A call to action to close the global digital divide in nursing: Clinical nursing information systems and standardized terminologies in low and middle-income countries

Abstract

Clinical Nursing Information Systems (CNISs) and Standardized Nursing Terminologies (SNTs) significantly enhance the quality of care, promote interoperability, and enable measurable nursing outcomes. However, their adoption remains inconsistent, particularly in low- and middle-income countries (LMICs). This commentary reframes the existing gap as an issue of equity and systems design while providing a feasibility-prioritized roadmap tailored for LMICs. The article supports a sequenced approach that distinguishes between short-term actions and longer-term initiatives. Short-term actions include stabilizing infrastructure, developing open-source CNIS models, initiating terminology localization pilots, implementing essential data privacy safeguards, and providing targeted in-service training. In contrast, longer-term initiatives involve establishing national standards and exchanges, securing sustainable financing, cultivating leadership pipelines and curricula, and promoting cross-border interoperability and evaluation. Furthermore, it delineates various financing mechanisms—including concessional loans, performance-based grants, and collective procurement—while also addressing strategic considerations related to policy and governance frameworks. The commentary concludes with an explicit call to action: policymakers, donors, nursing leaders, educators, and vendors must collaborate to integrate structured nursing data into routine care and national platforms. Bridging this gap will render nursing work more visible, enhance decision support, and foster learning health systems within hospitals and communities worldwide.

Keywords

nursing informatics electronic health records health information systems interoperability global health developing countries

Introduction

Clinical Nursing Information Systems (CNISs) and Standardized Nursing Terminologies (SNTs) are increasingly recognized as vital components of modern, data-driven healthcare. By enabling nurses to electronically document care using consistent, structured language, these tools have the potential to improve patient outcomes and enhance interoperability across health systems.^1,2 High-income countries (HICs) have led the way in adopting CNISs integrated with Electronic Health Records (EHRs), using SNTs to capture nursing assessments, interventions, and outcomes in a standardized manner.^3,4 However, in many low- and middle-income countries (LMICs), the uptake of these digital nursing tools remains limited. Most hospitals in resource-constrained settings still rely on paper charts or fragmented record systems, which undermines the ability to leverage nursing data for decision-making.^5,6 This commentary advocates for an urgent call to action: addressing the CNIS/SNT adoption gap is fundamentally a matter of health equity and health system effectiveness, rather than simply a technical preference. To support this stance, evidence was gathered through a focused scoping of PubMed, CINAHL, and Embase (covering the years 2010–2025), along with citation chaining and key policy sources, with a particular emphasis on LMIC contexts. The materials included in this article consist of peer-reviewed empirical studies, systematic reviews, editorials, and implementation reports relevant to CNIS, SNT, and nursing informatics in resource-constrained settings.

This work explicitly acknowledges the recent editorial by Cesare and Zega⁷ that advances the global case for CNISs based on SNTs. Building on that foundation, this commentary contributes three advances: (i) an LMIC-focused analysis of structural, economic, and educational barriers; (ii) a feasibility-prioritized roadmap that distinguishes near-term from longer-term actions across infrastructure, policy/governance, and education/leadership; and (iii) concrete financing and localization pathways tailored to constrained environments. Taken together, these elements sharpen the critical perspective and frame a pragmatic, equity-oriented agenda to ensure nurses worldwide can fully contribute to data-driven care.

Global evidence for CNIS and SNTs

A growing body of literature highlights how CNISs and standardized terminologies improve the quality and continuity of care globally. CNISs provide structured digital platforms for nurses to plan, document, and evaluate care with greater precision and consistency.⁸ When nursing data are entered in a structured format, they can be more easily retrieved and analyzed, supporting evidence-based clinical decisions at the bedside. SNTs, meanwhile, establish a common language for describing patient problems, nursing interventions, and outcomes. By using agreed-upon codes and definitions such as NANDA International diagnoses, Nursing Interventions Classification (NIC), Nursing Outcomes Classification (NOC), or the International Classification for Nursing Practice (ICNP), nurses and other clinicians can communicate more clearly across shifts, departments, and even different organizations.^9,10 This standardized communication reduces ambiguity and errors, ultimately promoting patient safety and continuity of care.¹

One of the key advantages of SNTs is their contribution to interoperability. In healthcare, interoperability refers to the ability of different information systems and providers to exchange and interpret shared data.¹¹ SNTs enable nursing documentation to be understood across various health information systems, ensuring that a patient’s care information “speaks the same language” whether they are in a hospital, a community clinic, or transitioning between care settings.¹² For example, using a standardized terminology allows nursing notes and care plans to be integrated into interdisciplinary EHRs and health information exchanges. This means that important nursing observations and interventions follow the patient, supporting seamless care transitions and informed decision-making by all team members.⁹ Standardized terms and codes in health records also facilitate data aggregation and analysis at a system level. When nursing data from thousands of patient encounters are coded uniformly, it becomes possible to mine these data for insights – identifying trends in patient responses, evaluating the effectiveness of interventions, and contributing to quality improvement and research.^13,14 In short, CNISs and SNTs turn scattered nursing notes into a rich source of health information that can drive improvements in care.

Critically, the use of CNISs and SNTs has been linked to improved patient outcomes. Studies indicate that when nurses document care using standardized languages within electronic systems, there are measurable benefits, such as better adherence to clinical guidelines, reduced errors, and more proactive risk management.^15,16 A recent systematic review and meta-analysis found that the implementation of SNTs significantly improved several patient and organizational outcomes compared to usual, non-standardized documentation.¹⁷ Complementing this global synthesis, recent European investigations have demonstrated that standardized nursing diagnoses captured within CNIS/EHR environments predict clinically salient outcomes across both adult and pediatric settings—such as ICU transfer risk, delayed discharge, and prolonged length of stay—thereby underscoring the clinical consequence of SNT-coded nursing data.^18–20 In adult surgical cohorts, the embedding of standardized nursing diagnoses within CNIS has also enabled robust risk stratification and actionable quality-improvement signals derived from electronic health data.²¹

For instance, with standardized documentation, nurses can more easily track a patient’s progress toward outcomes and adjust care plans accordingly, leading to more timely interventions. Furthermore, by encoding nursing contributions in the EHR, SNTs make the often “invisible” work of nursing visible and quantifiable.²² Nursing-sensitive indicators, such as pain management effectiveness, pressure injury rates, or patient education outcomes, can be monitored through the data captured by CNISs. This enables healthcare organizations to accurately assess the contribution of nursing care to patient outcomes, which is crucial for quality improvement and informed policy-making. In summary, global evidence suggests that CNISs and SNTs are not merely bureaucratic tools, but rather enablers of safer, more effective patient care and robust, data-driven practice. These benefits underscore why many healthcare leaders advocate for their widespread adoption as part of strengthening health systems worldwide. Yet, despite the accumulating evidence base, adoption in many LMICs remains limited—a gap driven less by uncertainty of impact than by infrastructural fragility, unfunded mandates, and persistent capacity constraints. This disjuncture between evidence and implementation is precisely why a focused, equity-oriented call to action is now urgent.

The gap in LMICs

Despite the clear benefits and the maturity of these technologies in high-resource settings, a stark gap exists in the adoption of CNISs and SNTs between high-income and LMICs. In HICs, the past two decades have seen a near-universal shift from paper to EHRs in hospitals and community care. For example, the United States (US) and other developed nations have integrated multiple standardized nursing languages into national health IT infrastructures – the American Nurses Association (ANA) has approved 13 standardized nursing terminologies and data sets for use in EHRs, encompassing diagnoses, interventions, and outcomes classifications.³ These terminologies, such as NANDA-I, NIC, NOC, Omaha System, Clinical Care Classification, ICNP, etc., are widely taught and used in clinical practice in many HIC settings, enabling nurses to document care in a way that is computable and comparable across institutions. Countries like Japan, Spain, and France have also adopted these originally North American nursing languages in their health systems, illustrating the international reach of SNTs in high-income contexts.^3,23,24 As a result, nurses in such environments work with advanced CNIS platforms that streamline their workflow and feed into national databases, quality monitoring systems, and research, creating a continuous learning loop for healthcare improvement.

In contrast, most LMIC health systems are still in the early stages of digitizing clinical nursing documentation, if they have started at all. Paper-based nursing notes remain the norm in the majority of hospitals and clinics in resource-limited countries. A 2021 survey across 68 LMICs found that 64.2% of responding healthcare institutions still relied on paper charts for recording patient encounters, while only 25.9% had any form of institutional EHR software in use.⁵ Even where basic EHRs exist in LMIC settings, they often lack nursing-specific modules or standardized terminology support, resulting in digital versions of siloed medical charts rather than integrated CNISs. Globally, only about 15% of low-income countries have managed to implement electronic health record systems in health institutions nationally.⁶ This lag is most pronounced in Sub-Saharan Africa and parts of South Asia, where the “digital divide” – a lack of access to reliable technology infrastructure and resources – continues to hinder health information technology (IT) adoption.^25,26 The consequence is that nurses in LMICs largely operate without the benefits of data-driven tools: their documentation is often unstandardized, making it challenging to extract meaningful data to inform care improvements or to share information efficiently between care settings.

The implications reach beyond technology. Without CNISs, nurses spend valuable time on duplicative paperwork and face obstacles in tracking longitudinal patient progress across fragmented systems. Where SNTs are not implemented, nursing data frequently appears as free text or inconsistent local terms, effectively “locking” information within single facilities and limiting care coordination or secondary use. By contrast, nurses in advanced health systems can leverage real-time decision support (e.g., alerts for abnormal patient parameters, reminders for care plans) and contribute to big-data analytics that shape clinical guidelines – advantages that their LMIC counterparts are largely missing out on. This gap perpetuates a cycle in which LMIC health systems struggle to learn from nursing data and to demonstrate nursing’s impact on outcomes, constraining evidence-based improvement.^27,28 At the population level, the absence of standardized nursing data from LMICs creates blind spots in global surveillance and performance tracking, skewing policy priorities and resource allocation away from actual needs.²⁹

Several examples underscore the implementation gap. In Nigeria, nationwide EHR efforts (a prerequisite for CNIS functionality) have been slowed by power instability and connectivity gaps, making consistent electronic documentation unreliable.³⁰ Studies from Nepal and Tanzania similarly highlight intermittent electricity and poor internet service as impediments, with reliance on generators and minimal bandwidth.^31,32 In such contexts, sophisticated information systems or standardized digital terminologies are unlikely to take root without first addressing foundational infrastructure. Even middle-income countries face hurdles: Brazil and the Philippines have piloted nursing information systems linked to EHRs, yet report ongoing challenges with informatics training and tailoring terminologies to local practice.³³ These experiences make clear that EHR availability alone does not guarantee that nursing data are captured in structured, interoperable formats. Compared to high-income settings, where policy mandates, financing, and education have aligned to normalize CNIS/SNT use, LMICs continue to experience a significant implementation gap.³⁴ This reality underscores the need for targeted, staged strategies that elevate feasibility in resource-constrained environments and directly link nursing data to patient and system outcomes.

Barriers to implementation in LMICs

Implementing CNISs and standardized terminologies in low-resource settings is a complex task. The barriers in LMICs are multidimensional—technical, financial, policy, and workforce—and reinforce each other, making piecemeal solutions ineffective. A brief overview of the most significant barriers follows.

• Digital Infrastructure Gaps: Many LMICs lack the basic technology infrastructure needed to support clinical information systems. Inadequate electrical power, limited computer hardware, and poor network connectivity are widespread problems.³⁰ Intermittent electricity and constrained connectivity (often necessitating generators and offline workarounds) undermine routine system uptime and user trust, making the continuous use of an electronic system impractical.³⁵ Even where the internet exists, low bandwidth or unreliable service can severely hamper cloud-based or networked CNIS platforms.³⁶ These conditions contrast sharply with high-income hospitals that enjoy robust IT departments and 24/7 connectivity. The infrastructure gap is a fundamental barrier – without reliable power and internet, even the best software cannot function. Furthermore, outdated or insufficient hardware (e.g., a limited number of computers on wards) constrains nurses’ access to CNIS tools. For example, if only one computer is available for an entire unit, nurses will likely revert to paper charting to save time. A systematic review noted that “poor infrastructure” is a primary barrier to EHR adoption in low-income countries,¹⁴ which directly affects the feasibility of implementing nursing systems. In short, without dependable power and networking, even well-designed CNISs fail at the point of care.

• Economic and Resource Constraints: The cost of acquiring, customizing, and maintaining CNISs is prohibitive for many LMIC health systems. Hospitals operate on tight budgets that must prioritize basics like medications and staffing over information technology. Total cost of ownership extends beyond software licenses to include implementation, localization, training, governance, and lifecycle maintenance—cost centers that are often underestimated. Commercial nursing software or licensing fees for standardized terminology sets can be expensive. While open-source EHR/CNIS stacks can lower acquisition costs and enable local adaptation, they still incur nontrivial integration and sustainment expenses.^5,37,38 Lack of financial commitment at the national level is another issue – few LMIC governments have dedicated budgets for nursing informatics innovation, and external funding for digital health tends to focus on physician-centric or public health systems rather than nursing care documentation. A review of EHR initiatives in LMICs pointed out a lack of management commitment and support as a barrier, noting that without leadership allocating resources and setting expectations, digital systems fail to take root.³⁹ In effect, constrained capital and recurrent funding—rather than software availability per se—drive the implementation shortfall.

• Insufficient Policy Support and Standards: Structural barriers also manifest in the policy realm. Many LMICs do not have national policies or guidelines mandating or incentivizing the use of SNTs. In HICs, governments and professional bodies often require that health records use certain standards for example, the United States Meaningful Use program pushed EHR adoption broadly, and some countries have national nursing data sets.⁴⁰ In LMICs, such top-down drivers are often missing. The absence of a policy framework means each hospital or region is left to decide if and how to implement CNISs, leading to fragmentation and inaction. Moreover, a lack of localized or culturally relevant terminologies poses a significant challenge. International standards like ICNP or NANDA-I were largely developed in English and in high-income healthcare contexts. Healthcare professionals in LMICs encounter challenges related to the applicability of certain concepts within their specific context. There may be instances where terminology does not have a direct equivalent in local languages, or where prevalent health issues differ significantly from those in higher-income settings (e.g., tropical diseases, indigenous health practices) are not well-represented in the terminology.⁴¹ As a result, even if a country adopts an EHR, the nursing module might be left with free-text fields or a rudimentary list of nursing activities that are not standardized beyond that institution. Interoperability suffers in this environment because, without standard codes, data cannot be aggregated or shared easily. A broad analysis of evidence implementation in LMICs noted that context-specific issues are often overlooked, with the mismatch of language or terminology being a barrier to applying standardized practices.⁴² Compounding this, unclear or nascent data-governance frameworks (privacy, security, data ownership) heighten legal and ethical risk, disincentivizing scale.^43,44

• Educational and Workforce Barriers: Perhaps one of the most underestimated challenges is the shortage of informatics knowledge and skills among nurses and nurse leaders in LMICs. To implement CNIS and use SNTs effectively, the nursing workforce needs at least basic computer literacy and some training in the concepts of standardized documentation. In many LMICs, nurses receive little or no exposure to health informatics during their education.⁴⁵ Nursing curricula are often densely packed with essential clinical content, and topics such as electronic documentation, terminology standards, or data analytics are omitted.⁴⁶ Additionally, faculty qualified to teach nursing informatics are scarce in these regions. This results in low system self-efficacy, workflow avoidance (reversion to paper), and inconsistent use of codes even when systems are available. Studies in resource-constrained settings have found that low computer literacy and lack of familiarity with EHRs significantly reduce health workers’ readiness to adopt such systems.⁴⁷ Beyond frontline nurses, even nursing management in LMICs does not fully recognize the value of informatics, resulting in few champions for these innovations.⁴⁸ Opportunities for continuing professional development are limited, restricting the emergence of local “super-users” and nurse informaticians essential for sustainable adoption.⁴⁹ In summary, under-investment in nursing education and training regarding digital health is holding back adoption. Without building local capacity – informatics nurses, IT support staff, and knowledgeable end-users – even well-designed CNISs can fail to be utilized effectively. As one review observed in LMIC implementation projects, common barriers included deficits in staff knowledge, skills, and attitudes towards change.⁴² The human-capital gap is thus a first-order barrier, not a secondary consideration.

Taken together, these barriers explain why CNIS and SNT uptake remains sporadic despite available software options and growing global evidence of benefit: infrastructure fragility impairs reliability; funding models overlook lifecycle costs; national standards and governance lag service realities; and workforce preparation has not kept pace with digital requirements.^38,39

Toward equitable adoption: Strategies and solutions

To translate consensus into actionable implementation in resource-constrained environments, this section categorizes strategies into short-term goals—initiatives that can be started within existing limitations—and longer-term goals, which focus on institutionalizing scale and sustainability. This prioritization, specific to LMICs, effectively converts the rationale behind CNISs/SNTs into a practical approach for execution (Figure 1).

Figure 1.

Roadmap to equitable CNIS/SNT adoption in LMICs.

Invest in Health IT Infrastructure

Short-Term Goals: Stabilize the clinical “digital backbone” utilized by nurses for care documentation by prioritizing power continuity—through the use of uninterruptible power supplies and solar or generator backups—and enhancing last-mile connectivity with provider-managed broadband or cellular routers for high-traffic wards and clinics. Complement these essential elements with focused CNIS pilot programs that adapt open-source building blocks (such as OpenMRS and DHIS2) to capture a minimum nursing dataset in a limited number of sites. This strategy allows for rapid configuration, localized labeling, and effective change management without incurring the costs associated with platform lock-in.^50–52

Longer-term Goals: Expand reliable power and connectivity to district and primary care facilities; develop shared hosting and help-desk services accessible to peripheral sites; and leverage national or regional pooled procurement to lower unit costs and reduce variability in CNIS components—an approach successfully adopted by countries that invested early in e-health infrastructure, including Sri Lanka and India.^53,54 It is essential to view health information infrastructure as a fundamental aspect of health system strengthening, supported by earmarked grants or low-interest loans that acknowledge the return on investment across nursing, laboratory, pharmacy, and telehealth functions.⁵⁰

Develop supportive Policies and Standards

Short-term goals: Establishing national nursing informatics task forces or working groups can ensure that nurses have a voice in policy development and implementation. These bodies can collaborate with international organizations to adapt terminologies like the International Classification for Nursing Practice (ICNP) to local needs. For instance, the International Council of Nurses (ICN) has mechanisms for countries to submit new concepts to ICNP and guidelines for translation.⁵⁵ Engaging local nursing experts in this process will help produce localized terminology subsets that are culturally and linguistically appropriate, increasing acceptance among staff. Some countries have begun this journey: ICNP has been translated into multiple languages and is maintained in partnership with SNOMED International, which allows LMICs that use Systematized Nomenclature of Medicine—Clinical Terms (SNOMED CT) to implement ICNP more easily.⁵⁵

Longer-term goals: International collaborations on interoperability standards are also crucial. LMICs should not have to reinvent the wheel; by adopting global health data standards (HL7 FHIR, SNOMED CT, LOINC, etc.) alongside nursing terminologies, they can ensure their systems are compatible with international norms.^56,57 For example, countries can follow the lead of those who included nursing datasets in the national health information exchange from the outset, guaranteeing that nursing data flows as part of routine health information. Ensuring legal frameworks for data privacy and security will further support digital adoption by building public trust in electronic records.

Strengthen Education and Training

Short-term goals: Deploy in-service, practice-proximate training on standardized documentation at pilot sites; designate nurse “super-users” per unit for peer coaching; brief chief nurses and middle managers to secure protected time and workstation access. Use micro-learning (short modules at the point-of-care) to build competence without removing staff from clinical roles. For instance, the Technology Informatics Guiding Education Reform (TIGER) initiative has created a framework for informatics competencies globally.⁵⁸ In fact, creative collaborations between countries can help bridge faculty gaps – partnerships where academic institutions in HICs support the development of informatics programs in LMIC nursing schools could rapidly expand expertise.¹

Longer-term goals: Integrate informatics competencies across pre-service curricula; create advanced certificate/master’s tracks in nursing informatics; and establish regional training hubs and fellowships (with ICN/WHO support) to build a durable cadre of nurse-informaticians who can lead implementations and adapt systems to local context. Recent national exemplars—such as Armenia’s health-informatics training program combining bootcamps, tailored modules, and applied projects—offer an adaptable template for scaling regional capacity.^1,58,59

Leverage international Collaboration and innovation

Short-term goals: Initiate a “twinning” program between LMIC and HIC hospitals to facilitate both remote and on-site mentorship in CNIS configuration, SNT mapping, and change management. This initiative will involve curating a shared repository of pilot playbooks, interface templates, and localized ICNP subsets, while also leveraging the integration of ICNP and SNOMED CT to minimize duplication across programs and expedite semantic alignment.^1,55 For instance, an international “think tank” of nursing informatics leaders has been suggested as a way to disseminate success stories and prevent stakeholders from reinventing the wheel for each implementation.¹

Longer-term goals: Institutionalize regional communities of practice and procurement compacts. Successful examples from one country can inspire another – for example, if one East African country manages to implement ICNP⁵⁵ in its community health nursing forms and shows improved maternal care outcomes, neighboring countries could learn and adapt that model. Organizations like ICN, WHO, and regional bodies such as the East African Community⁶⁰ or the Association of Southeast Asian Nations (ASEAN)⁶¹ could facilitate these cross-country learning exchanges. Importantly, international funding agencies such as the World Bank, United States Agency for International Development (USAID), or the Global Fund should incorporate nursing information system components into their health system strengthening grants. There is precedent for donor-funded programs focusing on health information, often for disease surveillance or HIV programs – these could be expanded to include nursing data capture as part of comprehensive care. Additionally, integrating global standards: the recent integration of ICNP into SNOMED CT is a prime example of collaboration that will benefit all countries by unifying terminology resources.⁵⁵ LMICs that are members of SNOMED International now have access to a wealth of nursing concepts without separate infrastructure.⁶²

Implementation caveat and urgency: Execution demands coordination across health, education, finance, and ICT sectors. Yet the cost of inaction is rising: as care becomes more data-centric, systems that leave nurses off the digital grid will face widening quality and equity gaps. COVID-19 underscored that countries with stronger information backbones adapt faster; LMICs cannot afford to exclude the largest segment of their workforce from digital transformation.^50,63 The sequenced actions above position frontline nursing observations to power learning health systems, inform national policy, and tangibly advance health equity.

Conclusion

Closing the CNIS/SNT gap between high- and low-resource settings is no longer about generating evidence but about resolving implementation issues. The tools are already developed, and the benefits for safety and equity are well proven. The cost of delaying action—such as data gaps, preventable harm, and the continued invisibility of nursing contributions—is significant. Progress now depends on coordinated decisions that regard nursing information infrastructure as essential to the health system performance.

A credible standard for success involves standardizing nursing data accessible at the point of care, facilitating routine data exchange across various settings, and demonstrating measurable improvements in nursing-sensitive outcomes. Achieving this goal necessitates intentional collaboration among policymakers, donors, nursing leaders, educators, and vendors within a single planning cycle, followed by an iterative scale-up process grounded in transparent measurement. The ethical and operational rationale is clear: the integration of CNISs and SNTs in LMICs is essential for establishing safe and equitable health systems, as the health of millions, along with the advancement of nursing practice, relies on this integration.

Footnotes

ORCID iD

Animesh Ghimire

Author Contributions

Conceptualization: AG; Formal Analysis: AG; Investigation: AG; Writing – Original Draft Preparation and Writing – Review & Editing: AG

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

Declaration of conflicting interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Data Availability Statement

Data sharing is not applicable to this article as no new data were created or analyzed in this study*.

References

Dunn Lopez

Heermann Langford

Kennedy

, et al. Future advancement of health care through standardized nursing terminologies: reflections from a friends of the National library of medicine workshop honouring Virginia K. Saba. J Am Med Inform Assoc 2023; 30: 1878–1884.

Forde-Johnston

Butcher

Aveyard

. An integrative review exploring the impact of Electronic Health Records (EHR) on the quality of nurse–patient interactions and communication. J Adv Nurs 2023; 79: 48–67.

Jones

Lunney

Keenan

, et al. Standardized nursing languages essential for the nursing workforce. Annu Rev Nurs Res 2021; 28: 253–294.

Monsen

Heermann Langford

Bakken

, et al. Standardized nursing terminologies come of age: advancing quality of care, population health, and health equity across the care continuum. J Am Med Inform Assoc 2023; 30: 1757–1759.

Ferry

Davis

Rumprecht

, et al. Medical documentation in Low- and middle-income countries: lessons learned from implementing specialized charting software. Plast Reconstr Surg Glob Open 2021; 9: e3651.

Ngusie

Kassie

Chereka

, et al. Healthcare providers’ readiness for electronic health record adoption: a cross-sectional study during pre-implementation phase. BMC Health Serv Res 2022; 22: 282.

Cesare

Zega

. Clinical nursing information systems based on standardized nursing terminologies: how are we doing? J Nurs Scholarsh 2024; 56: 625–627.

Huter

Krick

Domhoff

, et al. Effectiveness of digital technologies to support nursing care: results of a scoping review. J Multidiscip Healthc 2020; 13: 1905–1926.

De Groot

De Veer

AJE

Paans

, et al. Use of electronic health records and standardized terminologies: a nationwide survey of nursing staff experiences. Int J Nurs Stud 2020; 104: 103523.

10.

Fennelly

Grogan

Reed

, et al. Use of standardized terminologies in clinical practice: a scoping review. Int J Med Inform 2021; 149: 104431.

11.

Torab-Miandoab

Samad-Soltani

Jodati

, et al. Interoperability of heterogeneous health information systems: a systematic literature review. BMC Med Inform Decis Mak 2023; 23: 18.

12.

Shaw

Hines

Kielly-Carroll

. Impact of digital health on the safety and quality of health care . ACSQHC, 2018. https://www.safetyandquality.gov.au/our-work/e-health-safety/impact-digital-health-safety-and-quality-health-care (accessed 6th April 2025).

13.

Ahmed

Hou

Hameed

. Harnessing big data analytics for healthcare: a comprehensive review of frameworks, implications, applications, and impacts. IEEE Access 2023; 11: 1–38.

14.

Woldemariam

Jimma

. Adoption of electronic health record systems to enhance the quality of healthcare in low-income countries: a systematic review. BMJ Health Care Inform 2023; 30: e100704.

15.

Bjerkan

Valderaune

Olsen

. Patient safety through nursing documentation: barriers identified by healthcare professionals and students. Front Comput Sci 2021; 3: 624555.

16.

Jones

Lunney

Keenan

, et al. Standardized nursing languages essential for the nursing workforce. Annu Rev Nurs Res 2010; 28: 253–294.

17.

Bertocchi

Dante

La Cerra

, et al. Impact of standardized nursing terminologies on patient and organizational outcomes: a systematic review and meta-analysis. J Nurs Scholarsh 2023; 55: 1126–1153.

18.

Cesare

Cocchieri

. Can an increase in nursing care complexity raise the risk of intra-hospital and intensive care unit transfers in children? A retrospective observational study. J Pediatr Nurs 2025; 80: 91–99.

19.

Cesare

D’Agostino

Cocchieri

. Exploring the association between complexity of care, medical complexity, and length of stay in the paediatric setting using a nursing minimum data set: a study protocol. Nurs Rep 2024; 14: 2923–2934.

20.

Cesare

Nurchis

Nursing , et al. Ranking nursing diagnoses by predictive relevance for intensive care unit transfer risk in adult and pediatric patients: a machine learning approach with random forest. Healthcare 2025; 13: 1339.

21.

Cesare

D’agostino

Maurici

, et al. Standardized nursing diagnoses in a surgical hospital setting: a retrospective study based on electronic health data. SAGE Open Nurs 2023; 9: 23779608231158157.

22.

Laukvik

Lyngstad

Rotegård

, et al. Utilizing nursing standards in electronic health records: a descriptive qualitative study. Int J Med Inform 2024; 184: 105350.

23.

Henry

Warren

Lange

, et al. A review of major nursing vocabularies and the extent to which they have the characteristics required for implementation in computer-based systems. J Am Med Inform Assoc 1998; 5: 321–328.

24.

Kim

Coenen

Hardiker

, et al. Representation of nursing terminologies in UMLS. AMIA Annu Symp Proc 2011; 2011: 709–714.

25.

Aldosari

Ahmed

McDermott

, et al. The use of digital health by South Asian communities: A scoping review. IOS Press, 2024, pp. 681–682.

26.

Odekunle

Shankar

. Why Sub-Saharan Africa lags in electronic health record adoption and possible strategies to increase its adoption in this region. Int J Health Sci 2017; 11: 59–64. https://pmc.ncbi.nlm.nih.gov/articles/PMC5654179/

27.

Shayan

Kiwanuka

Nakaye

. Barriers associated with evidence-based practice among nurses in low- and middle-income countries: a systematic review. Worldviews Evid Based Nurs 2019; 16: 12–20.

28.

Soliman

Roberts

Elhaddad

, et al. 12 barriers and facilitators to using real-world data in healthcare settings in low-and middle-income countries: a systematic review of qualitative studies. BMJ Evidence-Based Medicine 2019; 24: A48.

29.

O‘Neil

Taylor

Sivasankaran

. Data equity to advance health and health equity in Low- and middle-income countries: a scoping review. Digit Health 2021; 7: 20552076211061922.

30.

Akwaowo

Sabi

Ekpenyong

, et al. Adoption of electronic medical records in developing countries—A multi-state study of the Nigerian healthcare system. Front Digit Health 2022; 4: 1017231.

31.

Mwogosi

Kibusi

. Unveiling barriers to EHR implementation for effective decision support in Tanzanian primary healthcare: insights from practitioners. Health Informatics J 2024; 30: 14604582241304698.

32.

Parajuli

Bohara

, et al. Challenges and opportunities for implementing digital health interventions in Nepal: a rapid review. Front Digit Health 2022; 4: 861019.

33.

Wang

Shi

Han

, et al. The digital transformation of nursing practice: an analysis of advanced IoT technologies and smart nursing systems. Front Med 2024; 11: 1471527.

34.

Mwogosi

Kibusi

. Critical success factors for EHR systems implementation in developing countries: a systematic review. Global Knowledge, Memory and Communication 2024; 1–20.

35.

Chawla

Kurani

Wren

, et al. Electricity and generator availability in LMIC hospitals: improving access to safe surgery. J Surg Res 2018; 223: 136–141.

36.

Faulkenberry

Luberti

Craig

. Electronic health records, mobile health, and the challenge of improving global health. Curr Probl Pediatr Adolesc Health Care 2022; 52: 101111.

37.

Huffstetler

Bandara

Bharali

, et al. The impacts of donor transitions on health systems in middle-income countries: a scoping review. Health Policy Plan 2022; 37: 1188–1202.

38.

Aminpour

Sadoughi

Ahamdi

. Utilization of open source electronic health record around the world: a systematic review. J Res Med Sci 2014; 19: 57–64. https://pmc.ncbi.nlm.nih.gov/articles/PMC3963324/

39.

Bostan

Johnson

Jaspersen

, et al. Contextual barriers to implementing open-source electronic health record systems for Low- and lower-middle-income countries: scoping review. J Med Internet Res 2024; 26: e45242, Review 1.8.2024.

40.

Evans

. Electronic health records: then, now, and in the future. Yearb Med Inform 2016; 25: S48–S61.

41.

MHDS

CADC

Oliveira

BGRBD

. Classificação Internacional para a Prática de Enfermagem - CIPE®: aplicação à realidade brasileira. Rev Bras Enferm 2015; 68: 730–731.

42.

Whitehorn

Porritt

, et al. Mapping clinical barriers and evidence-based implementation strategies in Low-to-Middle Income Countries (LMICs). Worldviews Evid Based Nurs 2021; 18: 190–200.

43.

Ross

Stevenson

Lau

, et al. Factors that influence the implementation of e-health: a systematic review of systematic reviews (an update). Implement Sci 2016; 11: 146.

44.

Singh

Muthuswamy

. Factors affecting the adoption of electronic health records by nurses. World Appl Sci J 2013; 28: 1531–1535.

45.

Asif

Khan

. Role of nurse informaticists in the implementation of electronic health records (EHRs) at resource-limited settings. Pak J Med Sci 2024; 40: 2156–2159.

46.

Patel

Oaikhena

Phan

TTD

, et al. The landscape of health informatics education in low-or middle-income countries. IEEE Xplore 2023; 1–5.

47.

Walle

Shibabaw

Tilahun

, et al. Readiness to use electronic medical record systems and its associated factors among health care professionals in Ethiopia: a systematic review and meta-analysis. Inform Med Unlocked 2023; 36: 101140.

48.

Yam

ELY

Cheruvettolil

, et al. Perspectives of digital health innovations in low- and middle-income health care systems from south and Southeast Asia. J Med Internet Res 2024; 26: e57612, Viewpoint 25.11.2024.

49.

Borges do Nascimento

Abdulazeem

Vasanthan

, et al. Barriers and facilitators to utilizing digital health technologies by healthcare professionals. npj Digit Med 2023; 6: 161.

50.

Javaid

Haleem

Singh

. Health informatics to enhance the healthcare industry’s culture: an extensive analysis of its features, contributions, applications and limitations. Informatics and Health 2024; 1: 123–148.

51.

Kobayashi

Falcón

Fraser

, et al. Using open source, open data, and civic technology to address the COVID-19 pandemic and infodemic. Yearb Med Inform 2021; 30: 038–043.

52.

Paton

Braa

Muhire

, et al. Open source digital health software for resilient, accessible and equitable healthcare systems. Yearb Med Inform 2022; 31: 067–073.

53.

Amarakoon

Gundersen

Muhire

, et al. Exploring health information system resilience during COVID-19 pandemic: case studies from Norway, Sri Lanka & Rwanda. BMC Health Serv Res 2023; 23: 1433.

54.

Srivastava

. Adoption of electronic health records: a roadmap for India. Healthc Inform Res 2016; 22: 261–269.

55.

ICN.

International classification for nursing practice . ICN, 2023. https://www.icn.ch/how-we-do-it/projects/ehealth-icnptm/about-icnp#:∼:text=implementation,setofguidelinesfortranslation (accessed 6th April 2025).

56.

Bodenreider

Cornet

Vreeman

. Recent developments in clinical terminologies — SNOMED CT, LOINC, and RxNorm. Yearb Med Inform 2018; 27: 129–139.

57.

Monsen

Heermann

Dunn-Lopez

. FHIR-up! advancing knowledge from clinical data through application of standardized nursing terminologies within HL7® FHIR®. J Am Med Inform Assoc 2023; 30: 1858–1864.

58.

Hübner

Shaw

Thye

, et al. Technology informatics guiding education reform – TIGER. Methods Inf Med 2018; 57: e30–e42.

59.

Lulejian

Kysh

Galoyan

, et al. Health informatics training program in low and middle income countries. In: Hägglund

(ed). Caring is sharing – exploiting the value in data for health and innovation. IOS Press, 2023, pp. 476–477.

60.

East African Community about EAC. East African

Community, 2025. https://www.eac.int/ (accessed 8th April 2025).

61.

ASEAN . About us. Association of Southeast Asian Nations, 2025. https://asean.org/about-us/ (accessed 8th April 2025).

62.

SNOMED International.

About us. SNOMED International, 2025. https://www.snomed.org/about-us (accessed 8th April 2025).

63.

Etowa

Vukic

Aston

, et al. Experiences of nurses and midwives in policy development in low- and middle-income countries: qualitative systematic review. Int J Nurs Stud Adv 2023; 5: 100116.