Barriers to safety and efficiency in robotic surgery docking

Human Factors Integration in Robotic Surgery

OBJECTIVE

Using the example of robotic-assisted surgery (RAS), we explore the methodological and practical challenges of technology integration in surgery, provide examples of evidence-based improvements, and discuss the importance of systems engineering and clinical human factors research and practice.

BACKGROUND

New operating room technologies offer potential benefits for patients and staff, yet also present challenges for physical, procedural, team, and organizational integration. Historically, RAS implementation has focused on establishing the technical skills of the surgeon on the console, and has not systematically addressed the new skills required for other team members, the use of the workspace, or the organizational changes.

RESULTS

Human factors studies of robotic surgery have demonstrated not just the effects of these hidden complexities on people, teams, processes, and proximal outcomes, but also have been able to analyze and explain in detail why they happen and offer methods to address them. We review studies on workload, communication, workflow, workspace, and coordination in robotic surgery, and then discuss the potential for improvement that these studies suggest within the wider healthcare system.

CONCLUSION

There is a growing need to understand and develop approaches to safety and quality improvement through human-systems integration at the frontline of care.Precis: The introduction of robotic surgery has exposed under-acknowledged complexities of introducing complex technology into operating rooms. We explore the methodological and practical challenges, provide examples of evidence-based improvements, and discuss the implications for systems engineering and clinical human factors research and practice.

Workflow disruptions in robot-assisted surgery

Surgical flow disruptions are unexpected deviations from the natural progression which can potentially compromise the safety of the operation. Separation of the surgeon from the patient and team members is the main contributor for flow disruptions (FDs) in robot-assisted surgery (RAS). FDs have been categorised as communication, coordination, surgeon task considerations, training, equipment/ technology, external factors, instrument changes, and environmental factors. There may be an association between FDs and task error rate. Intervention to counter FDs include training, operating room adjustments, checklists, teamwork, communication improvement, ergonomics, technology, guidelines, workflow optimisation, and team briefing. Future studies should focus on identifying the significant disruptive FDs and the impact of interventions on surgical flow during RAS.

Automatic patient positioning based on robot rotational workspace for extended reality

PURPOSE

Understanding the properties and aspects of the robotic system is essential to a successful medical intervention, as different capabilities and limits characterize each. Robot positioning is a crucial step in the surgical setup that ensures proper reachability to the desired port locations and facilitates docking procedures. This very demanding task requires much experience to master, especially with multiple trocars, increasing the barrier of entry for surgeons in training.

METHODS

Previously, we demonstrated an Augmented Reality-based system to visualize the rotational workspace of the robotic system and proved it helps the surgical staff to optimize patient positioning for single-port interventions. In this work, we implemented a new algorithm to allow for an automatic, real-time robotic arm positioning for multiple ports.

RESULTS

Our system, based on the rotational workspace data of the robotic arm and the set of trocar locations, can calculate the optimal position of the robotic arm in milliseconds for the positional and in seconds for the rotational workspace in virtual and augmented reality setups.

CONCLUSIONS

Following the previous work, we extended our system to support multiple ports to cover a broader range of surgical procedures and introduced the automatic positioning component. Our solution can decrease the surgical setup time and eliminate the need to repositioning the robot mid-procedure and is suitable both for the preoperative planning step using VR and in the operating room-running on an AR headset.

Improving Operating Room Efficiency of Robotic-Assisted Metabolic and Bariatric Surgery Through Standardization

INTRODUCTION

The use of robotic-assisted (RA) surgery in the field of metabolic and bariatric surgery (MBS) is controversial because of cost concerns and issues related to efficiency. The objective of this study is to evaluate the operating room efficiency in performing RA-MBS prior and after the implementation of a standardized surgical approach.

MATERIALS AND METHODS

All MBS cases entered into our database between October 2017 and October 2022 were collected and analyzed before and after the introduction of the standardized approach (SA). The outcome variables consisted of operation time (OT), turnover time (TT), wheels in-wheels out (WW), and console time (CT). Procedures were divided into Roux-en-Y gastric bypass (RYGB), sleeve gastrectomy (SG), and revisional bariatric surgery (RBS).

RESULTS

For RYGB (n = 185), we found a significant reduction in OT, TT, and WW after SA (129 min vs 139 min; 37 min vs 73 min; 165 min vs 175 min, respectively, p < 0.05). For SG (n = 253), we found a significant reduction in turnover time (TT) after SA. For RBS (n = 201), we also found a significant reduction in OT, TT, WW, and CT after SA ( 157 min vs 177 min; 36 min vs 72 min; 194 min vs 216 min; 119 min vs 134 min, respectively, p < 0.05).

CONCLUSION

Using a standardized surgical approach, we were able to demonstrate improved operation room efficiency as demonstrated by a reduction in operation length, turnover time, and the overall time of the procedure for primary RYGB and revisional procedures and turnover time for primary sleeve procedures.

Demands of surgical teams in robotic-assisted surgery: An assessment of intraoperative workload within different surgical specialties

BACKGROUND

Current approaches to assessing workload in robotic-assisted surgery (RAS) focus on surgeons and lack real-world data. Understanding how workload varies by role and specialty aids in identifying effective ways to optimize workload.

METHODS

SURG-TLX surveys with six domains of workload were administered to surgical staff at three sites. Staff reported workload perceptions for each domain on a 20-point Likert scale, and aggregate scores were determined per participant.

RESULTS

188 questionnaires were obtained across 90 RAS procedures. Significantly higher aggregate scores were reported for gynecology (Mdn ​= ​30.00) (p ​= ​0.034) and urology (Mdn ​= ​36.50) (p ​= ​0.006) than for general (Mdn ​= ​25.00). Surgeons reported significantly higher scores for task complexity (Mdn ​= ​8.00) than both technicians (Mdn ​= ​5.00) (p ​= ​0.007), and nurses (Mdn ​= ​5.00).

CONCLUSIONS

Staff reported significantly higher workload during urology and gynecology procedures, and experienced significant differences in domain workload by role and specialty, elucidating the need for tailored workload interventions.

Identifying Workflow Disruptions in Robotic-Assisted Bariatric Surgery: Elucidating Challenges Experienced by Surgical Teams

PURPOSE

Bariatric surgery is an effective and durable treatment for weight loss for patients with extreme obesity. Although traditionally approached laparoscopically, robotic bariatric surgery (RBS) has unique benefits for both surgeons and patients. Nonetheless, the technological complexity of robotic surgery presents new challenges for OR teams and the wider clinical system. Further assessment of the role of RBS in delivering quality care for patients with obesity is necessary and can be done through a human factors approach. This observational study sought to investigate the impact of RBS on the surgical work system via the study of flow disruptions (FDs), or deviations from the natural workflow progression.

MATERIALS AND METHODS

RBS procedures were observed between October 2019 and March 2022. FDs were recorded in real time and subsequently classified into one of nine work system categories. Coordination FDs were further classified into additional sub-categories.

RESULTS

Twenty-nine RBS procedures were observed at three sites. An average FD rate of 25.05 (CI =  ± 2.77) was observed overall. FDs were highest between insufflation and robot docking (M = 29.37, CI =  ± 4.01) and between patient closing and wheels out (M = 30.00, CI =  ± 6.03). FD rates due to coordination issues were highest overall, occurring once every 4 min during docking (M = 14.28, CI =  ± 3.11).

CONCLUSION

FDs occur roughly once every 2.4 min and happen most frequently during the final patient transfer and robot docking phases of RBS. Coordination challenges associated with waiting for staff/instruments not readily available and readjusting equipment contributed most to these disruptions.

Factors affecting workflow in robot-assisted surgery: a scoping review

BACKGROUND

Robot-assisted surgery is expanding worldwide. Most research in this field concentrates on surgeons' technical skills and patient outcome, but research from open and laparoscopic surgery shows that teamwork is crucial for patient safety. Team composition is changed in robot-assisted surgery with the surgeon placed away from the bedside, potentially altering teamwork and workflow in the operating theatre. This scoping review aimed to explore how factors affecting workflow as well as team members' social and cognitive skills during robot-assisted surgery are reported in the literature.

METHODS

A systematic search was performed in the databases Medline, EMBASE, PsycINFO, and Web of Science. Reports were screened according to the Preferred Reporting Item for Systematic reviews and Meta-Analysis for Scoping Review guidelines. Inclusion criteria were robot-assisted surgery, multi-professional teams, and workflow, flow disruptions, or non-technical skills.

RESULTS

A total of 12,527 references were screened, and 24 articles were included in the review. Articles were heterogeneous in terms of aim, methods and focus. The studies concentrated on two main fields: flow disruptions and the categorization of their causes and incidences; and non-technical skills describing the challenges of communication and effects on situation awareness.

CONCLUSION

Many studies focused on flow disruptions and found that communication, coordination, training, and equipment/technology were the most frequent causes. Another focus of studies was non-technical skills-primarily communication and situation awareness. Future studies could focus on how to prevent the most harmful flow disruptions and develop interventions for improving workflow.

A Novel Approach for Engagement in Team Training in High-Technology Surgery: The Robotic-Assisted Surgery Olympics

INTRODUCTION

There is ongoing interest in the development of technical and nontechnical skills in healthcare to improve safety and efficiency; however, barriers to developing and delivering related training programs make them difficult to implement. Unique approaches to training such as "serious games" may offer ways to motivate teams, reinforce skill acquisition, and promote teamwork. Given increased challenges to teamwork in robotic-assisted surgery (RAS), researchers aimed to develop the "RAS Olympics," a game-based educational competition to improve skills needed to successfully perform RAS.

METHODS

This pilot study was conducted at an academic medical center in Southern California. Robotic-assisted surgery staff were invited to participate in the "RAS Olympics" to develop their skills and identify opportunities to improve processes. Impact of the activity was assessed using surveys and debriefs.

RESULTS

Sixteen operating room team members participated and reacted favorably toward the RAS-Olympics (average score, 4.5/5). They enjoyed the activity, would recommend all staff participate, felt that it was relevant to their work, and believed that they practiced and learned new techniques that would improve their practice. Confidence in skills remained unchanged. Participants preferred the RAS Olympics to traditional training because it provided an interactive learning environment.

CONCLUSIONS

The successful implementation of the RAS Olympics provided insight into new opportunities to engage surgical staff members while also training technical and nontechnical skills. Furthermore, this shared experience allowed surgical staff members to gain a greater appreciation for their teammates and an understanding of the current challenges and methods to improve teamwork and communication while promoting safety and efficiency in RAS.

Room Size Influences Flow in Robotic-Assisted Surgery

The introduction of surgical technology into existing operating rooms (ORs) can place novel demands on staff and infrastructure. Despite the substantial physical size of the devices in robotic-assisted surgery (RAS), the workspace implications are rarely considered. This study aimed to explore the impact of OR size on the environmental causes of surgical flow disruptions (FDs) occurring during RAS. Fifty-six RAS procedures were observed at two academic hospitals between July 2019 and January 2021 across general, urologic, and gynecologic surgical specialties. A multiple regression analysis demonstrated significant effects of room size in the pre-docking phase (t = 2.170, df = 54, β = 0.017, p = 0.035) where the rate of FDs increased as room size increased, and docking phase (t = -2.488, df = 54, β = -0.017, p = 0.016) where the rate of FDs increased as room size decreased. Significant effects of site (pre-docking phase: p = 0.000 and docking phase: p = 0.000) were also demonstrated. Findings from this study demonstrate hitherto unrecognized spatial challenges involved with introducing surgical robots into the operating domain. While new technology may provide benefits towards patient safety, it is important to consider the needs of the technology prior to integration.