This study is a qualitative case report that describes and analyzes an HIT-related patient safety incident that occurred in a heart intensive care unit. A single-incident case design was chosen to enable an in-depth examination of technical, organizational, and human factors contributing to the event. Case reports are well suited for studying rare or complex HIT failures and for generating transferable safety insights when experimental or quantitative approaches are not feasible [1,3,12,24].

This study involved secondary analysis of a fully deidentified incident report obtained from a national incident reporting repository. The data contained no patient identifiers or confidential information. Ethical advice was obtained from the Ethical Advisory Board in southeast Sweden (Dnr 934‐2023). Based on the retrospective analysis of anonymized quality and safety data, formal ethical review and informed consent were not required under applicable regulations (the Swedish Ethical Review Act 2003:460) [25].

The incident analyzed in this study was obtained from a national incident reporting repository that captures medical device–related and HIT-related events reported by health care professionals and clinical engineering staff. The incident reports in the repository typically include structured fields and free-text narratives describing the incident, the results of any internal investigation, and measures taken in response [26-28]. A single incident was selected for analysis on the basis of the following criteria:

The incident report included 3 core narrative components: an incident description, a summary of investigation findings, and a summary of measures. These components formed the primary data for analysis (Textbox 1). The incident description was translated and linguistically adapted from the original incident report to improve clarity and readability, without altering the factual content.

The original incident report was written in a language other than English and contained informal clinical and technical expressions typical of voluntary incident reporting. The report was translated into English using a direct linguistic translation approach focused on preserving semantic accuracy. No interpretive synthesis or restructuring of factual content was performed. Following translation, minor language editing was undertaken to improve clarity, including grammatical corrections and standardization of terminology, without altering the original meaning or sequence of events. The translated version was reviewed by a clinical engineer experienced in medical device incident management to ensure technical accuracy and contextual fidelity.

To ensure confidentiality and compliance with ethical and publication standards, all potentially identifying information, including names of individuals, institutions, manufacturers, locations, dates, and device identifiers, was removed prior to analysis. The final narrative was reviewed to confirm that it remained fully deidentified while retaining sufficient detail for analytical purposes [29].

To provide a comprehensive and systems-oriented analysis, the incident was classified using five complementary patient safety and human factors frameworks: (1) ICPS, (2) HIT-CS, (3) the SEIPS 2.0 model, (4) the sociotechnical model for HIT safety proposed by Sittig and Singh, and (5) HFACS-Healthcare.

Each framework was applied independently to the same incident narrative to capture different dimensions of the event, including incident type and outcome (ICPS), HIT-specific failure mechanisms (HIT-CS), work system interactions (SEIPS 2.0), sociotechnical dependencies (Sittig and Singh sociotechnical model), and human and organizational contributors (HFACS-Healthcare).

The ICPS was used to classify the incident at a high level, including incident type, detection, patient outcome, and degree of harm. It provides an internationally standardized taxonomy for patient safety incidents and supports comparison across settings and reporting systems [8]. The framework was applied descriptively to characterize the incident without attributing causality.

The HIT-CS, developed by Magrabi, Jabin, and colleagues, was used to classify the HIT-specific characteristics of the incident, including the primary issue type, secondary technical issues, outcome manifestation, workflow location, and impact scale [6,10,11]. The HIT-CS focuses on information flow and technical failure modes and is particularly suited for identifying technology-induced risks and system unavailability events [30,31].

The SEIPS 2.0 framework was applied to examine how the incident affected and emerged from interactions among technology, tasks, people, organizational structures, environment, and processes [16]. This analysis emphasized that the surveillance system failure disrupted continuous monitoring and retrospective data review and that recovery depended on organizational coordination with IT services.

The sociotechnical model proposed by Sittig and Singh [18,19] was used to analyze interdependencies among technical infrastructure, clinical content, human-computer interfaces, workflows, organizational policies, and system monitoring. This framework was applied to identify latent conditions such as lack of system status visibility and centralized architecture that contributed to delayed detection and multi-patient impact [4,18,20].

HFACS-Healthcare was applied to examine human and organizational contributors to the incident across 4 levels: unsafe acts, preconditions for unsafe acts, unsafe supervision, and organizational influences [21,32]. This framework was used to distinguish between frontline performance and system-level factors and to assess whether human error contributed to the incident. HFACS-Healthcare was applied analytically rather than punitively, consistent with its use in health care safety research [22,23,33].

Figure 1 illustrates the conceptual relationship among the 5 classification frameworks applied in this study and their complementary roles in characterizing the incident.

The incident was analyzed using 5 complementary classification frameworks to capture its patient safety, technical, sociotechnical, and human factors dimensions. This section summarizes the classification results according to each framework. Together, these analyses demonstrated that the incident was primarily driven by HIT system failures and latent organizational and design factors rather than by frontline human error.

Across all 5 classification frameworks, the incident was consistently characterized as an HIT-driven system failure with delayed detection, multi-patient impact, and potential for severe harm. While each framework emphasized different aspects of the incident, all converged on the absence of frontline human error and the presence of latent technical, organizational, and design vulnerabilities. The combined use of the ICPS, HIT-CS, SEIPS 2.0, sociotechnical, and HFACS-Healthcare frameworks provided a comprehensive and complementary understanding of the incident and its patient safety implications. To facilitate comparison across frameworks and to synthesize key findings, Table 6 presents an alignment of major incident characteristics as identified by the ICPS, HIT-CS, SEIPS 2.0, Sittig and Singh sociotechnical model, and HFACS-Healthcare. Despite differences in terminology and analytical focus, the frameworks showed strong convergence in identifying the incident as a technology-driven failure with delayed detection, multi-patient impact, and predominant system-level and organizational contributors rather than frontline human error.

This case report demonstrates that an HIT failure in a high-acuity clinical setting can pose a substantial patient safety risk despite appropriate frontline actions. Across all 5 applied classification frameworks—that is, ICPS, HIT-CS, SEIPS 2.0, the Sittig and Singh sociotechnical model, and HFACS-Healthcare—the incident was consistently characterized as a system-driven failure involving loss of monitoring information, delayed detection, and multi-patient impact. The absence of frontline human error identified across frameworks aligns with prior research showing that many HIT-related safety events arise from latent system conditions rather than individual mistakes [3,4,11,19].

Importantly, the incident should not be interpreted as a benign technical malfunction. In high-acuity cardiac settings, centralized surveillance serves as a safety layer to detect arrhythmias and sudden physiological deterioration. Interruption of this layer introduces a window of vulnerability during which time-critical events may be missed or responses delayed, underscoring the clinical seriousness of HIT availability failures. Incident report analyses have demonstrated that software-related challenges frequently reflect latent system conditions rather than isolated user errors, reinforcing the importance of sociotechnical interpretation of HIT failures [34].

The alignment of findings across frameworks (Table 6) illustrates how different taxonomies emphasize complementary dimensions of the same event. The ICPS provided a standardized patient safety classification [7,8], the HIT-CS enabled precise characterization of HIT-specific failure mechanisms [9,10], and the SEIPS, sociotechnical, and HFACS-Healthcare frameworks highlighted how interactions among technology, workflows, supervision, and organizational context shaped the emergence and impact of incidents [13,15,19].

This study contributes methodologically by demonstrating the value of multi-framework incident classification. Prior work has noted that reliance on a single taxonomy may obscure important aspects of HIT-related risk [6,11]. In this case, the HIT-CS clarified technical failure modes; the SEIPS and sociotechnical models contextualized the disruption to clinical work; and the HFACS-Healthcare provided a structured explanation of how organizational and supervisory factors contributed to the disruption, without attributing blame to frontline staff [18,19].

The integrative alignment table (Table 6) supports synthesis across frameworks and reduces interpretive fragmentation. Similar alignment approaches have been recommended to strengthen learning from complex safety events and to improve translation of analytic findings into practice [22,24].

Failure of centralized HIT should be interpreted through the lens of systemic safety governance, rather than isolated technical faults. Complex care, such as extracorporeal treatments, requires structured communication, protocols, and organizational support to mitigate clinical risk. Nalesso et al [35] emphasize the importance of proactive and reactive safety management in critical care settings, including multidisciplinary risk assessment, procedural checklists, and safety culture tools to reduce clinical incidents in high-risk technologies.

High-risk technologies in critical care demand governance approaches that extend beyond device functionality to include organizational coordination, communication, and monitoring infrastructure. In complex therapies such as extracorporeal blood purification, safety governance includes structured protocols, feedback loops, and systematic risk analysis to prevent latent failures that could lead to patient harm [18,35]. Digital incident reporting systems themselves require quality governance to ensure meaningful learning from HIT failures. Prior work has demonstrated variability in documentation quality and analytic depth within digital reporting systems, underscoring the need for structured governance frameworks [28].

Remote monitoring and device control can improve patient care and staff safety, but such systems must be interpreted in context with cognitive load and alert management. Garzotto et al [36] describe how remote control of medical devices reduced risk and unnecessary interventions in the intensive care unit during the COVID-19 pandemic, highlighting operational benefits and the potential for remote surveillance to support safety in critical settings [37].

Remote monitoring and control of medical devices can reduce direct exposure risks and streamline clinical workflows, particularly during infectious outbreaks. Garzotto et al [36] reported that remote device control decreased unnecessary interventions and supported patient safety in intensive care environments [37,38]. However, as with all automated systems, trade-offs such as increased alert burden and clinician cognitive load must be considered to ensure that alerting improves situational awareness without inducing fatigue [37,38].

Although not specific to intensive care unit central surveillance, telemedicine and remote monitoring paradigms offer conceptual bridges for understanding distributed monitoring and alerting across care settings. Ricci and Ronco [39] describe telemedicine support for peritoneal dialysis, illustrating how continuous remote surveillance of treatment data provides clinical oversight and improves outcomes in chronic care. These principles parallel the reliability of centralized surveillance; both involve automated data capture, transmission, and interpretation to support timely clinical decisions [35,40,41].

Concepts from telemedicine and continuous remote monitoring provide relevant frameworks for ensuring the reliability of centralized surveillance. Remote monitoring of chronic therapies such as peritoneal dialysis involves continuous data transmission and clinician oversight, improving the detection of therapy deviations and enabling timely intervention [35,41,42].

Early literature emphasizes that IT in critical care therapies (such as continuous renal replacement therapy) impacts patient safety, monitoring, and documentation. Ricci and Ronco [39] underscored that IT contributes to practice variation, patient assessment, monitoring, and documentation in critical care, highlighting the potential for technology to influence safety outcomes when integrated with governance and quality systems [4].

IT implementation in critical care therapies can enhance patient assessment, documentation, and therapeutic monitoring, but it also introduces new failure modes that must be governed within broader safety and quality infrastructures [4,37,39].

A key strength of this study is the systematic application of 5 established classification frameworks to a single HIT-related incident, enabling a comprehensive and triangulated analysis. This approach aligns with calls for richer sociotechnical analyses of HIT safety events [15,16]. Additionally, focusing on an incident with high potential severity but no confirmed harm supports proactive safety learning, consistent with modern patient safety principles [5,23].

Several limitations should be acknowledged. The analysis is based on a single incident report, limiting generalizability and precluding causal inference. Patient outcomes could not be fully assessed due to missing monitoring data, which constrained the classification of harm under ICPS [7]. Furthermore, while the frameworks used are well established, their application involves interpretive judgment; alternative analysts might emphasize different dimensions. Nevertheless, the strong convergence of findings across frameworks suggests robust analytical results.

In addition, the reconstruction of the event relied primarily on the narrative documented in the incident report; it is possible that relevant contextual factors were not recorded and therefore could not be captured within the analytic frameworks applied.

Future research should examine whether similar patterns of delayed detection, limited recovery capacity, and organizational dependency are observed across larger samples of HIT-related incidents using comparable multi-framework approaches [11,28]. Comparative studies evaluating how different classification systems influence safety recommendations could further guide methodological choices in HIT safety research.

There is also a need for empirical evaluation of design and governance interventions, such as real-time HIT health monitoring, redundancy strategies, and improved IT-clinical coordination models, to assess their effectiveness in reducing risk in high-acuity settings [16,26,29]. Finally, integrating resilience-oriented concepts such as Safety-II into HIT safety analysis may offer additional insights into how systems can better support adaptive clinical work under failure conditions [22,23].

This case report demonstrates that HIT-related safety incidents in intensive care settings are best understood as emergent properties of complex sociotechnical systems rather than isolated technical malfunctions or individual errors. The combined application of ICPS, HIT-CS, SEIPS 2.0, the sociotechnical model, and HFACS-Healthcare revealed convergent system-level vulnerabilities involving delayed detection, limited recovery capacity, and architectural and organizational design factors. These findings underscore the importance of governance structures, redundancy planning, and human-centered alerting strategies in high-acuity monitoring environments. Integrative, multi-framework analysis may strengthen learning from HIT failures and support safer design, implementation, and resilience of clinical surveillance systems.