Publications

IMPORTANCE: The retropubic midurethral sling (RP-MUS) surgery poses unique training challenges due to blind trocar passage through a complex retropubic space near critical structures. A structured understanding of how experts teach these high-risk steps is essential to improve trainee learning and support safe surgical performance.

OBJECTIVE: The objective of this study was to generate a comprehensive catalog of teaching techniques to lead novice surgeons through RP-MUS surgery.

STUDY DESIGN: Semistructured cognitive task analysis (CTA) interviews were conducted with 11 surgeons from diverse institutions who teach the RP-MUS surgical procedure. Surgeons described their step-by-step teaching process; 4 also reviewed intraoperative videos of themselves instructing trainees. All teaching steps were extracted and mapped to operative CTA steps. Inquiry focused on strategies to help trainees visualize the retropubic space, orient instruments, and optimize ergonomics. Two researchers independently extracted data; 6 researchers iteratively analyzed findings until consensus and thematic saturation was achieved.

RESULTS: This CTA generated 458 teaching microsteps. Categories of teaching steps identified include the following: (1) technical advice, (2) demonstrations outside the surgical field, (3) instruction on how technique changes may cause complications, (4) ergonomic guidance, and (5) high-value assist steps that optimize safety. Many strategies specifically addressed cognitive challenges, including conceptualization of the blind retropubic space.

CONCLUSION: This study presents an innovative application of CTA to systematically characterize how experts teach the RP-MUS surgical procedure. These findings provide a foundation for iterative refinement of teaching practices and future studies linking specific instructional strategies to trainee performance, teacher confidence, and patient safety.

OBJECTIVE: Intraoperative video recording (IVR) is a valuable data collection modality for health professions education (HPE) research, and certifying organizations are increasingly adopting video-based operative assessments. No guidelines exist for the collection and use of this data. This scoping review characterizes current use of IVR in HPE research, with a focus on strategic, ethical, and technological considerations, to provide recommendations for future use.

METHODS: Two surgeons, 2 education specialists, a resident, and 1 medical student followed Arksey and O'Malley's scoping review approach. Assisted by a librarian, the team utilized a MeSH search strategy to identify abstracts for screening, covering articles between 1991 and 2025. Paired researchers screened abstracts for studies with IVR that took place in the operating room (OR), with learners present. The team then reviewed studies, extracting 29 data points with intermittent check-ins to prevent rater drift. Descriptive statistics summarized IVR use in HPE research.

RESULTS: A total of 7475 abstracts were screened, 291 full-text articles reviewed, 163 met inclusion criteria. Topics addressed included Surgical Performance (61.3%), Assessment (41.7%), and Teaching (33.1%). A total of 31.3% included an educational intervention. Studies included quantitative (94.5%) and qualitative (25.8%) analyses, of oral (21.5%) and/or nonverbal (10.4%) communication. Field of view was most frequently endoscopic (54.6%); 65.0% of studies included no audio. A total of 73.8% of studies reported Institutional/Ethics Review Board (IRB) status (Exempt 13.5%, Full Review 19.6%, "Approved" 39.9%). Consent was variably obtained from study participants and patients.

CONCLUSIONS: Researchers relied heavily on laparoscopic video which cannot capture body position, team interactions, teaching, or equipment use. We recommend precise reporting on how IVR data are collected, including information about recording devices and their placement in the OR, for study quality and reproducibility. Consent and IRB processes should be fully detailed. IVR can be better leveraged to study research questions about intraoperative teaching or communication, nonverbal cues essential for learning through thoughtful choices about theoretical guidance.

Hospital-acquired infections (HAIs) pose a significant challenge in healthcare settings, contributing substantially to patient morbidity, mortality, and increased healthcare costs. HAI incidence arises from complex interactions among healthcare workers, patients, and contaminated medical equipment. Agent-based modeling (ABM) is a well-known tool for simulating HAI dynamics, but existing ABM solutions have limited efficacy because of their reliance on synthetic/random human movement data. To address this gap, we deploy an Ultra-Wideband (UWB) Real-Time Location System (RTLS) in the post-surgery observation unit of a tertiary hospital in our city and collect high-resolution spatiotemporal location data of healthcare workers and medical devices in that unit. We develop an agent-based model of HAI transmission that incorporates epidemiological parameters specific to Clostridioides difficile (C. diff), capturing both direct and indirect transmission routes. The model is calibrated using assimilated RTLS data and is then applied to forecast exposure risk associated with asymptomatic carriers in the presence of biosecurity interventions (i.e., hand hygiene and surface disinfection). Our simulation results, generated in the AnyLogic simulation software, demonstrate that exposure levels vary due to movement behavior and infection control measures. These findings highlight the necessity of integrating real-time location data into ABMs to enhance predictive accuracy and optimize intervention strategies.

STUDY OBJECTIVE: To investigate the correlation between HbA1c and complications after hysterectomies.

DESIGN: Retrospective cohort analysis.

SETTING: Cerner Health Facts (519 million patient encounters from 750 hospitals).

PATIENTS: Patients undergoing laparoscopic±robotic-assisted, vaginal, or abdominal hysterectomy January 2010-November 2018. We included patients with perioperative HbA1c values, including those diagnosed with diabetes or undergoing screening.

INTERVENTIONS: We performed univariable and multivariable logistic regressions with the outcome a composite of any post-op complication. We adjusted for HbA1c as continuous and categorical variables using HbA1c (>8.0% based on guidelines and >8.85% based on ROC analysis from current dataset) as cut-points.

MEASUREMENTS AND MAIN RESULTS: Of 86,420 hysterectomies, 2,693 (3.1%) had non-excluded HbA1c, including 514 (19.1%) with complications and 2,179 (80.9%) without. Median time to complication was 18 days. Patients were 75.2% white, 83.2% in urban hospitals. Route was predominantly minimally invasive: 30.5% laparoscopic, 25.6% vaginal, 43.9% abdominal, with 69.8% adnexa removed. Patients with complications were older (60.5±15.9 vs 58.4±14.2 years, p=.004), more likely from the Southern U.S. (24.5% vs 18.5%, p=.006), used tobacco (26.7% vs 19.0%, p<.001), and were obese (32.3% vs 23.3%, p<.001). Most complications were infectious (87.4%). Higher HbA1c was associated with more complications, whether continuous (p=.03), HbA1c >8.0% (p=.01), or HbA1c >8.85% (p=.001). HbA1c >8.85% optimized test characteristics (sensitivity=.14, specificity=.91, PPV=.27, NPV=.82). Predictive value improved with confounders (AUC=.71-.72), and multivariable analysis demonstrated HbA1c >8.85% increased complications (OR=1.71, 95% CI=1.25-2.32). Obesity (OR=1.31, 95% CI=1.04-1.64) and urogynecological diagnoses including urinary frequency, history of urinary tract infection, and dysuria (OR=2.31, 3.71, 3.98, respectively) also increased complications.

CONCLUSION: HbA1c as a continuous variable impacted complications, so HbA1c reductions will likely decrease complications. For surgeons deciding to proceed with hysterectomy based on preoperative HbA1c, 8.0% and 8.85% are both acceptable cut-points. Multivariable models outperformed HbA1c alone, warranting further research to predict complications.

Methicillin-resistant Staphylococcus aureus (MRSA) is a strain of Staphylococcus aureus that poses significant challenges for effective treatment and infection control within healthcare settings. Recent research suggests that the incidence of healthcare-associated MRSA (HA-MRSA) is higher among patients treated in safety-net hospitals when compared to other healthcare settings. This study aimed to identify HA-MRSA transmission patterns across various nursing units of a safety-net hospital. This study employed Bayesian inference to investigate the transmission patterns of HA-MRSA across multiple nursing units within a safety-net hospital. Using surveillance data from 2019 to 2023, a compartmental disease model was constructed and validated to estimate MRSA transmission rates and basic reproduction number () for each nursing unit. Posterior probability distributions for MRSA transmission, Isolation, and hospital discharge rates were computed using the Delayed Rejection Adaptive Metropolis (DRAM) Bayesian algorithm. Analysis of 187,040 patient records revealed that inpatient nursing units exhibited the highest MRSA transmission rates in three out of the five years studied. Notable transmission rates were observed in certain inpatient and progressive care units (0.55 per individual per month; 0.018 per individual per day) and the surgical ICU (0.44 per individual per month; 0.015 per individual per day). In contrast, the Nursery NICU and Medical ICU had the lowest transmission rates. Although MRSA transmission rates significantly declined across all units in 2021, these rates rebounded to pre-pandemic levels in subsequent years. Notably, outbreaks emerged in units such as ICUs and progressive care units that had not experienced prior MRSA outbreaks since 2019. While MRSA transmission significantly declined during the initial phase of the pandemic, the pathogen reestablished itself in later years. These findings highlight the persistent and evolving challenge of HA-MRSA transmission in safety-net hospital settings, where resource constraints and patient vulnerability may contribute to elevated transmission risks.

BACKGROUND: Miscommunication in the robotic operating room occurs up to 3 times per hour and is a significant contributor to patient harm. In robotic surgery, environmental distractors exacerbate miscommunication and flow disruptions, elevating the risk of patient harm.

METHODS: We directly observed 75 robotic surgeries and assessed miscommunication associated with flow disruptions using a custom observational instrument. Observers collected data on speech communication interferences and their event evidence, contextual data, staff involvement in the event, communication flow, and flow disruption time. Data from each observed case was aggregated into case-level events and analyzed with a weighted Pearson correlation analysis.

RESULTS: Correlation coefficients for contextual and event-based weighted correlates demonstrate the strongest relationships between overlapping conversations and no responses during a miscommunication and the surgeon leaning out of the console and clarifications. Contextual and staff-based correlates showed strong relationships between the frequency of clarifications, multitasking, and the involvement of all staff. Finally, evidence of event- and staff-based correlates showed the strongest relationships between the frequency of loud machines and the medical student; the frequency of the surgeon leaning out of the console and the surgical resident, attending surgeon, and scrub tech. Multiple near misses associated with miscommunication were observed.

CONCLUSION: Miscommunications and flow disruptions increase case length and the risk of misunderstanding and patient harm. The surgeon often leans out of the console to clarify messages. The surgical resident, certified registered nurse anesthetist, and scrub tech often mediate messages. Loud machines are associated with higher rates of no response, and medical students often request clarifications.

BACKGROUND: Urinary tract infections (UTI) are among the most common infections encountered in both community and healthcare settings. Differentiating between community-associated UTI (CA-UTI) and healthcare-associated UTI (HA-UTI) is crucial for understanding their epidemiology, identifying risk factors, and developing appropriate treatment strategies. Machine learning (ML) techniques have shown significant potential in improving the accuracy of predicting these infections, enabling more effective interventions and better patient outcomes. While previous studies have demonstrated the utility of ML models in various healthcare settings, there is still a need for a comparative analysis of different ML approaches, particularly in distinguishing between CA-UTI and HA-UTI and assessing the risk of UTI among hospitalized patients.

OBJECTIVE: Using 2019-2023 patient demographics, hospital, and socioeconomic data, this study aims to build, validate, and compare machine learning models-Decision Tree (DT), Neural Network (NN), Logistic Regression (LR), Random Forest (RF), and Extreme Gradient Boosting (XGBoost) to differentiate between the incidences of HA-UTI and CA-UTI. Additionally, it seeks to identify key predictors of UTI using demographic, hospital, and socioeconomic variables.

RESULTS: The DT model demonstrated the highest sensitivity, particularly in handling the highly imbalanced data of HAI, with a sensitivity of 87%. LR achieved the best overall accuracy, at 95.9% for HA-UTI and 93.2% for HA-UTI vs. CA-UTI. RF performed best in cross-validation, reaching 99.1% for HA-UTI and 96.2% for HA-UTI vs. CA-UTI. NN showed the highest specificity, at 93.4%, for HA-UTI vs. CA-UTI. The AUC values further supported these findings, ranging from 71.9% for NN to 96% for RF, reflecting the robustness of these models across different annual datasets. Among patient demographics, hospital, and socioeconomic variables, all models consistently identified the nurse units (e.g., inpatient units and mental health units) as the most significant predictors of UTI. In addition to nurse units, LR and DT identified location (e.g., various clinics and medical centres) as a key predictor. For HA-UTI versus CA-UTI, variations were observed across the years, with patient age, median household income, and gender intermittently emerging as key predictors.

CONCLUSION: The predictive accuracy of the machine learning models is relatively the same, with some differences in sensitivity and specificity for identifying both HA-UTI vs. CA-UTI and HA-UTI. Nurse units consistently emerge as the most significant predictors across all years. The importance of all predictors, such as socioeconomic factors and location, varies from year to year, highlighting the need for incorporating those variables in the surveillance systems to optimize the accuracy of predictions.

IMPORTANCE: Sacral neuromodulation (SNM) requires removal for infectious complications in 3-11%.

OBJECTIVE: The objective of this study was to examine the effect of preoperative antibiotic choice on all-cause SNM device removal rates.

STUDY DESIGN: This was a retrospective cohort analysis, using the Health Facts Database, representing more than 750 hospitals. We included female patients undergoing SNM implantation from 2010 to 2018. Univariate and multivariate logistic regression identified factors associated with removal. Thirty-five comorbidities were evaluated. Those with P < 0.2 on univariate analysis were included in the multivariate analysis. We decided a priori to include prophylactic antibiotic choice in the final model.

RESULTS: Of 1,433 patients, 170 (11.9%) had device removal. Patients were 63.0 ± 14.9 years old, predominantly Caucasian (90.0%), treated in urban hospitals (94.1%), and married (54.2%). A total of 11.8% were obese, and 18.0% smoked. Those in the removal cohort were more likely from the Northeastern United States; 52.3% received first-gen cephalosporins (CPSN), 7.4% second- or third-generation CPSNs, 9.1% vancomycin, 13.4% aminoglycosides, 4.6% clindamycin, and 13.3% fluoroquinolones. Compared to vancomycin, more removals were associated with first-generation CPSNs (odds ratio [OR] = 3.1, 95% confidence interval [1.4, 6.8]); clindamycin (OR = 3.2, [1.2, 8.4]); second/third-generation CPSNs (OR = 3.1, [1.3, 7.6]); and aminoglycosides (OR = 3.1, [1.3, 7.4]). Additionally, patients treated in the Northeast were more likely to undergo removal (OR = 1.9, [1.0, 3.7]).

CONCLUSIONS: Vancomycin as a prophylactic antibiotic was associated with fewer device removals compared to most antibiotics in this retrospective cohort analysis. While prospective trials could confirm this benefit, low removal rates may make this impractical.

Miscommunication in the OR is a threat to patient safety and surgical efficiency. Our objective was to measure the frequency and causes of communication interference between robotic team members. We observed 78 robotic surgeries over 215 h. 65.4% were General Surgery, most commonly cholecystectomy, identifying Speech Communication Interference (SCI) events, defined as “surgery-related group discourse that is disrupted according to the goals of the communication or the physical and situational context of the exchange”. We noted the causes and strategies to correct the miscommunication, near misses, and case delays associated with each SCI event. Post-surgery interviews supported observations and were analyzed thematically. Overall, we observed 687 SCI events (mean 8.8 ± 6.5 per case, 3.2 per hour), ranging from one to 28 per case. 48 (7.0%) occurred during docking and 136 (19.8%) occurred during a critical moment. The most common causes were concurrent tasks (66.1%); loud noises (10.8%) from patient cart, lightbox fan, and suction machine; and overlapping conversations (4.2%). 94.8% resulted in a case delay. These events distracted from monitoring patient safety and resulted in near misses. Mitigating strategies included leaning out of the surgeon console to repeat the message and employing a messenger. These findings help characterize miscommunication in robotic surgery. Possible interventions include microphones and headsets, positioning the surgeon console closer to the bedside, moving loud equipment further away, and upgrading the patient cart speaker.

Keywords: Anesthesia; Communication; Interprofessional teamwork; OR nursing staff; Robotic surgery; Surgical error.

PURPOSE:

The surgical clerkship provides medical students with valuable hands-on experience. This study examined why medical students speak or remain silent in the OR to improve progression from novice to engaged surgical team member.

METHODS:

Using Constructivist Grounded Theory 37 interviews were conducted concerning expectations and behaviors that encourage or discourage students from speaking during their clerkship. Transcripts were coded, analyzed, and triangulated to develop a conceptual model.

RESULTS:

Students' decision to speak or remain silent was based on their perception of the OR as a safe learning space. Our findings suggest that better preparation, awareness of critical moments, and informal communication with team members encouraged student speech.

CONCLUSIONS:

Medical students remain conflicted about their speaking in the OR and their evaluation. Key to improving students' psychological safety is establishment of interpersonal relationships, awareness of OR mood, and assignment of case-related tasks to assist with OR assimilation and improved learning.