Policy Tag: Digital Health

Adopted by the 76th WMA General Assembly, Porto, Portugal, October 2025

 

PREAMBLE

  1. The World Medical Association (WMA) recognizes that artificial intelligence (AI) is rapidly transforming all sectors, including healthcare. In this statement, the WMA reaffirms its commitment to patient-centered, physician-led care by emphasizing the concept of augmented intelligence – a framing that highlights AI’s role in augmenting human judgment – by strengthening rather than supplanting it, while recognizing that in specific, well-defined tasks AI may perform independently but always under human accountability. Through augmentation, AI is supporting rather than replacing human judgment, empathy, and accountability.
  2. Building on lessons learned from early deployments, the WMA sets out principles that maximize AI’s benefits while mitigating its risks, ensuring that its development, regulation and use remain consistent with medical ethics, international human-rights standards and the public’s trust in the profession.

DEFINITIONS AND SCOPE

  1. To promote clarity across jurisdictions while embedding the augmented intelligence perspective, the WMA uses the following working definitions in the healthcare ecosystem:
  • Artificial Intelligence (AI): Computer systems designed to perform tasks that normally require human intelligence – such as learning, problem-solving, understanding language, and recognizing patterns.
  • Augmented Intelligence: Use of artificial intelligence designed to support—not replace—human capabilities in healthcare.
  • Physician-in-the-Loop (PITL): an extension of the general “human-in-the-loop” principle whereby a licensed physician—rather than any user—must review and retain final authority over all AI outputs before they shape clinical care. Where clinical care involves multidisciplinary teams, PITL implementation should ensure that all relevant licensed professionals are adequately consulted, while the physician retains ultimate clinical responsibility.
  1. Emphasis on “augmented”
  • The term signals a human-centered approach to AI—one that reinforces the physician’s role as the final decision-maker. Rather than viewing AI as a replacement, augmented intelligence frames these tools as extensions of clinical expertise, designed to support – not replace – professional judgment, empathy, and responsibility.
  • While “AI” is widely understood as artificial intelligence, emphasizing the augmented perspective helps ensure that systems are designed, validated, regulated, and trusted with the right ethical priorities.
  • For the medical profession, this framing also enables more effective advocacy—especially when engaging with policymakers, regulators, and stakeholders who default to the broader term AI. It equips physicians to promote technologies that truly align with the goals of ethical, patient-centered care.
  1. Scope and audience
  • This statement aims to apply to all uses of AI in medicine, including clinical care and research, where AI primarily augments human decision-making. AI systems in administrative and educational contexts should be applied responsibly and with appropriate human oversight.
  • Its principles address physicians, other healthcare professionals, healthcare organizations, developers, regulators, payers, academic institutions, and industry partners, each of whom shares responsibility for ensuring that AI remains a safe, equitable, transparent, and ethically-governed tool in the delivery of healthcare worldwide.

GUIDING PRINCIPLES FOR AI IN HEALTHCARE

  1. Human-centricity: Human-centricity in AI prioritizes human needs, values, and wellbeing above technological capabilities or performance metrics. This principle includes:
  • Maintaining and respecting patient dignity, autonomy, and rights through meaningful consent for AI use.
  • Preserving patient health and well-being, and the human connection as the paramount considerations.
  • Embedding cultural competence to ensure AI systems respect diverse patient values, clinical needs, languages, and health beliefs.
  1. Physician well-being: The well-being of physicians and other clinicians must be safeguarded, recognizing that reducing administrative burden and avoiding unnecessary cognitive load are essential not only for supporting healthcare professionals but also for ensuring the quality and safety of patient care.
  2. AI is a Tool: AI should serve as a means to support healthcare goals rather than an end in itself. Unlike traditional medical tools, AI systems may appear to learn and adapt without continuous human input, making it essential to pair their use with strong human oversight and ethical governance.
  3. Accountability: AI integration does not diminish physician responsibility for patient welfare and advocacy. Consistent with the PITL principle, physicians must continue exercising professional judgment, and the final responsibility and accountability for diagnosis, indication, and therapy must always lie with the physician. At the same time, the growing prevalence of these tools necessitates clearly distributed accountability. Responsibility should be appropriately allocated among all stakeholders, including but not limited to developers, healthcare organizations, regulators, researchers and clinicians.
  4. Transparency, Explainability, and Trustworthiness:
  • AI systems must be designed and developed in ways that ensure their outputs and recommendations can be meaningfully understood by their intended end users—whether physicians, other healthcare professionals, or patients—within the relevant clinical context. Transparency extends beyond the “black box” paradigm, while explainability provides insight into the basis for specific outputs, thereby fostering trust and enabling responsible use. Transparency requirements and disclosures must be tailored to the needs of physicians and patients without adding paperwork or extra administrative tasks. Ensuring these qualities is a shared responsibility across all stakeholders, including developers, healthcare organizations, regulators, researchers, and clinicians.
  • Mechanisms should exist for meaningful challenges of healthcare AI outputs, enabling patients and clinicians – including physicians – to question, review, or override AI recommendations when appropriate. This capacity is essential for building clinical trust, without which clinicians may reject valuable AI tools or overly rely on opaque systems.
  • Explainability exists on a spectrum, with some complex models functioning as “black boxes” where only input/output relationships can be observed. The level of explainability required should generally be proportional to the clinical risk involved and the degree of autonomy granted to the system. In high-stakes contexts such as life-and-death decision-making, additional safeguards and oversight must be in place whenever full explainability cannot be achieved.
  1. Safe deployment: Safe deployment of AI in healthcare requires real-world validation demonstrating consistent performance, clinical efficacy, and usability before widespread adoption. Before clinical deployment, AI systems must also undergo rigorous ethical and health equity impact assessments that are context-sensitive and adapted to the specific healthcare setting and population, with particular attention to vulnerable and underrepresented groups. Implementation must include continuous performance monitoring, feedback mechanisms, and iterative improvement protocols to ensure sustained benefit and global accessibility. Risks and harmful consequences, including bias, must be properly understood, anticipated, and mitigated.
  2. Equitable implementation: New and beneficial AI healthcare tools must be developed and deployed equitably, with the goal of being accessible worldwide. Equitable implementation should ultimately bridge gaps in healthcare access, treatment, and outcomes., while expanding access to technology across disparate health care facilities.
  3. Data governance: All stakeholders must maintain the highest standards of data collection, storage, processing, and sharing to protect patient privacy, and institutional trust. This principle is foundational because healthcare AI depends on data access. Transparency around data provenance – including the origin, diversity, and quality of datasets used to train AI systems – must also be ensured to build trust and verify that data appropriately represents the patients being served.
  4. Environmental impact: Effective implementation of AI in healthcare requires careful consideration of its environmental impact and a strong commitment to sustainability. Environmental responsibility must be integrated alongside clinical validation to ensure that new technologies improve care while minimizing harm to the planet.

PHYSICIAN ROLES AND RESPONSIBILITIES

  1. Clinical Judgment and Accountability: As emphasized in the PITL principle, physician judgment remains essential when using AI in healthcare, serving as both an ethical imperative and a practical necessity. Physicians must maintain professional autonomy and clinical independence to act in the best interests of patients, consistent with the WMA Declaration of Seoul.
  2. Patient Advocacy: Physicians must safeguard patient health, well-being, and safety, ensuring that AI tools are only used in ways that genuinely benefit patients. Patient safety must remain a fundamental priority, whether or not augmented intelligence is applied.
  3. AI tool development: Physicians should be involved throughout the development and implementation of AI technologies in healthcare. They must participate in decision-making processes about technology and its use from the outset and be empowered to scrutinize new innovations, including for usability.
  4. Maintenance of competencies: Physicians must maintain core clinical expertise while also being educated and trained to work responsibly with AI systems. Delegation of tasks to AI must not erode the human capability required for safe, safety-critical care or for continuity when AI systems are unavailable or unreliable. Healthcare organizations should support this through ongoing education, simulation-based refreshers, periodic skills maintenance, and documented failover procedures that enable clinicians to critically appraise, override, and – when necessary – perform essential tasks independently.
  5. Incident reporting: Physicians must be empowered to report incidents and question outcomes resulting from the use of AI in healthcare.

PATIENT RIGHTS AND ENGAGEMENT

  1. While core patient rights are covered in existing WMA policies, AI introduces new risks – especially due to its reliance on data – that require focused ethical attention.
  2. Informed consent: Given AI systems’ reliance on patient health information, appropriate safeguards for data use are crucial. The principles of informed consent and transparency, building upon the WMA Declaration of Lisbon’s affirmation of patients’ rights to information and self-determination, must be rigorously applied in healthcare involving AI. Where possible, patients should be informed about the role AI plays in their care in ways that are understandable and meaningful, while physicians retain responsibility for ensuring safe and appropriate use of AI. In circumstances where full technical comprehension is impractical, informed consent may reasonably extend to a ‘consent for governance’ model, whereby patients place justified trust in physicians, healthcare institutions, and regulatory oversight to uphold their rights, safety, and welfare.
  3. Data rights: Patients must be informed about AI systems’ limitations and potential for error, as well as how physician oversight helps to ensures their protection. Patients should retain the right to request removal of their data from AI systems where feasible and legally permissible, and the right to understand how their data contributes to their care.
  4. Patient autonomy and explanation rights: Patient autonomy must be preserved through meaningful consent processes. Patients should retain the right, where feasible, to refuse AI-mediated interventions and request human-only assessment. Where such refusal is not possible due to systemic integration of AI, safeguards must ensure that patients’ data remain anonymous and non-traceable. Patients must have access to understandable and non-biased explanations of how AI contributes to their care, tailored to their information needs and preferences. They must also retain the right to dispute AI-generated recommendations they believe to be erroneous and to seek appropriate redress. This must extend to health insurer use of AI to determine patient care, payment, and coverage.
  5. Vulnerable patient population: Vulnerable patient cohorts, such as those with reduced decision-making capabilities, must not be disadvantaged or harmed through the use of AI in healthcare. Safeguards must include proactive bias mitigation, inclusive dataset development, and tailored consent or governance procedures to protect those unable to fully exercise autonomy. Particular attention must be given to ensuring that informed consent and data rights principles are applied in ways that do not reinforce structural inequities or exclude vulnerable groups from fair access to care.

GOVERNANCE, REGULATION, AND LIABILITY

  1. Up-to-date standards: Regulation, standards, and guidance must be suitably robust to safeguard patient safety and to ensure that the ethical rules of the medical profession are considered, with regulators empowered to stay up to date with developments and enforce legislation. Health care AI policies should be coordinated and consistent across government entities.
  2. Liability: Clear lines of legal liability must be established, including the AI developers, as well as physicians, and healthcare organisations. Accountability should be shared and proportional, reflecting each actor’s role in design, deployment, and use, rather than defaulting to a single actor alone.
  3. Continuous audit: There should be regular reviews and audits of regulatory processes and bodies surrounding AI in healthcare, including bias audits, ethical reviews, and participatory governance with physician input.

CLINICAL INTEGRATION AND IMPLEMENTATION OF HEALTH AI

  1. Tool evaluation and governance support: AI systems implemented in clinical settings must be validated for clinical relevance, safety, and effectiveness. Regular updates must be implemented to maintain security and ensure systems remain compatible with evolving clinical practices. In complex delivery environments, AI adoption must also be supported by appropriate governance structures that align clinical teams, leadership, and technology teams to ensure safe and responsible implementation.
  2. Workflow integration: AI tool implementation requires seamless integration within existing workflows to enhance usability and function as supportive additions rather than disruptive elements that impede efficient care delivery. Mechanisms should be established for tracking AI recommendations and their relationship to final clinical decisions.
  3. Post-deployment monitoring: Robust post-deployment monitoring is critical to ensure AI systems continue performing as intended. AI systems can drift from initial performance parameters when encountering new patient populations not represented in training data, as clinical practices evolve, or even within the same populations over time. Special attention should be directed toward monitoring outcomes in patient groups not adequately represented in training datasets.

DATA GOVERNANCE IMPLEMENTATION

  1. Patient data: All patient-identifiable information used or generated by AI systems must be collected, stored, and processed in strict accordance with the WMA Declaration of Taipei on Ethical Considerations Regarding Health Databases and Biobanks, as well as all applicable laws and regulations. Security safeguards are mandatory to preserve confidentiality, prevent unauthorised access, and uphold the therapeutic trust that underpins the patient–physician relationship. Additionally, patient data use must follow the same ethical safeguards applied to clinician data, including purpose limitation, transparency and consent, protection against misuse, and, where feasible, anonymisation and minimisation of data collected.
  2. Clinician data: AI systems are increasingly capturing granular data about clinicians (e.g., keystrokes, voice recordings, workflow metrics, prescribing patterns). Such information can support quality improvement and safety, but it also carries a risk of surveillance, punitive misuse, or erosion of professional autonomy. Therefore:
  • Purpose limitation: Clinician-identifiable data may be used only for clearly defined clinical, educational, or quality-improvement objectives that have been disclosed to—and agreed by—those clinicians.
  • Transparency and consent: Physicians must be informed, in advance and in comprehensible terms, what data are collected, how they will be analyzed, and who will have access. Explicit consent is required for uses beyond direct patient care or clinician-requested feedback.
  • Protection against misuse: Data must not be repurposed to penalize clinicians, set unrealistic performance quotas, or otherwise undermine the patient-physician relationship. Any secondary use (e.g. commercial analytics, administrative oversight) requires separate ethical review and consent.
  • Anonymization and minimization: Where feasible, clinician data should be de-identified or aggregated, and collection limited to the minimum necessary to achieve the stated purpose.
  1. Governance and oversight: Healthcare organisations must establish independent oversight mechanisms – such as, and not limited to, data protection officers, ethics committees, and periodic external audits – to verify compliance with safeguards for both patient and clinician data. Breaches or unauthorised uses must trigger transparent disclosure, remediation, and, where appropriate, sanctions. In addition, AI system developers must implement and support robust cybersecurity policies and controls to protect the confidentiality, integrity, and availability of health data throughout the AI system’s lifecycle.

MEDICAL EDUCATION AND CAPACITY BUILDING

  1. AI literacy requirements: Physicians must maintain appropriate AI literacy in the rapidly evolving AI landscape, including the knowledge and skills to use AI tools properly and the ability to critically understand and assess AI literacy must be systematically integrated into undergraduate medical curricula to ensure all physicians acquire a foundational understanding of these technologies. In addition, AI literacy should be reinforced through mandatory continuing professional development programs, enabling physicians to keep pace with evolving tools and to ensure their safe, ethical, and informed use in practice.
  2. Global equity: Focused attention must be directed toward bridging AI education gaps between regions, with particular emphasis on enhancing capacity in low- and middle-income countries (LMICs). Equitable distribution of educational resources and opportunities is essential to prevent widening disparities in AI implementation and ensure global benefit from these technological advances.

RESEARCH, INNOVATION AND EVALUATION

  1. Medical research standards: Any medical research involving AI, whether as the tool or object of study, must abide by accepted international standards of medical research, including, but not limited to, Good Clinical Practice, the WMA Declaration of Helsinki, and the WMA Declaration of Taipei.

GLOBAL CONSIDERATIONS AND COLLABORATION

  1. Cross-jurisdiction applicability: AI policies and infrastructures should, as far as possible, be aligned to have applicability across jurisdictions.
  2. Diverse healthcare environments: Appropriate AI solutions must be pursued across diverse healthcare environments, including low-resource settings. This requires supporting locally developed, context-sensitive innovations to ensure AI systems are responsive to local needs, realities, and resource constraints.
  3. Cultural Sensitivity: AI policies should respect varied cultural approaches while ensuring alignment with fundamental ethical principles, such as respect for human dignity, rights, and wellbeing.

RECOMMENDATIONS

  1. For physicians and medical associations: Medical professionals and their representative organizations should promote the development of comprehensive AI literacy programs, actively engage in AI governance structures – including contributing to the development of best practices for AI use in medicine – and uphold rigorous ethical standards to ensure quality patient care in an AI-enhanced healthcare environment. They should also consider creating educational materials for patients to support transparency and informed understanding of AI in healthcare.
  2. For healthcare facilities: Healthcare institutions must establish robust governance frameworks for the safe adoption of AI technologies and implement continuous monitoring processes. Organizations should balance innovation with safety considerations and maintain respect for clinical judgment when deploying AI systems. Importantly, AI implementation should be pursued when it demonstrably serves patients’ interests, without mandating AI use as a condition for licensure, participation, or reimbursement.
  3. For technology developers: Technology companies and AI developers must prioritize co-design approaches with practicing physicians and provide transparency in system development, deployment and use. Sustained collaboration between clinical and technical experts throughout the entire development lifecycle is essential to create tools that enhance healthcare quality and equity and that effectively support clinical activity.
  4. For regulators and policymakers: In consultation with medical associations (and other health professions organisations), craft physician-informed regulations and foster international cooperation.
  5. For educational institutions: Embed AI training in curricula and support global capacity building.
  6. For researchers and innovators: Pursue ethical, equitable, and evidence-based AI advancements.

 

Appendix

Narrow AI:
Domain-specific applications confined to clearly defined clinical or administrative objectives.

Generative AI:
Models, often large-language models, that create new clinical content—such as documentation drafts or treatment-plan suggestions—based on training data.

Foundational Models:
Broad, continuously trained models that underpin multiple healthcare applications and therefore require ongoing domain-specific oversight.

Machine learning:
A subset of artificial intelligence in which computer algorithms autonomously improve their performance at a specific task by learning complex relationships or identifying patterns in data, rather than by following explicit, pre-programmed instructions.

Patient-Physician Relationship:
Trust can be enhanced in the patient-physician relationship when:

-Physicians transparently discuss the role of AI in patient care
-AI systems demonstrably improve quality or safety outcomes
-Patients clearly understand how their data is used and protected and how data governance is organized.
-Patients are offered more time with their physician

 

Adopted by the 60th WMA General Assembly, New Delhi, India, October 2009
and revised by the 73rd WMA General Assembly, Berlin, Germany, October 2022

 

PREAMBLE

  1. Digital health is a broad term that refers to “the use of information and communication technologies in medicine and other health professions to manage illnesses and health risks and to promote wellness.” Digital health encompasses electronic health (eHealth) and developing areas such as the use of advanced computer sciences (including ‘big data’, bioinformatics and artificial intelligence). The term also includes telehealth, telemedicine, and mobile health (mHealth).
  2. The term “digital health” may be used interchangeably with “eHealth.” These terms also include within them: Telehealth” or “Telemedicine,” which both utilize information and communications technology to deliver healthcare services and information at a distance (large or small). They are used for remote clinical services, including real-time patient monitoring such as in critical care settings. Also, they serve for patient-physician consultation where access is limited due to physicians’/patients’ schedules or preferences, or patient limitations such as physical disability. Alternatively, they can be used for consultation between two or more physicians. The difference between the two terms is that “Telehealth” refers also to remote clinical and non-clinical services: preventive health support, research, training, and continuing medical education for health professionals.
  3. Technological developments and the increasing availability and affordability of mobile devices have led to an exponential increase in the number and variety of digital health services in use in both developed and developing countries. Simultaneously, this relatively new and rapidly evolving sector remains largely unregulated, which could have potential patient safety and ethical implications.
  4. The driving force behind digital health should be improving quality of care, patient safety and equity of access to services otherwise unavailable.
  5. Digital health differs from conventional health care in the medium used, its accessibility, and its effect on the patient-physician relationship, as well as on the traditional principles of patient care.
  6. The development and application of digital health has expanded access to health care and health education in both regular and emergency situations. At the same time, its effect on the patient-physician relationship, accountability, patient safety, multistakeholder interactions, privacy and data confidentiality, fair access, and other social and ethical principles should be taken into consideration. However, the scope and application of digital health, telemedicine or telehealth are context-dependent. Factors such as human resources for health, size of service area and level of healthcare facilities should also be taken into consideration.
  7. Physicians should be involved in the development and implementation of digital health solutions to be used in health care, in order to ensure they meet the needs of patients and health professionals.
  8. Consistent with the mandate of the WMA, this statement is addressed primarily to physicians and their role in the health care setting. The WMA encourages others who are involved in healthcare to develop and adhere to similar principles, as appropriate to their role in the healthcare system.

Physician autonomy

  1. Acceptable boundaries in the patient-physician relationship necessary for the provision of optimal care, should exist in digital as well as physical practice. The nearly continuous availability of digital health care has the potential to unduly interfere with a physician’s work-life balance due to theoretical 24/7 availability. The physician should inform patients about his or her availability and recommend services when he or she is not available.
  2. Physicians should exercise their professional autonomy in deciding whether digital health consultation is appropriate. This autonomy should consider the type of visit scheduled, the physician’s comfort with the medium, and the physician’s assessment, together with the patient, of the patient’s comfort level with this type of care.

Patient-physician relationship

  1. Face to face consultation should be the gold standard where a physical examination is required to establish a diagnosis, or where there is a wish on the part of the physician or patient to communicate in person as part of establishing a trusted physician-patient relationship. Face to face consultations may be preferable in some circumstances to take stock of non-verbal cues, and for consultations where there may be communication barriers or discussion of sensitive matters. Ideally, the patient-physician relationship in the context of digital health, should be based on a previously established relationship and sufficient knowledge of the patient’s medical history.
  2. However, in emergency and critical situations, or in settings where access to doctors is not available other than via telemedicine, delivery of care via telemedicine should be prioritized even when a prior patient-physician relationship was not established. Telemedicine can be employed when a physician cannot be physically present within a safe and acceptable period. It can also be used to manage patients remotely including self-management and for chronic conditions or follow-up after initial treatment, where it has been proven to be safe and effective.
  3. The physician providing telemedicine services should be familiar with the technology and/or should receive sufficient resources, training and orientation in effective digital communication. Additionally, the physician should strive to ensure that quality of communication during a digital health encounter is maximized. It is also important that the patient is comfortable using the technology employed. Any significant technical deficiencies should be noted in the documentation of the consultation and reported, if applicable.
  4. The patient-physician relationship is based on mutual trust and respect. Therefore, the physician and the patient must identify each other reliably when telemedicine is employed. However, it must be recognized that sometimes third parties or ‘surrogates’ such as a family member should become involved in the case of minors, the frail, the elderly, or in an emergency situation.
  5. The physician should give clear and explicit direction to the patient during the telemedicine encounter regarding who has ongoing responsibility for any required follow-up and ongoing health care.
  6. In a digital consultation between two or more professionals, the primary physician remains responsible for the patient’s care and coordination.  The primary physician remains responsible for protocols, conferencing, and medical record review in all settings and circumstances. Physicians providing consultation should be able to contact other health professionals and technicians, as well as patients, in a timely manner.

Informed consent

  1. Proper informed consent requires that the patient be informed of, have capacity for, and provide consent specific to the type of digital health being used. All necessary information regarding the distinctive features of digital health, in general, and telemedicine, in particular, must be explained fully to patients including, but not limited to: how telemedicine works, how to schedule appointments, privacy concerns, the possibility of technological failure, including confidentiality breaches; possible secondary use of data; protocols for contact during virtual visits, prescribing policies and coordinating care with other health professionals.  This information should be provided clearly and understandably without coercion or undue influence of the patient’s voluntary choices, while taking into account the patient’s perceived level of health literacy and other resource limitations specific to the type of digital health being used.

Quality of care

  1. The physician must ensure the standard of care delivered via digital health is at least equivalent to any other type of care given to the patient, considering the specific context, location and timing, and relative availability of face to face care. If the standard of care cannot be satisfied via digital technology, the physician should inform the patient and suggest an alternative form of healthcare delivery.
  2. The physician should have clear and transparent protocols for delivering digital health care such as clinical practice guidelines, whenever possible, to guide the delivery of care in the digital setting, recognizing that certain modifications may need to be made to accommodate specific circumstances. Changes to clinical practice guidelines for the digital setting should be approved by the appropriate governing and/or regulatory body or association. If the digital health solution is equipped with automated clinical practice support, this support must be strictly professionally based and not influenced by economic interests in any way.
  3. The physician providing digital services should follow all regulatory requirements and relevant protocols and procedures related to informed consent (verbal, written, and recorded); privacy and confidentiality; documentation; ownership of patient records; and appropriate video/telephone behaviors.
  4. The physician providing care by means of telehealth should keep a clear and detailed record of the advice delivered, the information on which the advice was based and the patient’s informed consent.
  5. The physician should be aware of and respect the particular challenges and uncertainties that may arise when in contact with the patient through telecommunication. The physician must be prepared to recommend direct patient-physician contact whenever possible if he/she believes it is in the patient’s best interests or will improve compliance.
  6. The possibilities and weaknesses of digital health in emergencies must be duly identified. If it is necessary to use telemedicine in an emergency, the advice and treatment suggestions will be influenced by the severity of the patient’s medical condition and the patient’s technological and health literacy. To ensure patient safety, entities that deliver telemedicine services should establish protocols for referrals in emergency situations.

Clinical Outcomes

  1. Entities providing digital health programs should monitor and continuously strive to improve the quality of services to achieve the best possible outcomes.
  2. Entities providing digital health programs should have a systematic protocol for collecting, evaluating, monitoring and reporting meaningful health care outcomes, safety data and clinical effectiveness. Quality indicators should be identified and utilized. Like all health care interventions, digital technology must be tested for its effectiveness, efficiency, safety, feasibility, and cost-effectiveness. Quality assurance and improvement data should be shared to improve its equitable use.
  3. Entities implementing digital health are urged to report unintended consequences to help improve patient safety and further the overall development of the field. Countries are encouraged to implement these guiding principles in their own legislation and regulation.

Equity of care

  1. Although digital health can provide greater access to distant and underserved populations, it may also exacerbate existing inequalities due to, among other things, age, race, socioeconomic status, cultural factors, or literacy issues. Physicians must be aware that certain digital technologies might be unavailable or unaffordable to patients, impeding access and further widening the health outcomes gaps.
  2. Digital technologies should be implemented and monitored carefully to avoid inequity of access to these technologies. Where appropriate, social or healthcare services should facilitate access to technologies as part of basic benefit packages while taking all necessary precautions to guarantee data security and privacy. Access to vital technologies should not be denied to anyone based on financial status or a lack of technical expertise.

Confidentiality and data security

  1. In order to ensure data confidentiality, officially recognized data protection measures must be used. Data obtained during a digital consultation must be secured to avoid unauthorized access and breaches of identifiable patient information through appropriate and up-to-date security and privacy measures. If data breaches do occur, the patient must be notified immediately in accordance with the law.
  2. Digital health technologies generally involve the measurement or manual input of medical, physiological, lifestyle, activity, and environmental data to fulfill their primary purpose. The large amount of data generated also may be used for research or other purposes to improve healthcare and disease prevention. However, secondary uses of personal mHealth data can result in misuse and abuse.
  3. Robust policies and safeguards to regulate and secure the collection, storage, protection, and processing of digital health users’ data, especially personal health data, must be implemented to assure valid informed consent and guarantee patients’ rights.
  4. If patients believe that their privacy rights have been violated, they may file a complaint with the covered entity’s Privacy Officer or data protection authorities, in accordance with local regulations.

Legal principles

  1. A clear legal framework must be drawn up to address potential liability arising from the use of digital technologies. Physicians should only practice telemedicine in countries/jurisdictions where they are licensed to practice and should adhere to the legal framework and regulations as defined by the country/jurisdiction where the physician originates care and the countries in which they practice. Physicians should ensure that their medical indemnity includes telemedicine and digital health coverage.
  2. Reimbursement models must be set up in consultation with national medical associations and healthcare providers to ensure that physicians receive appropriate reimbursement for providing digital health services.

Specific principles of mHealth technology

  1. Mobile health (mHealth) is a form of electronic health (eHealth) for which there is no fixed definition. It has been described as medical and public health practice supported by mobile devices, such as mobile phones, patient monitoring devices, personal digital assistants (PDAs), and other devices intended to be used in connection with mobile devices. It includes voice and short messaging services (SMS), applications (apps), and the use of the global positioning system (GPS).
  2. A clear distinction must be made between mHealth technologies used for lifestyle purposes and those that require physicians’ medical expertise and meet the definition of medical devices. The latter must be appropriately regulated, and users must be able to verify the source of medical information provided, as these applications could potentially recommend non-scientific or non-evidence-based treatments. The information provided must be comprehensive, clear, reliable, non-technical, and easily understood by laypeople.
  3. Concerted work must improve the interoperability, reliability, functionality, and safety of mHealth technologies, e.g., through the development of standards and certification schemes.
  4. Comprehensive and independent evaluations must be carried out regularly by competent authorities with appropriate medical expertise to assess the functionality, limitations, data integrity, security, and privacy of mHealth technologies. This information must be made publicly available.
  5. mHealth can only positively contribute to improvements in care if services are based on sound medical rationale. As evidence of clinical usefulness is developed, findings should be published in peer-reviewed journals and be reproducible.

 

RECOMMENDATIONS

  1. The WMA recognizes the value of digital health to supplement traditional ways of managing health and delivering healthcare. The driving force behind digital health should be improving quality of care and equity of access to services otherwise unavailable.
  2. The WMA emphasizes that the principles of medical ethics, as outlined in The Declaration of Geneva: The Physician’s Pledge and the International Code of Medical Ethics, must be respected in the practice of all forms of digital health.
  3. The WMA recommends that the training of digital health literacy and skills be included in medical education and continuing professional development.
  4. The WMA urges patients and physicians to be discerning in their use of digital health and to be mindful of potential risks and implications.
  5. The WMA recommends further research in digital health to assess safety, efficacy, cost-effectiveness, feasibility of implementation, and patient outcomes.
  6. The WMA recommends monitoring the risks of excessive or inappropriate use of digital health technologies and the potential psychological impact on patients and ensuring that the benefits of such technologies outweigh the risks.
  7. The WMA recommends special attention be given to patients’ disabilities (audio-visual or physical) and patients who are minors, when using digital healthcare.
  8. Where appropriate, National Medical Associations should encourage the development and update of ethical norms, practice guidelines, national legislation, and international agreements on digital health.
  9. The WMA recommends that other regulatory bodies, professional societies, organizations, institutions, and private industry, monitor the proper use of digital health technologies and share these findings widely.