Use of natural language processing method to identify regional anesthesia from clinical notes.

INTRODUCTION: Accurate data capture is integral for research and quality improvement efforts. Unfortunately, limited guidance for defining and documenting regional anesthesia has resulted in wide variation in documentation practices, even within individual hospitals, which can lead to missing and inaccurate data. This cross-sectional study sought to evaluate the performance of a natural language processing (NLP)-based algorithm developed to identify regional anesthesia within unstructured clinical notes. METHODS: We obtained postoperative clinical notes for all patients undergoing elective non-cardiac surgery with general anesthesia at one of six Veterans Health Administration hospitals in California between January 1, 2017, and December 31, 2022. After developing and executing our algorithm, we compared our results to a frequently used referent, the Corporate Data Warehouse structured data, to assess the completeness and accuracy of the currently available data. Measures of agreement included sensitivity, positive predictive value, false negative rate, and accuracy. RESULTS: We identified 27,713 procedures, of which 9310 (33.6%) received regional anesthesia. 96.6% of all referent regional anesthesia cases were identified in the clinic notes with a very low false negative rate and good accuracy (false negative rate=0.8%, accuracy=82.5%). Surprisingly, the clinic notes documented more than two times the number of regional anesthesia cases that were documented in the referent (algorithm n=9154 vs referent n=4606). DISCUSSION: While our algorithm identified nearly all regional anesthesia cases from the referent, it also identified more than two times as many regional anesthesia cases as the referent, raising concerns about the accuracy and completeness of regional anesthesia documentation in administrative and clinical databases. We found that NLP was a promising alternative for identifying clinical information when existing databases lack complete documentation.

Mapping the Discharge Process After Surgery.

Importance: Care transition models are structured approaches used to ensure the smooth transfer of patients between health care settings or levels of care, but none currently are tailored to the surgical patient. Tailoring care transition models to the unique needs of surgical patients may lead to significant improvements in surgical outcomes and reduced care fragmentation. The first step to developing surgical care transition models is to understand the surgical discharge process. Objective: To map the surgical discharge process in a sample of US hospitals and identify key components and potential challenges specific to a patient's discharge after surgery. Design, Setting, and Participants: This qualitative study followed a cognitive task analysis framework conducted between January 1, 2022, and April 1, 2023, in Veterans Health Administration (VHA) hospitals. Observations (n = 16) of discharge from inpatient care after a surgical procedure were conducted in 2 separate VHA surgical units. Interviews (n = 13) were conducted among VHA health care professionals nationwide. Exposure: Postoperative hospital discharge. Main Outcomes and Measures: Data were coded according to the principles of thematic analysis, and a swim lane process map was developed to represent the study findings. Results: At the hospitals in this study, the discharge process observed for a surgical patient involved multidisciplinary coordination across the surgery team, nursing team, case managers, dieticians, social services, occupational and physical therapy, and pharmacy. Important components for a surgical discharge that were not incorporated in the current care transition models included wound care education and supplies; pain control; approvals for nonhome postdischarge locations; and follow-up plans for wounds, ostomies, tubes, and drains at discharge. Potential challenges to the surgical discharge process included social situations (eg, home environment and caregiver availability), team communication issues, and postdischarge care coordination. Conclusions and Relevance: These findings suggest that current and ongoing studies of discharge care transitions for a patient after surgery should consider pain control; wounds, ostomies, tubes, and drains; and the impact of challenging social situations and interdisciplinary team coordination on discharge success.

Haptic Guidance and Haptic Error Amplification in a Virtual Surgical Robotic Training Environment.

Teleoperated robotic systems have introduced more intuitive control for minimally invasive surgery, but the optimal method for training remains unknown. Recent motor learning studies have demonstrated that exaggeration of errors helps trainees learn to perform tasks with greater speed and accuracy. We hypothesized that training in a force field that pushes the user away from a desired path would improve their performance on a virtual reality ring-on-wire task. Thirty-eight surgical novices trained under a no-force, guidance, or error-amplifying force field over five days. Completion time, translational and rotational path error, and combined errortime were evaluated under no force field on the final day. The groups significantly differed in combined error-time, with the guidance group performing the worst. Error-amplifying field participants did not plateau in their performance during training, suggesting that learning was still ongoing. Guidance field participants had the worst performance on the final day, confirming the guidance hypothesis. Observed trends also suggested that participants who had high initial path error benefited more from guidance. Error-amplifying and error-reducing haptic training for robot-assisted telesurgery benefits trainees of different abilities differently, with our results indicating that participants with high initial combined error-time benefited more from guidance and error-amplifying force field training.