The evolving role of robotic colorectal surgery

Surgeons' physical discomfort and symptoms during robotic surgery: a comprehensive ergonomic survey study

BACKGROUND

It is commonly believed that robotic surgery systems provide surgeons with an ergonomically sound work environment; however, the actual experience of surgeons practicing robotic surgery (RS) has not been thoroughly researched. In this ergonomics survey study, we investigated surgeons' physical symptom reports and their association with factors including demographics, specialties, and robotic systems.

METHODS

Four hundred and thirty-two surgeons regularly practicing RS completed this comprehensive survey comprising 20 questions in four categories: demographics, systems, ergonomics, and physical symptoms. Chi-square and multinomial logistic regression analyses were used for statistical analysis.

RESULTS

Two hundred and thirty-six surgeons (56.1 %) reported physical symptoms or discomfort. Among those symptoms, neck stiffness, finger, and eye fatigues were the most common. With the newest robot, eye symptom rate was considerably reduced, while neck and finger symptoms did not improve significantly. A high rate of lower back stiffness was correlated with higher annual robotic case volume, and eye symptoms were more common with longer years practicing robotic surgery (p < 0.05). The symptom report rate from urology surgeons was significantly higher than other specialties (p < 0.05). Noticeably, surgeons with higher confidence and helpfulness levels with their ergonomic settings reported lower symptom report rates. Symptoms were not correlated with age and gender.

CONCLUSION

Although RS provides relatively better ergonomics, this study demonstrates that 56.1 % of regularly practicing robotic surgeons still experience related physical symptoms or discomfort. In addition to system improvement, surgeon education in optimizing the ergonomic settings may be necessary to maximize the ergonomic benefits in RS.

An initial experience with 85 consecutive robotic-assisted rectal dissections: improved operating times and lower costs with experience

BACKGROUND

Data are limited about the robotic platform in rectal dissections, and its use may be perceived as prohibitively expensive or difficult to learn. We report our experience with the initial robotic-assisted rectal dissections performed by a single surgeon, assessing learning curve and cost.

METHODS

Following IRB approval, a retrospective chart review was conducted of the first 85 robotic-assisted rectal dissections performed by a single surgeon between 9/1/2010 and 12/31/2012. Patient demographic, clinicopathologic, procedure, and outcome data were gathered. Cost data were obtained from the University HealthSystem Consortium (UHC) database. The first 43 cases (Time 1) were compared to the next 42 cases (Time 2) using multivariate linear and logistic regression models.

RESULTS

Indications for surgery were cancer for 51 patients (60 %), inflammatory bowel disease for 18 (21 %), and rectal prolapse for 16 (19 %). The most common procedures were low anterior resection (n = 25, 29 %) and abdominoperineal resection (n = 21, 25 %). The patient body mass index (BMI) was statistically different between the two patient groups (Time 1, 26.1 kg/m(2) vs. Time 2, 29.4 kg/m(2), p = 0.02). Complication and conversion rates did not differ between the groups. Mean operating time was significantly shorter for Time 2 (267 min vs. 224 min, p = 0.049) and remained significant in multivariate analysis. Though not reaching statistical significance, the mean observed direct hospital cost decreased ($17,349 for Time 1 vs. $13,680 for Time 2, p = 0.2). The observed/expected cost ratio significantly decreased (1.47 for Time 1 vs. 1.05 for Time 2, p = 0.007) but did not remain statistically significant in multivariate analyses.

CONCLUSIONS

Over the series, we demonstrated a significant improvement in operating times. Though not statistically significant, direct hospital costs trended down over time. Studies of larger patient groups are needed to confirm these findings and to correlate them with procedure volume to better define the learning curve process.