The Effect of Positive and Negative Verbal Feedback on Surgical Skills Performance and Motivation

There is considerable effort and time invested in providing feedback to medical students and residents during their time in training. However, little effort has been made to measure the effects of positive and negative verbal feedback on skills performance and motivation to learn and practice. To probe these questions, first-year medical students (n = 25) were recruited to perform a peg transfer task on Fundamentals of Laparoscopic Surgery box trainers. Time to completion and number of errors were recorded. The students were then randomized to receive either positive or negative verbal feedback from an expert in the field of laparoscopic surgery. After this delivery of feedback, the students repeated the peg transfer task. Differences in performance pre- and post-feedback and also between the groups who received positive feedback (PF) vs negative feedback (NF) were analyzed. A survey was then completed by all the participants. Baseline task times were similar between groups (PF 209.3 seconds; NF 203 seconds, p = 0.58). The PF group averaged 1.83 first-time errors while the NF group 1 (p = 0.84). Post-feedback task times were significantly decreased for both groups (PF 159.75 seconds, p = 0.05; NF 132.08 seconds, p = 0.002). While the NF group demonstrated a greater improvement in mean time than the PF group, this was not statistically significant. Both groups also made fewer errors (PF 0.33 errors, p = 0.04; NF 0.38 errors, p = 0.23). When surveyed about their responses to standardized feedback scenarios, the students stated that both positive and negative verbal feedback could be potent stimulants for improved performance and motivation. Further research is required to better understand the effects of feedback on learner motivation and the interpersonal dynamic between mentors and their trainees.     

Using Multiple Array Sensing and Non-Invasive Data Capture As a Model For Polypharmacy Error Detection

Developing standards and technology models that will facilitate e-prescribing is one of the key action items in the federal government’s plan to build a nationwide electronic health information infrastructure in the United States. E-prescribing has the potential to drive change in the healthcare industry, but the unavailability of diagnostic testing and detection equipment outside of clinical settings makes expanded collection and use of information problematic. Most solutions are provider-based, and limited by organization-wide startup & maintenance costs, and risk-averse data distribution policies. Objective, consumer-provided standardized data can facilitate the use of distributed information networks in polypharmacy detection and avoidance. In this technology review we propose here one promising model for polypharmacy management and integrated diagnostics through the use of breath-based, multiple array sensing and data capture.