Dwight Meglan

Minimizing Surgical Error by Incorporating Objective Assessment into Surgical Education

H T p e e t p l ( ( 2 E a p t e c c e q t e n e ackground n 2000, the Institute of Medicine issued its landmark reort, To Err is Human, which revealed that as many as 8,000 in-hospital deaths result from medical errors each ear, and calculated a complication rate of 2.9% to 3.7%. he medical literature, however, reports rates three to four imes the Institute of Medicine-reported rates. Healey and olleagues report complication rates in the 11.0% to 6.6% range, and surgical adverse event rates span the 9% o 11% and 7% to 16% ranges. Surgical adverse events ccounted for two-thirds of all adverse events in Colorado nd Utah, and a recent retrospective study by a fellow from he US Department of Health and Human Services Agency or Healthcare Research and Quality revealed that surgical dverse events were responsible for 12.5% of hospital eaths. When randomly sampled settled surgical malpracice claims were reviewed, more than 50% were from techical error. The literature concurs that as many as half of urgical adverse events are avoidable. Because surgeons regularly encounter adverse or unexected conditions (eg, abnormal anatomy, friable tissue dhesions), optimization of technical approach to specific ircumstances, error anticipation or recognition, and tactial recovery are important in their cognitive and technical urgical training. Assessment of trainees’ decision-making apabilities is the other indispensable component to surgical kills training, and special emphasis needs to be placed on ecision-making in knowledgeor information-constrained ettings. The contemporary mandate of surgical education is emodied in the challenge to provide training in decisionaking and technical skills outside the normative center of extbook practice and in an environment of decreasing sur-

Data structures and transformations for physically based simulation on a GPU

As general purpose computing on Graphics Processing Units (GPGPU) matures, more complicated scientific applications are being targeted to utilize the data-level parallelism available on a GPU. Implementing physically-based simulation on data-parallel hardware requires preprocessing overhead which affects application performance. We discuss our implementation of physics-based data structures that provide significant performance improvements when used on data-parallel hardware. These data structures allow us to maintain a physics-based abstraction of the underlying data, reduce programmer effort and obtain 6×-8× speedup over previously implemented GPU kernels.

A Preliminary Evaluation of a Burr Hole Drilling Simulator for Craniotomy

A training transfer paradigm was used to evaluate a virtual reality (VR) simulator for performing the burr hole drilling portion of a craniotomy procedure. Participants used a VR simulator that had a physical drill handle interfaced with a haptic force/torque feedback system. They practiced until they met a priori performance criteria and then repeated the procedure with a genuine drill and foam model of a skull. The results showed some positive transfer related to hand steadiness, but not for drilling time. The drilling model incorporated in the simulator may require more effort to maintain the drill position and more revolutions of the drill bit to penetrate the model skull. Thus, practice on the VR simulator enabled the participants to maintain better drill position on the physical model. From a clinical perspective, the findings underscore the advantages of practicing a critical surgical procedure on a simulator prior to performing on a genuine patient.