solve it. Or have worked in the industry, identified a gap, and pursued it.

They often have a long list of improvements to address, and, often, they aren’t performing well financially. Their reimbursement processes are a little murky, and this uncertainty might increase in 2025.

How much time do they have to integrate innovations that don’t directly impact their outcomes, quality, access or bottom line? Because everything’s being looked at in that piece.

We need to understand that the situation changes as the market evolves. What is the critical point where innovation intersects with standard operating procedures, and what does it look like? How can information from one area influence the other? We must determine how to identify the good and the bad and how to present them to the market.

What happens at the global conference? What happens in chapter meetings? What happens in the papers that are submitted? What happens in the insights? Much of this depends on how these elements connect.

Bashe: Many diverse health information communities come together yearly at HIMSS. You have consistently made this gathering relevant. This year, a new startup section called Emerge addresses a critical need within the HIMSS community. You just started with the story about a startup enterprise. Could you share some important and innovative aspects that will be highlighted at this year’s gathering?

Wolf: That’s a pressing question, so I’ll divide it into multiple parts because it’s challenging.

We’re part of the industry, so let’s return to your original premises to find the answer. You mentioned “competing,” which refers to people competing against each other. However, you also have specific points of interest to consider.

The benefit engine can determine how much money you’ll receive in reimbursement for a particular service. However, if you’re on the insurance or payer side, the configuration may depend on whether it’s for North America or a Ministry of Health. Here, the goal is to anticipate the costs incurred in treating a patient. This perspective aligns with an actuarial professional trying to understand and guide the process forward.

I’ve observed hospital systems are starting to integrate AI functionalities, but currently, only 5 to 7% are using it effectively, from an operational standpoint,

For example, when someone walks to the front desk and says their right arm is hurting, the staff collects the information by typing it into a form. They ask a couple of questions, such as “Who’s your primary care doctor? Who’s your insurance company? Are you on managed care? Is this new? What other ailments do you have? What other prescriptions do you have?” They must ask those questions if they don’t have that information readily available. That data feeds into new algorithms on the AI side.

In the background, AI analyzes the information and makes predictive models about how long this person would be in the hospital, what resources they will consume, and how much revenue it will generate. It’s occurring in the background, without the front desk staff being aware of AI’s calculations; meanwhile, the administrator is beginning to recognize the impact of these advancements. The inbound process begins with appropriate testing, questioning, and, if necessary, into a bed.

Meanwhile, the hospital administrator or the system managing the situation assesses the resources the patient will need, whether they will need a specialist and whether the specialist will be available when needed. By the time an exam takes place, they can inform the patient about what to expect in the next 24 to 48 hours, whether they will be staying at the hospital or going home. This preparation and communication represent the positive aspects of the process.  

The dark side is that the hospital engine in the background might say, “We’re not going to get a lot of money out of this. This is not a good use of our beds/time. If we maximize profits, we should send this person on and see what the next person will bring because our algorithm told us that five people would come in with congestive heart failure, and we do make money on that.”

The person writing this down may never realize what is happening; they won’t know that the system will indicate that the hospital is full, even though there might be capacity. Instead, they will tell the person to go down the street to Acme Community Hospital, which can take care of them, explaining that their system lacks resources. That is a dark coin flip to what could happen.

Bashe: Many hospital networks are acquiring primary care practices as feeder systems in their facilities. For example, if a patient is told, “You need to do a cardiac stress test. Do you have a cardiologist?” and they respond with “No?” the primary care provider can then say, “Why don’t you let me arrange that for you.” The primary care satellite site is closing the loop on a fragmented system. While the hospital system benefits economically from the service, patients benefit, and the primary care satellite site serves as a conduit for care.

I’m always thinking about the benefits of technology in enhancing the hospital and primary care systems. Imagine a doctor saying, “You know, you’ll have many questions. I will be here to help you frame your thinking around those questions. Our system has an LLM model. Let’s call it Dr. Hal. You can ask Dr. Hal every question regarding your congestive heart failure or prostate cancer. Dr. Hal is the composite wisdom of all the experts in our system and is here to address your questions.”

The creative aspect of our discipline, combined with information, is becoming a superpower. We use data to guide our supply chain resourcing and leverage information to promptly provide patients with confidence and comfort. We ensure greater access to accurate information vetted by the system, so patients do not depend solely on Dr. Google.

Wolf: The actual value of AI is knowledge management. It allows a very broad and capable synthesis of vast amounts of data and information that no human can keep up with. For example, in the 1970s, clinicians had access to three to four journals, where editors picked what was important enough to be published. These journals had to be printed and mailed out, resulting in about 400 peer-reviewed articles per year reaching healthcare professionals. If you read one a day, you could keep up. Today, more than 10,000 articles will be published this year alone. All that information, knowledge management, and sharing will occur collaboratively, and there is no way for anyone to synthesize all that.

AI plays a crucial role in operational and clinical decision support by turning information into knowledge, with recommendations that lead to changes in operations, suggestions, and care.

In clinical care, pharmacy, or whatever path you’re on, these recommendations are communicated back to physicians with an explanation of why they are a recommendation and the source of that information.

I think part of the maturity that we’re seeing, and you’ll see at HIMSS 2025, is the evolution of AI since our session three years ago. Back then, we held a session titled, “What is AI, and what does it mean?” The panel discussed its potential application in healthcare, and at that time, chat had just been introduced, and people were starting to look at it. Some people were on stage calling for a six-month hiatus before we allowed anything to go forward.

Last year, we saw glimpses of initial uses of AI being deployed operationally, albeit only in a few hospital systems. But it was beginning to happen, and we knew that AI was in the background of devices or operational considerations. Where would the benefit engines come from? The algorithms were starting to be built, and we had a particular point of looking out for biases. We started talking about biases within AI and realized that no matter what you do, there will always be some biases. It’s unavoidable. What was the source information for AI, and how do I ensure I utilize it to the best of my ability?

You’ll see the presentation of how people are using it on a scale. What are examples of its success, and what are some of its limitations? Numerous applications are set to emerge. You’re going to see them on the floor, where people are using components of AI in the background to produce better products that are more efficient and can guide operations as well as at-home care, and all those pieces are being brought forward. The common link between it and your point is on the information side of the house. How good is the starting information, and how broad does it go? Where is the opportunity from a linking standpoint?

To achieve this, a private-public partnership is essential. If you’re looking at algorithms and information that utilize global data that gets turned into global information, it has an impact. Most healthcare systems around the globe are publicly held. They’re not privately held. The United States is an aberration due to its vast amounts of privately held institutions versus publicly held ones.

Bashe:  Are you seeing more of that regarding the technology being used proactively?

Wolf: Yes, and that’s a good thing. We’ve always wanted to see that proper reimbursement takes place and proper services rendered. Many things in a system can get missed, but not an overwhelming amount.  If hospital systems perform well, they typically operate on a 2 percent to three percent margin, but many run at a loss, making proper reimbursements difficult. Large actuarial departments played a key role in the past, with various organizations providing revenue support, which was a huge thing even 15 years ago. However, over time, those efforts began to converge.

The real opportunity lies beneath the surface. It must coincide with an understanding of the care that was delivered. Right next to that benefit realization is the value proposition. What was the quality of what was rendered? Was the care appropriately given? Did we miss something in the diagnosis?

Bashe: One of the things that I worry about is not New York City or Los Angeles. Medical centers such as Mount Sinai, NYU Langone, Weil-Cornell, Columbia Presbyterian, and Memorial Sloan Kettering, much like their counterparts in Boston, Chicago, and the Bay area, provide excellent care. However, in rural America, someone can live three hours from a tertiary care center.

Your approach of using information to improve the care of almost 29 percent of the US population applies, I think, to other nations where people live far from centers of excellence. What are your thoughts about devices, wearables, remote patient monitoring and information, and protecting the information from your standpoint?

Wolf:  Wearables and home monitoring have transformed patient engagement, making health data more immediate and actionable. It’s fascinating. My wife and I compare our Oura daily. How did you sleep? How’s your heart rhythm? We’re finding the features and working through them. She lives anonymously. We are very engaged in our health. How far did we walk? What was our heart rate? Let’s do the 6-minute walk today and see. Were you snoring last night?

All of that is going on, and that’s an engagement level. The information flows from me to my ring, and then my ring says, do you want to share it with Apple? I said yes, and my wife said no; she didn’t want it to flow to another company. Apple will know how well I sleep – I don’t worry about it. If they want to dive into it, there is a profile about me and my general health. They could also derive that from the stuff I buy and the credit card information. That’s always been the case.

If you remember, back in the 1980s, we were already using demographic data with Donnelly overlays. I worked for Time Warner in the early 1990s when Time magazines were delivered to your door. The Time magazine that arrived at your next-door neighbor was different from yours—not the content, but the cover and the ads in the back. You may have gotten an ad for a sports car, and your neighbor may have gotten one for a minivan.

It was specifically designed based on the Donnelly reports, which provided insights about the household. We’re starting to shape recommendations at the personal level of the care an individual should receive.

Why wouldn’t a physician or a clinician want every piece of information on this Oura ring to be included in a patient’s profile? This information would help complete the picture needed to utilize sophisticated knowledge management systems, tapping into tens of thousands of research papers and combining that data with the person’s unique health details. The richer that information becomes, the more accurate it becomes, the more mistakes it makes, the more positively it helps the next person.

Bashe: Often, when I speak to doctors and nurses in the health system, we talk about the Electronic Medical Record (EMR). They’re candid: “We have an EMR system—it’s not perfect, we know that, and it’s getting better and better.” Yet, they often say, “Did you read that patient’s EMR data?” and then they’ll say, “I don’t have time to read the EMR.” While best practices come from committees, you play a unique role as an advisor to corporations. You’re the sounding board for major corporations, whether AWS, Epic, Microsoft, or Oracle. I’m sure they will listen to you because you’re the voice of the global community.

Wolf: We don’t have a dog in the race against them as a competitor.

Bashe: As a not-for-profit society that operates at a global NGO level, when you look at your role and the challenges you face, how does HIMSS address constant sector transformation? HIMSS and its members are constantly evolving because you represent applied information. The system is getting more interesting and more creative.

Look at the challenges that HIMSS owns and represents and your mission, which is obviously to improve access to care. As the organization’s leader, you’re clear and committed to this role, but you’ve seen difficulties implementing cultural or systemic changes.

What’s your guidance for the community? Please don’t take out a ruler and slap people on their hands. You’re obviously about supporting the system’s evolution, making it better. Can you share insights on how you’re filtering down best practices within this evolving landscape? How do you reflect on these challenges that arise and guide systems to understand that care is delivered to the front lines and is not always in hospitals? It’s specialists or primary care—physicians in their little offices worldwide.

Wolf: This is precisely what we discuss daily at HIMSS, and it’s central to our global work. Let’s walk through our view of the ecosystem and how we influence what you just related to because it’s our core.

Hospital systems – or, more importantly, governments worldwide – including our own, recognize their fundamental responsibility to care for their populations. Let’s set the United States aside for a second.

Ministries of Health in countries around the world are accountable for the well-being of their citizens. We’ll talk about citizens for a minute. The people living within their country want a healthy population, which improves the economy. They’re smart enough to know that a healthier population, or one cared for, efficiently reduces the constant increase in costs within healthcare systems. No one is looking to save money; instead, the goal is to slow the escalating costs of healthcare systems, which seem to rise every year.  

Information is the driver behind everything, but to your point, the combination of people, processes, and technology shapes the outcome. Technology is rarely an issue here; the challenge lies in implementing and changing culture. The pandemic forced a significant global cultural change, and while it may seem that it’s deeply in our rear-view mirror, its impact is still felt. Telemedicine, the idea of using information, and the idea of remote care to alleviate the pressures on the front line became a standard feature, and people recognize that.

We see the impact in our relationships with organizations like the WHO in Europe. Take Romania, for example, where we just signed an agreement to help them develop a strategic plan to deliver digital health transformation. HIMSS is focused on four major points.

1. First is digital health transformation.
2. Second is the deployment and utilization of AI as a tool.
3. Third is cyber security to protect that information and ensure that it works for the betterment of their ecosystem with less hassle.
4. Fourth is workforce development, which trains people to understand these tools before they can utilize them to their fullest extent.

Those are our four main areas. When we think about digital health transformation, we start with the HIMSS maturity models from five to seven years ago.

Back then, our maturity models were a checklist of technology. Do you have that technology? Are you wired? That used to be the baseline, what we now consider table stakes. It’s not table stakes anymore.

We’ve transformed our maturity models to reflect quality, access, correctness, and fundamental value. How are you using the information? How does it improve the flow?

From an IT standpoint, we began looking at our maturity models like a stack. It starts with the information layer. What does the infrastructure look like? How is it laid out? How does your data need to be laid out? Where does the electronic medical record go on top of that? How do those two pieces feed into each other? How do you utilize the radiology and the pictures that are in there? How does that flow? What’s your analytical layer? How does this work?

Where are you getting your information, and how are you handling your analytics? How does that tie itself back into the infrastructure? How does that information flow from your reporting back into your EMR and the data layer? How does that data layer tie in when discussing the imaging ecosystem? What’s your continuity of care, the CCMM? How does it flow across the board to ensure you’re not dropping a patient?

We’ve created a stack of maturity models that form the foundation of how information flows from the patient across hospitals, clinics, and homes, wherever the case is provided, to ensure you can keep up with them. And we present these maturity models not just as a technology checklist. Anyone can do that—it’s not meaningless—but anyone can do that. The true focus is on how you use these technologies.

How do you ensure that the relationship between the patient and pharmacy utilization, as well as the benefit realization, is maintained? And how does all this tie together?

Whether it’s community service, a hospital system, or a home, what we’ve created in those stacks is a blueprint that any hospital system, country, or large-scale region can use to identify the technology needed and deploy it for its maximum benefit. 

People do assessments in hospital systems. For HIMSS members in the United States, these assessments are part of the membership, allowing them unlimited access to evaluate their systems. They can conduct these assessments online, check their status, and aim for levels 6 and 7, which is when all those benefits kick in. That’s when we do our validations.

We also do white papers, thought leadership, and HIMSS events, panels and educational programs. More than 300 academic programs are coming up at HIMSS in 2025, with more than 150 offering CE credits.

But these experiences are all based on the output, what worked, and what didn’t work. As you know, learning from others’ mistakes is just as valuable as learning from their successes. Some of the most impactful lessons come from those who try something, fail, and then fix it.

That’s where HIMSS and advisory services come in. We’re presenting the aggregated global knowledge of what’s working and what isn’t.

Most ecosystems don’t work the same way the United States does because most don’t have the same amount of money invested in it. We draw from many healthcare systems- from the U.S., to Romania, Italy, Germany, Singapore, Indonesia, Malaysia and Australia. We learn from all these countries, bring it together in our membership, and figure out what we have learned. How does it impact the models? We do these reviews in a constant session. That’s how we make the society work.

Bashe: It’s a brilliant use of human capital and composite wisdom. As we’re gearing toward the end of our conversation, I wanted to ask you about the Emerge Innovation Experience– this is a first-time gathering, but the concept of start-ups at HIMSS is nothing new. What’s different now is that you’ve recognized that start-ups are a unique culture with unique needs. You’ve assembled a cohort of leaders dedicated to helping these start-ups succeed. What are your expectations from Emerge?

Wolf: First, I’m very interested in the outcome of Emerge. This is the first time that we’re going to try to bring that mesh point I mentioned earlier, where innovations meet operations. They’ve chosen some excellent examples of what can come forward. I think it’s got the right practicality and innovative forethought. From what I’ve heard from people involved in it and talking to people on the committee, I’ve listened to everything from “Wow! This is fantastic and very innovative!” to “It could have been stronger.”

If I talk to heavy innovator startups, they reply that it doesn’t go far enough and could be really “wow.” Meanwhile, those focused on operations often reply that it’s “really pushing the edge.” What that tells me is that it’s in the right mesh point.

What I’m curious to see is how it is received. Many smart people have been working on it, focusing on what will have the biggest impact on operations and be ready for prime time tomorrow, especially in areas like AI utilization and operational impact. What is one step beyond? We also have an incubator ecosystem there.

The Emerge Innovation Experience will be unique, and I look forward to that outcome.

Bashe: I always value your candor.

Wolf: Sometimes, I can get criticized for it, but I believe in absolute transparency. The beautiful part about thought leadership is that we share these thoughts, which makes HIMSS thrive. Transparency is a strength in a positive society. If we’re not transparent with each other, we can’t advance. My grandfather taught me a long time ago that the three most essential phrases in business are “I don’t know, I’ll find out, and I’ll get back to you.” “I don’t know” is critically important.

What’s beautiful about healthcare is that you learn something new every day. It’s impossible to be in the health sector without learning something new every day unless you don’t ask a single question, read nothing, or stay in a room and shut the door.  

Just today, I learned something about HIMSS. I didn’t know because I was asking about a process. A question was raised, and I followed the thread through the organization and found one I’d never seen before, which was exciting.

I appreciate the philosophical and real questions you’re asking. We’re excited about HIMSS 2025 and the learning opportunities it will offer. It’s also about the big picture of what’s happening globally. We call it the Global Conference because it brings together Ministries of Health and NGOs worldwide. It all comes together. This is our largest membership meeting, and we’re thrilled that there’s at least a 35% increase in people signing up for HIMSS membership compared to last year.

Bashe: I’ll be attending this year. As always, thank you for the in-depth exchange. *****

In this insightful conversation, Hal Wolf, President and CEO of HIMSS, explores the critical forces shaping the sector’s future. As industry and governments navigate a rapidly evolving policy landscape under a new administration’s eyes, hospital systems, startups and policymakers must adapt to changing regulations, funding challenges, and digital transformation. Wolf highlights HIMSS’ role as a global leader in uniting a diverse ecosystem to advance health equity, interoperability, and patient-centered care.

A key theme of the discussion is how data and AI are transforming payer, provider and product innovation operations – how information can improve people’s lives. Wolf explains how AI-driven predictive models are integrated to optimize patient care and resource allocation. However, he also warns of ethical concerns—such as the potential for financial-driven decision-making that could prioritize revenue over patient needs. HIMSS plays a vital role in ensuring there is a balance between digital health innovation aligns with quality care and equitable access.

As digital tools, AI, and large language models (LLMs) become more integrated into healthcare, Wolf and Bashe discuss how these advancements can empower providers and patient-enhancing decision-making, improving operational efficiency and offering trusted, system-vetted health information.

As HIMSS prepares for its annual global conference, Wolf emphasizes its role in shaping industry priorities. HIMSS is not just an event; it’s a society that defines strategies, policies, and innovations that drive the future of health. With an expanding ecosystem of startups and industry veterans, HIMSS remains a pivotal force in bridging technology, policy and patient care.

The post The Future of Health Information and Innovation: A Conversation with HIMSS CEO Hal Wolf appeared first on Medika Life.

“The use of technology in healthcare offers numerous benefits.  But it must be easy for clinicians to use and document the care provided without barriers or interruptions to their train of thought.  Not an easy task.”

Bradley leads an HIMSS team of 40 professionals working with health systems to manage the professional society’s Analytics Maturity Models.  These effort include working with the health provider systems, software vendors, certified consultants and peer-review teams to ensure their health information systems are geared to do their best work supporting patient care. Phil also serves as the point of contact for all Stage 7 validations across the Americas.

Gil and Phil discuss the uptake and efficacy of electronic medical records and related digital health IT platforms, emphasizing clinician-user experience, workplace fatigue, and the need for clinician engagement to be “actionable.”

Listen to their conversation live – captured by Health Unabashed Executive Produce Gregg Masters, MPH, straight from the HIMSS exhibit floor:

The post Exclusive From HIMSS24: Meet Phil Bradley, HIMSS Global Digital Health Strategist appeared first on Medika Life.