Governing AI-Assisted Clinical Deterioration Prediction and Alerts in Viet Nam: Bedside Assessment, Timely Escalation and Patient Safety

Governing AI-Assisted Clinical Deterioration Prediction and Alerts in Viet Nam: Bedside Assessment, Timely Escalation and Patient Safety

IMPLEMENTATION INSIGHTS

PRACTICAL FRAMEWORKS & TOOLS

Last reviewed: 20 October 2026

A patient recovering from pneumonia is being cared for on a general medical ward. During the night, the patient's respiratory rate increases, oxygen requirements rise, and an AI-assisted monitoring system identifies a higher risk of clinical deterioration.

The warning appears on the hospital's electronic dashboard. A nurse has already noticed changes in the patient's breathing, but the physician responsible for the ward is attending another patient. The intensive care unit (ICU) is operating near capacity, and it is not immediately clear whether the patient needs urgent critical care assessment or who should coordinate the response.

The AI system may have identified an important change before a major clinical event occurred. But its potential benefit depends on what happens next.

Will someone assess the patient promptly? Will the physician receive enough information to make a treatment decision? If the patient's condition continues to worsen, can an appropriate emergency response be activated? And if ICU-level care is required, can the hospital provide that care without an unsafe delay?

These questions illustrate a central challenge in AI-assisted deterioration monitoring. Predicting that a patient may become critically ill is not the same as ensuring that the patient receives appropriate care.

An AI system may help identify patients who need attention, but it cannot independently examine a patient, determine the cause of deterioration or ensure that the necessary clinical resources are available.

For hospitals in Viet Nam, the challenge is to connect AI-generated information with reliable physiological monitoring, nursing assessment, medical decision-making and timely escalation.

The objective is not simply to produce earlier warnings. It is to help healthcare teams recognize clinically important deterioration, assess patients in time and deliver appropriate care before avoidable harm occurs.

Predicting deterioration and responding to it are different tasks

Clinical deterioration often develops through changes in a patient's physiological condition before a major event becomes apparent.

An increasing respiratory rate, worsening oxygen saturation, falling blood pressure, altered consciousness or reduced urine output may indicate that a patient needs further assessment. Some changes are obvious, while others become concerning only when considered together or compared with earlier observations.

Traditional early warning systems use physiological measurements to help healthcare professionals recognize these changes. The National Early Warning Score 2 (NEWS2), developed by the Royal College of Physicians, provides a structured approach to assessing acute illness severity and supporting clinical escalation [1].

AI-based systems may analyze additional information, including electronic medical records, laboratory findings, physiological trends and nursing documentation, to estimate the likelihood of deterioration within a defined period.

These systems do not necessarily communicate their predictions in the same way.

Some display a risk score or trajectory on an electronic dashboard. Others generate active notifications when specified conditions are met. Certain applications may also help prioritize patients for additional clinical review.

The differences have practical consequences.

A risk score displayed on a ward screen may not receive immediate attention. An active alert may notify a specific nurse or physician. A system that automatically initiates a response pathway creates further operational responsibilities.

The clinical value of these functions depends on whether the information reaches an appropriate healthcare professional, whether its significance is understood and whether it leads to timely action when needed.

A prediction model may perform well statistically but contribute little to patient safety if the hospital lacks an effective way to respond.

Conversely, a hospital may achieve meaningful improvements through reliable vital sign monitoring, effective communication and appropriate escalation procedures without using a particularly complex prediction model.

AI-assisted deterioration prediction should therefore be evaluated within the hospital's existing recognition-and-response process rather than as an isolated technology.

What recent evidence tells us about AI deterioration monitoring

Recent international studies provide encouraging but mixed evidence about AI-supported deterioration monitoring.

In April 2025, Rossetti and colleagues published a pragmatic cluster-randomized controlled trial in Nature Medicine evaluating the CONCERN early warning system [2].

The system uses patterns in nursing documentation to estimate deterioration risk. The study included 60,893 hospital encounters across 74 clinical units in two healthcare systems.

Compared with usual care, the intervention group had a lower adjusted hazard of in-hospital death (hazard ratio 0.64; 95% confidence interval 0.53–0.78) and a higher adjusted hazard of unanticipated ICU transfer (hazard ratio 1.25; 95% confidence interval 1.09–1.43).

These findings suggest that AI-supported surveillance integrated into clinical practice may improve selected patient outcomes.

They also demonstrate why an increase in ICU transfers should not automatically be considered an unfavorable result. Earlier recognition of deterioration may allow patients to receive appropriate critical care before experiencing cardiac arrest or another severe event.

However, the study did not establish that every additional ICU transfer was clinically necessary or that the increased transfers directly caused the observed mortality benefit.

An author correction published in April 2026 revised the study's reported estimates for hospital length of stay and 30-day readmission because of a modeling error [3]. The mortality and ICU transfer findings discussed here were not among the results requiring correction.

A different outcome was reported in February 2026.

Keim-Malpass and colleagues published a pragmatic randomized controlled trial in Scientific Reports involving 10,422 inpatient visits on cardiology and cardiac surgical wards [4].

The intervention displayed AI-based risk trajectories without requiring clinicians to follow a specific response protocol.

The study did not demonstrate a statistically significant improvement in its primary outcome, which measured time free from defined clinical deterioration events.

Interpretation requires caution. The study was conducted during the COVID-19 pandemic, and clinician-directed movement of patients between intervention and control beds complicated aspects of the randomized design.

Nevertheless, the findings raise an important implementation question. Providing clinicians with predictive information may not be sufficient to improve patient outcomes unless that information is incorporated into effective clinical decision-making and response processes.

The two trials evaluated different technologies, patient populations and implementation arrangements. They should not be treated as a direct comparison establishing that one alerting strategy is universally superior to another.

Their combined relevance lies in demonstrating why predictive performance and clinical effectiveness should be evaluated separately.

A systematic review and meta-analysis by Chua and colleagues, published in the March 2026 issue of Critical Care Medicine, examined 18 studies involving 349,818 patients [5].

The review evaluated real-time automated deterioration alert and trigger systems that notified healthcare personnel beyond the patient's assigned bedside nurse.

Among patients meeting alert criteria, the pooled analysis found an association with fewer in-hospital cardiac arrests. However, it did not demonstrate a statistically significant reduction in hospital mortality.

The evidence had important limitations. Most included studies used before-and-after designs, and all were assessed as having at least moderate risk of bias.

Not every system included in the review used AI. Its findings are therefore relevant to clinical response design but should not be presented as direct evidence of AI effectiveness.

A 2026 umbrella review by Lee and Kim in Intensive and Critical Care Nursing also highlighted the gap between the potential effectiveness of hospital rapid response systems and their implementation in practice [6].

The review synthesized 48 previous reviews and identified persistent barriers involving staffing, escalation arrangements and professional hierarchies that can discourage healthcare workers from raising concerns.

These findings reinforce an important distinction. Earlier recognition can create opportunities to improve care, but the hospital must be capable of responding appropriately.

Technology can help identify a patient who needs attention. Whether that patient receives timely and effective care depends on the clinical system surrounding the technology.

Reliable vital sign monitoring remains the foundation

AI deterioration prediction depends on the quality and timeliness of the information available to the system.

On general wards, physiological observations may be recorded intermittently rather than continuously. Some measurements come from electronic monitors, while others are obtained and entered manually by healthcare professionals.

A prediction may therefore reflect information that is incomplete, delayed or no longer representative of the patient's current condition.

Consider a patient whose oxygen saturation was acceptable during an earlier assessment. The patient's respiratory condition subsequently worsens, but updated measurements have not yet been recorded.

The AI system may continue displaying a relatively low risk estimate because the information needed to recognize the change is unavailable.

The absence of a high-risk prediction should not be interpreted as confirmation that the patient remains clinically stable.

The opposite problem can also occur. Incorrect measurements, monitoring artifacts or data-entry errors may generate an elevated prediction that does not accurately represent the patient's condition.

Healthcare professionals need to interpret physiological information in its clinical context rather than rely solely on a numerical output.

International guidance provides useful principles for physiological surveillance.

The National Institute for Health and Care Excellence (NICE) guideline on recognizing and responding to deterioration in acutely ill hospitalized adults recommends structured monitoring of respiratory rate, heart rate, systolic blood pressure, oxygen saturation, temperature and level of consciousness [7].

It emphasizes that monitoring frequency should reflect the patient's condition and that abnormal physiological findings should prompt appropriate reassessment and clinical response.

These recommendations provide an international reference rather than a universal monitoring schedule mandated for Vietnamese hospitals.

The appropriate observation plan should reflect the patient's diagnosis, clinical condition, treatment requirements, applicable professional standards and the hospital's established procedures.

For AI implementation, hospitals should consider whether their information systems can distinguish recent measurements from older observations and whether missing information is recognizable to clinical users.

The organization should also understand how frequently predictions are updated and whether displayed results continue to represent the patient's current condition.

AI cannot compensate for a clinical monitoring process in which essential observations are consistently missing or recorded too late to support timely care.

Nurses are central to recognizing deterioration and initiating assessment

In general inpatient care, nurses often have more frequent direct contact with patients than other members of the clinical team.

They observe breathing, consciousness, mobility, discomfort and other changes that may not be fully represented by electronic data.

A nurse may notice that a patient has become unusually confused, appears more breathless than earlier or requires increasing assistance with activities that were previously manageable.

These observations may justify further assessment even when an AI system has not generated a warning.

When an AI alert appears, the nurse's responsibility is not simply to acknowledge it electronically.

Depending on the patient's condition and the hospital's procedures, an appropriate response may involve confirming the patient's identity, reviewing recent physiological observations, assessing the current clinical condition, verifying questionable measurements and identifying signs requiring urgent medical attention.

Where the patient appears acutely unstable, escalation should not be delayed merely to repeat measurements or confirm the AI score.

The nurse should communicate important findings to the responsible physician or activate the appropriate emergency response process within the applicable professional scope and institutional arrangements.

Effective communication needs to convey more than the presence of an elevated risk score.

A physician responding to the concern may need to know that the patient's respiratory rate has increased, oxygen requirements have changed, blood pressure is falling or consciousness is deteriorating.

These observations help establish clinical urgency and support appropriate decisions about assessment and treatment.

Evidence from Viet Nam highlights the importance of practical training and consistent application of early warning processes.

In July 2025, Lê Thị Hằng and colleagues published a cross-sectional study in the Vietnam Medical Journal examining NEWS2 knowledge, attitudes and application among 176 nurses at Military Hospital 175 [8].

The researchers reported that 41.5% of participating nurses met the study's criteria for good NEWS2 application.

Only 36.3% applied NEWS2 at the required assessment times under the hospital's evaluated procedures, while 56.3% complied with reassessment requirements and 62.5% applied the specified clinical responses when NEWS2 scores changed.

These findings reflect one hospital and the criteria used in that study. They should not be generalized to nursing practice throughout Viet Nam or interpreted as evidence about AI system performance.

However, they illustrate a relevant implementation challenge.

Introducing a structured early warning tool does not necessarily mean that observation schedules, reassessment and clinical response practices will be applied consistently.

For hospitals adopting AI-assisted deterioration monitoring, nursing education should therefore focus on actual clinical situations rather than simply explaining how to interpret a risk score.

Staff need to understand which findings require further assessment, when to communicate concerns, how to obtain medical review and what to do when the patient's condition becomes unstable.

Training should also reinforce that professional clinical concern remains important even when the prediction system does not identify high risk.

The treating physician must be able to assess and respond

When a nurse identifies deterioration or receives a clinically important warning, the next step often depends on timely assessment by the responsible medical team.

The treating physician needs sufficient information to understand what has changed and whether urgent intervention is required.

This may include the patient's diagnosis, recent physiological trends, symptoms, oxygen requirements, relevant laboratory findings and treatment already provided.

The physician must then evaluate the clinical situation and determine whether the patient requires additional monitoring, diagnostic assessment, treatment changes or escalation to a higher level of care.

An AI prediction may influence the urgency of the assessment, but it does not establish a diagnosis or determine the treatment that should follow.

A patient with worsening respiratory status may be experiencing progression of infection, fluid overload, pulmonary embolism or another condition. Appropriate management depends on clinical findings rather than the predicted risk score alone.

Hospitals also need to address situations in which the treating physician cannot attend immediately.

A warning that reaches a nurse without an effective route to medical assessment may provide limited clinical benefit.

The local response process should clarify who receives urgent communication, which qualified professional can respond when the treating physician is unavailable and how further escalation occurs if the patient has not been assessed within a clinically appropriate timeframe.

This is particularly important overnight, during weekends and during periods of high workload.

The process should not depend on one clinician continuously monitoring an electronic dashboard or remaining available to answer every notification.

Clear on-call arrangements, handovers and access to alternative qualified responders are necessary to make deterioration alerts useful in routine hospital operations.

The organization should also ensure that communication responsibilities are understood across shifts.

An important change identified shortly before a shift handover should not lose priority merely because responsibility for the patient is being transferred to another team.

The clinical concern, actions already taken and required reassessment should remain clear to the professionals continuing care.

Rapid response arrangements must reflect the hospital's capabilities

Rapid response systems are designed to support timely assessment and management of patients who are becoming critically ill outside critical care areas.

Depending on the hospital, the response may involve a rapid response team, medical emergency team, critical care outreach service or another organized group of professionals with emergency and critical care competencies.

These arrangements can differ substantially across healthcare facilities.

A large tertiary hospital may have a dedicated response team available across several departments. Another hospital may depend on emergency physicians, anesthesiologists, ICU personnel or clinicians assigned to an on-call emergency response arrangement.

It would be inaccurate to assume that every hospital in Viet Nam operates an identical rapid response model.

AI-assisted deterioration monitoring should therefore be linked to the response resources actually available.

The organization needs to establish which findings justify urgent assessment by the treating team and which require immediate involvement of personnel with additional emergency or critical care expertise.

An elevated AI score should not automatically activate the highest-level emergency response in every case.

The appropriate escalation depends on the patient's clinical condition, the significance and reliability of the warning, and the hospital's established response criteria.

At the same time, obvious clinical instability should never have to wait for an AI threshold to be reached.

The response arrangement must also clarify who can initiate escalation.

Nurses should understand when and how they can request urgent assistance under the applicable professional and institutional framework. Physicians should know when critical care consultation is necessary, and response personnel need a dependable way to receive and prioritize requests.

Where hospital procedures permit nurse-initiated rapid response activation, unnecessary intermediate approval steps should not delay urgent assessment.

A rapid response involves more than the arrival of a team.

The patient may require immediate treatment, reassessment of physiological observations, closer monitoring or a decision about transfer to a higher level of care.

If the condition remains unstable after initial intervention, further escalation may be necessary.

The response should not be considered complete simply because the team attended the bedside.

What matters is whether appropriate care was provided and whether the patient has a safe ongoing treatment and monitoring plan.

ICU capacity is part of the clinical response

Early recognition is valuable only if the hospital can provide the level of care the patient needs.

A patient may be assessed promptly and referred appropriately, yet remain at risk because an ICU bed, suitable monitoring equipment or qualified critical care personnel are unavailable.

This creates an important distinction between recognizing the need for higher-level care and being able to deliver it.

When ICU admission is clinically indicated, the responsible teams need to determine how appropriate assessment, stabilization, treatment and monitoring will continue while transfer arrangements are made.

Depending on the patient's condition and the hospital's resources, this may involve support from critical care personnel at the current location, transfer to another suitably equipped area or referral to another healthcare facility when necessary and feasible.

Such arrangements must be consistent with the capabilities of available staff, appropriate clinical standards and established emergency procedures.

Continuing treatment on a general ward should not automatically be regarded as an adequate substitute for ICU-level care merely because an ICU bed is unavailable.

Hospitals should also distinguish physical bed availability from actual critical care capacity.

A nominally available ICU bed may not be usable without sufficient qualified personnel, monitoring equipment and other resources required for the patient's condition.

Introducing AI deterioration prediction may increase the number of patients identified as needing medical assessment or critical care consultation.

That does not mean every flagged patient requires ICU admission.

However, the hospital should understand whether its response teams and critical care services can manage the additional workload that may result from implementation.

If AI increases requests for urgent review without improving response capacity, delays may simply move from the recognition stage to the assessment or transfer stage.

Decisions about higher-level care must also take account of individual clinical circumstances, the likely benefits of treatment and any relevant, appropriately established goals or limitations of care.

ICU admission is not automatically the right outcome for every patient with a high predicted risk.

Hospital leaders should therefore consider clinical response capacity, ICU resources and appropriate care planning when evaluating the potential benefits of deterioration prediction.

ICU transfer rates must be interpreted in their clinical context

Unplanned ICU transfer is frequently used as an outcome when evaluating deterioration monitoring systems.

It can provide useful information, but it should not be interpreted in isolation.

An increase in ICU transfers may indicate that more patients are becoming critically ill. It may also indicate that clinicians are identifying deterioration earlier and transferring appropriate patients before a more serious event occurs.

Conversely, a reduction in ICU transfers may reflect improved management on general wards, but it could also result from limited ICU capacity, different admission practices or delayed recognition.

The CONCERN trial illustrates why this distinction matters.

The intervention was associated with a higher adjusted hazard of unanticipated ICU transfer and a lower adjusted hazard of in-hospital death [2].

These findings are consistent with the possibility that earlier recognition and escalation contributed to improved outcomes.

However, they do not prove that every additional transfer was appropriate or that the transfers themselves caused the mortality benefit.

Healthcare organizations should therefore examine the clinical circumstances surrounding ICU transfer decisions.

Was the patient assessed promptly? Was escalation clinically justified? Did necessary treatment begin before the patient's condition worsened further? Was the patient transferred within an appropriate timeframe after ICU-level care was considered necessary?

If transfer was delayed, the organization should examine whether the delay was related to communication, staffing, bed availability, equipment or another operational constraint.

The patient's physiological condition during the waiting period is also relevant.

A hospital should not describe a rising ICU transfer rate as a failure without considering whether those transfers represented earlier and more appropriate care.

Similarly, fewer ICU transfers should not automatically be presented as evidence that AI improved patient safety.

The more meaningful question is whether patients received the right level of care at the right time.

Clinical concern must remain actionable without an AI warning

A deterioration prediction system may fail to identify a clinically important change.

The patient may develop a condition outside the model's intended prediction scope. Relevant physiological measurements may be missing, or the clinical situation may change between model updates.

A relatively low predicted risk should therefore not prevent clinicians from reassessing a patient with concerning symptoms or physiological abnormalities.

A patient who develops respiratory distress, altered consciousness or circulatory instability may need urgent medical attention regardless of the current AI score.

Hospitals should preserve escalation pathways based on professional clinical concern as well as structured early warning scores and automated predictions.

NICE guidance recognizes both physiological warning scores and clinical concern as reasons to initiate an appropriate response [7].

Patients and family members may also provide useful information.

A relative may recognize that the patient is less responsive, behaves unusually or appears substantially different from their normal condition.

These observations should be considered through appropriate clinical communication and assessment rather than dismissed because no AI alert is present.

The important safeguard is that technology should add information to clinical surveillance without becoming the only accepted basis for requesting further assessment.

A practical pathway from prediction to bedside action

A hospital introducing AI-assisted deterioration monitoring should establish a practical process connecting predictive information with existing clinical response arrangements.

The process begins when the system identifies a clinically important increase in risk or generates a notification requiring attention under the hospital's agreed procedures.

The receiving healthcare professional should be able to confirm which patient is involved, understand the significance of the warning and determine whether immediate bedside assessment is required.

If the patient is clearly unstable, the appropriate emergency response should proceed without waiting for additional confirmation from AI.

For other warnings, the responsible professional should review available physiological observations and assess the patient's current clinical condition according to the urgency and responsibilities established by the hospital.

If medical review is needed, the relevant findings should be communicated to the treating physician or another appropriately qualified clinician.

Communication should describe the observed clinical change, recent physiological trends and the specific concern requiring assessment.

Simply reporting that the AI score has increased may not provide enough information for effective clinical decision-making.

The physician should then assess the patient as clinically indicated and determine whether additional monitoring, investigation, treatment or escalation is necessary.

Where the patient's condition requires emergency or critical care expertise, the established response pathway should be activated.

The process must also address what happens if the intended responder is unavailable.

An important clinical concern should not remain unresolved because the first person contacted is attending another patient.

If the patient remains on the ward, the clinical team should establish an appropriate monitoring and reassessment plan.

This may include more frequent vital sign observations, evaluation of treatment response or further medical review.

If transfer to a higher level of care is required, the response should continue until an appropriate clinical plan has been established and the necessary care or transfer has been arranged and delivered.

An electronic acknowledgement alone should not be interpreted as evidence that the patient has been assessed or that the clinical concern has been resolved.

The pathway should also distinguish between patients requiring immediate emergency intervention and those whose increased predicted risk warrants closer observation without activating an emergency response.

These arrangements should reflect the organization's clinical services, staffing and professional responsibilities.

The proposed pathway is a practical organizational approach, not a mandatory Vietnamese Ministry of Health AI deterioration protocol or an independently validated clinical instrument.

Measure bedside response and patient outcomes, not just predictive accuracy

A deterioration prediction model may have strong discrimination or calibration and still fail to improve patient care.

Hospitals therefore need to examine what happens after clinically important risk information becomes available.

One useful measure is the time between an actionable AI notification and the first documented bedside assessment.

However, the relevant events need to be distinguished.

The model may calculate a risk score at one time, display it later, send a notification after a configured threshold is reached and receive an electronic acknowledgement before anyone examines the patient.

These are not equivalent stages of care.

Hospitals should define the starting and ending points for each response-time measure and use timestamps that can be interpreted reliably.

For systems that display predictions without active notifications, the organization should establish what constitutes an actionable prediction and whether the beginning of the response interval can be measured meaningfully.

The time from identification of a clinical concern to communication with the treating physician is another useful measure.

When rapid response activation is required, the interval from the decision to activate the team to its arrival and clinical assessment may help identify delays.

Where ICU transfer is indicated, hospitals may examine the time from the decision to request higher-level care to the actual delivery of that care.

However, speed alone does not establish clinical appropriateness.

A patient may be assessed quickly but receive an inadequate response. Another may be assessed promptly and appropriately remain on the general ward because ICU admission is not indicated.

Evaluation should therefore examine whether the response matched the patient's clinical condition.

This may require selected case reviews by appropriately qualified clinicians to assess the reason for escalation, the actions taken, the patient's response and whether further intervention was necessary.

Relevant patient outcomes may include in-hospital cardiac arrest, emergency intubation, mortality, deterioration requiring unplanned critical care and hospital or ICU length of stay.

These outcomes should be interpreted in the context of patient severity, clinical case mix, baseline performance and changes in hospital resources.

The organization should also examine cases in which important deterioration occurred without an AI warning.

Otherwise, the hospital may overestimate the safety of its monitoring process by reviewing only patients the system successfully identified.

Differences between wards and shifts deserve attention as well.

An alerting process may function well during daytime hours but produce less timely responses overnight when staffing and access to medical review differ.

Where feasible, hospitals should examine unusually long response delays as well as typical response times.

A small number of seriously delayed responses may be important even when the overall average appears acceptable.

Evaluation should also consider whether changes in observed outcomes can reasonably be attributed to implementation. Differences in patient populations, staffing, ICU capacity, clinical protocols or other simultaneous improvement initiatives may influence results.

The most useful performance assessment follows the patient from recognition of a potential problem through bedside assessment, appropriate escalation, treatment and subsequent clinical outcome.

Local evaluation should begin with existing response capability

Before introducing AI deterioration prediction, hospitals should understand how their current recognition-and-response systems perform.

This includes examining whether vital signs are recorded reliably, how nurses communicate deterioration, how quickly physicians respond and whether emergency escalation arrangements are functioning as intended.

The hospital should also assess how patients requiring ICU-level care are currently identified and managed.

Without understanding existing practice, it may be difficult to determine whether AI improves care or simply adds another source of notifications.

A local evaluation may begin with a defined population, such as adult patients on selected general medical or surgical wards.

The organization should confirm whether the proposed system has been developed and evaluated for comparable patients and clinical circumstances.

A model intended for adult general wards should not automatically be extended to pediatric, obstetric or specialized critical care settings without appropriate evidence.

Testing should examine the quality and timing of physiological data, the relevance of predictions and the expected number of patients requiring additional review.

The hospital should also determine how the system behaves when essential information is missing or data transmission is interrupted.

An initial evaluation may be conducted without allowing AI outputs to influence patient care, comparing predictions with existing surveillance and clinical outcomes.

This can help identify data limitations, unexpected alert patterns and unsuitable alert thresholds before routine deployment.

However, noninterventional testing does not establish that the system will improve patient outcomes after implementation.

Clinical behavior and workload may change once healthcare professionals begin receiving and acting on predictions.

Training should therefore include realistic situations.

Nurses and physicians may need to practice responding to a warning during a busy shift, communicating deterioration when the responsible physician is unavailable and escalating concerns when a patient appears unstable despite a relatively low AI risk estimate.

The hospital should also consider whether the expected alert volume is manageable.

An implementation that produces frequent urgent notifications without adequate clinical response capacity may create delays elsewhere in the hospital.

Feedback from nursing staff, treating physicians and emergency or critical care personnel is particularly valuable because these professionals experience the practical consequences of the system.

Where software updates, changes in clinical data or modifications to monitoring interfaces affect the prediction process, the organization should determine whether further evaluation is needed.

AI-assisted monitoring should be introduced at a pace consistent with the hospital's ability to provide safe and timely responses.

Vietnamese law reinforces existing clinical responsibilities

AI-assisted deterioration prediction operates within the professional and organizational responsibilities established by Vietnamese healthcare law.

The Law on Medical Examination and Treatment No. 15/2023/QH15, as amended and reflected in Consolidated Document No. 26/VBHN-VPQH dated February 26, 2026, provides an important legal foundation for professional practice and patient care [9].

Article 70 addresses the organization of continuous medical duty in relevant healthcare establishments, including arrangements for clinical services and emergency care.

These provisions are relevant when hospitals organize medical coverage and emergency response outside routine working hours.

Article 71 addresses the prevention of medical incidents through identification, reporting, analysis of causes and preventive measures. It also establishes responsibilities for healthcare establishments and the people working within them.

These provisions reinforce the importance of appropriate clinical organization and patient-safety management. They do not prescribe a specific AI deterioration threshold or require every hospital to establish an identical rapid response team.

For nursing activities, Circular No. 31/2021/TT-BYT provides a relevant professional framework [10].

The Circular addresses patient assessment, identification of risks, nursing interventions, monitoring and coordination with physicians.

Article 6 includes responsibilities concerning respiratory, circulatory and temperature-related care, including timely notification of physicians and coordination in managing abnormal patient conditions.

These provisions are directly relevant when AI supports recognition of clinical deterioration.

An AI-generated warning does not replace professional nursing assessment or independently authorize clinical interventions outside the applicable professional scope.

Hospitals should ensure that AI supports existing responsibilities rather than creates uncertainty about who must assess, communicate and respond when a patient's condition worsens.

Circular No. 19/2013/TT-BYT provides a framework for hospital quality management [11], while Circular No. 43/2018/TT-BYT addresses the prevention, reporting, analysis and management of medical incidents in healthcare facilities [12].

These instruments are relevant when hospitals investigate whether delayed recognition, communication failures or problems with escalation contributed to a patient-safety incident.

However, an elevated AI risk score is not automatically a reportable medical incident. Reporting obligations depend on the nature and circumstances of the event and the applicable requirements.

The legal status of the AI technology itself requires separate assessment.

Viet Nam's Law on Artificial Intelligence No. 134/2025/QH15 took effect on March 1, 2026, with Decree No. 142/2026/NĐ-CP providing further implementation provisions from May 1, 2026 [13, 14].

Decision No. 33/2026/QĐ-TTg, effective August 15, 2026, establishes the list of high-risk AI systems [15].

As of this article's review date, the healthcare-specific entries in that list concern defined AI-assisted surgical and robotic surgical systems meeting particular conditions.

Ordinary AI-assisted clinical deterioration prediction is not separately identified among those healthcare entries.

This does not establish that every such application is legally low-risk or clinically safe.

The regulatory classification of an individual system must be assessed according to its intended purpose, actual functions and applicable legal provisions.

Where relevant, medical device regulation and personal data protection requirements must also be evaluated separately.

Clinical risk and statutory AI risk classification should not be treated as the same determination.

For hospitals, the practical implication is that adopting AI does not remove existing professional duties or patient-safety responsibilities.

The organization must determine how the technology fits its clinical services, professional roles, emergency response arrangements and applicable legal obligations.

Making deterioration prediction work in Vietnamese hospitals

The most useful starting point for AI-assisted deterioration monitoring is a clearly identified patient-safety problem.

A hospital may find that deterioration is recognized late on certain wards. Another may have reliable physiological observations but experience delays between nursing concern and medical assessment. A third may identify deterioration promptly yet struggle to provide timely critical care because of limited response capacity.

These problems require different interventions.

AI may contribute by identifying meaningful risk patterns earlier or helping teams recognize patients who warrant closer assessment.

However, if the main problem is delayed response after deterioration has already been recognized, improving communication, staffing or emergency escalation arrangements may be more immediately valuable than introducing another predictive system.

Hospital leaders should therefore examine how AI fits into the existing clinical response process rather than assume technology alone will resolve its weaknesses.

Nursing managers, treating physicians, emergency and critical care personnel, quality teams and information technology staff should participate in implementation planning.

The response arrangements should reflect the hospital's actual clinical resources.

A large tertiary hospital may be able to provide rapid critical care assessment through dedicated personnel. A smaller hospital may need different arrangements based on its available services and emergency referral capabilities.

The organization should also create conditions in which nurses and other healthcare professionals can raise clinical concerns promptly.

A warning system offers limited protection if staff are reluctant to communicate deterioration or encounter unnecessary barriers when requesting medical review.

Training, communication practices and appropriate professional support are therefore part of the clinical safety response.

The success of implementation should be assessed by whether the hospital becomes better at recognizing deterioration, providing timely bedside assessment, escalating appropriately and delivering the level of care patients require.

More alerts are not necessarily better care.

From earlier recognition to safer inpatient care

AI-assisted deterioration prediction may help healthcare teams identify clinically important changes before patients experience severe events.

Its value depends on what happens between recognition and response.

Reliable physiological monitoring provides the foundation. Nurses contribute bedside surveillance and early identification of concerning changes. Treating physicians determine the clinical significance of those changes and the appropriate treatment. Rapid response and critical care personnel provide additional expertise when patients require urgent assessment or higher-level intervention.

The hospital must connect these responsibilities through workable communication, escalation and reassessment processes.

ICU capacity and the ability to provide safe care while transfer is arranged are also essential considerations.

An increase in ICU transfers should not automatically be considered a failure, just as fewer transfers should not automatically be considered a successful outcome.

The important question is whether patients who require critical care are identified appropriately and receive the care they need without avoidable delay.

For hospitals in Viet Nam, responsible AI-assisted deterioration monitoring is not simply about generating earlier or more accurate predictions.

It is about ensuring that important clinical information reaches the right healthcare professionals, leads to appropriate bedside assessment and supports timely treatment within the hospital's actual care capabilities.

The real value of AI-assisted deterioration prediction lies not in how early a warning appears, but in whether that warning helps healthcare teams deliver safer and more timely care to patients who need it.

Professional Education Notice

This article is intended for professional education and knowledge exchange. It does not constitute individualized medical, nursing, emergency care, legal, regulatory or patient-safety advice.

The clinical scenarios are illustrative and do not represent documented patient cases.

The proposed clinical response and evaluation approaches are practical organizational recommendations. They are not standardized Vietnamese Ministry of Health AI deterioration protocols, mandatory international procedures or independently validated assessment instruments.

Recommendations concerning physiological monitoring, nursing assessment, medical review, rapid response activation, escalation timing, critical care consultation, ICU transfer and documentation should not be interpreted as identical statutory obligations for every healthcare facility.

Professional responsibilities depend on applicable laws, scope of practice, patient condition, healthcare services, institutional procedures and available clinical resources.

International guidance, including NEWS2 and NICE recommendations, should not automatically be treated as binding clinical requirements in Viet Nam.

Evidence from conventional early warning systems, automated alerting technologies and AI-based deterioration prediction should be distinguished. Findings from individual research studies should not be generalized automatically to all healthcare settings or patient populations.

Legal AI risk classification, medical device status, personal data protection and medical incident reporting requirements must be assessed separately under the applicable regulatory frameworks.

References

1. Royal College of Physicians. National Early Warning Score (NEWS) 2: Standardising the Assessment of Acute-Illness Severity in the NHS. Updated report of a working party. London: Royal College of Physicians; 2017.

2. Rossetti SC, Dykes PC, Knaplund C, et al. Real-Time Surveillance System for Patient Deterioration: A Pragmatic Cluster-Randomized Controlled Trial. Nature Medicine. 2025;31:1895–1902. DOI: 10.1038/s41591-025-03609-7.

3. Rossetti SC, Dykes PC, Knaplund C, et al. Author Correction: Real-Time Surveillance System for Patient Deterioration: A Pragmatic Cluster-Randomized Controlled Trial. Nature Medicine. 2026;32:1556. Published April 13, 2026. DOI: 10.1038/s41591-026-04382-x.

4. Keim-Malpass J, Ratcliffe SJ, Clark MT, et al. A Randomized Controlled Trial of Artificial Intelligence-Based Analytics for Clinical Deterioration. Scientific Reports. 2026;16:7345. Published February 5, 2026. DOI: 10.1038/s41598-026-39051-z.

5. Chua WL, Azimirad M, Chia M, Jones D, Woon PY. Effects of Real-Time Automated Clinical Deterioration Alert and Trigger Systems on Clinical Outcomes in Adult General Ward Patients: A Systematic Review and Meta-Analysis. Critical Care Medicine. 2026;54(3):557–570. DOI: 10.1097/CCM.0000000000006960.

6. Lee S, Kim G. Rapid Response Systems in Hospital Settings: An Umbrella Review. Intensive and Critical Care Nursing. 2026;95:104463. DOI: 10.1016/j.iccn.2026.104463.

7. National Institute for Health and Care Excellence. Acutely Ill Adults in Hospital: Recognising and Responding to Deterioration. Clinical Guideline CG50. Published July 25, 2007; recommendations amended April 2019; surveillance review 2020.

8. Lê Thị Hằng, Trần Thiện Trung, Nguyễn Gia Hiệp. Kiến thức và thái độ của điều dưỡng khi áp dụng thang điểm cảnh báo sớm (NEWS2). Tạp chí Y học Việt Nam (Vietnam Medical Journal). 2025;552(2). Published July 24, 2025. DOI: 10.51298/vmj.v552i2.14978.

9. National Assembly of Viet Nam. Law No. 15/2023/QH15 on Medical Examination and Treatment. Adopted January 9, 2023; effective January 1, 2024, as amended. Consolidated Document No. 26/VBHN-VPQH, issued February 26, 2026.

10. Ministry of Health of Viet Nam. Circular No. 31/2021/TT-BYT on Nursing Activities in Hospitals. Issued December 28, 2021; effective February 27, 2022.

11. Ministry of Health of Viet Nam. Circular No. 19/2013/TT-BYT on Guidelines for Healthcare Service Quality Management in Hospitals. Issued July 12, 2013; effective September 15, 2013.

12. Ministry of Health of Viet Nam. Circular No. 43/2018/TT-BYT on Guidelines for Prevention of Medical Incidents in Healthcare Facilities. Issued December 26, 2018; effective March 1, 2019.

13. National Assembly of Viet Nam. Law No. 134/2025/QH15 on Artificial Intelligence. Adopted December 10, 2025; effective March 1, 2026.

14. Government of Viet Nam. Decree No. 142/2026/NĐ-CP Detailing Certain Provisions and Measures for Implementing the Law on Artificial Intelligence. Issued April 30, 2026; effective May 1, 2026.

15. Prime Minister of Viet Nam. Decision No. 33/2026/QĐ-TTg Promulgating the List of High-Risk Artificial Intelligence Systems. Issued June 30, 2026; effective August 15, 2026.