BackgroundThe amount of scientific literature available is often overwhelming, making it difficult for researchers to have a good overview of the literature and to see relations between different developments. Visualisation techniques based on bibliometric data are helpful in obtaining an overview of the literature on complex research topics, and have been applied here to the topic of patient safety (PS).MethodsOn the basis of title words and citation relations, publications in the period 2000–2010 related to PS were identified in the Scopus bibliographic database. A visualisation of the most frequently cited PS publications was produced based on direct and indirect citation relations between publications. Terms were extracted from titles and abstracts of the publications, and a visualisation of the most important terms was created. The main PS-related topics studied in the literature were identified using a technique for clustering publications and terms.ResultsA total of 8480 publications were identified, of which the 1462 most frequently cited ones were included in the visualisation. The publications were clustered into 19 clusters, which were grouped into three categories: (1) magnitude of PS problems (42% of all included publications); (2) PS risk factors (31%) and (3) implementation of solutions (19%). In the visualisation of PS-related terms, five clusters were identified: (1) medication; (2) measuring harm; (3) PS culture; (4) physician; (5) training, education and communication. Both analysis at publication and term level indicate an increasing focus on risk factors.ConclusionsA bibliometric visualisation approach makes it possible to analyse large amounts of literature. This approach is very useful for improving one's understanding of a complex research topic such as PS and for suggesting new research directions or alternative research priorities. For PS research, the approach suggests that more research on implementing PS improvement initiatives might be needed.

When equipped with motion and force sensors, box-trainers can be good alternatives for relatively expensive Virtual Reality (VR) trainers. As in VR trainers, the sensors in a box trainer could provide the trainee with objective information about his performance. Recently, multiple tracking systems were developed for classification of participants based on motion and time parameters. The aim of this study is the development of force parameters that reflect the trainee's performance in a suture task. Our second goal is to investigate if the level of the participant's skills can be classified as experts or novice level. In the experiment, experts (n = 11) and novices (n = 21) performed a two-handed needle driving and knot tying task on artificial tissue inside a box trainer. The tissue was mounted on the Force platform that was used to measure the force, which the subject applied on the tissue in three directions. We evaluated the potential of 16 different performance parameters, related to the magnitude, direction, and variability of applied forces, to distinguish between different levels of surgical expertise. Nine of the parameters showed significant differences between experts and novices. Principal Component Analysis was used to convert these nine partly correlating parameters, such as peak force, mean force, and main direction of force, into two uncorrelated variables. By performing a Leave-One-Out-Cross Validation with Linear Discriminant Analysis on each participants' score on these two variables, it was possible to correctly classify 84 percent of all participants as an expert or novice. We conclude that force measurements in a box trainer can be used to classify the level of performance of trainees and can contribute to objective assessment of suture skills.

BackgroundTo improve endoscopic surgical skills, an increasing number of surgical residents practice on box or virtual-reality (VR) trainers. Current training is mainly focused on hand–eye coordination. Training methods that focus on applying the right amount of force are not yet available.MethodsThe aim of this project is to develop a system to measure forces and torques during laparoscopic training tasks as well as the development of force parameters that assess tissue manipulation tasks. The force and torque measurement range of the developed force platform are 0–4 N and 1 Nm (torque), respectively. To show the potential of the developed force platform, a pilot study was conducted in which five surgeons experienced in intracorporeal suturing and five novices performed a suture task in a box trainer.ResultsDuring the pilot study, the maximum and mean absolute nonzero force that the novice used were 4.7 N (SD 1.3 N) and 2.1 N (SD 0.6 N), respectively. With a maximum force of 2.6 N (SD 0.4 N) and mean nonzero force of 0.9 N (SD 0.3 N), the force exerted by the experts was significantly lower.ConclusionsThe designed platform is easy to build, affordable, and accurate and sensitive enough to reflect the most important differences in, e.g., maximal force, mean force, and standard deviation. Furthermore, the compact design makes it possible to use the force platform in most box trainers.

BackgroundTo improve endoscopic surgical skills, an increasing number of surgical residents practice on box or virtual reality (VR) trainers. Current training is focused mainly on hand–eye coordination. Training methods that focus on applying the right amount of force are not yet available.MethodsThe aim of this project is to develop a low-cost training system that measures the interaction force between tissue and instruments and displays a visual representation of the applied forces inside the camera image. This visual representation continuously informs the subject about the magnitude and the direction of applied forces. To show the potential of the developed training system, a pilot study was conducted in which six novices performed a needle-driving task in a box trainer with visual feedback of the force, and six novices performed the same task without visual feedback of the force. All subjects performed the training task five times and were subsequently tested in a post-test without visual feedback.ResultsThe subjects who received visual feedback during training exerted on average 1.3 N (STD 0.6 N) to drive the needle through the tissue during the post-test. This value was considerably higher for the group that received no feedback (2.6 N, STD 0.9 N). The maximum interaction force during the post-test was noticeably lower for the feedback group (4.1 N, STD 1.1 N) compared with that of the control group (8.0 N, STD 3.3 N).ConclusionsThe force-sensing training system provides us with the unique possibility to objectively assess tissue-handling skills in a laboratory setting. The real-time visualization of applied forces during training may facilitate acquisition of tissue-handling skills in complex laparoscopic tasks and could stimulate proficiency gain curves of trainees. However, larger randomized trials that also include other tasks are necessary to determine whether training with visual feedback about forces reduces the interaction force during laparoscopic surgery.

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