Health care systems comprise all organizations, individuals, and activities aimed at promoting, restoring, or maintaining health [1], including efforts to influence health determinants and implement improvement initiatives [2]. Despite this broad scope, many descriptions of health care systems adopt a reductionist standpoint [3,4], neglecting the interrelationships among components and essential functions, such as responsiveness to legitimate expectations, respect for dignity, fair financing, service provision, and enabling functions (eg, stewardship, financing, and resource generation) [5]. This conceptual diversity has led to variations in health care systems worldwide, shaped by regional needs and resources [6]. While health care system performance in some countries is adequate, many still struggle with access, quality, and equity [7], highlighting the need for systemic improvements across processes and organizations [8].

The pursuit of improved quality and efficiency has long motivated the integration of technologies into health care [9,10]. In the 1960s, computers were first applied to standardize and share medical data [11]. During the late 1970s, personal computers introduced new technological solutions to the health care sector [12]. For example, the early electronic medical record (EMRs) initiatives demonstrated how computers could store and retrieve patient data more efficiently, improving record accuracy and accessibility. The advent of the internet in the 1990s further expanded the role of technology in health care [13], such as the use of online medical information platforms, allowing patients to access reliable health information without needing to visit a provider. More recently, the advances of the Fourth Industrial Revolution have intensified digitalization efforts aimed at creating more efficient therapeutic structures and support processes [14,15]. For instance, artificial intelligence (AI) tools have been applied in various ways in health care, from providing 24/7 patient support, symptom checking, and appointment management [16] to assisting with medical imaging and diagnostics [17]. These advances have been enabled by the availability of more effective and affordable technologies, miniaturized sensors with greater portability, and enhanced data acquisition and management capacities [18-20].

The digital transformation of health care continues to face substantial barriers. Beyond technological constraints, political and economic interests, as well as pressures from organizations, associations, and lobbyists, hinder effective implementation [21,22]. Progress has also been uneven across countries and over time [23-25], raising concerns about the consistency and sustainability of digitalization efforts. Given that digital transformation is widely viewed as a key driver of competitiveness and resilience in health care systems [26-28], these barriers highlight the need for a more systematic and sector-specific approach.

This paper presents a viewpoint about the digitalization of health care through debate, advocacy, and refutation [29]. To frame the discussion, we adopt the sociotechnical systems (STS) theory, which emphasizes the joint optimization of social and technical subsystems [30-32]. Literature focusing on the integration of digital technologies into hospitals and clinics also provided the basis for our discussion. This led to the formulation of research propositions (RPs) according to each STS dimension, which serves as guidance for future testing and validation.

The paper contributes by offering a structured examination of health care digital transformation from both theoretical and practical perspectives, identifying elements that may favor (ie, on-track elements for a successful health care digital transformation) or hinder (ie, off-track elements for a successful health care digital transformation) its effective implementation. It highlights key sociotechnical factors shaping digitalization and outlines future research opportunities to advance more effective and resilient health care systems. These outcomes may be useful for different stakeholders, such as health care practitioners handling digital transformation, academics whose research focuses on health care digitalization, and policymakers whose activities are impacted by novel advances in digital technologies.

Digital transformation is the process of incorporating technologies into processes, products, and services to change how organizations operate, deliver value, and adapt to evolving needs [33,34]. It is also about redesigning processes, culture, and experiences to improve efficiency and agility while creating opportunities for growth and innovation [35,36]. Health care digitalization has been distinctly termed as e-Health [37], Health 4.0 [38], Healthcare 4.0 [39], Care 4.0 [40], and Smart Health [41]. It has also been classified into four categories [42]: (1) diagnosis, (2) treatment, (3) follow-up, and (4) supply chain. Despite differences in nomenclature [43], all these approaches similarly foster health care digitalization, advancing treatments, administrative processes, and the supply chain [44,45]. New technologies improve treatments in the short term and yield gradual gains in administrative processes over time [46]. Health care digital transformation aims to create a more patient-centric care system [9,47], though its success depends on multiple factors. Capital investment, skilled labor, and the socioeconomic context influence digitalization capacity [48,49]. Clinical infrastructure and data regulation are also critical enablers [50]. Technologies include hardware and software to support storage, sharing, and use of health care information for communication and decision-making [51,52]. These advances can enhance patient welfare, staff motivation, and productivity while reducing errors, delays, and costs [53-55]. For instance, AI has been commonly used to monitor patient vitals in real time, providing proactive health insights and standing out as a disruptive technology in the health care setting [56].

Another potential contribution of digital transformation is related to preventive health care, which reduces the incidence and impact of disease by focusing on early detection, risk reduction, and health promotion [57]. Digitalization strengthens this approach by enabling continuous health monitoring through wearables, mobile apps, and connected devices, generating real-time data on behaviors and physiological conditions [58]. When combined with electronic health records (EHRs) and advanced analytics, these data allow health care providers to identify risk patterns, predict potential conditions, and intervene earlier with personalized strategies [59]. This shift moves health care from a reactive, treatment-focused model to a proactive system centered on maintaining health and preventing illness. Moreover, digitalization enhances patient engagement and supports population health management [60]. Telemedicine, patient portals, and digital platforms empower individuals to access information, communicate with providers, and adopt healthier behaviors through personalized guidance and reminders [61]. At a broader level, aggregated data help organizations and policymakers detect trends, target high-risk populations, and design more effective preventive interventions [62].

The World Health Organization’s (WHO) Global Strategy on Digital Health focuses on strengthening health systems by applying digital technologies that empower patients and promote the vision of health for all [63]. Digital health care strategies encompass a wide range of tools, including EHRs, telemedicine, mobile health, wearables, and data analytics. Therefore, national digital health strategies are essential to guiding governments’ efforts in this domain [64]. In Europe, most countries have national digital health strategies or related policies, though with heterogeneous approaches [65]. A notable regional initiative is the Cross-Border eHealth Information Services, enabling secure and interoperable exchange of prescriptions and patient summaries among European countries [66]. Estonia exemplifies advanced integration with a countrywide digital record for all patients and progress toward personalized medicine through genomics [67], which contrasts with the results observed in Australia. Generally, wealthier countries tend to achieve higher adoption rates, such as Canada, where nearly 90% of clinicians use EMRs [68]. In Latin America, EMRs are legally regulated in 16 of 21 countries, though adoption varies significantly between public and private sectors [69]. In Mexico, up to 45% of private professionals conduct remote consultations and 43% use EMRs [70]. Across Africa, digital health varies widely; most sub-Saharan countries face fragmentation, limited scalability, and unequal investment [71,72].

Despite the differences in digitalization initiatives and health care application contexts (Table 1), it is noteworthy that health care digitalization has been a key strategy in large economies. Efforts on how to improve, standardize, and ensure the ethical handling of electronic medical data records have been commonly observed across these countries [7,73]. Additionally, France, Brazil, and Australia are focused on expanding telemedicine (or telehealth) to improve people’s access and reduce the overload on existing health care infrastructure [74,75].

Despite well-documented benefits and available technologies, health care digitalization has traditionally faced resistance [86]. Kassirer [87] reported the unpreparedness of physicians to integrate the internet into medical practice in the 1990s, and later studies showed concerns about the negative implications of technology adoption on physicians’ examination and diagnostic skills [88]. Resistance among health care workers may derive from the disruption of routines, generational differences, or limited competencies [89-91]. These findings highlight the importance of addressing the social dimensions of digitalization, which emphasize the joint optimization of social and technical elements [92].

STS theory is based on 2 core principles. First, the interaction between social and technical aspects determines organizational performance [93]. Such interaction includes both linear and nonlinear relationships, intentional or emergent [94]. Second, optimizing social or technical aspects alone tends to generate unintended or counterproductive outcomes [31,95]. Consequently, STS theory fosters joint optimization, in which the design of social and technical subsystems is concurrently conducted [30]. Such integration enables new possibilities for work and supports technological change [32].

A widely used model for assessing STS is the Leavitt [96] framework, later refined by several researchers [97-99]. Originally encompassing 4 dimensions (people, task, structure, and technologies), it evolved to six interrelated dimensions: (1) people, (2) culture, (3) goals, (4) processes/procedures, (5) infrastructure, and (6) technology [100,101]. These dimensions provide a systemic structure for analyzing complex STS, such as health care.

Given that digital transformation integrates new technologies and relies on human-technology interaction, it can be viewed as a sociotechnical process [34,102]. Successful implementation requires a holistic organizational approach that simultaneously addresses human, structural, and technological dimensions [91]. Many studies have adopted STS theory to present complementary findings. For instance, Sony and Naik [22] proposed an STS-based framework for designing and integrating digital technologies. Imran et al [103] demonstrated that leadership, structures, and culture are critical enablers of digital transformation. Thomas [104] identified the key drivers of knowledge management in this context, while Iden and Bygstad [105] emphasized the need for the joint consideration of social and technical factors to facilitate digital transformation. Overall, the literature indicates that STS theory offers a robust theoretical foundation for understanding and guiding digital transformation in health care.

The topic was examined through multiple perspectives and by drawing on existing scholarship to encourage critical reflection and support informed discussion. Although partly grounded in original research, discussion papers typically situate their analysis within the broader body of literature, addressing issues in a comprehensive and balanced manner [106]. Such work is commonly found in academic, organizational, and policy-related settings. Generally, the interpretations and conclusions presented in discussion papers reflect the viewpoints of their respective authors [107-109]. In this sense, discussion papers function as a basis for reasoned debate and decision-making, offering a structured way to explore complex issues, fostering stakeholder engagement, and stimulating the development of new ideas and solutions [110].

Accordingly, using both advocacy and counterargument approaches [29], we discussed cases of health care digital transformation at different stages of maturity and the strategies adopted in each context. To conceptually address the challenges of digital transformation in health care and to differentiate initiatives progressing effectively from those that are not, we adopted STS theory, as articulated by Cooper and Foster [30] and Trist [32]. This perspective emphasizes that organizational outcomes are shaped by the interplay between social and technical components. Drawing on both the literature and our combined academic and practical experience—each author contributing over a decade of work in health care digital transformation—we discussed current digitalization efforts from a sociotechnical standpoint.

Each identified element, if present, was classified according to whether it had been observed in practice, research, or both, informed by both prior studies and the authors’ empirical insights. Building on this analysis, we proposed a distinction between elements that support (“on-track”) or hinder (“off-track”) health care digitalization across each sociotechnical dimension. The initial classification of these elements was conducted independently by each author, followed by a collective discussion to reach alignment. In cases where agreement could not be achieved, the majority view prevailed, with no possibility of a tie due to the odd number of contributors. This reflective process led to the development of RPs intended for further exploration, refinement, and empirical validation.

This structured approach enables a clear understanding of how social and technical factors jointly influence digital transformation in health care, guiding future empirical investigations and managerial decision-making. While these propositions were organized according to individual STS dimensions to enhance clarity and analytical precision, it is important to note that health care organizations are more likely to succeed when these dimensions are advanced simultaneously. We also recognize that interdependencies and overlaps may exist among these dimensions, although these relationships were not explicitly examined in this study.

The developed RPs enable the identification of on- and off-track elements and contribute to a more systematic understanding of health care digitalization. Viewing health care digitalization through an STS lens allows for a holistic comprehension of its complexity, although the interrelations among social and technical dimensions and potential trade-offs (eg, legacy infrastructure may exacerbate staff burnout, suggesting isolated technical upgrades risk suboptimal outcomes) were beyond the scope of this discussion. This strengthens theoretical coherence without overextending scope.

Our work suggests that, despite cross-country differences in health systems, the elements that encourage or hinder health care digitalization can be delineated. This supports the establishment of more effective government policies, the design of private initiatives, and the adjustment of regulatory frameworks. From a research perspective, interdisciplinary avenues emerge:

From a practical standpoint, health systems pursuing digitalization should simultaneously strengthen social and technical dimensions, reinforcing on-track elements while redirecting off-track ones. Given the contextual nature of health care delivery, digital transformation must be tailored to each system’s specific needs, adding complexity to efforts by policymakers, organizations, academia, and regulators. This study contributes by mapping the sociotechnical factors influencing health care digitalization and by drawing practitioners’ attention to potential barriers, enablers, and strategies for achieving digital progress.

Regarding limitations, since this is a viewpoint paper, it does not claim systematic empirical verification of all proposed relationships. Instead, the formulated RPs should be used as input for future studies, so that they can be tested and validated. Additionally, STS theory states that the joint optimization of social and technical elements leads to successful approaches and greater performance. We discussed social and technical elements separately disregarding their interrelationship. Further research should be conducted to verify how such an interaction can either favor or hinder health care digitalization. Furthermore, our work mainly discusses health care digitalization based on evidence collected in major economies, where data and literature tend to be more available. However, regions such as Africa present a unique context in which fragmentation, limited scalability, and unequal investments pose additional challenges [177,178]. More research should be conducted to properly explore health care digitalization from a more global perspective. Finally, an effective digital transformation may be subject to other aspects beyond the ones discussed here. The interests of stakeholders such as lobby agencies, government, and regulatory institutions might influence the speed of digitalization changes, raising additional challenges. Research on these aspects should be carried out to enhance the holistic view of the implications of digitalization.