Abstract
Despite advances in medical technology, intraoperative adverse events (IAE) continue to occur in hospital’s complex systems. Current approaches to understanding and mitigating IAEs rely on retrospective data analyses to recognize the series of events leading to an IAE. This paper discusses current challenges and highlights the potential of the Operating Room Blackbox (ORBB) system use to enhance patient safety.
Introduction
Despite technological advances in medicine, ranging from electronic medical records (EMRs) to sophisticated robotic technologies for minimally invasive surgeries, intraoperative adverse events (IAEs) that lead to morbidity and mortality (M&M) remain a significant issue in healthcare. A study on the incidence and causes of IAEs showed that out of the 7,926 reviewed patient records, IAEs accounted for 744, of which 40.5% were deemed preventable (Zegers et al., 2011). Furthermore, a more recent survey published in 2019 revealed that over a six-month period, there were 182 operations with IAEs out of a total of 5365 surgeries (3.4%) and attributed 106 out of the 182 IAEs to human performance deficiencies (Suliburk et al., 2019). Such IAEs occur predominantly because the operating room (OR) is a dynamic and complex environment requiring an intricate interaction between the surgical team and its environment, which encompass both technical and non-technical characteristics such as teamwork and communication (ElBardissi & Sundt, 2012; Lingard et al., 2004).
To understand and eventually mitigate IAEs, current patient safety and outcomes approaches rely on reviewing and analyzing retrospective data to understand the progression of events that lead to IAEs (Goldenberg & Elterman, 2020); these reviews are primarily performed with case reports. However, these reports are based on personal accounts and may be subject to recall or confirmation biases; for example, a recent study of a surgical team (composed of a lead surgeon, a fellow, and eleven trainees) revealed that the recollection of major details of 25 routine cases revealed significant inaccuracies in their accounts (Alsubaie et al., 2019). Reducing recall bias require objective and directly observable factors be captured and used in the reporting. One such method is the Operating Room Black Box™ (ORBB).
The ORBB was pioneered to increase objective, recordable variables or events that lead up to errors that adversely impact patient safety and surgical outcomes. The system is based on the prospective data capture and analysis principles adapted from high-reliability organizations, such as aviation and oil exploration (Jung et al., 2020). The system consists of video (both in room and at the surgical site) and audio recording functionality with an added capability of synchronizing multiple streams of data including OR equipment data (e.g. patient heart rate or oxygen saturation). Data can be stratified into short durations for objective data-driven IAE review and OR teamwork and efficiency studies. (Smith et al., 2022). Video is collected via four cameras: four wide-angle cameras recording surgical team activity and either an operative light fixture, lendoscope or robotic intracorporeal camera. The system can synchronize all data and encrypt it on a server where specialized algorithms and trained analysts can explore intraoperative variables of interest (Møller et al., 2022). For privacy and confidentiality purposes, the system’s AI algorithms de-identify faces, skin tone, body shape and identifiers like personalized surgical caps and redacts any personal information mentioned during surgery (Smith et al., 2022).
Current and Future Prospective Of The Orbb System
The ORBB system holds tremendous potential for safety and intraoperative quality improvement. A review by Goldenberg & Elterman (2020) indicated that IAE reporting is not only subject to recall bias, but the combination of technical and non-technical factors leading up to the IAE can be completely missed, causing missed opportunities to improve. With the data recorded and de-identified via the OR ORBB system, researchers can retrospectively pinpoint the time and combination of variables leading up to IAEs. For example, Jung et al. (2020) used audio recordings from the ORBB system to quantify intraoperative distractions, events, and errors and compare the lead surgeon’s technical skills with that of the trainees in laparoscopic surgery. Their results showed a median number of 20 errors per case, of which underestimation of distance between laparoscopic instrument and target organ or insufficient force application on the laparoscopic handle were the predominant source. They reported that these errors occurred often during the dissection, resection and reconstruction phase of the surgery, resulting in unanticipated bleeding. Furthermore, they identified cognitive and auditory distractions resulting from absent or malfunctioning equipment and OR door opening, respectively.
Another area where the systems like the ORBB can be indispensable is pioneering new surgical techniques. For example, it is well known that the transitions from open to minimally invasive surgical approaches may have clinical benefits such as reduced blood loss, improved recovery time, and cosmesis (Jaffray, 2005; Morgantini et al., 2022). However, such transitions change the team dynamics and individual workloads (Norasi et al., 2022), especially when considering the learning curves associated with technique changes (Bennett et al., 1997; Vigneswaran et al., 2020). The ORBB system, with its audiovisual (AV) capability, provides the avenue to objectively quantify teamwork and workload variables without the possibility of recall bias from memory or observations. These data can then serve as a baseline for comparison with teamwork and workload variables when exploring a new surgical approach. With this workflow analysis, researchers will be able to pinpoint procedural differences that lead to increased workload or negatively impact teamwork and the best interventions to address the drivers of these challenges.
Finally, contextual usability studies of medical devices in surgery to minimize flow disruption is another prime opportunity with the capabilities of the a system like the ORBB. A recent study of workflow using ORBB in cardiac surgery showed that device usability errors accounted for approximately 20% out of a total of 1,080 flow disruptions observed in the ten cases studied (Palmer et al., 2013). It is worth noting that device or technology-related errors are frequently related to contextual usability factors (Pennathur et al., 2013); hence, Palmer et al. (2013) recommended staff training and a collaboration between device manufacturers and OR staff members during equipment design. A practical extension of this collaboration could be the heuristic evaluation of medical devices in the context in which they are designed for using the AV data from the ORBB system. The AV data can be used to identify usability flaws, enabling designers to develop fixes for such flaws.
Conclusion
With the quest to improve patient safety and operative outcome in modern healthcare delivery, there is the need for continuous exploration of means to address non-technical variables that might hinder progress. The ORBB system presents researchers with many opportunities to address some of the OR challenges to improve patient safety and surgical outcomes.
