Governing AI-Assisted Inpatient Falls Prevention in Viet Nam: Comprehensive Assessment, Individualized Prevention and Post-Fall Learning

Governing AI-Assisted Inpatient Falls Prevention in Viet Nam: Comprehensive Assessment, Individualized Prevention and Post-Fall Learning

An older patient is recovering from surgery in a hospital ward. The patient can walk short distances with assistance but feels dizzy when standing and needs to use the bathroom frequently during the night.

A bedside monitoring system uses artificial intelligence (AI) to recognize movements suggesting that the patient may be attempting to leave the bed without assistance. Late one evening, the system generates an alert. The nurse responsible for the patient is attending to someone else and cannot respond immediately. Before help arrives, the patient loses balance and falls.

The technology recognized a potentially unsafe movement, but the fall still occurred.

Understanding what happened requires more than checking whether the alert was technically accurate. Was the patient's dizziness properly assessed? Was there a plan for assisted nighttime toileting? Could the patient reach and use the call bell? Was assistance realistically available, and did the monitoring system provide enough time for someone to intervene?

These questions illustrate why inpatient falls prevention cannot be reduced to predicting risk or detecting movements that might precede a fall.

AI may help recognize changes in clinical information, identify certain movements or notify staff when assistance could be needed. Its contribution to patient safety, however, depends on how those functions connect with comprehensive assessment, individualized care and the hospital's capacity to respond.

For healthcare organizations in Viet Nam, the challenge is to use technology without weakening clinical judgment, unnecessarily restricting mobility or creating a false sense of protection.

Patients need protection from avoidable harm, but they also need opportunities to move, recover and maintain independence. Effective falls prevention must address both.

Falls Prevention Begins With Understanding Why Patients Fall

Falls are an important patient-safety concern because they can cause additional harm during an already vulnerable period of hospitalization.

A fall may result in a fracture, head injury, pain or loss of confidence. Some patients experience delayed rehabilitation, prolonged hospitalization or a persistent fear of falling that affects their willingness to move independently. Even when no physical injury is apparent, a fall may reveal an underlying clinical problem or weakness in the care process.

The circumstances leading to a fall are often complex. A patient may feel dizzy after standing, become confused during an episode of delirium or experience impaired balance following surgery. Medicines affecting alertness, blood pressure or coordination may contribute. An unfamiliar environment, inadequate lighting, inaccessible walking aids or an urgent need to reach the bathroom can create additional hazards.

Several of these factors may be present at the same time. A patient recovering from surgery, for example, may have reduced muscle strength, receive opioid analgesia and require assistance with toileting. The interaction between these circumstances may be more important than any single factor considered separately.

Risk can also change throughout hospitalization. Someone who walked independently on admission may become unstable after a procedure, acute illness or medication adjustment. Another patient may gradually regain mobility but still need supervision when transferring from bed to chair.

Falls prevention therefore requires continuing assessment rather than a risk score completed only when the patient arrives.

There is also a balance to maintain. Prolonged bed rest can contribute to muscle weakness, physical deconditioning, functional decline and loss of independence. Restricting movement simply because a patient might fall can introduce additional harm.

The aim should be to identify and address the factors that make movement unsafe while supporting mobility appropriate to the patient's condition.

This is a more meaningful clinical objective than attempting to eliminate every movement that could potentially result in a fall.

What the 2025 NICE Guideline Means for Falls Risk Prediction

The National Institute for Health and Care Excellence (NICE) published guideline NG249, Falls: Assessment and Prevention in Older People and in People 50 and Over at Higher Risk, on 29 April 2025.

The guideline represents an important development in how falls risk assessment is approached.

NICE recommends against using falls risk prediction tools to predict whether a person will fall. Instead, it emphasizes comprehensive assessment of individual risk factors and interventions tailored to those findings.

This recommendation applies within the guideline's defined population: people aged 65 years or older and people aged 50–64 years who have at least one factor that could increase their risk of falling.

For people within this population receiving inpatient care, NICE recommends comprehensive falls assessment and management rather than relying on a prediction score to determine who requires attention.

This distinction matters for hospitals that routinely use instruments such as the Morse Fall Scale or Hendrich II Fall Risk Model.

A numerical score may summarize selected characteristics, but it does not necessarily explain why an individual patient may fall or identify which intervention would be most appropriate.

Consider two patients with similar scores. One experiences orthostatic hypotension and becomes dizzy when standing. The other has delirium and difficulty understanding how to request assistance in an unfamiliar ward.

Although both may be considered at increased risk, their clinical needs are different. Addressing dizziness, reviewing medication and planning assisted mobilization may be appropriate for the first patient. The second may require assessment and management of delirium, communication support and closer supervision.

Assigning the same risk category to both patients does not resolve these differences.

The NICE recommendation should not be interpreted as a prohibition on AI research or all technologies associated with falls. Nor should it be generalized without qualification to patients outside the guideline's defined population.

Its central message is that predicting falls is not an adequate substitute for understanding and managing the factors contributing to them.

The 2022 World Guidelines for Falls Prevention and Management for Older Adults reinforce this approach. For hospitalized older adults, the guidelines recommend multifactorial assessment and personalized prevention. They also advise against relying on scored falls risk screening tools as substitutes for comprehensive assessment.

For Vietnamese hospitals, these recommendations provide an opportunity to review established practice. If substantial nursing time is devoted to repeatedly completing risk scores, hospital leaders should examine whether those activities lead to meaningful assessment and appropriate preventive care.

An AI-generated risk classification should not become a reason to withhold comprehensive assessment from patients labeled low-risk or impose unnecessary restrictions on those labeled high-risk.

International recommendations can inform local clinical protocols, although they do not automatically become binding Vietnamese legal requirements.

AI-Assisted Falls Prevention Includes Different Technologies

The term AI-assisted falls prevention can describe technologies with very different purposes.

Some applications analyze electronic health records to estimate the probability that a patient will fall during hospitalization. These models may use information about age, previous falls, diagnoses, medications, mobility assessments and other documented clinical characteristics.

Other applications use cameras, radar, depth sensors, pressure sensors or wearable devices to recognize movement patterns.

For example, a monitoring system may attempt to identify when a patient approaches the edge of a bed, begins to stand or moves in a way that suggests assistance may be needed.

A third category focuses on detecting a fall after it has occurred. Such systems may notify staff that a patient appears to have fallen or remains on the floor.

These functions are not interchangeable.

A prediction model estimates future risk. A movement monitoring system identifies particular activities. A fall detection system attempts to recognize an event that may already have occurred.

A hospital should not assume that a system designed for one purpose has demonstrated reliable performance in another.

Conventional bed-exit alarms also need to be distinguished from AI. A pressure-sensitive mat that activates an alarm when weight is removed may operate through a simple predefined rule rather than machine learning. The presence of a camera, sensor or electronic dashboard does not automatically establish that AI is involved.

This distinction matters when evaluating products and their claims.

A system may accurately identify bed-exit movements without reducing falls. A prediction model may classify patients into risk categories without explaining which interventions are needed. A fall detection system may support faster assistance after an event without preventing the event itself.

Before considering implementation, healthcare organizations should define exactly what the technology is intended to accomplish and evaluate the evidence supporting that function.

Comprehensive Assessment Must Lead to Individualized Prevention

A comprehensive falls assessment should identify why a particular patient may fall and what can reasonably be done to reduce the risk.

Previous falls provide important information, but assessment needs to go further.

Mobility, gait, balance, muscle strength and the ability to transfer safely between bed and chair may require examination. Cognitive impairment, delirium and changes in awareness can affect how a patient responds to the hospital environment.

Cardiovascular assessment may identify orthostatic hypotension or other causes of dizziness and instability. Visual impairment, unsuitable footwear, nutritional problems and difficulties using mobility aids may also contribute.

Medication review is especially relevant.

Sedatives, opioids, some psychotropic medicines and medications affecting blood pressure or alertness can contribute to falls in certain circumstances. Medication-related hypoglycemia and other adverse effects may also be involved.

However, the presence of a potentially relevant medication does not automatically justify stopping it. The clinical indication, expected benefits, possible harms and alternatives need to be considered by an appropriately qualified professional.

NICE NG249 identifies several domains for comprehensive assessment, including cardiovascular findings, cognition, delirium, dizziness, medication use, mobility, functional ability, vision, hearing, footwear, nutrition and urinary continence.

The assessment should lead to a practical care plan.

If a patient becomes dizzy when standing, the team may need to investigate the cause and agree on an appropriate method of mobilization. If the patient cannot use the call bell effectively, repeatedly instructing the patient to request help is unlikely to solve the problem.

If the main concern is frequent nighttime toileting, arranging timely assistance may be more appropriate than telling the patient not to leave the bed.

The patient's understanding and preferences also matter. Family members and caregivers may contribute information about usual mobility, previous falls, communication needs or behaviors that differ from the patient's normal condition.

AI may help bring relevant clinical information together, but it cannot replace these conversations and bedside observations.

A useful system should make it easier for the team to recognize important changes and determine what support the patient actually needs.

Preventing Falls Should Not Mean Preventing Movement

Safe mobility is an important part of recovery.

A patient recovering from surgery may need assisted walking to regain strength. Another patient may benefit from physiotherapy, a suitable mobility aid or additional supervision during transfers.

Falls prevention should support these activities while addressing specific hazards.

The 2025 NICE guideline recommends that inpatient interventions be tailored to identified risk factors, including environmental factors and problems that can reasonably be addressed during hospitalization. It also encourages patients to remain active when clinically appropriate.

The 2026 Cochrane review, Interventions for Preventing Falls in Older People in Hospitals, provides an updated evidence base.

Published on 5 May 2026, the review included 55 randomized trials involving 104,474 participants.

The findings indicate that education involving patients, families and staff probably reduces fall rates and the risk of falling. Changes in how acute hospitals organize and deliver care also probably reduce fall rates.

Multifactorial interventions probably reduce falls and the risk of falling, although the confidence intervals remain compatible with little or no benefit, or even a small increase, in some comparisons.

The effects of several individual interventions remain uncertain.

The review also suggests that prevention approaches incorporating features such as adaptation to the local setting, tailoring to patient needs and engagement with patients or caregivers may be more effective than approaches lacking these elements.

These findings have practical implications.

Patients need understandable information about how to move safely, when assistance is recommended and how to use unfamiliar equipment. Staff need a shared understanding of each patient's mobility needs.

Environmental measures may include appropriate lighting, accessible call bells, suitable footwear, available walking aids and timely assistance with transfers or toileting.

These interventions should be selected because they address identified problems, rather than simply because they appear on a standard prevention checklist.

Restrictive measures require particular caution.

Physical restraints, unnecessary sedation and prolonged immobility should not be treated as routine methods of preventing falls. Such measures can cause distress, reduce functional ability and introduce additional clinical risks.

The 2022 World Guidelines found insufficient robust evidence to recommend bed or chair alarms, nonslip socks or physical restraints solely for inpatient fall prevention.

A patient who remains in bed throughout hospitalization may have fewer opportunities to fall, but this does not necessarily mean the overall quality of care has improved.

The goal should be safer movement and functional recovery, not immobility.

What the 2026 Evidence Shows About Sensors and AI

Despite increasing commercial interest in AI-assisted monitoring, evidence that these technologies consistently reduce inpatient falls remains limited.

A systematic review and meta-analysis by Zheng and colleagues, published in Worldviews on Evidence-Based Nursing in October 2026, examined sensor-based fall prevention systems in hospitals.

The review included 16 studies, with seven contributing to the quantitative meta-analysis.

Across the pooled studies, sensor-based systems did not demonstrate a statistically significant overall reduction in fall rates, the proportion of patients who fell or fall-related injuries.

The pooled incidence rate ratio for falls was 0.77, with a 95% confidence interval of 0.38–1.58. The wide interval and substantial heterogeneity indicate considerable uncertainty about the overall effect.

For fall-related injuries, the pooled incidence rate ratio was 0.98, with a 95% confidence interval of 0.81–1.18.

A subgroup analysis of machine-learning-based systems showed a more favorable association with reduced fall rates, with an incidence rate ratio of 0.50 and a 95% confidence interval of 0.32–0.79.

This finding is encouraging but requires careful interpretation.

The subgroup evidence came from a limited number of studies involving different technologies, patient populations and implementation arrangements. The certainty of evidence across the review ranged from very low to low.

The favorable subgroup result should therefore be treated as a reason for further investigation rather than definitive proof that machine-learning systems prevent hospital falls.

Another systematic review and meta-analysis by Haines and colleagues, published in the Journal of the American Geriatrics Society in August 2026, examined mobilization alarms used in hospitals.

Across randomized trial comparisons, the pooled incidence rate ratio for falls was 0.93, with a 95% confidence interval of 0.83–1.04.

For injurious falls, the corresponding estimate was 1.00, with a 95% confidence interval of 0.87–1.16.

The authors concluded that routine hospital-wide use of mobilization alarms appeared ineffective for preventing inpatient falls.

This review evaluated mobilization alarms broadly, rather than exclusively AI-based products. Its conclusions should not be presented as a direct assessment of every contemporary AI monitoring system.

Nevertheless, the findings challenge the assumption that increasing the number of alerts will automatically reduce falls.

An accurate alarm is only one part of prevention. If staff cannot respond promptly or the underlying reasons for unsafe movement remain unaddressed, the clinical benefit may be limited.

Lessons From a 2026 Korean Hospital Study

A study by Lee and colleagues, published in Healthcare on 14 September 2026, provides a relevant example of AI-assisted monitoring in real hospital practice.

The researchers evaluated an AI-assisted video monitoring system at a tertiary hospital in South Korea using a retrospective matched cohort design.

Patients admitted before and after implementation were compared using propensity score matching, producing two groups of 3,002 cases each.

The reported fall incidence rate was 1.017 per 1,000 patient-days in the AI-monitoring group, compared with 1.286 per 1,000 patient-days in the comparison group.

Although the observed rate was lower after implementation, adjusted analyses did not demonstrate a statistically significant reduction in falls.

The study illustrates why technical capability and clinical effectiveness need to be evaluated separately.

The monitoring system used ceiling-mounted cameras and AI-assisted movement recognition. Coverage was limited to eight shared patient rooms rather than all areas of the ward.

Single-occupancy rooms, hallways and other locations were outside the system's monitoring coverage.

This limitation matters because falls may occur in bathrooms, corridors or other locations where a patient is not directly monitored.

Even within monitored rooms, identifying movement does not guarantee that assistance will arrive before a fall.

The study's retrospective design also limits causal interpretation. Differences in patient characteristics, clinical practices and other changes over time may have influenced the results.

The relatively small number of observed fall events further limits the precision of the estimates.

Nevertheless, the study is useful because it examines a system operating in actual clinical conditions rather than relying solely on laboratory measurements of detection accuracy.

For Vietnamese hospitals, the relevant question is whether monitoring provides useful information early enough, in the right location and through a workflow that allows staff to act.

A system may accurately recognize certain movements while providing limited preventive benefit under particular staffing or operational conditions.

AI Alerts Need a Realistic Nursing Response

The clinical usefulness of movement monitoring depends on what happens after an alert is generated.

A notification displayed at the nursing station may be overlooked when staff are attending to other patients. An alert sent simultaneously to several people may appear well distributed without making anyone clearly responsible for responding.

Hospitals should therefore define who receives alerts, how urgency is communicated, how responses are coordinated and what happens when the designated professional is unavailable.

These arrangements need to reflect actual staffing conditions.

A system that appears manageable during daytime testing may create difficulties overnight, when fewer staff members are available and patients may require more assistance with toileting.

Response time is particularly important because some falls occur rapidly.

If an alert is generated only when a patient begins standing, the available time for intervention may be very short. Even a technically accurate alert may provide insufficient opportunity for a nurse to reach the bedside.

Hospitals should therefore evaluate the complete sequence from movement recognition to staff response.

When did the patient begin moving? When did the system identify the movement? When was the notification delivered? Was the responsible professional available, and did assistance arrive in time?

False-positive alerts can create additional problems. Staff may repeatedly interrupt care for other patients to investigate movements that do not require assistance. Over time, this may increase workload and contribute to alert fatigue.

False-negative or delayed alerts create a different concern. Patients and staff may develop confidence in monitoring that the system cannot reliably provide.

The hospital should also consider what happens during technical failures, maintenance or interruptions in connectivity.

Established nursing assessment and prevention procedures must continue when AI is unavailable.

The technology may support the care team, but it cannot replace appropriate staffing, supervision or direct clinical care.

Privacy, Dignity and Patient Autonomy in AI Video Monitoring

Video-based monitoring raises patient-safety and ethical questions that differ from those associated with conventional risk assessment scores.

A hospital room is a clinical environment, but it is also a place where patients may be physically exposed, distressed, confused or dependent on others for personal care.

Continuous observation can affect privacy, dignity and trust.

The concerns become more complex in shared rooms, where cameras may capture several patients, visitors and healthcare workers at the same time.

Before introducing AI-enabled cameras, a hospital should determine what information is collected, whether video is recorded or analyzed only in real time, where processing occurs, who can access the information and how long it is retained.

It should also examine whether the intended safety function can be achieved through a less intrusive method.

For example, a system using radar or other non-image movement information may present different privacy risks from a camera that continuously records identifiable footage.

Processing data locally without routinely storing or transmitting identifiable video may reduce some risks. However, local processing does not automatically eliminate data protection obligations.

Removing names or obscuring faces also does not necessarily make information anonymous. Body movements, location, timing and contextual details may still permit identification.

Particular caution is needed in settings involving personal hygiene, changing clothes or other activities where privacy expectations are especially high.

Hospitals should determine the applicable lawful grounds for monitoring, provide appropriate information to affected individuals and assess any required consent or other authorization for the specific arrangement.

Where a patient has impaired decision-making capacity, the relevant legal and professional requirements must be considered. Monitoring should not be assumed to be automatically justified by the patient's condition.

The clinical purpose should also remain clearly defined.

A system introduced to support fall prevention should not become unrestricted surveillance of patients or a means of evaluating staff behavior for unrelated purposes without appropriate justification and safeguards.

External technology suppliers should not be permitted to reuse identifiable patient footage for unrelated model training, product development or commercial activities without an appropriate legal basis and necessary protections.

Access restrictions, security arrangements, retention policies and procedures for responding to privacy or cybersecurity incidents should be established before deployment.

The potential benefits of monitoring must be weighed against effects on privacy, autonomy and the patient's experience of care.

An intervention intended to reduce harm should not create avoidable surveillance-related risks.

The Vietnamese Nursing and Patient-Safety Context

Fall prevention is already part of the broader responsibilities of nursing care and patient safety in Vietnamese hospitals.

Ministry of Health Circular No. 31/2021/TT-BYT, effective from 27 February 2022, regulates nursing activities in hospitals.

The Circular emphasizes comprehensive, continuous, safe and individualized nursing care. Its provisions address clinical assessment, nursing interventions, monitoring, documentation and evaluation of patient care.

These requirements provide an important professional foundation for preventing inpatient falls.

However, the Circular does not establish a specific AI-assisted falls prevention model or require hospitals to implement a particular prediction tool.

Circular No. 43/2018/TT-BYT provides the framework for reporting, analyzing, responding to and preventing medical incidents in healthcare facilities.

Circular No. 19/2013/TT-BYT on hospital quality management also addresses organizational responsibilities for monitoring and improving patient safety.

Together, these instruments support the integration of falls prevention into routine nursing and quality management.

Recent Vietnamese research provides additional context.

A study published in the Vietnam Journal of Community Medicine on 15 January 2026 examined fall prevention practices among 312 nurses at four district-level hospitals in Ho Chi Minh City.

Data were collected between August and October 2024.

The study reported a mean fall prevention practice score of 3.26 out of 5, indicating a moderate level of reported practice.

Participation in fall prevention training was associated with more favorable practice scores, alongside other professional and demographic factors.

The findings suggest opportunities to strengthen continuing education, supervision and organizational support.

However, the study was cross-sectional and involved a limited number of hospitals. Its results should not be generalized to all Vietnamese hospitals or interpreted as proof that training alone causes a reduction in falls.

The study also did not evaluate AI-assisted monitoring.

Its contribution is to highlight the importance of nursing capability and organizational practice.

For hospitals considering technology, this suggests that implementation should begin with an understanding of existing prevention processes and workforce needs rather than assuming that an automated alert system will correct underlying weaknesses.

After a Fall, Clinical Assessment Comes First

When a patient falls, the immediate priority is the patient's clinical condition.

The care team must assess for injury, physiological instability and other urgent concerns.

A patient who has struck their head, lost consciousness, developed new neurological symptoms or sustained a possible fracture may require urgent medical evaluation.

The possibility of serious injury should be considered before the patient is assisted to stand or moved unnecessarily, while recognizing that immediate intervention may be required in life-threatening circumstances.

Head injuries deserve particular attention in patients receiving anticoagulants or certain antiplatelet medicines.

International guidance, including NICE NG232 on head injury assessment and early management, provides criteria for considering imaging and other clinical interventions.

For example, NICE recommends considering computed tomography after head injury in people receiving anticoagulants or antiplatelet treatment other than aspirin monotherapy, even when other indications for scanning are absent. The recommendation specifies timing according to the circumstances of presentation.

These recommendations are intended for interpretation within their clinical context and do not automatically become Vietnamese legal requirements.

Decisions about imaging, observation and treatment must be made by appropriately qualified healthcare professionals according to clinical findings and applicable local procedures.

A fall detection notification cannot establish whether a patient has sustained an intracranial injury, fracture or other complication unless the system has been specifically designed and validated for that purpose.

Even then, direct clinical assessment remains necessary.

After immediate care has been provided, the event should be documented and communicated through the hospital's established incident reporting process.

The record should describe the circumstances of the fall, the patient's condition, relevant assessments and the actions taken.

Where monitoring technology was involved, the organization should determine whether an alert was generated, when it appeared, who received it and what response followed.

This information may support subsequent review, but collecting it must not delay necessary treatment.

Post-Fall Learning Should Improve the Care Plan

A post-fall review serves two related purposes.

The first is to identify and address the individual patient's continuing risks. The second is to understand whether the hospital's care processes can be improved.

The 2022 World Guidelines recommend post-fall assessment for hospitalized older adults to identify the circumstances and mechanism of the fall, resulting injuries and contributing clinical factors.

The assessment should inform changes to the patient's prevention plan.

A fall may reveal previously unrecognized orthostatic hypotension, worsening delirium, an acute illness or difficulty using a mobility aid. The patient's condition may also have changed because of the fall itself.

The care team should determine whether additional treatment, assistance, rehabilitation or monitoring is required.

At the organizational level, a multidisciplinary review can examine the sequence of events.

The patient may have attempted to reach the bathroom because assistance was not readily available. A walking aid may have been placed outside the patient's reach, or the call bell may have been difficult to use.

The monitoring system may have generated a warning, but the notification may have arrived too late for staff to intervene.

Alternatively, the technology may have performed as intended during an event that could not reasonably have been prevented through the available measures.

These circumstances require different responses.

Not every fall establishes professional negligence, and a fall occurring after an AI alert does not automatically prove that the system failed.

The purpose of review is to identify missed opportunities for prevention, weaknesses in care coordination and practical ways to reduce recurrence.

Near misses are also important.

A patient who begins to fall but is safely assisted by staff may reveal a problem with mobility planning even though no injury occurred.

A supportive reporting culture encourages clinicians to discuss these events, raise concerns about monitoring systems and identify improvements without assuming that every incident represents an individual failure.

The result of a review should be practical.

If delayed toileting assistance contributed, the response may require changes in staffing or care coordination. If an unsuitable walking aid was involved, equipment assessment may need improvement.

If repeated AI alerts were ignored because they frequently proved irrelevant, the technology and associated workflow should be reassessed.

Completing an incident report is only one step. Meaningful learning occurs when the findings lead to better care.

Measuring Whether Falls Prevention Actually Improves

Hospitals need reliable outcome measures before evaluating AI-assisted falls prevention.

A commonly used indicator is the inpatient fall rate per 1,000 patient-days.

This allows organizations to compare the number of falls relative to the amount of inpatient exposure.

However, the definition of a fall must remain consistent.

The hospital should specify how assisted falls, events without injury and other relevant circumstances are classified and reported.

A change in definitions or reporting practices can make results appear better or worse even when actual clinical performance has not changed.

Injurious falls should also be monitored separately.

Hospitals may examine falls causing minor injury, major injury or other specified harm using a consistent severity classification.

Events resulting in fractures, intracranial injuries or other serious consequences are particularly important, although they may occur infrequently within individual departments.

Interpretation also needs to account for patient characteristics and ward environments.

An acute medical ward, rehabilitation unit and intensive care unit may have different mobility patterns, patient populations and opportunities for independent movement.

Comparing fall rates without considering these differences may produce misleading conclusions.

Reporting quality is another important factor.

If staff become more willing to report minor falls and near misses, the recorded rate may initially increase without actual deterioration in safety.

Conversely, a declining reported rate may conceal underreporting if the organizational culture discourages disclosure.

Process measures provide additional information.

Hospitals can examine whether comprehensive assessments are completed for eligible patients, whether identified risk factors lead to appropriate interventions and whether prevention plans are reviewed following changes in clinical condition.

Measures may also include timely mobility and toileting assistance, availability of appropriate walking aids and the quality of patient and caregiver education.

For AI-assisted systems, monitoring should include clinically relevant alerts, false-positive alerts, missed events, detection delays, staff response times and periods when the technology was unavailable.

The evaluation should also consider whether monitoring coverage included the locations where falls occurred.

An alert acknowledgment alone should not be counted as successful prevention.

The organization should determine whether the alert led to meaningful assistance or clinical action when such action was necessary.

Balancing measures are equally important.

These may include unnecessary restrictions on mobility, physical restraint use, patient distress, sleep disruption, avoidable staff interruptions and increased workload.

A system that reduces recorded falls by discouraging appropriate movement may not improve overall patient care.

Safety outcomes should therefore be assessed alongside functional recovery, rehabilitation needs, patient experience and the ability to maintain independence.

Evaluating AI Against Existing Care

The relevant comparison for AI-assisted falls prevention is not necessarily a hospital ward without any preventive measures.

Most hospitals already use combinations of nursing assessment, mobility assistance, environmental safeguards, patient education and incident reporting.

The important question is whether AI provides additional value beyond those existing practices.

A meaningful evaluation should begin by identifying the main circumstances associated with falls in the intended clinical setting.

If many falls occur during transfers to the bathroom, better toileting assistance and mobility planning may offer greater benefit than another sensor.

If falls frequently go undiscovered for prolonged periods, a reliable fall detection system may address a different safety concern by helping staff provide assistance sooner.

These are different clinical use cases and should not be combined into a single claim about AI effectiveness.

For movement monitoring, technical evaluation may examine detection sensitivity, false alarms and detection latency.

These measures describe how the system performs technically, but they are not sufficient to establish clinical benefit.

An alert generated several seconds before a fall may be technically accurate yet provide too little time for meaningful intervention.

Similarly, a fall detected immediately afterward may support faster clinical assessment, but it should not be counted as a prevented fall.

Clinical evaluation should therefore consider the complete sequence from signal generation to staff response and patient outcome.

Where feasible, prospective studies using appropriate comparison groups or staggered implementation across comparable wards may provide stronger evidence than simple before-and-after comparisons.

Results should account for differences in patient characteristics, staffing, monitoring coverage and concurrent quality-improvement activities.

If a hospital introduces new nursing education, revised mobility protocols and AI monitoring at the same time, an improvement in outcomes should not automatically be attributed to AI.

The limitations of the evaluation should be reported transparently.

Legal and Regulatory Considerations in Viet Nam

AI-assisted inpatient falls prevention must be considered within Viet Nam's legal framework governing healthcare practice, artificial intelligence, medical devices and personal data protection.

The Law on Medical Examination and Treatment No. 15/2023/QH15 provides the broader legal framework for professional practice, patient rights, medical records and the responsibilities of healthcare organizations.

Ministry of Health Circular No. 31/2021/TT-BYT establishes relevant requirements concerning nursing activities, including assessment, planning, implementation, monitoring and evaluation.

Circular No. 43/2018/TT-BYT addresses the prevention, reporting and management of medical incidents, while Circular No. 19/2013/TT-BYT concerns hospital quality management.

These instruments provide a professional and organizational foundation for inpatient falls prevention and post-fall learning.

They should not be described as requiring mandatory AI-based monitoring or a particular falls prediction tool.

Viet Nam's Law on Artificial Intelligence No. 134/2025/QH15 took effect on 1 March 2026.

Decree No. 142/2026/ND-CP, effective from 1 May 2026, provides detailed implementing provisions.

Decision No. 33/2026/QD-TTg, effective from 15 August 2026, establishes the current list of high-risk AI systems.

The healthcare entries expressly identified in that list concern specified AI-assisted surgical and robotic surgical systems.

AI-assisted inpatient fall prediction and movement monitoring are not separately named in those healthcare entries.

However, this does not establish that every such application is automatically low-risk or exempt from applicable AI obligations.

A product-specific assessment must consider its intended purpose, actual functions, degree of automation, deployment arrangements and relevant statutory criteria.

Formal regulatory classification and clinical risk are also distinct questions.

A system that generates misleading alerts, misses important events or contributes to unnecessary restrictions on patient movement may create significant safety concerns regardless of its formal AI classification.

Healthcare organizations should also determine whether a particular product falls within the medical device regulatory framework.

Decree No. 98/2021/ND-CP on medical device management, as amended, and Ministry of Health Circular No. 24/2026/TT-BYT, effective from 1 July 2026, may apply depending on the product's intended medical purpose and characteristics.

The legal status of a monitoring system should not be determined solely by the presence of a camera, sensor or AI model.

Software intended for clinical fall monitoring may raise different regulatory considerations from a general building security system.

The intended medical function and actual capabilities require assessment.

Personal data protection is particularly important when AI systems process clinical records, images, video or identifiable movement information.

The Law on Personal Data Protection No. 91/2025/QH15 and Decree No. 356/2025/ND-CP, both effective from 1 January 2026, establish relevant requirements for personal data processing and protection.

Decree No. 102/2025/ND-CP on health data management, effective from 1 July 2025, is also relevant to the governance, processing, protection and use of digital health data.

Healthcare organizations should determine the applicable lawful grounds and purposes for processing information, limit collection to what is necessary and establish appropriate access, security and retention arrangements.

Where external technology suppliers are involved, contractual responsibilities should address data processing, hosting, maintenance, security incidents and permitted uses of patient information.

Video monitoring may also involve other patients, visitors and healthcare workers whose information requires appropriate consideration.

Cross-border data transfers, overseas hosting and access by external service providers should be assessed under the applicable legal provisions.

Identifiable clinical records or video should not automatically be reused for unrelated AI model training or commercial product development merely because the information was originally collected for patient-safety purposes.

Such secondary use requires separate consideration of the applicable legal and ethical requirements.

These matters should be addressed before identifiable information is processed through a live monitoring system in patient care areas.

International clinical guidelines and the practical safeguards discussed in this article may inform local implementation, but they do not automatically become binding Vietnamese legal requirements.

Likewise, recommended organizational controls should be distinguished from statutory obligations unless applicable legal provisions make them mandatory.

A Practical Starting Point for Vietnamese Hospitals

A hospital considering AI-assisted falls prevention should begin by examining how reliably its existing prevention and post-fall learning processes work.

A representative review of recent inpatient falls and near misses can help identify the greatest opportunities for improvement.

The organization should examine whether comprehensive assessments were completed, whether individual risk factors were addressed and whether patients had access to appropriate mobility and toileting assistance.

It should also review the timeliness of clinical response and the quality of incident reporting.

These findings may reveal weaknesses that require organizational rather than technological solutions.

One ward may have complete falls risk documentation but insufficient assistance for patients who need to use the bathroom at night.

Another may have effective mobility planning but inconsistent communication when patients transfer between departments.

A third may experience delays in recognizing falls that occur outside direct staff observation.

Each situation requires a different response.

Where AI is expected to provide additional value, the hospital should select a narrow, clearly defined clinical use case.

For example, an initial evaluation might examine whether AI-assisted movement monitoring can identify specific situations requiring assistance in selected adult inpatient rooms.

Before live monitoring or processing of identifiable patient information begins, the necessary clinical governance, privacy, security and legal safeguards should be established.

Technical evaluation should consider detection performance, alert timing, false-positive rates and the adequacy of monitoring coverage.

It should also assess whether available staff can realistically respond to the expected alert volume.

Where appropriate, an initial silent evaluation may help assess system performance without allowing AI outputs to direct clinical decisions, while established prevention procedures continue unchanged.

If results justify further investigation, the hospital may consider a limited supervised prospective pilot.

The pilot should maintain comprehensive nursing assessment, individualized prevention planning and established clinical escalation procedures.

Nursing staff should participate in designing and evaluating the workflow because their practical experience is essential for understanding what happens after an alert appears.

Patients and caregivers may also provide valuable feedback about comfort, trust, mobility and the acceptability of monitoring.

The organization should monitor whether AI changes clinical behavior, creates unnecessary workload or affects patient mobility and dignity.

Criteria for modifying, restricting or suspending the system should be established before broader deployment.

If evaluation shows that the system generates excessive alerts, cannot provide sufficient time for intervention or introduces disproportionate privacy concerns, the organization should be prepared to reconsider its use.

Expansion to additional departments should depend on evidence from the intended clinical setting.

A system evaluated in an adult medical ward should not automatically be introduced into pediatric, psychiatric, rehabilitation or other specialized settings without appropriate assessment.

Different patient populations and care environments may require different preventive approaches and raise different legal, ethical or operational concerns.

The objective is to strengthen an existing falls prevention and learning system, not make safe nursing care dependent on a particular technology.

From Detecting Movement to Preventing Harm

AI-assisted inpatient falls prevention offers opportunities to help healthcare organizations recognize certain movements, identify changes in clinical information and respond more promptly when assistance may be needed.

These capabilities may be useful in selected circumstances.

However, the international evidence available in 2026 does not establish that sensor-based or AI-assisted monitoring consistently reduces falls and fall-related injuries across hospital environments.

Comprehensive assessment, individualized interventions, patient participation, appropriate staffing and reliable care processes remain the foundation of prevention.

The 2025 NICE guideline emphasizes understanding the factors contributing to falls rather than relying on prediction scores. The 2026 Cochrane review highlights the potential value of education, changes in care delivery and interventions tailored to patient needs and local conditions.

For healthcare organizations in Viet Nam, responsible AI implementation should build on these principles and existing professional responsibilities for nursing care, patient safety and quality improvement.

An AI system may recognize a patient attempting to leave bed, but the care team still needs to understand why the patient is moving and how that movement can be supported safely.

A sensor may detect a fall, but healthcare professionals must assess the patient, manage any injuries and determine whether the prevention plan needs to change.

An incident report may document what happened, but meaningful improvement requires the organization to act on what it learns.

The measure of success is not how many patients receive a high-risk score, how many movements are detected or how many notifications appear on a dashboard.

It is whether patients receive appropriate assessment and assistance, remain mobile and independent as safely as their condition allows, experience fewer avoidable injuries and benefit from a healthcare organization that learns from its own performance.

That is where AI-assisted falls prevention has the potential to make a meaningful contribution to healthcare quality and patient safety.

Selected References and Regulatory Sources

International Clinical Guidelines

National Institute for Health and Care Excellence. Falls: Assessment and Prevention in Older People and in People 50 and Over at Higher Risk. NICE Guideline NG249. Published 29 April 2025.

Montero-Odasso M, van der Velde N, Martin FC, et al. World Guidelines for Falls Prevention and Management for Older Adults: A Global Initiative. Age and Ageing. 2022;51(9). DOI: 10.1093/ageing/afac205.

National Institute for Health and Care Excellence. Head Injury: Assessment and Early Management. NICE Guideline NG232. Published 18 May 2023.

Evidence on Hospital Falls Prevention and AI

McLennan C, Dyer SM, Kwok WS, et al. Interventions for Preventing Falls in Older People in Hospitals. Cochrane Database of Systematic Reviews. 2026;5. DOI: 10.1002/14651858.CD016065.

Zheng M, Yan T, Wang X, et al. Effectiveness of Sensor-Based Fall Prevention Systems in Hospitals: A Systematic Review and Meta-Analysis. Worldviews on Evidence-Based Nursing. 2026;23(5). DOI: 10.1111/wvn.70184.

Haines TP, Stephen K, Manohar R, et al. Systematic Review and Meta-Analyses of Mobilization Alarms for Prevention of Falls in Hospital. Journal of the American Geriatrics Society. Published online 19 August 2026. DOI: 10.1111/jgs.70651.

Lee DS, Kim YJ, Park HW, et al. Evaluation of Artificial Intelligence-Assisted Video Monitoring for Inpatient Fall Prevention: A Retrospective Matched Cohort Study. Healthcare. 2026;14(18):2999. DOI: 10.3390/healthcare14182999.

Vietnamese Nursing and Patient-Safety Evidence

Le Thi Nhung, Nguyen Thi Anh, Le Kim Tha, et al. Fall Prevention Practice Among Nurses at Level II Government Hospitals in Ho Chi Minh City, Vietnam. Vietnam Journal of Community Medicine. 2026;67(1):326–331. DOI: 10.52163/yhc.v67i1.4193.

Relevant Vietnamese Legal and Regulatory Framework

Law on Medical Examination and Treatment No. 15/2023/QH15.

Ministry of Health Circular No. 31/2021/TT-BYT on nursing activities in hospitals.

Ministry of Health Circular No. 43/2018/TT-BYT on prevention of medical incidents in healthcare facilities.

Ministry of Health Circular No. 19/2013/TT-BYT on hospital quality management.

Law on Artificial Intelligence No. 134/2025/QH15; Decree No. 142/2026/ND-CP; Decision No. 33/2026/QD-TTg.

Law on Personal Data Protection No. 91/2025/QH15; Decree No. 356/2025/ND-CP.

Decree No. 102/2025/ND-CP on health data management.

Decree No. 98/2021/ND-CP on medical device management, as amended; Ministry of Health Circular No. 24/2026/TT-BYT.

Editorial and Clinical Notice

This article is intended for professional education, responsible healthcare AI governance and healthcare quality-improvement discussion. It draws on international clinical guidance, published research, Vietnamese professional requirements and relevant legal and regulatory sources.

It does not establish a mandatory AI implementation standard, provide patient-specific medical advice, authorize automated clinical decisions or constitute a legal determination of any particular AI product's regulatory status.

The clinical examples and proposed implementation approaches are illustrative. Falls assessment, individualized prevention planning, clinical response to falls, injury management and post-fall evaluation must remain under the responsibility of appropriately qualified healthcare professionals, consistent with applicable Vietnamese requirements, current clinical guidance and locally approved procedures.

AI-assisted monitoring should not replace comprehensive assessment, justify unnecessary restrictions on mobility or create disproportionate surveillance of patients. Any use of identifiable images, video or clinical data requires assessment under the applicable privacy, data protection and professional requirements.

The studies discussed do not establish that AI-assisted inpatient falls prevention necessarily reduces falls, serious injuries, mortality or healthcare costs in Vietnamese hospitals. Claims of clinical effectiveness require appropriate evidence from the intended clinical setting, including local evaluation, professional oversight and ongoing monitoring.

Digital Medicine Vietnam (DMVN) | Evidence and regulatory review: 1 November 2026.