Characteristics of Patients Presenting at an Emergency Department for a Heroin Overdose vs Detoxification.

Purpose: This study compares substance use, treatment histories, and sociodemographic characteristics of patients presenting to an emergency department (ED) following a heroin overdose or seeking detoxification services for heroin and examines risk factors for a subsequent return to the ED for a substance-related problem. Methods: A convenience sample of patients presenting for an overdose or detoxification at an urban teaching ED was recruited for this study. During their ED visit, patients were interviewed regarding demographics, substance use experiences, and treatment history. Subsequently, a review of patient records for past and subsequent ED use was performed. Results: Patients requesting detox and those with an overdose were similar in terms of prior treatment. Both groups had similar extensive polysubstance histories. As a group, however, patients presenting for detox were more likely to report use of each of three substances (benzodiazepines, opioid pain medications, and heroin) more than three times per week, compared to those presenting for overdose. Detox patients had higher scores on the 3-item Alcohol Use Disorder Identification Test-C and the drug problems scale compared to overdose patients. Overall, 28% of the patients returned to the ED within 90 days for a drug-related issue, including 8% that returned for an overdose. Factors predictive of a return ED visit included ED visits for substance use in the previous year and recent frequent heroin use. Conclusion: Patients requesting detox were similar in most domains to those presenting following an overdose. Notably, overdose patients were less likely to use heroin more than three times per week compared to detox patients. Both groups were equally likely to return for an SUD reason within 3-months, however for both groups, previous ED visits and recent frequent heroin use predicted a return visit.

Robotic Simulators for Tissue Examination Training With Multimodal Sensory Feedback.

Tissue examination by hand remains an essential technique in clinical practice. The effective application depends on skills in sensorimotor coordination, mainly involving haptic, visual, and auditory feedback. The skills clinicians have to learn can be as subtle as regulating finger pressure with breathing, choosing palpation action, monitoring involuntary facial and vocal expressions in response to palpation, and using pain expressions both as a source of information and as a constraint on physical examination. Patient simulators can provide a safe learning platform to novice physicians before trying real patients. This paper reviews state-of-the-art medical simulators for the training for the first time with a consideration of providing multimodal feedback to learn as many manual examination techniques as possible. The study summarizes current advances in tissue examination training devices simulating different medical conditions and providing different types of feedback modalities. Opportunities with the development of pain expression, tissue modeling, actuation, and sensing are also analyzed to support the future design of effective tissue examination simulators.

A Surrogate Model Based on a Finite Element Model of Abdomen for Real-Time Visualisation of Tissue Stress during Physical Examination Training.

Robotic patients show great potential for helping to improve medical palpation training, as they can provide feedback that cannot be obtained in a real patient. They provide information about internal organ deformation that can significantly enhance palpation training by giving medical trainees visual insight based on the pressure they apply for palpation. This can be achieved by using computational models of abdomen mechanics. However, such models are computationally expensive, and thus unable to provide real-time predictions. In this work, we proposed an innovative surrogate model of abdomen mechanics by using machine learning (ML) and finite element (FE) modelling to virtually render internal tissue deformation in real time. We first developed a new high-fidelity FE model of the abdomen mechanics from computerized tomography (CT) images. We performed palpation simulations to produce a large database of stress distribution on the liver edge, an area of interest in most examinations. We then used artificial neural networks (ANNs) to develop the surrogate model and demonstrated its application in an experimental palpation platform. Our FE simulations took 1.5 h to predict stress distribution for each palpation while this only took a fraction of a second for the surrogate model. Our results show that our artificial neural network (ANN) surrogate has an accuracy of 92.6%. We also showed that the surrogate model is able to use the experimental input of palpation location and force to provide real-time projections onto the robotics platform. This enhanced robotics platform has the potential to be used as a training simulator for trainees to hone their palpation skills.

Magnetic Surgical Instruments for Robotic Abdominal Surgery.

This review looks at the implementation of magnetic-based approaches in surgical instruments for abdominal surgeries. As abdominal surgical techniques advance toward minimizing surgical trauma, surgical instruments are enhanced to support such an objective through the exploration of magnetic-based systems. With this design approach, surgical devices are given the capabilities to be fully inserted intraabdominally to achieve access to all abdominal quadrants, without the conventional rigid link connection with the external unit. The variety of intraabdominal surgical devices are anchored, guided, and actuated by external units, with power and torque transmitted across the abdominal wall through magnetic linkage. This addresses many constraints encountered by conventional laparoscopic tools, such as loss of triangulation, fulcrum effect, and loss/lack of dexterity for surgical tasks. Design requirements of clinical considerations to aid the successful development of magnetic surgical instruments, are also discussed.

Modeling and analysis of coagulated liver tissue and its interaction with a scalpel blade.

Radiofrequency-assisted methods have been used in hepatectomy--the resection process of removing liver tissue which encapsulates the tumor from the liver organ. A prototype was built to enable smooth surgical transition between radiofrequency ablation and liver resection. There is a lack of literature on mechanical properties of radiofrequency-ablated liver tissue and the tool-tissue interaction during cutting. This led to our study on coagulated tissue mechanical properties and modeling of its dynamic interaction with a scalpel blade. A novel mechanical model was proposed to mimic the mechanical behavior of radiofrequency-ablated liver tissue. The model is able to account for the viscoelastic behavior of the ablated tissue in both compression and relaxation tests. Experiments were performed to validate the proposed model. In addition, a knife blade-tissue interaction model is proposed to demonstrate the potential of integrating the proposed model for application in device design.