Применение цифровой технологии в области управления качеством медицинских сестер: обзор аналитического исследования.

Introduction

Nursing quality management is a multidimensional and systematic process that involves planning, organizing, coordinating, and controlling key factors influencing nursing quality—including patient safety, satisfaction, service efficiency, and care appropriateness—based on quality formation principles [ 1 ]. For the purpose of this review, nursing quality management is operationally defined as the systematic administrative processes—including planning, organizing, coordinating, and controlling—that utilize structural resources, standardized processes, and outcome monitoring to achieve, maintain, and improve nursing care quality, consistent with the Donabedian framework [ 2 ]. This distinguishes it from nursing quality, which refers to the degree of excellence in care outcomes themselves.

Traditional nursing quality management models, which are predominantly manual, have many limitations [ 3 ], such as non-standard nursing documentation and omissions in data recording. Data transmission often suffers from delays, and information sharing between departments is untimely, hindering cross-departmental collaboration. Quality assessments are mostly retrospective, making it difficult to provide feedback and intervention during care processes, which is not conducive to preventing adverse events. With the rapid development of information technology, the digital transformation of nursing quality management has become an inevitable requirement for future development [ 4 ] .

Digital technology has gradually penetrated all aspects of healthcare, including data tracking and statistics, electronic health records, smart wearable devices, full-cycle tracking management systems, the Internet of Things, telemedicine, and AI-assisted diagnosis, aiming to achieve rapid transmission, efficient processing, and sharing of nursing information, thereby improving the standardization of nursing work and the timeliness of quality evaluation [ 5 ].

In recent years, digital technology has demonstrated significant potential in optimizing healthcare delivery, with existing reviews highlighting its broad applications in digital health, patient safety and nurse-facing tools. However, these reviews have notable gaps that limit their guidance for nursing quality management practice. For example, Teixeira [ 4 ] explored digital maturity in general practice but did not focus on the unique context of nursing quality control; Han [ 5 ] conducted a concept analysis of digital nursing quality management without systematically mapping practical applications or evidence gaps; while other reviews [ 6 , 7 ] concentrated on single technologies rather than comprehensive integration across nursing quality management domains. Additionally, existing studies on digital technology in nursing quality management are fragmented, with unclear conceptual boundaries of core terms (e.g., “digital technology” and “nursing quality management”) and heterogeneous evidence distribution, making it difficult to form a unified understanding of application effects and implementation strategies [ 8 , 9 ] .

Given that the current research field is characterized by conceptually ambiguous definitions, heterogeneous evidence scattered across multiple application scenarios, and the absence of a comprehensive mapping of digital technology’s role in nursing quality management, a scoping review represents the most appropriate methodological approach. Unlike systematic reviews, which typically address narrowly defined clinical questions, scoping reviews are particularly well-suited to clarifying conceptual boundaries, mapping diverse evidence, and identifying research gaps in emerging or under‑synthesized areas [ 10 ]. Accordingly, this review aims to systematically map the application landscape of digital technology in nursing quality management and to identify key themes, evidence patterns, and knowledge gaps to inform future research and practice.

Accordingly, this review aims to systematically map the application landscape of digital technology in nursing quality management and to identify key themes, evidence patterns, and knowledge gaps to inform future research and practice. Theoretically, this review addresses a critical gap by establishing conceptual boundaries between digital technology applications and nursing quality management, providing an organizing framework that integrates the Donabedian model with digital health evidence. This synthesis offers a foundation for developing context-sensitive implementation strategies and standardized evaluation metrics in this emerging field.

Objectives

This scoping review will provide a comprehensive overview of current digital technology applications in nursing quality management. By synthesizing available evidence, this review intends to (1) clarify the conceptual structure and application boundaries of digital technology in nursing quality management; (2) summarize its effectiveness, limitations, and influencing factors across different contexts; (3) identify existing research gaps; and (4) offer evidence-based recommendations for practical implementation and future research. This review will contribute to standardizing the application of digital technology in nursing quality management and support the development of guidelines for its sustainable integration into clinical practice.

Methods

This scoping review was conducted in accordance with the Arksey & O’Malley framework [ 10 ], which includes five stages: identifying the research question, identifying relevant studies, study selection, charting the data, and collating/summarizing. The reporting adheres to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews [ 11 ].

Identifying research questions

The research questions of this review were as follows: (1) In diverse contexts, what are the main application directions of digital technology in nursing quality management for the target population? This will be addressed through inductive thematic analysis of technology types and application domains.(2) What positive effects do these digital technology applications have on nursing quality outcomes? This will be examined via narrative synthesis organized around the Donabedian structure-process-outcome framework.(3) What methodological and practical limitations exist in the application of digital technology in nursing quality management? This will be identified through critical appraisal of study limitations and influencing factors reported in included studies.

Eligibility criteria

The study selection process was guided by the PCC (Population, Concept, Context) [ 12 ] framework to ensure alignment with the review objectives.

Population

The review included registered nurses or patients receiving nursing care. Nurses were required to be involved in or affected by digital technology interventions aimed at quality management. Patients were those whose care was directly influenced by such technologies. There were no restrictions regarding gender, age, or clinical specialty.

Concept

The focus was on studies evaluating or describing the application of digital technology within nursing quality management. This included technologies such as electronic health records, clinical decision support systems, wearable devices, telemedicine platforms, and data analytics tools used for monitoring, improving, or assessing nursing care quality, safety, efficiency, or patient outcomes.

Context

All healthcare settings where nursing care is delivered were considered, including but not limited to hospitals, outpatient clinics, community health centers, and long-term care facilities. Studies from any geographical location were eligible. Studies were included if they: (1) reported primary research on digital technology applications in nursing quality management; (2) involved nurses or patients as participants; (3) were original qualitative, quantitative, or mixed-methods studies; and (4) were published in English or Chinese.

Exclusion criteria

Studies were excluded if they: (1) focused solely on the technical development or design of a digital system without evaluation in a nursing quality context; (2) involved healthcare professionals other than nurses without separable nursing data; (3) were review articles, editorials, commentaries, conference abstracts without results, or study protocols; (4) were not published in English or Chinese; or (5) had unavailable full text.

Search strategy

A systematic and comprehensive literature search was conducted to identify all relevant published studies. The search strategy was developed iteratively through consultation with a medical librarian and team discussion, based on the key concepts derived from the research questions: “digital technology,” “nursing,” and “quality management.“For each key concept, a combination of Medical Subject Headings (MeSH) or equivalent subject terms and free-text keywords (including synonyms, related terms, and abbreviations) was used. The Boolean operators “AND” were used to combine the concepts, and “OR” was used within each concept set to ensure breadth. The search strategy was adapted to the specific syntax of each database. The following electronic bibliographic databases were searched from their inception to June 1, 2025: PubMed, Embase, Web of Science Core Collection, Cochrane Library, China National Knowledge Infrastructure (CNKI), Wanfang Database, China Science and Technology Journal Database (VIP), China Biomedical Database (CBM). The detailed search strategy for PubMed is presented in Table 1 . To align with the linguistic capabilities of the research team and ensure the feasibility of screening and data extraction, the search was restricted to articles published in English or Chinese only; no other language restrictions were applied. No restrictions were placed on publication date or study design during the initial search to maximize retrieval. To minimize publication bias and identify additional grey literature, we manually searched the reference lists of all included studies and relevant systematic/scoping reviews. A targeted search of Google Scholar was also performed using key combinations of search terms.

Table 1 Search strategy in PubMed NO. Search strategy #1 “Nursing Care“[Mesh] OR “Nursing“[Mesh] OR nurs*[tiab] OR “patient care“[tiab] #2 “Quality of Health Care“[Mesh] OR “Quality Improvement“[Mesh] OR “quality management“[tiab] OR “quality control“[tiab] OR “quality indicator*“[tiab] OR “quality outcome*“[tiab] OR “patient safety“[tiab] #3 “Digital Technology“[Mesh] OR “Health Information Systems“[Mesh] OR “Telemedicine“[Mesh] OR “Artificial Intelligence“[Mesh] OR digital[tiab] OR electronic[tiab] OR informat*[tiab] OR telemedicine[tiab] OR “smart device*“[tiab] OR wearable[tiab] OR “decision support“[tiab] #4 #1 AND #2 AND #3 Note: [Mesh] = Medical Subject Headings; [tiab] = title and abstract; * = wildcard for truncated words in PubMed database

Data selection

Records retrieved from databases and supplementary sources ( n = 992) were imported into EndNote X9 for management. After removing duplicates, two reviewers (XA W. and XY Z.) independently performed a two-stage screening process. Screening was conducted using EndNote X9 with manual review; no automated screening tools (e.g., Rayyan, Covidence) were used due to institutional access constraints. First, titles and abstracts were screened against the eligibility criteria. Subsequently, the full texts of potentially relevant articles were assessed for final inclusion. During screening, any discrepancies between reviewers were resolved through discussion to reach consensus. If agreement could not be reached, a third reviewer (CL Y.) was consulted for arbitration. A formal risk of bias or quality assessment of individual studies was not conducted, as the primary aim of this scoping review was to map the breadth and nature of evidence, rather than to synthesize or appraise the effectiveness of interventions, which aligns with established scoping review methodology.

Data extraction and charting

A standardized data extraction form was developed using Microsoft Excel and piloted on a subset of included studies. Two reviewers independently extracted data from all studies. The extracted information encompassed study identification details (authors, publication year, country), study characteristics (research design, setting, sample size), participant descriptions (nurses or patients), the intervention or key concept (type of digital technology and its specific application in nursing quality management), and key outcomes (reported effects on nursing quality indicators, limitations, and influencing factors). The reviewers cross-checked the extracted data, and any discrepancies were resolved through iterative discussion. The data charting form was continuously refined throughout the process to ensure comprehensiveness and accuracy.

Data synthesis approach

An inductive thematic analysis approach was employed for data synthesis. Initially, two researchers independently reviewed the extracted data to identify recurring themes, patterns, and categories related to the application and effects of digital technology. Inter-rater reliability was ensured through weekly consensus meetings to resolve discrepancies; a third reviewer (CL Y.) adjudicated persistent disagreements. While formal Kappa statistics were not calculated, iterative discussion and triangulation with a third reviewer ensured analytical rigor. The frequency of key themes was noted. Findings were then compared, and differences in interpretation were discussed until consensus was reached. A third reviewer was available to adjudicate if needed. Subsequently, the identified themes were organized into coherent analytical domains, which collectively mapped the evidence landscape. A narrative synthesis was structured around the Donabedian (Structure–Process–Outcome) framework to conceptually explain how digital technologies influence different dimensions of nursing quality management.

Results

Literature screening results

A comprehensive search was conducted across selected databases and grey literature sources, yielding a total of 992 records. Following removal of duplicates ( n = 428), 564 articles were screened based on title and abstract, of which 545 were excluded. Nineteen articles proceeded to full-text evaluation, with 3 subsequently excluded (one unavailable full text, one with non-extractable data, and one with an incompatible study design). Finally, 16 studies were included in the systematic review (4 Chinese and 12 English publications). The process is illustrated in Fig. 1 .

Fig. 1 PRISMA-ScR flow chart for study selection

Basic characteristics of included studies

The 16 included studies involved nurses ( n = 7) and patients ( n = 9). Study types included randomized controlled trials ( n = 9), qualitative studies ( n = 1), mixed-methods studies ( n = 4), and survey studies ( n = 2). There were 4 domestic studies and 12 international studies, involving 9 countries. The top three contributing countries were China ( n = 4), Germany ( n = 3), and the United States and the United Kingdom ( n = 2 each). Details are shown in Table 2 .

Table 2 Basic Characteristics of Included Studies ( n = 16) Author/ Country Research Duration Research Object Research Type Sample Size Technology Type Research Scenario Research Content Key Findings Limitations Funding Support 2 A Information Management and Decision Support Systems ( n = 7) (Including: Information Systems, Clinical Decision Support Systems) Ungaro CMet al.[ 6 ]/ June - August 2019 Immunosuppressed or transplant patients visiting the emergency department Cohort study 585/344 a Information system a community-based tertiary care hospital Patients who are admitted to the hospital through the priority care pathway for immunosuppression or transplantation of patients are defined as the exposed group, and are classified as yellow during triage to distinguish them. Improved efficiency/quality; Reduced waiting time; Better patient experience Not mentioned Not mentioned Argentina Sbaffi Let al.[ 7 ]/ Summer 2019 Emergency department medical staff Survey research 101 Information System English NHS hospitals The survey data included demographic information and the emergency doctors’ views on the overload of medical information. Ensured quality of data collection; Information overload reported Not mentioned Not mentioned Britain Nathalie et al.[ 9 ]/ November 2018 - December 31, 2018 Cervical cancer patients Sequential mixed research method 480/8 b Decision support system Community Health Center A survey was conducted on all healthcare workers who used the 2018 version of the CC tool between November 1, 2018 and December 31, 2020. Qualitative interviews were also conducted with personnel from different geographical locations and with various medical roles at community health centers. Improved efficiency/quality; Reduced waiting time Technology immature; Study design needs improvement The National Cancer Institute of the National Institutes of Health USA Kathrin Set al.[ 15 ] / March - June 2019 Nurse Sequential mixed research method 1335/14 b Information system Nursing homes, mobile care services, day care centers and hospitals A self-designed questionnaire consisting of 21 closed-ended questions and 10 open-ended questions was used for the survey. Participants in the online survey were recruited to conduct qualitative research. Improved efficiency/quality; Reduced waiting time Not mentioned National-level Project Germany Liu Yuzhong et al.[ 20 ]/ November 2020 - August 2022 Breast cancer chemotherapy patients RCT 50/50 a Decision support system Grade III Class A Hospital The control group implemented intelligent nursing through the hospital-developed intelligent nursing decision support system. Improved efficiency/quality; Enhanced professional identity Technology immature; Study design needs improvement; Limited promotion Institutional-level project China Ma Juan et al.[ 23 ]/ January 2019 - December 2020 Neurology department severe patients HCT 120/120 a IAD prevention decision support system Grade III Class A Hospital Compared IAD prevention compliance, incidence, and data collection time pre- and post-DSS implementation. Improved efficiency/quality; Improved medical decision-making quality Technology immature; Study design needs improvement Provincial-level project China Li Na et al. [ 24 ]/ October 2023 - December 2023 Nurse RCT 30/30 a Decision support system Grade III Class A Traditional Chinese Medicine Hospital Experimental group: TCM nursing model assisted by DSS; control group: traditional method. Compared nurses’ clinical decision-making ability, quality, efficiency, and usage experience. Improved efficiency/quality; Improved medical decision-making quality Limited promotion Provincial-level project China 2B Telemedicine and Monitoring Technologies ( n = 5) (Including: Telemedicine , Virtual Nursing , Remote Monitoring , Wearable Smart Devices) Spies C Det al.[ 8 ]/ September 4, 2018 - March 31, 2020 Severe patients RCT 414 /1048 a Remote healthcare ICU The experimental group consisted of eight German acute ICU nursing quality indicators and daily remote medical rounds guided by expert consultation. The control group, on the other hand, had daily on-site rounds conducted by the local hospital. Improved efficiency/quality Not mentioned National-level Project Germany Müller et al.[ 16 ]/ 8 quarters (exact dates not provided) Online visiting patients NRCTs 300/1500 a Telemedicine home The intervention group implemented non-medical assistance, nursing services and medical services through the remote medical network. Improved efficiency/quality; Reduced medical costs; Enhanced self-management; Improved continuity/coordination Limited promotion National-level Project Germany Khairat Set al.[ 19 ]/ August 2022 - December 2023 Emergency patients Retrospective cohort study 11,343/4092 b Virtual nursing Emergency Room Set the face-to-face contact participants as the control group, and the virtual contact participants as the exposed group. Enhanced professional identity; Reduced waiting time; Better patient experience High economic cost; Limited target population National-level Project USA Tahir H Net al.[ 21 ]/ April 2023 - May 2024 Nurse Mixed studies 60/324 c Remote healthcare Hospital Survey (27 items, six dimensions): nursing quality perception, facility infrastructure, service quality, provider behavior, communication, and accessibility. Interviews covered diverse ages, genders, and government departments. Improved efficiency/quality; Improved nurse capability; Improved continuity/coordination Limited promotion None Pakistan Kasimovskaya Net al.[ 22 ]/ January 2022 - December 2024 Hypertension patients RCT 400/400 a Remote monitoring, wearable smart devices medical institution The experimental group received digital intelligent care, while the control group received traditional care. Improved efficiency/quality; Enhanced self-management; Improved continuity/coordination Not mentioned None Russia 2 C Innovative Interaction and Digital Care Technologies ( n = 4) (Including: Virtual Reality , Sensing Communication Technologies , Interactive Digital Technologies , Digital Care Pathways Sutarno M et al.[ 13 ]/ January 2022 -September 2022 Nurse Survey research 337 Sensing Communication Technologies and Processing Actuation Technologies 11 private hospitals, The survey tools include five questions on perception of communication technology, four questions on processing-driven technology, three questions on improving the quality of the medical system, two questions on reducing medical costs, and three questions on managing the medical supply chain. In total, there are 17 questions. Improved efficiency/quality; Reduced medical costs Technology immature; High economic cost Not mentioned Indonesia 9 public hospitals Zhao Menglu et al. [ 14 ]/ July 2022 - August 2023 Newly recruited nurse RCT 63/63 a Virtual reality technology Grade III Class A Hospital The subjects were randomly divided into the control group and the intervention group. The intervention period lasted for 12 months. Clinical practice evaluations, the New Nurse Transformation Shock Evaluation Scale, and satisfaction surveys were conducted respectively before the intervention, at 6 months into the intervention, and at 12 months into the intervention. Improved nurse capability; Increased training satisfaction; Enhanced professional identity Study design needs improvement Municipal-level project China McVey et al.[ 17 ]/ November 30, 2017 - June 6, 2018 Medical staff Qualitative research 54 interactive digital technologies English NHS hospitals The interview content includes the background and roles of the participants, as well as their experiences and feedback during the process of using NCA data. Improved efficiency/quality; Ensured quality of data collection Technology immature National-level Project Britain Vesinurm Met al.[ 18 ]/ February 1, 2021 - August 30, 2022 Multiple sclerosis patients CCS 112/63 b DCP Helsinki University Hospital Set patients who have logged into DCP at least once and meet the inclusion/exclusion criteria as the exposure group, while those who have not logged in will be classified as the control group. Reduced waiting time; Better patient experience Technology immature; High economic cost; Limited target population Not mentioned Finland Note: Sample size: a Control group/Intervention group; b Control group/Exposed group; c Quantitative study/Qualitative study. HCT: Historical Controlled Trial; RCT: Randomized Controlled Trial; NRCTs: Non-Randomized Controlled Trials; CCS: Case-Control Study; IAD: Incontinence-Associated Dermatitis; NCA: National Cancer Audit; DCP: Digital Care Pathway; CC: Cervical Cancer; NHS: National Health Service; ICU: Intensive Care Unit; DSS: Decision Support System; TCM: Traditional Chinese Medicine

Evidence mapping of digital technology applications

Based on the Donabedian framework, the applications of digital technology in nursing quality management were mapped into structural, process, and outcome dimensions. Details are shown in Table 3 .

Table 3 Evidence mapping of digital technology applications in nursing quality management Dimension Core Domains Technology Types Key Outcomes Structural Intelligent upgrading of electronic nursing record systems Electronic health records, AI-assisted verification systems Standardized documentation, improved data accessibility Process Precise scheduling of training resources Virtual reality simulation, digital training platforms Enhanced nurses’ professional competence Process Real-time monitoring and evaluation Wearable sensors, IoT, real-time dashboards Dynamic quality control, timely intervention Process Digital safety control Barcode scanning, smart infusion pumps, blockchain Reduced operational errors, ensured patient safety Process Full-cycle tracking management Telemedicine, full-process tracking programs Improved care continuity Process Optimization of patient participation Satisfaction evaluation systems, digital health education platforms Increased patient satisfaction and engagement Outcome Nursing efficiency Multiple technologies Improved work efficiency Outcome Patient safety Multiple technologies Reduced adverse events

Table 3 illustrates how specific digital technologies map onto distinct quality management functions. Structural technologies (e.g., EHRs) establish the foundational data infrastructure; process technologies (e.g., IoT sensors, telemedicine) enable real-time care delivery and monitoring; while outcome-focused applications reported variable impacts on efficiency, safety, and satisfaction across individual studies. This mapping reveals that most technologies span multiple Donabedian dimensions. Because the 16 included studies comprised 9 RCTs, 1 qualitative study, 4 mixed-methods studies, and 2 surveys, the strength and generalizability of these associations differ by study design; readers should interpret the mapped outcomes as descriptive patterns rather than pooled evidence of effectiveness.

Effectiveness and limitations

The results show varying effects of digital technology on nursing quality management. Influenced by social and environmental factors, the application and promotion of digital technology in this field have accelerated significantly since 2020 [ 4 , 13 ]. However, the included studies differed markedly in design: 9 RCTs, 4 mixed-methods studies, 2 surveys, and 1 qualitative study. RCTs and mixed-methods studies described improvements in nurses’ work efficiency [ 14 ] and perceived professional competence [ 15 ], while surveys and mixed-methods studies suggested enhanced care continuity and coordination [ 6 ] and increased patient satisfaction [ 16 , 17 ]. These findings are descriptive and should not be interpreted as pooled or generalizable estimates of effectiveness. Among the 16 included studies, 13 mentioned limitations of digital technology application, including immature technology, need for further development of functional modules [ 18 ], high initial construction costs [ 19 ], insufficient acceptance and recognition by patients and nurses [ 20 ], and difficulties in promotion.

Influencing factors

Current research on influencing factors of digital technology application primarily focuses on digital technology level, healthcare scale, regional economic level, government policies and support, technology-personnel compatibility, users’ psychological cognition and behavioral tendencies, and age. The evidence base for these factors is mixed: RCTs and mixed-methods studies [ 6 – 8 , 15 – 17 , 19 – 23 ] indicated that a high level of digital technology and well-developed information systems are key facilitators for improving nursing quality management efficiency, supporting large-scale application. Survey and mixed-methods studies [ 7 – 9 , 17 , 20 – 25 ] pointed out major barriers, including insufficient technological adaptability due to an aging user structure, inadequate training and guidance systems, lagging infrastructure, high economic costs of application and maintenance, and low psychological acceptance of digital technology among users. Because these observations derive from heterogeneous designs and contexts, they should be understood as exploratory themes rather than confirmed causal determinants.

Discussion

This scoping review systematically mapped the applications, effects, and limitations of digital technology in nursing quality management. Through evidence synthesis based on the Donabedian framework, we identified how digital technologies influence structural elements, care processes, and quality outcomes. The findings reveal both promising advancements and persistent challenges, offering insights for future implementation and research.

It is important to note that the 16 included studies varied substantially in methodological design (nine RCTs, one qualitative study, four mixed-methods studies, and two surveys), participant populations (seven focused on nurses, nine on patients), settings, and outcomes. The narrative synthesis below therefore presents themes as reported in individual studies; these should not be read as review-level conclusions about effectiveness.

Structural dimension: digital infrastructure and resources

Digital technology has fundamentally reshaped the structural foundation of nursing quality management. Survey and mixed-methods studies suggest that electronic health records (EHRs) and AI-assisted verification systems have standardized documentation and improved data accessibility [ 24 ]. Participants in these studies reported that such systems reduce manual entry errors and enhance data traceability, which are essential for quality monitoring. However, these benefits were largely described in observational and survey data; RCT evidence on structural outcomes remains limited. However, structural barriers remain evident. Several studies noted system incompatibility across departments or institutions, leading to “data silos” that hinder information sharing [ 26 ]. Furthermore, disparities in technological infrastructure between regions and healthcare settings affect equitable implementation [ 27 ]. In economically underdeveloped areas, limited funding slows the adoption of advanced systems, widening the digital divide [ 13 ]. These structural limitations underscore the need for interoperable systems and targeted investments to ensure broad and equitable integration. Future research should address interoperability standards and evaluate implementation equity across resource-limited settings. Healthcare administrators must prioritize infrastructure investment in underdeveloped regions to prevent widening of the digital divide.

Process dimension: enhancing care delivery and workflow

Digital tools have transformed key nursing processes, promoting efficiency, safety, and consistency.

Documentation and Decision Support: RCTs and mixed-methods studies indicate that intelligent EHR systems automate verification and provide real-time alerts, reducing documentation omissions and supporting clinical decision-making [ 28 ]. Nurses in included studies reported that such systems enable synchronous multi-terminal input, allowing data recording at the point of service, thereby enhancing accuracy and timeliness. These process improvements were observed across multiple designs but have not been systematically quantified in pooled analyses.

Training and Competency Development: Digital platforms, including virtual reality (VR) simulations and e-learning modules [ 29 ], enable personalized and scenario-based training. An RCT [ 15 ] and mixed-methods studies [ 30 , 31 ] reported that VR-based training improves skill acquisition and confidence, leading to better clinical performance and reduced error rates. These findings are promising but derive from a small number of studies with varied outcome measures. However, the effectiveness of such training depends on institutional support and nurses’ openness to adopting new learning tools [ 20 ].

Real-Time Monitoring and Safety Controls: IoT sensors, wearable devices, and smart infusion pumps allow continuous monitoring of vital signs and medication administration [ 32 , 33 ]. These technologies facilitate early warning of adverse events (e.g., falls, pressure injuries) and enable proactive interventions [ 25 , 32 ]. Barcode scanning and blockchain-based audit trails further enhance medication safety and procedural compliance [ 34 , 35 ].

Care Coordination and Patient Engagement: Telemedicine and digital tracking systems support seamless care transitions and longitudinal follow-up [ 8 , 36 , 37 ]. Patient-facing tools, such as digital health education platforms and satisfaction feedback systems, foster active participation and improve nurse-patient communication [ 38 , 39 ]. These processes collectively strengthen care continuity and personalized service delivery [ 23 ].

Despite these advances, process-level challenges persist, including variable user acceptance, the need for ongoing training, and the risk of over-reliance on technology, which may inadvertently diminish critical thinking and hands-on clinical judgment [ 9 , 40 ].

There is a need for longitudinal studies on the optimal balance between technology assistance and clinical autonomy. Institutions should implement safeguards to prevent over-reliance on automated systems and maintain nurses’ critical thinking capabilities through regular skills-based training.

Outcome dimension: measuring impact on quality and safety

The integration of digital technology was associated with reported benefits across multiple outcome indicators, though the strength of evidence varies by design.

Nursing Efficiency and Professional Competence: RCTs and mixed-methods studies reported time savings in documentation, scheduling, and care coordination, allowing nurses to allocate more time to direct patient care [ 14 , 28 ]. One RCT [ 15 ] and mixed-methods evaluations [ 31 ] found that digital training interventions correlate with higher competency scores and improved clinical readiness.

Patient Safety and Clinical Outcomes: In RCTs and cohort studies, the use of monitoring technologies and decision support systems was associated with reduced medication errors, fewer adverse events, and enhanced compliance with safety protocols [ 25 , 34 ]. These safety outcomes were primarily reported in hospital-based RCTs and may not generalize to other settings. For instance, smart infusion systems and barcode verification have significantly decreased dosing errors in high-risk settings [ 33 ].

Patient Satisfaction and Experience: Digital feedback mechanisms and participatory tools enable real-time evaluation of care experiences, leading to responsive service adjustments and higher satisfaction ratings [ 16 , 22 , 39 ]. Enhanced communication and education also contribute to greater patient engagement and trust [ 38 , 41 ].

Nevertheless, outcome evidence remains uneven. Most studies focus on short-term efficiency gains, with limited data on long-term clinical outcomes, cost-effectiveness, or impacts in community and long-term care settings [ 19 , 21 ]. Furthermore, outcome measures are often heterogeneous, complicating cross-study comparisons and meta-level synthesis.

Standardized outcome metrics for digital nursing interventions are urgently needed. Future studies should incorporate cost-effectiveness analyses and patient-reported outcome measures to support sustainable implementation decisions.

Potential adverse effects and unintended consequences

While digital technology offers substantial benefits, potential negative effects warrant consideration. First, qualitative and survey studies suggest that excessive reliance on automated decision support may lead to “deskilling” among nurses, potentially diminishing clinical judgment and autonomous decision-making capabilities [ 40 ]. This concern is based on staff perceptions rather than observed outcome data. Second, the “double-edged sword” of continuous connectivity may contribute to nurse burnout, as digital tools blur boundaries between work and personal life, increasing cognitive load [ 41 ]. Third, alert fatigue from frequent system notifications can desensitize clinicians to critical warnings, paradoxically reducing patient safety [ 7 ]. Finally, technology-mediated communication may depersonalize nurse-patient relationships, potentially compromising the therapeutic alliance central to nursing care. These risks underscore the importance of human-centered design and implementation strategies that preserve nursing’s relational core.

Limitations

This review has several methodological limitations. First, the search was restricted to English and Chinese literature, which may exclude relevant studies published in other languages. Second, while we followed established scoping review guidelines, no formal quality appraisal of individual studies was conducted, consistent with the mapping purpose of scoping reviews. This means that findings from studies with varying methodological rigor are presented without differentiation, potentially affecting the strength of evidence for specific conclusions. Readers should interpret results with caution, recognizing that effect sizes and associations reported may be influenced by study design limitations. Third, grey literature and unpublished studies were not systematically searched, though we manually checked reference lists of included studies and relevant reviews. Fourth, the included studies predominantly originated from high-income countries (Germany, USA, UK) and urban tertiary hospitals in China, potentially limiting applicability to low-resource settings, primary care environments, or different healthcare financing systems. The organizational and cultural context of nursing practice varies substantially across countries; for example, nurse-patient ratios, scope of practice regulations, and technology infrastructure differ markedly between settings. Caution should be exercised when transferring findings to contexts with disparate healthcare delivery models.

Implications for practice, policy, and future research

To maximize the potential of digital technology in nursing quality management, several strategic actions are recommended:

Practice: Healthcare institutions should invest in interoperable systems and provide continuous digital literacy training for nurses. Emphasis should be placed on balancing technology use with clinical judgment to avoid deskilling.

Policy: Policymakers and administrators should develop clear data governance frameworks, ensure equitable funding for digital infrastructure, and promote standards for system compatibility and data security.

Research: Future studies should prioritize longitudinal and mixed-methods designs to assess sustainability, cost-effectiveness, and patient-centered outcomes. Research in underserved settings and on human-technology interaction is also needed to guide context-sensitive implementation.

Conclusion

Digital technology holds substantial promise for enhancing nursing quality management across structural, process, and outcome dimensions. While current evidence supports its positive impact on efficiency, safety, and satisfaction, significant challenges related to integration, equity, and outcome measurement remain. A coordinated effort among clinicians, managers, policymakers, and researchers is essential to harness digital innovation responsibly and effectively, ensuring that technological advances translate into meaningful and equitable quality improvements in nursing care. To maximize the potential of digital technology in nursing quality management, several strategic actions are recommended:

Research priorities: Future studies should prioritize (1) community and long-term care settings where evidence remains critically scarce; (2) longitudinal evaluations with at least 12-month follow-up to assess sustainability of benefits; (3) standardized, validated outcome measures to enable cross-study comparison; and (4) implementation science research to identify context-sensitive adoption strategies in resource-limited environments.

Broader implications

As digital transformation accelerates, ethical considerations regarding data privacy, algorithmic bias, and equitable access must be prioritized. Future innovations should embrace patient-centered co-design principles, ensuring that technological advances serve human needs rather than replacing the essential human connection that defines quality nursing care.

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Acknowledgements

Not applicable.

Author contributions

C.L.Y. conceived the study idea, designed the research framework, and supervised the overall process. C.L.Y. and X.A.W. conducted the literature search and screening based on predefined inclusion and exclusion criteria. C.L.Y. and X.Y.Z. were responsible for data extraction, quality assessment, and risk of bias evaluation. L.P.Z. contributed to the critical review of the manuscript, provided expert insights, and revised the manuscript. All authors approved the final version of the manuscript.

Funding

This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.

Data availability

No datasets were generated or analysed during the current study.

Declarations

Ethics approval and consent to participate

A scoping review does not involve human subjects, human material, human tissues or human data. Therefore, the approval of an ethics committee was not necessary.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Abbreviations

Artificial intelligence

Electronic health records

Internet of things

Preferred reporting items for systematic reviews and meta-analyses extension for scoping reviews

Randomized controlled trial

Non-randomized controlled trial

Historical control trial

Case-control study

Virtual reality

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