Digital Medicine in Viet Nam: What It Is, How It Differs from Digital Health, and Why Evidence Matters

Digital Medicine in Viet Nam: What It Is, How It Differs from Digital Health, and Why Evidence Matters

Healthcare is becoming increasingly digital. Electronic medical records are expanding, telemedicine has become more familiar, and wearables and connected devices can collect health information outside traditional healthcare settings. Artificial intelligence is entering medical imaging, clinical decision support, documentation, monitoring, patient communication, and hospital operations. Software is increasingly becoming part of how healthcare is delivered.

These developments are often discussed under the broad term Digital Health. But not every digital technology used in health is Digital Medicine. The distinction becomes particularly important when a technology begins to measure health, monitor patients, influence clinical decisions, or deliver an intervention. At that point, one of the most important questions is no longer simply what the technology can do, but what evidence supports using it for this purpose, with these patients, in this healthcare setting.

That question sits at the heart of Digital Medicine.

Digital Health is the broader field

The World Health Organization defines Digital Health as the field of knowledge and practice associated with the development and use of digital technologies to improve health. It is deliberately broad and encompasses, or is closely related to, areas such as health information systems, telemedicine, connected technologies, artificial intelligence, big data, interoperability, and other digital applications across healthcare and public health.

A hospital information system is therefore part of Digital Health. So is an online appointment platform. Electronic prescribing, teleconsultation, electronic medical records, patient portals, administrative automation, wellness applications, connected devices, and many uses of artificial intelligence can all sit within the wider Digital Health ecosystem.

Digital Medicine has a more specific focus within this broader landscape. The Digital Medicine Society describes Digital Medicine as a field concerned with technologies used as tools for measurement, intervention, and enhancing human health. Its Evidence DEFINED framework describes Digital Medicine products as evidence-based software and/or hardware products that measure and/or intervene in the service of human health. The emphasis on evidence is therefore not an additional feature of Digital Medicine. It is central to the concept.

A practical way to understand the distinction is that Digital Health describes the broader digital ecosystem for health, while Digital Medicine focuses more specifically on evidence-based technologies used to measure or intervene in human health.

This is primarily a conceptual and professional distinction rather than a legal classification. In Viet Nam, relevant technologies are currently regulated through applicable frameworks such as those governing artificial intelligence, personal data, data, cybersecurity, information security, medical devices, healthcare services, and other sector-specific requirements. Which rules apply depends on what a particular technology does, its intended use, the claims being made, its level of risk, the data it processes, and the context in which it is developed, supplied, or deployed.

What can Digital Medicine include?

Digital Medicine can take many forms. A wearable sensor may measure physiological or behavioral signals and generate digital measures that help clinicians understand changes in a patient's condition. A remote monitoring system may allow patients with chronic disease to be followed outside a hospital or clinic. Software may analyze medical images and provide information that supports clinical interpretation. An artificial intelligence system may help identify patients at increased risk of deterioration. Connected technologies may combine sensors, software, algorithms, communication systems, and clinical workflows to support ongoing care.

Digital Medicine can therefore include areas such as digital measures, remote patient monitoring, certain clinical applications of artificial intelligence, software-based clinical decision support, digital therapeutics, connected medical technologies, and digitally enabled models of care. These areas can overlap, and the boundaries between them continue to evolve as technologies become more integrated.

What matters, however, is not simply the underlying technology. More important questions are what the technology is intended to do, what claim is being made about it, what decision or intervention it may influence, what could happen if it performs poorly, and what evidence supports its intended use.

Consider two products using similar technologies. A consumer activity tracker that counts steps for general wellness and a wearable system intended to detect clinically meaningful changes in a patient's condition may both contain sensors and software. Their intended purposes, potential consequences, evidence requirements, and regulatory implications can nevertheless be very different.

The same principle applies to artificial intelligence. An AI tool used to help schedule appointments should not be evaluated in the same way as an AI system used to support the identification of a potentially serious abnormality on a medical image. Both are digital technologies, and both may use sophisticated algorithms, but the consequences of error and the evidence needed to support their use are not the same.

Digital Medicine is not the same as Digital Therapeutics

Digital Therapeutics are another part of the digital health landscape. The Digital Therapeutics Alliance currently describes Digital Therapeutics as health software intended to treat or alleviate a disease, disorder, condition, or injury by generating and delivering a medical intervention with demonstrable positive therapeutic impact on a patient's health.

Digital Therapeutics are therefore therapeutic in purpose. Digital Medicine is broader and can include technologies used for measurement, monitoring, clinical decision support, research, or intervention. The two concepts are related, but they should not be treated as interchangeable.

It is also better not to assume that Digital Health, Digital Medicine, and Digital Therapeutics form a universally agreed legal hierarchy. Different professional and industry frameworks organize the digital health landscape in somewhat different ways, while regulators generally classify products according to characteristics such as intended use, functionality, claims, and risk rather than simply according to the label a developer gives them.

Why evidence is central to Digital Medicine

Healthcare has always involved decisions under uncertainty. Medicines do not work equally well for every patient, diagnostic tests have limitations, and clinical interventions have benefits as well as risks. Digital technologies do not remove this uncertainty. In some situations, they introduce new forms of it.

A digital tool may perform differently in another patient population. A machine-learning model may perform differently when the data encountered in routine care differ from the data used during development. A wearable sensor may generate reliable measurements in a controlled study but perform less consistently during everyday use. A clinical decision-support tool may be technically accurate but poorly integrated into clinical workflow. An artificial intelligence system may provide useful information while also creating false reassurance, unnecessary alerts, automation bias, or additional workload.

For this reason, Digital Medicine should not be evaluated by asking only whether a technology “works.” A more useful assessment asks what the technology is intended to do, how well it performs that task, for whom it has been evaluated, under what conditions, compared with what alternative, what happens when people actually use it, and whether its use improves something that matters to patients, clinicians, or the health system.

Evidence helps answer those questions.

Evidence should match the claim

There is no single evidence standard appropriate for every Digital Medicine technology. The type and strength of evidence should be proportionate to the intended use, the claims being made, the remaining uncertainty, and the potential consequences if the technology performs poorly.

A low-risk wellness application should not require the same evidence as software used to influence diagnosis or treatment. Similarly, evidence that a sensor can accurately measure a physiological signal does not by itself demonstrate that using the sensor improves patient outcomes. Evidence that an algorithm can identify an abnormality does not automatically establish that introducing the algorithm into clinical practice will result in earlier diagnosis, better treatment decisions, shorter waiting times, fewer complications, or better outcomes.

These are different questions and may require different forms of evidence. Depending on the technology and intended use, evaluation may involve technical verification, analytical validation, clinical validation or clinical performance, diagnostic accuracy, usability and human factors assessment, clinical utility, comparative effectiveness, implementation evaluation, health economic analysis, and post-deployment performance monitoring.

The important principle is fit for purpose. Evidence should be strong enough to support the specific claim being made and the context in which the technology will actually be used.

Good evidence does not always mean a randomized controlled trial

Evidence-based Digital Medicine should not be reduced to a single type of study. Randomized controlled trials can provide strong evidence for certain questions, particularly when the objective is to determine whether an intervention causes an improvement in a clinical outcome. They are not, however, the only appropriate form of evidence.

Depending on the technology and the question being asked, useful evidence may come from technical testing, analytical validation, diagnostic accuracy studies, prospective clinical studies, usability and human factors research, implementation studies, qualitative research, real-world evidence, health economic evaluation, or ongoing performance monitoring.

A remote monitoring program, for example, may require evidence that measurements are sufficiently reliable, clinically important changes can be detected, alerts lead to appropriate action, the workflow can be managed safely by the clinical team, and the overall model of care provides meaningful benefit. A clinical AI system may require evidence of technical performance, external evaluation, applicability to the intended patient population, performance across relevant groups, interaction with clinicians, workflow fit, and ongoing monitoring after deployment.

The most useful question is therefore not simply “Do you have a clinical trial?” It is “What evidence is needed to support this particular claim and this particular use?”

Evidence generated elsewhere may not fully answer the question in Viet Nam

This issue is particularly important for healthcare organizations in Viet Nam. A Digital Medicine product developed and evaluated in the United States, Europe, Korea, Japan, China, Singapore, or another market may also perform well in Viet Nam, but that should be assessed rather than assumed.

Patient characteristics, disease prevalence, clinical workflows, staffing models, referral patterns, equipment, infrastructure, language, documentation practices, health literacy, connectivity, and data quality can differ between settings. The way clinicians and patients interact with a technology can also differ.

These differences can be particularly important for artificial intelligence because performance may depend on the data, population, equipment, workflow, and environment in which a model is used. A language model performing well in English cannot automatically be assumed to perform equally well with Vietnamese clinical language. An imaging algorithm developed largely using data from other populations, institutions, or equipment environments may require local verification. A remote monitoring model designed around a particular level of connectivity, digital literacy, or clinical staffing may not transfer directly into another healthcare setting.

This does not mean that every Digital Medicine technology needs to repeat its entire research and development program in Viet Nam. It means healthcare organizations should identify what uncertainty remains when evidence is transferred from one setting to another. Depending on the intended use and level of risk, local evaluation might involve verification using representative local data, usability testing, silent-mode evaluation, a monitored pilot, prospective evaluation, or other forms of implementation evidence.

The objective is not to create unnecessary barriers to innovation. It is to understand whether evidence generated elsewhere is sufficiently applicable to the patients, professionals, workflows, and environment in which the technology will actually be used.

Viet Nam is becoming increasingly ready for Digital Medicine, but digitization alone is not enough

Viet Nam's health system is undergoing substantial digital transformation. The Ministry of Health's Digital Transformation Program to 2025, with orientation to 2030, established directions for developing digital foundations, health data, digital platforms, smart healthcare, and health system governance. In 2026, the Ministry of Health issued Decision No. 965/QD-BYT approving the national electronic medical record implementation plan for 2026–2030. The plan sets a goal that by 2030, 100 percent of healthcare facilities nationwide will implement electronic medical records without maintaining parallel paper medical records.

This expanding digital infrastructure matters. Electronic medical records, connected information systems, more structured health data, and increasingly digital workflows can create important foundations for Digital Medicine. They can make it easier to integrate remote monitoring, clinical decision support, digital measures, artificial intelligence, and other technologies into care.

But digitizing healthcare and practicing evidence-based Digital Medicine are not the same thing. Converting a paper process into an electronic process is part of digital transformation. Introducing technology that meaningfully measures health, informs clinical care, or delivers an intervention creates additional questions about evidence, patient safety, governance, clinical accountability, privacy, data protection, cybersecurity, and real-world performance.

For that reason, the maturity of Digital Medicine should not be measured simply by how much technology a healthcare organization has purchased. A more meaningful question is whether the organization has the capability to determine which technologies address a genuine healthcare need, whether their claims are supported by appropriate evidence, whether they can be implemented safely and effectively, and whether they continue to perform as expected after deployment.

Regulation and evidence answer different questions

As Digital Medicine develops in Viet Nam, it is important to distinguish regulatory compliance from evidence of value and fitness for purpose. They are related, but they do not answer the same question.

Viet Nam's regulatory environment relevant to digital health technologies has developed significantly. The Law on Artificial Intelligence No. 134/2025/QH15 took effect on March 1, 2026. Decree No. 142/2026/ND-CP, effective May 1, 2026, provides detailed provisions and measures for implementation of the Law. Decision No. 33/2026/QD-TTg, effective August 15, 2026, establishes the current list of high-risk artificial intelligence systems, including specified applications in healthcare such as certain AI-enabled surgical and surgical-robot systems. Importantly, this does not mean that every AI system used in a healthcare organization is automatically classified as high-risk. The applicable classification depends on the characteristics and intended use of the particular system and the criteria established by law.

Health data also require careful governance. The Law on Personal Data Protection No. 91/2025/QH15 and Decree No. 356/2025/ND-CP took effect on January 1, 2026. The Law includes specific provisions concerning personal data related to health and establishes requirements for its collection and processing, subject to the exceptions provided by law. Other legal frameworks, including the Law on Data No. 60/2024/QH15 and applicable cybersecurity and information security requirements, may also be relevant depending on the technology and its data flows.

Medical device regulation may also apply to some Digital Medicine technologies. Viet Nam's medical device framework under Decree No. 98/2021/ND-CP, as amended and supplemented, expressly includes software within the definition of a medical device when the relevant criteria and intended medical purposes are met. The framework has subsequently been amended, including by Decree No. 07/2023/ND-CP and Decree No. 04/2025/ND-CP, and is reflected in the Ministry of Health's current consolidated text. Circular No. 24/2026/TT-BYT, effective July 1, 2026, further addresses the determination of risk levels and management measures for medical device products.

Not every Digital Medicine technology will fall into all of these frameworks, and other requirements may apply depending on the product, organization, data flows, service model, and deployment context. The appropriate regulatory analysis therefore starts with the actual technology and its intended use rather than with the label “Digital Medicine.”

Regulation asks, in essence, what legal requirements apply to this technology and organization, and have they been met? Evidence asks a different question: do we have sufficient reason to believe that using this technology in this way will be safe, useful, effective, and appropriate?

A product's regulatory status does not automatically establish that it is the right choice for a particular hospital, patient population, or clinical workflow. Conversely, promising research evidence does not remove the need to comply with applicable law. Responsible Digital Medicine requires both perspectives.

Evidence should continue after implementation

Digital technologies can change after they enter practice. Software is updated, algorithms may be modified, artificial intelligence models may be retrained, data sources can change, clinical workflows evolve, new populations begin using the technology, and staff may gradually use a system in ways that were not anticipated when it was first introduced.

Evidence should therefore not always be treated as something reviewed once during procurement. Healthcare organizations also need to understand what happens after implementation. They need to know whether the technology is being used as intended, whether performance remains consistent with expectations, whether false alerts are increasing, whether clinicians are frequently overriding recommendations, whether performance differs across relevant patient groups, whether software updates have materially changed the system, and whether new safety issues are emerging.

For many Digital Medicine technologies, particularly those that influence clinical care, real-world monitoring becomes part of the evidence lifecycle. The important question is not only whether a technology worked when it was adopted, but how the organization will know that it continues to work safely and effectively over time.

Digital Medicine is ultimately about healthcare, not technology

It is easy for conversations about Digital Medicine to become conversations about artificial intelligence, devices, software, sensors, platforms, or data. But the purpose is not to make healthcare more digital for its own sake. The purpose is to improve health and healthcare.

A sophisticated technology that does not solve an important problem may add little value. A highly accurate algorithm that cannot be integrated into clinical practice may have limited impact. A remote monitoring system that generates more alerts than a clinical team can safely manage may introduce new risk. A digital intervention that performs well in a study but cannot be accessed or used by the people who need it may widen rather than reduce gaps in care.

The starting point should therefore be the healthcare need. Organizations should ask what problem they are trying to solve, who is expected to benefit, what will change in the care pathway, what outcomes matter, what could go wrong, what level of evidence would provide sufficient confidence, how the technology will fit into existing people and processes, and how they will know whether it produced the expected value.

Those questions are more important than whether a technology appears innovative.

What should healthcare organizations in Viet Nam ask?

Healthcare organizations do not need to become technology research centers before they can innovate. They do, however, need the capability to ask better questions.

Before adopting a technology that may influence health or clinical care, leaders should understand its intended use, the population for whom it was developed and evaluated, the evidence supporting its claims, the relevance of that evidence to the local setting, known limitations and risks, applicable regulatory requirements, data flows, privacy and cybersecurity considerations, workflow implications, human oversight requirements, and how its performance will be monitored after implementation.

Most importantly, organizations should distinguish between three different propositions: the technology is innovative; there is evidence that the technology works; and the technology is appropriate for us to use for this purpose, with these patients, under these conditions.

The third proposition requires much more than technology. It requires evidence, governance, implementation capability, and ongoing learning.

Building Digital Medicine in Viet Nam

Viet Nam has an opportunity to develop Digital Medicine at a time when both its healthcare system and the international technology landscape are changing rapidly. Continued expansion of digital health infrastructure can provide an important foundation. Artificial intelligence, connected technologies, remote care, digital measures, and software-based interventions may create new ways to improve access, support healthcare professionals, strengthen clinical decision-making, and make care more proactive and personalized.

But successful Digital Medicine will depend on more than access to new technologies. It will depend on whether healthcare organizations can evaluate evidence critically, understand uncertainty, govern risk, protect data, integrate technology into real clinical workflows, monitor performance, and learn from local implementation.

This becomes increasingly important as digital technologies move closer to decisions affecting diagnosis, treatment, monitoring, and patient safety. Digital Medicine should therefore not be understood simply as the next stage of technology adoption in healthcare. It asks a more disciplined question: How can digital technologies be used as credible, evidence-based tools to measure, understand, and improve human health?

For Viet Nam, answering that question well may be more important than adopting technology quickly. The goal should not simply be to create the most digital healthcare system. It should be to build a health system that knows when digital technology adds value, why it can be trusted, how it should be implemented, and whether it is actually improving care.

That is where Digital Medicine begins.

Key references

World Health Organization. Global Strategy on Digital Health 2020–2027. The Seventy-eighth World Health Assembly extended the original Global Strategy on Digital Health through 2027.

World Health Organization. Digital Health. WHO describes Digital Health as the field of knowledge and practice associated with the development and use of digital technologies to improve health.

Digital Medicine Society. Defining Digital Medicine.

Digital Medicine Society. Evidence DEFINED.

Digital Therapeutics Alliance. What is a DTx?

Ministry of Health of Viet Nam. Decision No. 5316/QD-BYT, Digital Transformation Program for Health to 2025, with orientation to 2030.

Ministry of Health of Viet Nam. Decision No. 965/QD-BYT, Plan for Implementation of Electronic Medical Records at Healthcare Facilities Nationwide for 2026–2030.

National Assembly of Viet Nam. Law on Artificial Intelligence No. 134/2025/QH15, effective March 1, 2026.

Government of Viet Nam. Decree No. 142/2026/ND-CP detailing provisions and measures for implementation of the Law on Artificial Intelligence, effective May 1, 2026.

Prime Minister of Viet Nam. Decision No. 33/2026/QD-TTg promulgating the List of High-Risk Artificial Intelligence Systems, effective August 15, 2026.

National Assembly of Viet Nam. Law on Personal Data Protection No. 91/2025/QH15, effective January 1, 2026.

Government of Viet Nam. Decree No. 356/2025/ND-CP detailing provisions and measures for implementation of the Law on Personal Data Protection, effective January 1, 2026.

National Assembly of Viet Nam. Law on Data No. 60/2024/QH15, effective July 1, 2025.

Government of Viet Nam. Decree No. 98/2021/ND-CP on medical device management, as amended and supplemented by subsequent regulations, including Decree No. 07/2023/ND-CP and Decree No. 04/2025/ND-CP.

Ministry of Health of Viet Nam. Consolidated Document No. 08/VBHN-BYT dated March 6, 2026, on medical device management.

Ministry of Health of Viet Nam. Circular No. 24/2026/TT-BYT on determining risk levels and management measures for medical device products, effective July 1, 2026.

This article is intended for educational and informational purposes. It does not constitute legal, regulatory, clinical, research, or technology procurement advice. The evidence and regulatory requirements applicable to a particular technology should be assessed according to its specific intended use, claims, risk, patient population, organizational role, data practices, and operating environment.

Updated: September 18, 2026

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