Acute care surgery (ACS), which encompasses trauma, emergency general surgery, and surgical critical care, is thematically defined by the notion of surgical rescue, in which ACS teams must maintain a high state of anticipation for unexpected and potentially devastating events [1]. While ACS surgeons find purpose in the care and rescue of critically ill patients, they continue to face burnout at alarming rates as high as 67% [2,3]. A major contributor to this crisis is the escalating burden of administrative tasks, particularly the documentation of complex clinical data. Compared to their peers, ACS surgeons spend 60% more time on electronic health records (EHRs) [4-6]. The resulting cognitive overload undermines high-stakes decision-making while eroding their sense of purpose, ultimately impacting outcomes in rapidly evolving surgical emergencies [7].

Since the introduction of the Health Information Technology for Economic and Clinical Health (HITECH) Act [8] in 2009, EHRs have become the core infrastructure for data-driven technologies in health care. Yet EHR adoption continues to face challenges, including poor data standardization, interoperability issues, and difficulties integrating data into clinical workflows. While EHRs are widely used for documentation and billing, their role in supporting effective decision-making, particularly in acute settings, remains poorly defined. Moreover, few studies have evaluated the effectiveness of EHR-based tools in comparison to the bureaucratic counterfactual: the existing, nontechnical workflows that shape clinical practice [9,10]. Understanding the clinical and organizational context into which these digital tools are introduced is critical to ensuring they enhance, rather than disrupt, patient care, particularly in busy, high-stakes environments like ACS.

Accordingly, a broader sociotechnical perspective situates EHRs within complex clinical workflows, team interactions, and organizational constraints that shape their real-world use and impact [11,12]. Ethnographic studies have shown that clinical work is inherently distributed, iterative, and context-dependent, with EHR use reflecting adaptation to workflow complexity rather than deviation from intended design [13]. From this view, EHRs function as evolving information infrastructures, shaped over time by policy, institutional priorities, and user adaptation [14]. Ethnographic insights from field observations and stakeholder perspectives may therefore clarify how digital tools are embedded in practice, informing efforts to better support decision-making and surgical rescue.

Given that clinical decisions in ACS often occur in rapid, high-stakes settings without the benefit of preoperative planning, ACS represents a key opportunity to understand how EHRs and related workflows can be optimized [15]. This qualitative, ethnographic study aims to examine how ACS teams interact with EHR systems, focusing on how data are accessed, interpreted, and used in decision-making. By mapping existing workflows and patterns of engagement, we seek to identify barriers and opportunities for future implementation of digital tools that align with the realities of frontline surgical care.

This ethnographic study examined the EHR workflows through which ACS clinicians make data-driven decisions, identifying both opportunities and barriers to implementing digital tools that support surgical care delivery. Opaque billing requirements were a key driver of documentation overload, contributing to note bloat and errors. ACS clinicians responded to this burden either passively or actively. Decision support through risk assessment emerged as a central use of EHR data, with clinical decisions hinging on the timely identification and integration of relevant data points that could facilitate surgical rescue. Clinicians emphasized that digital tools must be designed with attention to data input, output, accuracy, and clinical relevance to augment, rather than impede, effective decision-making. These findings align with broader sociotechnical perspectives on health informatics, showing that system performance emerges from interactions among technical design and the social, organizational, and cognitive contexts of use.

ACS may be viewed as an extreme environment in which high acuity, uncertainty, and time pressure, compounded by worsening workforce shortages, magnify the impact of documentation burden and note bloat [4-6,29]. Yet, prior work suggests that such extreme contexts can serve as revealing cases, exposing latent inefficiencies that might otherwise remain obscured, as constraints of time, uncertainty, and limited resources amplify their effects [30]. In this study, these dynamics were evident in users selectively bypassing bloated notes, relying on synchronous communication, or developing workarounds to rapidly locate high-value information. These behaviors highlight how excessive or poorly structured documentation can hinder information retrieval and clinical decision-making. Although such adaptations may be particularly necessary in ACS, they likely reflect broader, systemic challenges in EHR use, where the consequences of documentation burden may be less immediate but remain consequential.

The competing demands of administrative compliance and clinical care have shaped the evolution of EHRs from single-purpose clinical tools into multipurpose systems. The HITECH Act, for example, catalyzed EHR adoption by strengthening the financial rationale for implementation among hospital administrators [31]. Since then, documentation standards designed to support financial and legal compliance, such as those related to Current Procedural Terminology (CPT) codes and the International Classification of Diseases (ICD), have increasingly influenced digital and clinical workflows alongside persistent interoperability and data fragmentation challenges [32]. Such requirements have further exacerbated misalignment between system-imposed documentation structures and the cognitive and workflow needs of frontline clinicians [12,14].

This is particularly consequential for medically complex patients, where, as Naik and Singh [33] emphasize, asynchronous communication methods such as the EHR are critical for transmitting pertinent information across multiple receivers without interruption. In ACS, there is a natural bias toward synchronous, interruptive communication (eg, face-to-face conversations and phone calls) due to the time-sensitive nature of care. While necessary and efficient in urgent or life-threatening situations, overreliance on synchronous communication alone can lead to cognitive overload, increased dependence on short-term memory, work-related stress, and higher risk of medical errors [34,35]. Ethnographic studies of EHR use have similarly shown that clinicians rely on both formal documentation and informal communication practices to manage complex, time-sensitive care, with breakdowns often occurring when digital systems fail to support this distributed coordination [13]. In ACS, there is thus a strong incentive to develop noninterruptive EHR systems aligned with surgeon workflows to support shared decision-making, with prior work demonstrating that targeted EHR-based redesign can improve timeliness of care [36].

While greater clinical experience and seniority might suggest a shift toward intuitive decision-making over time, previous research shows that surgical judgment, particularly in assessing risks and benefits, remains highly variable regardless of experience level, especially in areas lacking clear guidelines [37,38]. As a result, EHR-enabled decision support tools are increasingly valued for their ability to promote deliberative reflection and counteract maladaptive cognitive biases and heuristics [39]. Harnessing this potential will therefore require documentation practices that balance efficiency with the effective inclusion and retrieval of clinically important information.

A key strategy may be to shift from systems-based to problem-based charting. Interviews revealed that documentation standards for billing are often poorly understood, with some believing that systems-based documentation is required for certain services when it is not. Increasing transparency around billing requirements could help dispel these misconceptions and encourage more focused documentation of high-value information without compromising financial-legal standards. Accordingly, a previous study of problem-based charting demonstrated significant improvements in capturing certain critical care diagnoses for billing purposes [40]. Such a transition would facilitate the identification and use of relevant EHR information to support surgical and perioperative decision-making.

In this study, EHR data were often leveraged for risk assessment, such as to support critical decisions around when and whether to operate. However, the manual search and entry of pertinent information remain significant barriers to adopting EHR-embedded decision support tools, indicating “hidden work” in the form of cognitive and navigational tasks not accounted for in system design [41]. Recent advances in AI, including models capable of generating real-time predictions or recommendations, have made it increasingly feasible to overcome these limitations [42,43]. These insights must also be clinically meaningful or actionable, particularly in settings where decision-making already depends on heterogeneous or uncertain clinical data [44]. For example, predicting specific complications such as postoperative respiratory failure should be coupled with standardized, risk-stratified protocols that align predicted risk with appropriate interventions, such as tailored monitoring and recovery strategies. In contrast, predictions of more general outcomes like mortality or overall morbidity may be most useful in situations of clinical uncertainty, supporting decisions and shared decision-making during equipoise through quantitative data, or serving as a second opinion when clinicians seek additional validation. Analogous decision support frameworks in other high uncertainty domains have used fuzzy multicriteria decision-making models to integrate expert judgment, conflicting criteria, and ambiguous data, illustrating the broader relevance of structured approaches when decisions cannot be reduced to a single quantitative output [45,46].

While there are certain perceptions that clinical gestalt may ultimately override any outputs from EHR-enabled decision-making, there is also general agreement that digital workflows can be better optimized to mitigate human cognitive biases [47]. For example, studies have demonstrated improved predictive accuracy when surgeons are supported by risk calculators, and, in some cases, the tools alone can outperform both unaided and aided surgeons [48,49]. This underscores the importance of educating clinicians to meaningfully integrate decision-making processes into digital workflows without falling into patterns of overreliance or reflexive skepticism. One suggested model is a Bayesian-like approach, where AI output is treated as an informed second opinion from a colleague with a distinct expertise profile, neither overriding nor subservient to clinical judgment, but contributing to a well-calibrated decision [50]. Translating this model of collaboration into routine practice will require prospective validation across dynamic environments, subgroup populations, and ACS care models, with attention to calibration and clinically actionable outcome definitions. Clinician trust will also depend on whether outputs are interpretable, available at the right moment in the workflow, and linked to clear responsibilities and care pathways. To ensure these tools augment care delivery, future implementation should therefore evaluate not only predictive performance, but also effects on alert fatigue, documentation burden, communication patterns, and unintended changes in surgical decision-making.

This study has several limitations. First, as a single-center ethnographic study, findings may not be generalizable to institutions with different EHR systems, workflows, or models of ACS care. Documentation practices are also shaped by local institutional culture, remuneration and billing structures, and national regulatory context, which may further limit transferability. However, several findings likely reflect broader sociotechnical dynamics relevant beyond academic hospitals, including documentation burden, note bloat, reliance on workarounds, difficulty retrieving high-value information, and the need for decision support tools embedded within existing workflows. In nonacademic hospitals or smaller health systems, these challenges may be amplified by leaner staffing models, fewer APPs or trainees, and less information technology support, although simpler organizational structures may also facilitate more direct communication. Thus, while the specific EHR behaviors observed in this study should be interpreted in context, the broader need to align documentation, information retrieval, and decision support with frontline clinical workflows is likely applicable across diverse acute surgical care settings. Second, the presence of observers may have introduced reactivity or response bias, and any concurrent EHR-related initiatives may have influenced participant perspectives. Third, our purposive sampling strategy may have introduced selection bias and underrepresented certain groups, including those with limited availability (eg, night-shift staff) and early-career providers. In addition, we did not fully capture EHR interactions involving consulting services or bedside nursing staff. Nevertheless, the inclusion of all attending surgeons alongside APPs and trainees, combined with a dual-observer fieldwork approach and in situ ethnographic interviews, supports the breadth and validity of the findings. Finally, while we identified opportunities for EHR-enabled decision support, we did not evaluate the implementation or impact of such tools, which will require future prospective and intervention-based research.

EHR systems are critical parts of the ACS workflow, shaping how information is documented as well as how decisions are made. This study highlights the dual burden of administrative documentation and clinical complexity, demonstrating that EHR practices are both a source of friction and a potential site for innovation. Meaningful improvement will likely require reconsideration of how documentation, communication, and decision-making processes are structured within the broader clinical system. While ACS clinicians vary in their engagement with the EHR, there is broad recognition that better integration of clinically valuable decision support, tailored to the ACS context, could improve surgical workflows and patient outcomes. Aligning EHR design, documentation standards, and digital tools with frontline ACS workflows is essential to advance a more efficient, accurate, and cognitively supportive environment for high-stakes surgical care delivery.