Introduction
The COVID-19 pandemic demonstrated the need for countries worldwide to implement a national digital health infrastructure needed to respond to health emergencies and to achieve the Sustainable Development Goals (SDGs) [1]. As part of this digital transformation effort, digital health technologies (DHTs), such as electronic medical record (EMR) systems, laboratory information systems (LISs), and picture archiving and communication systems (PACSs), have become widespread. In recent years, Vietnam has developed and begun implementation of an ambitious national digital health strategy that includes the deployment of hospital-based EMRs and an electronic health insurance claim system [2].
As the cost of hardware continues to drop and the range and capabilities of DHTs continue to expand [3], digitization of health systems is becoming increasingly feasible in low- and middle-income countries (LMICs). New technologies, such as the Internet of Things, wearable devices, and artificial intelligence (AI), have begun to be adopted in health care settings and could be an important enabler for realizing the World Health Organization’s (WHO) Triple Billion Targets (more than 1 billion people benefiting from universal health care; better protected from health emergencies; and enjoying better health and well-being) and to accelerate SDG achievement [4]. In 2023, WHO announced the Global Initiative for Digital Health to help LMICs achieve the aims of the Global Strategy on Digital Health by developing their national digital health infrastructure, adopting international data standards, and fostering increased international collaboration in digital health [1,4].
In global health research, there is increasing interest in implementing new technologies, such as AI or smart wearables, for clinical decision support [5,6]. However, for these systems to be practicable and to take advantage of increasingly available “big data,” the local provision of technological infrastructure and implementation capabilities is necessary. For example, hospitals that have implemented EMR systems could use data from the EMRs to train AI models and enable the implementation of clinical decision support systems (CDSS) [7]. In addition, the adoption of international interoperability standards, such as Health Level Seven (HL7) Fast Healthcare Interoperability Resources (FHIR), is also necessary to allow efficient data exchange between information systems and new DHTs [8].
In hospital settings, digital maturity models, such as the Electronic Medical Record Adoption Model (EMRAM) from the Healthcare Information and Management Systems Society (HIMSS), are often used to guide DHT adoption and implementation [9]. These models can be used on a national or international scale and can target different dimensions, including technology, strategy, interoperability, analytics, and governance. Currently, there is no international consensus on how to best measure digital capabilities in health care institutions [9,10], and variations in digital maturity models across different countries may reflect an adaptation to national priorities and contexts, especially when being implemented in LMICs. In 2017, the Vietnam Ministry of Health (MoH) issued the Vietnam Health Information Technology (HIT) Maturity Model, originally known as Circular 54/2017/TT-BYT, as a roadmap for Vietnamese public hospitals toward a paperless and smart hospital model [2,11]. Although this guidance defines 7 digital maturity stages similar to EMRAM, the criteria to achieve a specific level have been significantly modified. For example, the Vietnam model includes the requirement for hospital information systems (HIS) to connect to the Vietnam Social Security claim portal and assesses the integration of the national standardized vocabularies into the digital systems [11].
The Vietnam ICU Translational Applications Laboratory (VITAL) project focuses on developing new technologies for improving outcomes of critically ill patients in Vietnam, such as CDSS using AI algorithms trained on local data [12-14]. In the national DHT policy landscape, regulations, infrastructure, and professional workflows are important elements [2,15] to ensure the sustainability and successful implementation of DHTs [6,16]. For this reason, in this study, we aimed to explore the current status and likely short-term development of the DHT infrastructure in 5 major public hospitals in Vietnam. Although the findings may not be generalizable to all Vietnamese hospitals regarding the state of their DHT adoption, we expect they can inform discussions around how new DHTs can be integrated with the existing IT infrastructure in Vietnamese public hospitals.
Methods
Study Design
This study used a structured questionnaire to explore the current adoption of DHTs in 5 public hospitals in Vietnam. The assessment items were developed based on the Vietnam HIT Maturity Model, which comprises 8 domains related to technology adoption and digital capabilities in hospitals [11]. The 8 domains are IT infrastructure, HIS, LIS, radiology information systems–picture archiving and communication systems (RIS-PACS), EMR, administrative and operation software, security and information safety, and nonfunctional criteria. Except for security and information safety and nonfunctional criteria, each domain includes a set of infrastructure requirements, such as having a server room, or software functional criteria, such as having laboratory test management functionality. The model also sets out further digital capabilities focusing on assisting clinical practice, such as CDSS, clinical data repositories (CDRs), and medication management; these are named extra capabilities.
Following a stage-based approach, a hospital reaches a digital maturity level in a domain if a predefined set of criteria is met. The hospital can only move to a higher digital maturity level when all criteria of the lower levels have been satisfied. The domains of IT infrastructure and HIS have 7 digital maturity levels (1-7), while the other domains have 2 levels (basic and advanced). Multimedia Appendix 1 presents the digital maturity levels and their scoring criteria based on the domains and extra capabilities.
Questionnaire, Data Collection, and Analysis
Since the focus of this study was the adoption of digital health systems in hospitals, we developed an assessment tool based on the criteria from the IT infrastructure, HIS, LIS, RIS-PACS, EMR, and extra capabilities domains (see Multimedia Appendix 2). The administrative and operation software, security and information safety, and nonfunctional criteria domains were not included in this study as we considered these less relevant to the study’s focus despite their importance to the overall digital maturity of a health care institution.
For each domain, we gathered information through the hospitals’ IT departments to determine whether the hospitals met the component criteria or whether they planned to achieve the criteria in the next 3-5 years. Results of the assessments were discussed with the heads of the IT departments to ensure accuracy. Data collection was carried out from July 2022 to March 2023. Information about the digital maturity of each hospital was entered into Microsoft Excel for initial data cleaning and then analyzed using R software (R Foundation for Statistical Computing) [16].
Our aim was to provide a descriptive summary of what infrastructure and functionalities, or criteria, were available in each hospital across the domains rather than calculating scores for their digital maturity. The proportion of criteria that each hospital met in each domain was calculated. A radar plot was created using the fmsb package in R [17] to depict the difference in the proportions of criteria met among the hospitals by domain. Radar plots are regarded as an efficient tool to compare various groups on multiple variables, as discussed by Saary [18]. In addition to summary statistics, we further compared technology adoption between the 5hospitals by delving into the individual domains. Tile graphs were created using the ggplot2 package in R [19] to illustrate the criteria that the hospitals had satisfied within each domain. These graphs allow for the visualization of individual data points (eg, which criterion was met by a specific hospital) as opposed to visualizing the counts or proportions of hospitals that met a particular criterion, as in the case of bar charts.
Study Population
Until the beginning of 2024, it was estimated that there were nearly 1500 public and over 300 private hospitals in Vietnam [20]. Five hospitals were purposively selected for this study based on previous interest in DHT innovation and participation in digital health research activities. Our approach aimed to sample hospitals where DHT innovation was likely to occur the soonest, rather than providing a comprehensive view of all government hospitals. Of the 5 hospitals that participated in this assessment, 4 (80%) were in Hanoi or Ho Chi Minh City, the 2 largest cities in Vietnam, and 1 (20%) was in a Mekong Delta (ie, rural) province. All are top-tier teaching public hospitals with a capacity of over 500 beds; 2 (40%) are tertiary hospitals that focus on a limited number of specialties, while 3 (60%) are general hospitals that provide care in a wide range of specialties. Most hospitals have between 8 and 12 IT staff per 1000 beds, except for 1 (20%) hospital that has an approximate ratio of over 50 per 1000 beds. Characteristics of the hospitals are shown in Table 1. To ensure confidentiality, we withheld information that could be used to identify the hospitals.
Table 1.Characteristics of the 5 hospitals participating in this assessment.
| Hospital | Tier | Hospital type | Number of beds | Teaching hospital | IT staff:bed ratio |
| A | 1 | General hospital | > 500 | Yes | 50:1000 |
| B | 1 | Tertiary hospital | > 500 | Yes | 13:1000 |
| C | 1 | Tertiary hospital | > 500 | Yes | 11:1000 |
| D | 1 | General hospital | > 500 | Yes | 8:1000 |
| E | 1 | General hospital | > 500 | Yes | 12:1000 |
Ethical Considerations
This project was exempted from ethical review as it constituted a service review, as defined by the Oxford Tropical Research Ethics Committee (OxTREC) [21,22]. The selected hospitals were identified from a professional network that the study’s principal investigators (authors LT and CP) were part of. The hospitals were invited to participate in the assessment through an invitation letter explaining the purpose and scope of the study. Upon approval by the hospitals’ authorities, the hospital IT directors were provided with an information sheet, the questionnaire, and an instruction document. Queries regarding the assessment, if any, were clarified by the first author (DMT).
The IT directors were informed in writing that they could refuse to provide any data if they wished and that the data collected would be securely managed and only be used for the purpose of the study. Participating departments were able to review and agree on the contents of the manuscript prior to submission. No personally identifiable data were collected during the research. The research data were managed as per the Oxford University Clinical Research Unit’s data management policy. Information that can be used to identify the hospitals, such as names and the number of beds, was deidentified to ensure confidentiality. No compensation was given to the hospitals for participating in this study.
Information Systems in Vietnamese Public Hospitals
HIS, LIS, radiology information systems (RIS), PACS, and EMRs are popular information systems in health care institutions worldwide. However, their functionalities and use cases may differ to some extent depending on the national context that they are implemented in. In this section, we provide a Vietnam-oriented description of these information systems to support the interpretation of the study’s findings.
Hospital Information Systems
Also known as hospital management systems, HIS are usually prioritized by Vietnamese hospitals among other information systems. They are typically used to manage outpatient services, as well as administrative and billing data emerging from all wards and departments in a hospital. HIS can be interfaced with RIS and LIS to send orders or receive imaging and laboratory results. A significant proportion of HIS in Vietnamese hospitals have been developed and provided by local vendors, such as the Corporation for Financing Promoting Technology (known as FPT), the Vietnam Posts and Telecommunications Group (known as VNPT), and Viettel [23].
Laboratory Information Systems
LIS allow clinical laboratories to track and use a wide range of data related to orders, specimens, results, and consumables. They reduce the turnaround time from ordering tests to collecting laboratory results through automatic processes, such as integrating with HIS to transfer orders to the laboratory and connecting with laboratory instruments to automatically receive results. Clinicians can use LIS installed in their wards or HIS interfaced with LIS to access laboratory results, when available. Many public hospitals in Vietnam have chosen LIS developed by local vendors, such as LABCONN by LABSoft (deployed in over 200 laboratories) and LABMDSOFT by MDsoft.
Radiology Information Systems–Picture Archiving and Communication Systems
RIS and PACS are often used together in medical imaging departments. RIS typically support managing orders, patients, and imaging procedure information. RIS can receive order information and send images and report back to HIS using HL7 messages. RIS can also send orders to imaging machines and receive processed images from PACS.
PACS comprise 4 components: imaging instruments, a secure network to transfer the images, workstations that allow doctors to view the images, and a storage system that can archive the images. PACS can thus facilitate image viewing, editing, storing, and sharing. With PACS, hospitals can eliminate physical film usage and provide multisite access to medical images through a web interface.
Electronic Medical Records
In Vietnam, EMR systems refer to systems implemented in hospitals, especially in inpatient wards, to manage patient care information, such as medical histories, problems, orders, tests, and medications, similar to paper charts [24]. EMRs are defined differently than electronic health records (EHRs) in Vietnam, which are defined as health records updated throughout a person’s lifetime and can cover a comprehensive range of health information, such as allergies, vaccination, family history, and outpatient visits rather than specialized care information [25]. Although the data collected by EMR systems are independently managed by hospitals, EHR data are synchronized from multiple sources, such as hospital and primary care facility visits to a centralized platform managed by the MoH [26]. In the Vietnam HIT Maturity Model, EMR systems were assessed on 4 areas as follows:
In the Vietnam HIT Maturity Model, each digital maturity level requires several extra digital capabilities, in addition to the principal information systems, such as HIS, LIS, and EMRs (see Multimedia Appendix 1). These capabilities can be grouped as follows:
Assessment Criteria
Although we were not able to collect information about 3-5-year investment plans from the 5 hospitals, we were able to collect data on all the assessment criteria from the maturity model. The radar plot in Figure 1 presents the percentage of criteria the 5 hospitals met in each domain.
Enlarge this image.Figure 1. Percentage of HIT criteria satisfied in each domain across the 5 hospitals (IT infrastructure, EMR, LIS, HIS, RIS-PACS, and extra capabilities). EMR: electronic medical record; HIS: hospital information systems; HIT: health information technology; LIS: laboratory information systems; RIS-PACS: radiology information systems–picture archiving and communication systems.
In general, HIS, LIS, and RIS-PACS were implemented in all 5 hospitals, albeit with varied digital maturity levels. Two hospitals were not using any EMR system or fulfilling any extra digital capability. One hospital reported meeting all the criteria across the 6 domains. In the following sections, we describe the domains in more detail and compare the implementation strategies between the hospitals.
IT Infrastructure
Figure 2 shows the IT Infrastructure criteria that were met by the 5 hospitals. Level 1 criteria were met by all 5 hospitals, and most hospitals reported meeting level 2-5 criteria.
Enlarge this image.Figure 2. Criteria of IT infrastructure in the Vietnam HIT Maturity Model met by the 5 hospitals in this assessment, where a maximum of level 7 could be obtained. The color coding represents the maturity group that a criterion belongs to, not the entire domain’s maturity level of a hospital. Although for official purposes, a hospital needs to meet all the criteria of a lower level to progress to a higher level, this figure represents any criteria attained. HIT: health information technology; LAN: local area network; NAS: network attached storage; SAN: storage area network.
All 5 hospitals had sufficient workstations for staff. They were connected to the internet and local area networks. All 5 hospitals’ digital systems and databases were run on dedicated servers, and 3 (60%) hospitals had their own on-premise server rooms with physical controls. The hospitals’ data were stored on storage devices that were supported by storage networks, such as network attached storage (NAS) or storage area network (SAN) systems. Backup storage systems were available in 3 (60%) of the 5 hospitals.
Almost all hospitals in this assessment adopted technologies to help reduce errors and improve patient flow. These included barcode readers, barcode printers, queue management systems, screens displaying queue orders, and electronic notice boards. Information kiosks were less common as only 3 (60%) of the 5 hospitals implemented this technology.
Level 6 and 7 criteria were less available in the hospitals. Only 1 (20%) hospital reported adopting mobile devices, such as tablets and smartphones, and a wireless local area network (LAN) to support hospital activities.
Hospital Information Systems
All 5 hospitals reported that their HIS fully met the level 1 and 2 criteria (Figure 3). These systems were able to support patient registration, outpatient services, and pharmacy stock management. Hospital fees, including social health insurance payments, were also managed in these HIS, where insurance claims were formatted into XML files following a national standard and routinely submitted to the Vietnam Social Security portal. The HIS were reportedly able to integrate Vietnam’s national coding systems to standardize the classifications of some administrative units, such as the Vietnam ethnic classification system, and clinical services, such as the WHO International Classification of Disease [27]. Medical orders and test results were assigned unique patient IDs and uploaded to the HIS. Inpatient service management and reporting functionalities (level 3) were also available in the HIS across the 5 hospitals.
Enlarge this image.Figure 3. Criteria of HIS in the Vietnam HIT Maturity Model met by the 5 hospitals in this assessment, where a maximum of level 7 can be obtained. The color coding represents the maturity group that a criterion belongs to, not the entire domain’s maturity level of a hospital. Although for official purposes, a hospital needs to meet all the criteria of a lower level to progress to a higher level, this figure represents any criteria attained. EMR: electronic medical record; HIS: hospital information systems; HIT: health information technology; PACS: picture archiving and communication systems.
Of the 5 hospitals, 4 (80%) reported that their HIS integrated with PACS and cashless payment technology. However, modules for treatment protocols, professional procedures, and drug interaction management were implemented in only 3 (60%) hospitals.
We found the functionalities that were least adopted were the ones that required integration with EMR systems (ie, voice recognition for EMRs and EMR management, available in 1, 20%, hospital). In addition, only 2 (40%) hospitals reported that their HIS could interface with information kiosks, electronic patient cards, and mobile devices.
Laboratory Information Systems
Results of our assessment showed that all 5 hospitals reported that their LIS met the basic criteria (Figure 4). All the LIS could manage hospital laboratory orders and results, as well as generate reports. Most of these systems could automatically send orders to and receive results from laboratory machines. One hospital reported that its LIS could receive results from laboratory instruments, but the orders could only be entered manually. The national dictionary system for laboratory tests was adopted across all 5 hospitals to standardize laboratory coding. The LIS of 4 (80%) hospitals satisfied all the advanced criteria, which included the ability to integrate with HIS for exchanging orders and results, set alert thresholds for laboratory results, manage chemical supply stocks, and manage laboratory specimens.
Enlarge this image.Figure 4. Criteria of LIS in the Vietnam HIT Maturity Model met by the 5 hospitals in this assessment, where a maximum of 2 levels can be obtained. The color coding represents the maturity group that a criterion belongs to, not the entire domain’s maturity level of a hospital. Although for official purposes a hospital needs to meet all the criteria of a lower level to progress to a higher level, this figure represents any criteria attained. HIS: hospital information systems; HIT: health information technology; LIS: laboratory information systems.
Radiology Information Systems—Picture Archiving and Communication Systems
All 5 hospitals were using RIS-PACS at the time of this assessment, although with different degrees of implementation (Figure 5). Of the 5 hospitals, 2 (40%; A and C) had RIS-PACS that met all the basic and advanced criteria, 1 (20%) hospital (B) nearly satisfied all the basic and advanced criteria, and RIS-PACS from the remaining 2 (40%) hospitals (D and E) were largely operated by external vendors and only implemented a limited number of functionalities of RIS-PACS.
Enlarge this image.Figure 5. Criteria of RIS-PACS in the Vietnam HIT Maturity Model met by the 5 hospitals in this assessment, where a maximum of 2 levels can be obtained. The color coding represents the maturity group that a criterion belongs to, not the entire domain’s maturity level of a hospital. Although for official purposes a hospital needs to meet all the criteria of a lower level to progress to a higher level, this figure represents any criteria attained. DICOM: Digital Imaging and Communications in Medicine; HIS: hospital information systems; HIT: health information technology; HL7: Health Level Seven; RIS-PACS: radiology information systems–picture archiving and communication systems.
RIS-PACS meeting the basic criteria were able to retrieve Digital Imaging and Communications in Medicine (DICOM) images of common imaging modalities, such as X-ray, magnetic resonance imaging, and ultrasound, through 2-way interfaces. The RIS, PACS, and HIS could exchange orders and images with one another based on HL7 messaging standards. The PACS could convert DICOM images to JPEG format, and any changes to images in the PACS could be promptly updated to the HIS. Orders and radiologists’ reading result management, measurement, and reporting were also ensured by these RIS-PACS.
Hospitals meeting the advanced criteria of RIS-PACS could carry out multisite consultations through web-based access to DICOM images. One hospital reported that its PACS had adopted the HL7 FHIR standard.
Electronic Medical Records
Generally, the EMR status was less mature than other domains (Figure 6), with only 3 (60%) hospitals reporting that they were implementing EMR systems. Only 1 (20%) of these satisfied sufficient criteria in the regulations for EMR systems [24], which allowed the hospital to completely eliminate archiving of paper records for backup and legal purposes. Nevertheless, the other EMR systems could provide clinical data management functions, including managing medical histories, clinical documents, orders, laboratory and imaging results, treatments, and prescriptions.
Enlarge this image.Figure 6. Criteria of EMRs in the Vietnam HIT Maturity Model met by the 5 hospitals in this assessment, where a maximum of 2 levels can be obtained. The color coding represents the maturity group that a criterion belongs to, not the entire domain’s maturity level of a hospital. Although for official purposes a hospital needs to meet all the criteria of a lower level to progress to a higher level, this figure represents any criteria attained. CCD: Continuity of Care Document; CDA: Clinical Document Architecture; EMR: electronic medical record; HIT: health information technology; HL7: Health Level Seven.
An EMR system fully compliant with the regulations for EMRs could ensure storage capacity, backup, and recovery capabilities. Interoperability standards, such as the HL7 CDA, the HL7 CCD, and HL7 FHIR, were implemented. Information security measures, such as authentication, audit trail, and encryption, were in place. Digital signatures were used to authenticate medical records and orders.
Extra Capabilities
The availability of these capabilities in the 5 hospitals is shown in Figure 7. Notably, 1 (20%) hospital reported achieving all the extra capabilities, with 2 (40%) hospitals confident about sharing clinical data using HL7 standards (although they were not currently sharing data at the time of the study). The adoption of CDRs and CDSS to support clinical tasks, such as giving orders, prescribing, and clinical decision-making, was only available in 1 (20%) hospital. However, all the hospitals with an EMR system reported being able to digitize all clinical documents and allow clinicians to give orders in the electronic environment.
Enlarge this image.Figure 7. Extra capability criteria in the Vietnam HIT Maturity Model met by the 5 hospitals in this assessment. CDR: clinical data repository; CDSS: clinical decision support systems; HIT: health information technology; HL7: Health Level Seven.
Discussion
Principal Findings
This study assessed the implementation of some key digital health systems in 5 top-tier hospitals in Vietnam. Overall, many processes related to administration, laboratory tests, and medical imaging were digitized in the hospitals through the implementation of HIS, LIS, and RIS-PACS on the basis of adequate basic IT infrastructures. The digital maturity of these systems, however, varied significantly between the hospitals, although all are large top-tier hospitals. EMR systems were implemented in 3 of the 5 hospitals. Although these systems could satisfy a wide range of data collection needs, their integration with CDRs and CDSS was sparse.
Comparison With Prior Works
This is the first original research that examined digital health system implementation in Vietnamese hospitals since the Vietnam MoH enacted the National EMR plan and the Vietnam HIT Maturity Model in 2018 as part of the sectorwide digital transformation agenda [11,21,24]. Academic publications on adoption and implementation of hospital-based digital health systems are limited [2]. Most of the previous studies have been conducted before 2014 and focused on the readiness to transition from paper-based processes to electronic systems rather than describing DHT adoption [28,29]. Other publications have described the development and pilots of technical solutions, such as LIS, data retrieval from medical devices, and data visualization for EMRs. We could not find any research exploring the digital systems currently used in hospitals in Vietnam [30-33].
Muinga et al [34] showed that most of the digital health systems adopted in Kenyan public hospitals from 2014 to 2016 were aimed at administrative and billing purposes rather than supporting clinical tasks. Systems such as LIS, RIS, and PACS, when available, were usually stand-alone systems and lacked interoperability with other systems in the same institution [34]. Our study found that all the Vietnamese hospitals assessed had gone beyond administrative systems and had used clinical information systems that are interoperable with each other, especially with HIS. EMRs, as a crucial clinical information system, had been implemented in 3 of the 5 hospitals to capture a wide range of data, such as medical histories, clinical documents, orders, and test results. However, the adoption of EMR capabilities, such as CDRs and CDSS, was limited, even though these are large teaching hospitals at the top tier of the Vietnamese health system. This is inferior to the digital maturity status of public hospitals in Turkey, as over 98% of the Turkish hospitals surveyed reported having CDRs regardless of their size [35]. In addition, CDSS for medication orders and nonmedication orders were implemented in 71% and over 57% of Turkish public hospitals, respectively. The early use of EMRAM to guide the national digital health care transformation may be an important facilitator for this wide adoption of CDRs and CDSS in Turkish hospitals [36].
The Vietnam HIT Maturity Model [11] that this study’s questionnaire was based on and the HIMSS EMRAM [9] share several similarities regarding assessment criteria. For example, CDRs, CDSS, closed-loop medication administration, and electronic documentation are included in both models, including the maturity stage that these systems belong to. However, EMRAM particularly focuses on EMR capabilities, in which systems such as laboratory, radiology, and pharmacy are seen as EMR ancillaries, while CDRs and CDSS are crucial components deciding the maturity of the EMR system. All the ancillary systems need to be implemented as early as in stage 1 of EMRAM. The Vietnam HIT Maturity Model, in contrast, splits the ancillaries into basic and more advanced maturity levels along the digital maturity roadmap. Systems such as CDRs and CDSS are the extra capabilities that should be met, alongside other domains, such as HIS, LIS, RIS, and PACS, so that a hospital can move up to a higher digital maturity level. Although the division of digital systems into smaller milestones may allow hospitals to better benchmark their digital maturity and plan for investments, the requirement to meet digital maturity criteria across multiple domains can be challenging for hospitals in a resource-limited setting such as Vietnam. Duncan et al [10] discussed that most published maturity models, including EMRAM, are not assessed in LMICs, questioning their applicability in cultures different from those of high-income countries, such as the United States and the United Kingdom. In addition, the implementation of EMRAM poses the risk of investing in complex systems without meeting the organization’s local needs [37].
Integrating New DHTs Into the Existing System
Most hospitals participating in this assessment had a low adoption rate of EMRs, CDSS, and CDRs. The low number of complete EMR systems may be caused by the cost required to upgrade the infrastructure and a lack of the required technical capabilities to meet all the requirements in EMR regulations. It is difficult for hospitals with incomplete EMR systems and CDRs to embark on using CDSS. Integrating, implementing, and maintaining CDSS is usually complex and depends on multiple factors, such as the availability and interoperability of data, especially from EMRs; an updated knowledge base; and understanding of the hospital’s workflow [38].
The wide availability of HIS, LIS, and RIS-PACS in the hospitals participating in our study suggests that digital systems for administrative and billing purposes and simpler systems to implement tend to be prioritized. Many hospitals may choose to implement clinical documentation functions before upgrading the other components to fully comply with EMR regulation. This leads to an increasing amount of medical data that are electronically available and useful for AI training and implementation even without full EMR implementation. However, the scarcity of CDSS, CDRs, and standard-based data-sharing experience found in this assessment suggests challenges that stakeholders may face when seeking to integrate AI-enabled solutions in Vietnamese hospitals. Although these challenges should be explored in further research, we believe a lack of national frameworks and detailed guidance for meaningful and secure clinical data exchange is 1 of the key barriers. In addition, recently, Chanh et al [39] mentioned exploring system interoperability, collaboration with local stakeholders from multiple disciplines, and understanding local data-sharing policies as some of the key considerations for applying AI in Vietnam.
Strengths and Limitations
We examined the adoption of digital health systems using the criteria published in the Vietnam HIT Maturity Model. This is a national benchmarking tool for HIT implementation in Vietnamese public hospitals, making it familiar to the hospitals participating in this study.
Only 5 public hospitals at the top-tier level were included in this assessment, so our findings do not represent the overall picture of HIT implementation in Vietnamese hospitals. The hospitals participating in this study were ones that had previous interest in DHT implementation and digital health innovation research. However, it is likely that hospitals at low tiers and located in less-funded regions have lower digital maturity across all the domains, especially in the EMR and extra capabilities domains.
Implications for Further Study
The adoption of digital health systems in this study was assessed based on technological criteria, such as the software functions that were available. Future work could address other dimensions related to the implementation of these systems, such as the organizational context and human factors [40]. The use of qualitative methods can be particularly suitable for such research questions as they can unveil the characteristics unique to technology adoption in hospitals in LMIC settings, such as Vietnam.
Conclusion
Several major public hospitals in Vietnam have a sound digital infrastructure in place to support fundamental administrative and clinical tasks, such as patient management, insurance claims, laboratory result management, and medical imaging inspection. Most of the hospitals have implemented EMR systems to a basic level that prioritizes data collection, management, and access. Nevertheless, the more advanced level of data management and use via CDRs and CDSS is not common. This can be seen as a barrier to the introduction of new DHTs in these hospitals. Along with the increased amount of data collected by the systems, the adoption of HL7, DICOM, and other international standards can be seen as a facilitator for new DHTs, such as AI-based CDSS, to be implemented in these hospitals.
Acknowledgments
This study was supported by funding from the Wellcome Trust United Kingdom (215010/Z/18/Z). The Vietnam Health Information Technology Maturity Model (Circular 54/2017/TT-BYT) was originally published in Vietnamese and was interpreted into English by DMT.
The members of Vietnam ICU Translational Applications Laboratory (VITAL) investigators are as follows (alphabetic order by surname):
OUCRU inclusive authorship list in Vietnam: Dang Phuong Thao, Dang Trung Kien, Doan Bui Xuan Thy, Dong Huu Khanh Trinh, Du Hong Duc, Ronald Geskus, Ho Bich Hai, Ho Quang Chanh, Ho Van Hien, Huynh Trung Trieu, Evelyne Kestelyn, Lam Minh Yen, Le Dinh Van Khoa, Le Thanh Phuong, Luu Hoai Bao Tran, Luu Phuoc An, Angela Mcbride, Nguyen Lam Vuong, Ngan Nguyen Lyle, Nguyen Quang Huy, Nguyen Than Ha Quyen, Nguyen Thanh Ngoc, Nguyen Thi Giang, Nguyen Thi Diem Trinh, Nguyen Thi Kim Anh, Nguyen Thi Le Thanh, Nguyen Thi Phuong Dung, Nguyen Thi Phuong Thao, Ninh Thi Thanh Van, Pham Tieu Kieu, Phan Nguyen Quoc Khanh, Phung Khanh Lam, Phung Tran Huy Nhat, Guy Thwaites, Louise Thwaites, Tran Minh Duc, Trinh Manh Hung, Hugo Turner, Jennifer Ilo Van Nuil, Vo Tan Hoang, Vu Ngo Thanh Huyen, Sophie Yacoub | Hospital for Tropical Diseases, Ho Chi Minh City: Cao Thi Tam, Ha Thi Hai Duong, Ho Dang Trung Nghia, Le Buu Chau, Le Mau Toan, Nguyen Hoan Phu, Nguyen Quoc Viet, Nguyen Thanh Dung, Nguyen Thanh Nguyen, Nguyen Thanh Phong, Nguyen Thi Cam Huong, Nguyen Van Hao, Nguyen Van Thanh Duoc, Pham Kieu Nguyet Oanh, Phan Thi Hong Van, Phan Tu Qui, Phan Vinh Tho, Truong Thi Phuong Thao | University of Oxford: Natasha Ali, James Anibal, David Clifton, Mike English, Ping Lu, Jacob McKnight, Chris Paton, Tingting Zhu | Imperial College London: Pantelis Georgiou, Bernard Hernandez Perez, Kerri Hill-Cawthorne, Alison Holmes, Stefan Karolcik, Damien Ming, Nicolas Moser, Jesus Rodriguez Manzano | King’s College London: Liane Canas, Alberto Gomez, Hamideh Kerdegari, Andrew King, Marc Modat, Reza Razavi | University of Ulm: Walter Karlen | The University of Melbourne: Linda Denehy, Thomas Rollinson | Mahidol Oxford Tropical Medicine Research Unit (MORU): Luigi Pisani, Marcus Schultz
Data Availability
The data set generated and analyzed during this study is available upon request through the Oxford University Clinical Research Unit’s managed data access scheme [41].
Authors' Contributions
All authors made substantial contributions to the conception and design of the study, drafted the work and revised it critically for important intellectual content, and provided final approval of the version to be published. DMT, NTD, CMD, HNH, LMK, NPH, DTTH, HTLT, TVD, and LMY were responsible for data acquisition and analysis, while DMT, CLT, and CP interpreted the data for the study. DMT, NTD, CMD, HNH, LMK, NPH, DTTH, HTLT, and TVD made the agreement to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work were appropriately investigated and resolved. CLT and CP managed funding acquisition. The authors attest that there was no use of generative artificial intelligence technology in the generation of text, figures, or other informational content of this manuscript.
Conflicts of Interest
None declared.
------------
Abbreviations
| AI: artificial intelligence |
| CCD: Continuity of Care Document |
| CDA: Clinical Document Architecture |
| CDR: clinical data repository |
| CDSS: clinical decision support systems |
| DHT: digital health technology |
| DICOM: Digital Imaging and Communications in Medicine |
| EMR: electronic medical record |
| EMRAM: Electronic Medical Record Adoption Model |
| FHIR: Fast Healthcare Interoperability Resources |
| HIMSS: Healthcare Information and Management Systems Society |
| HIS: hospital information systems |
| HIT: health information technology |
| HL7: Health Level Seven |
| LAN: local area network |
| LIS: laboratory information systems |
| LMIC: low- and middle-income country |
| MoH: Ministry of Health |
| NAS: network attached storage |
| PACS: picture archiving and communication systems |
| RIS: radiology information systems |
| RIS-PACS: radiology information systems–picture archiving and communication systems |
| SAN: storage area network |
| SDG: Sustainable Development Goal |
| WHO: World Health Organization |
------------
Edited by A Mavragani; submitted 04.12.23; peer-reviewed by A Hadianfard, S Lloyd; comments to author 27.06.24; revised version received 11.09.24; accepted 13.11.24; published 06.02.25.
Global Initiative on Digital Health (GIDH) executive summary. World Health Organization. Feb 1, 2023. URL: https://cdn.who.int/media/docs/default-source/digital-health-documents/global-initiative-on-digital-health_executive-summary-31072023.pdf?sfvrsn=5282e32f_1 [accessed 2025-03-01]
Tran DM, Thwaites CL, Van Nuil JI, McKnight J, Luu AP, Paton C, et al. Vietnam ICU Translational Applications Laboratory (VITAL). Digital health policy and programs for hospital care in Vietnam: scoping review. J Med Internet Res. Feb 09, 2022;24(2):e32392.
Moore GE. Cramming more components onto integrated circuits. Proc IEEE. Jan 1998;86(1):82-85.
World Health Organization. Global Strategy on Digital Health 2020-2025. Geneva. World Health Organization; 2021.
Liverani M, Ir P, Perel P, Khan M, Balabanova D, Wiseman V. Assessing the potential of wearable health monitors for health system strengthening in low- and middle-income countries: a prospective study of technology adoption in Cambodia. Health Policy Plan. Sep 13, 2022;37(8):943-951.
Schwalbe N, Wahl B. Artificial intelligence and the future of global health. Lancet. May 2020;395(10236):1579-1586.
Lee S, Kim H. Prospect of artificial intelligence based on electronic medical record. J Lipid Atheroscler. Sep 2021;10(3):282-290.
Bender D, Sartipi K. HL7 FHIR: an agile and RESTful approach to healthcare information exchange. 2013. Presented at: 26th IEEE International Symposium on Computer-Based Medical Systems; June 20-22, 2013; Porto, Portugal.
EMRAM: a strategic roadmap for effective EMR adoption and maturity. HIMSS Eurasia. Sep 10, 2024. URL: https://himsseurasia.com/en/himss-emram-standards/ [accessed 2025-01-07]
Duncan R, Eden R, Woods L, Wong I, Sullivan C. Synthesizing dimensions of digital maturity in hospitals: systematic review. J Med Internet Res. Mar 30, 2022;24(3):e32994.
Thông tư 54/2017/TT-BYT về Bộ tiêu chí ứng dụng công nghệ thông tin tại các cơ sở khám bệnh, chữa bệnh. Vietnam Ministry of Health. 2017. URL: https://thuvienphapluat.vn/van-ban/Cong-nghe-thong-tin/Thong-tu-54-2017-TT-BYT-Bo-tieu-chi-ung-dung-cong-nghe-thong-tin-tai-cac-co-so-kham-chua-benh-373292.aspx [accessed 2023-07-15]
Ming DK, Hernandez B, Sangkaew S, Vuong NL, Lam PK, Nguyet NM, et al. Vietnam ICU Translational Applications Laboratory (VITAL) investigators. Applied machine learning for the risk-stratification and clinical decision support of hospitalised patients with dengue in Vietnam. PLOS Digit Health. Jan 18, 2022;1(1):e0000005.
The Vietnam ICU Translational Applications Laboratory (VITAL). Oxford University Clinical Research Unit. URL: http://vital.oucru.org/ [accessed 2023-07-01]
Van HMT, Hao NV, Phan Nguyen Quoc K, Hai HB, Khoa LDV, Yen LM, et al. Vital sign monitoring using wearable devices in a Vietnamese intensive care unit. BMJ Innov. Mar 31, 2021;7(Suppl 1):s7-s11.
Nguyen QH, Ming DK, Luu AP, Chanh HQ, Tam DTH, Truong NT, et al. Vietnam ICU Translational Applications Laboratory (VITAL) investigators. Mapping patient pathways and understanding clinical decision-making in dengue management to inform the development of digital health tools. BMC Med Inform Decis Mak. Feb 02, 2023;23(1):24.
R Core Team. R: A Language and Environment for Statistical Computing. Vienna, Austria. R Foundation for Statistical Computing; 2023.
Nakazawa M. Package ‘fmsb’: functions for medical statistics book with some demographic data. Comprehensive R Archive Network. 2023. URL: https://cran.r-project.org/web/packages/fmsb/fmsb.pdf [accessed 2025-01-07]
Saary M. Radar plots: a useful way for presenting multivariate health care data. J Clin Epidemiol. Apr 2008;61(4):311-317.
Wickham H. ggplot2: Elegant Graphics for Data Analysis. New York, NY. Springer-Verlag; 2016.
Le N. Cả nước chỉ 70 bệnh viện dùng bệnh án điện tử. VN Express. 2024. URL: https://vnexpress.net/ca-nuoc-chi-70-benh-vien-dung-benh-an-dien-tu-4732748.html [accessed 2024-09-10]
OxTREC application process. University of Oxford. 2024. URL: https://researchsupport.admin.ox.ac.uk/governance/ethics/apply/oxtrec#collapse404361 [accessed 2024-10-17]
Definition of terms: research, audit and service review. University of Oxford. 2024. URL: https://researchsupport.admin.ox.ac.uk/governance/ethics/apply/oxtrec/definitions [accessed 2024-10-18]
KPMG Department for International Trade, Oxford University Clinical Research Unit. Digital health in Vietnam. Market intelligence report. KPMG. Dec 2020. URL: https://assets.kpmg.com/content/dam/kpmg/vn/pdf/publication/2021/digital-health-vietnam-2020-twopage.pdf [accessed 2025-01-07]
Thông tư 46/2018/TT-BYT quy định về hồ sơ bệnh án điện tử. Vietnam Ministry of Health. 2018. URL: https://thuvienphapluat.vn/van-ban/Cong-nghe-thong-tin/Thong-tu-46-2018-TT-BYT-su-dung-va-quan-ly-ho-so-benh-an-dien-tu-391438.aspx [accessed 2023-07-15]
Quyết định 5349/QĐ-BYT năm 2019 về phê duyệt Kế hoạch triển khai hồ sơ sức khỏe điện tử. Vietnam Ministry of Health. 2019. URL: https://thuvienphapluat.vn/van-ban/The-thao-Y-te/Quyet-dinh-5349-QD-BYT-2019-phe-duyet-Ke-hoach-trien-khai-ho-so-suc-khoe-dien-tu-428071.aspx [accessed 2023-07-16]
Quyết định 1923/QĐ-BYT năm 2023 phê duyệt Kế hoạch triển khai Nền tảng Hồ sơ sức khỏe điện tử năm. Vietnam Ministry of Health. 2023. URL: https://thuvienphapluat.vn/van-ban/Cong-nghe-thong-tin/Quyet-dinh-1923-QD-BYT-2023-Ke-hoach-trien-khai-Nen-tang-Ho-so-suc-khoe-dien-tu-564088.aspx [accessed 2023-07-17]
Quyết định 2035/QĐ-BYT năm 2013 công bố Danh mục kỹ thuật về ứng dụng công nghệ thông tin. Vietnam Ministry of Health. 2013. URL: https://thuvienphapluat.vn/van-ban/Cong-nghe-thong-tin/Quyet-dinh-2035-QD-BYT-nam-2013-Danh-muc-ky-thuat-ung-dung-cong-nghe-thong-tin-trong-y-te-199624.aspx [accessed 2024-09-01]
Hochwarter S, Cuong DD, Chuc NTK, Larsson M. Towards an electronic health record system in Vietnam: a core readiness assessment. J Health Inform Dev Countries. Sep 21, 2014;8(2):93-103.
Nguyen KT. Electronic health record readiness assessment in Thai Binh Hospital, Vietnam. IJSR. Mar 2018;7(3):1277-1281.
Landgraf KM, Kakkar R, Meigs M, Jankauskas PT, Phan TTH, Nguyen VN, et al. Open-source LIMS in Vietnam: the path toward sustainability and host country ownership. Int J Med Inform. Sep 2016;93:92-102.
Vo C, CAO T, Doan N, Tu S, Nguyen A, Nguyen B, et al. EMRVisualization for patient progress tracking: interface design and system implementation. IJAE. 2019;18(3):117-128.
Vu DH, Nguyen DT. Design of laboratory information system for healthcare in Vietnam BK-LIS. 2010. Presented at: International Conference on Communications and Electronics 2010; August 11-13, 2010; Nha Trang, Vietnam.
Hai VD, Thuan ND, Ngoc PP. Automatic retrieving data from medical equipment to create electronic medical records (EMRs) for an e-hospital model in Vietnam. 2011. Presented at: 5th International Symposium on Bio and Medical Informatics and Cybernetics (BMIC); July 19-22, 2011; Orlando FL.
Muinga N, Magare S, Monda J, English M, Fraser H, Powell J, et al. Digital health Systems in Kenyan public hospitals: a mixed-methods survey. BMC Med Inform Decis Mak. Jan 06, 2020;20(1):2.
Kose I, Rayner J, Birinci S, Ulgu MM, Yilmaz I, Guner S, HIMSS Analytics Team, et al. Team MoH. Adoption rates of electronic health records in Turkish hospitals and the relation with hospital sizes. BMC Health Serv Res. Oct 21, 2020;20(1):967.
Miliard M. HIMSS helps Turkish hospitals go digital. Healthcare IT News. Nov 15, 2013. URL: https://www.healthcareitnews.com/news/himss-helps-turkish-hospitals-go-digital [accessed 2024-09-11]
Cresswell K, Sheikh A, Krasuska M, Heeney C, Franklin BD, Lane W, et al. Reconceptualising the digital maturity of health systems. Lancet Digital Health. Sep 2019;1(5):e200-e201.
Middleton B, Sittig DF, Wright A. Clinical decision support: a 25 year retrospective and a 25 year vision. Yearb Med Inform. Mar 06, 2018;25(S 01):S103-S116.
Chanh HQ, Ming D, Nguyen QH, Duc TM, An LP, Trieu HT, et al. Applying artificial intelligence and digital health technologies, Viet Nam. Bull World Health Org. Jul 01, 2023;101(07):487-492.
Cresswell K, Williams R, Sheikh A. Developing and applying a formative evaluation framework for health information technology implementations: qualitative investigation. J Med Internet Res. Jun 10, 2020;22(6):e15068.
Data sharing policy. Oxford University Clinical Research Unit. URL: https://www.oucru.org/data-sharing-policy/ [accessed 2025-01-07]
© 2025. This work is licensed under https://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
Details
; Nguyen, Thanh Dung
; Chau, Minh Duc
; Huynh, Ngoc Hon
; Le, Minh Khoi
; Nguyen, Phuc Hau
; Duong Thi Thu Huyen
; Huynh Thi Le Thu
; Tran Van Duc
; VITAL (Vietnam ICU Translational Applications Laboratory) Investigators
; Thwaites, C Louise
; Paton, Chris