Teaching students to teach patients: A theory-guided approach

Nurses in every setting provide patient teaching on a routine basis, often several times a day. Patient teaching skills are essential competencies to be developed during pre-licensure nursing education. While students learn what to teach for specific conditions, they often lack competence in how to teach in ways that individualize and optimize patient learning. The ultimate goal of patient teaching is to arm patients with the knowledge and skills, and the desire and confidence in their ability to reach their targeted health outcomes. We describe the creation of a theoretical framework to guide development of patient teaching skills. The framework, rooted in the contemporary health care values of patient-centered care, is a synthesis of four evidence-based approaches to patient teaching: patient engagement, motivational interviewing, adult learning theory, and teach-back method. Specific patient teaching skills, derived from each of the approaches, are applied within the context of discharge teaching, an important nursing practice linked to patient outcomes. This exemplar emphasizes the use of critical teaching process skills and targeted informational content. An online student learning module based on the theoretical framework and combined with simulation experiences provides the nurse educator with one strategy for use with nursing students. The theoretical framework has applicability for skill development during pre-licensure education and skill refinement for nurses in clinical practice

Patient-specific simulation for autonomous surgery

An Autonomous Robotic Surgical System (ARSS) has to interact with the complex anatomical environment, which is deforming and whose properties are often uncertain. Within this context, an ARSS can benefit from the availability of patient-specific simulation of the anatomy. For example, simulation can provide a safe and controlled environment for the design, test and validation of the autonomous capabilities. Moreover, it can be used to generate large amounts of patient-specific data that can be exploited to learn models and/or tasks. The aim of this Thesis is to investigate the different ways in which simulation can support an ARSS and to propose solutions to favor its employability in robotic surgery. We first address all the phases needed to create such a simulation, from model choice in the pre-operative phase based on the available knowledge to its intra-operative update to compensate for inaccurate parametrization. We propose to rely on deep neural networks trained with synthetic data both to generate a patient-specific model and to design a strategy to update model parametrization starting directly from intra-operative sensor data. Afterwards, we test how simulation can assist the ARSS, both for task learning and during task execution. We show that simulation can be used to efficiently train approaches that require multiple interactions with the environment, compensating for the riskiness to acquire data from real surgical robotic systems. Finally, we propose a modular framework for autonomous surgery that includes deliberative functions to handle real anatomical environments with uncertain parameters. The integration of a personalized simulation proves fundamental both for optimal task planning and to enhance and monitor real execution. The contributions presented in this Thesis have the potential to introduce significant step changes in the development and actual performance of autonomous robotic surgical systems, making them closer to applicability to real clinical conditions

First Ex-Vivo Validation of a Radioguided Surgery Technique with beta- Radiation

Purpose: A radio-guided surgery technique with beta- -emitting radio-tracers was suggested to overcome the effect of the large penetration of gamma radiation. The feasibility studies in the case of brain tumors and abdominal neuro-endocrine tumors were based on simulations starting from PET images with several underlying assumptions. This paper reports, as proof-of-principle of this technique, an ex-vivo test on a meningioma patient. This test allowed to validate the whole chain, from the evaluation of the SUV of the tumor, to the assumptions on the bio-distribution and the signal detection. Methods: A patient affected by meningioma was administered 300 MBq of 90Y-DOTATOC. Several samples extracted from the meningioma and the nearby Dura Mater were analyzed with a beta- probe designed specifically for this radio-guided surgery technique. The observed signals were compared both with the evaluation from the histology and with the Monte Carlo simulation. Results: we obtained a large signal on the bulk tumor (105 cps) and a significant signal on residuals of $\sim$0.2 ml (28 cps). We also show that simulations predict correctly the observed yields and this allows us to estimate that the healthy tissues would return negligible signals (~1 cps). This test also demonstrated that the exposure of the medical staff is negligible and that among the biological wastes only urine has a significant activity. Conclusions: This proof-of-principle test on a patient assessed that the technique is feasible with negligible background to medical personnel and confirmed that the expectations obtained with Monte Carlo simulations starting from diagnostic PET images are correct.Comment: 17 pages, 4 Figs, Accepted by Physica Medic

Mathematical modeling of thrombus formation in idealized models of aortic dissection: Initial findings and potential applications

Aortic dissection is a major aortic catastrophe with a high morbidity and mortality risk caused by the formation of a tear in the aortic wall. The development of a second blood filled region defined as the “false lumen” causes highly disturbed flow patterns and creates local hemodynamic conditions likely to promote the formation of thrombus in the false lumen. Previous research has shown that patient prognosis is influenced by the level of thrombosis in the false lumen, with false lumen patency and partial thrombosis being associated with late complications and complete thrombosis of the false lumen having beneficial effects on patient outcomes. In this paper, a new hemodynamics-based model is proposed to predict the formation of thrombus in Type B dissection. Shear rates, fluid residence time, and platelet distribution are employed to evaluate the likelihood for thrombosis and to simulate the growth of thrombus and its effects on blood flow over time. The model is applied to different idealized aortic dissections to investigate the effect of geometric features on thrombus formation. Our results are in qualitative agreement with in-vivo observations, and show the potential applicability of such a modeling approach to predict the progression of aortic dissection in anatomically realistic geometries

Evaluating the effectiveness of the Emergency Neurological Life Support educational framework in low-income countries.

BackgroundThe Emergency Neurological Life Support (ENLS) is an educational initiative designed to improve the acute management of neurological injuries. However, the applicability of the course in low-income countries in unknown. We evaluated the impact of the course on knowledge, decision-making skills and preparedness to manage neurological emergencies in a resource-limited country.MethodsA prospective cohort study design was implemented for the first ENLS course held in Asia. Knowledge and decision-making skills for neurological emergencies were assessed at baseline, post-course and at 6 months following course completion. To determine perceived knowledge and preparedness, data were collected using surveys administered immediately post-course and 6 months later.ResultsA total of 34 acute care physicians from across Nepal attended the course. Knowledge and decision-making skills significantly improved following the course (p=0.0008). Knowledge and decision-making skills remained significantly improved after 6 months, compared with before the course (p=0.02), with no significant loss of skills immediately following the course to the 6-month follow-up (p=0.16). At 6 months, the willingness to participate in continuing medical education activities remained evident, with 77% (10/13) of participants reporting a change in their clinical practice and decision-making, with the repeated use of ENLS protocols as the main driver of change.ConclusionsUsing the ENLS framework, neurocritical care education can be delivered in low-income countries to improve knowledge uptake, with evidence of knowledge retention up to 6 months

Wavelet entropy as a measure of ventricular beat suppression from the electrocardiogram in atrial fibrillation

A novel method of quantifying the effectiveness of the suppression of ventricular activity from electrocardiograms (ECGs) in atrial fibrillation is proposed. The temporal distribution of the energy of wavelet coefficients is quantified by wavelet entropy at each ventricular beat. More effective ventricular activity suppression yields increased entropies at scales dominated by the ventricular and atrial components of the ECG. Two studies are undertaken to demonstrate the efficacy of the method: first, using synthesised ECGs with controlled levels of residual ventricular activity, and second, using patient recordings with ventricular activity suppressed by an average beat template subtraction algorithm. In both cases wavelet entropy is shown to be a good measure of the effectiveness of ventricular beat suppression

Identifying Attrition Phases in Survey Data: Applicability and Assessment Study

Background: Although Web-based questionnaires are an efficient, increasingly popular mode of data collection, their utility is often challenged by high participant dropout. Researchers can gain insight into potential causes of high participant dropout by analyzing the dropout patterns. Objective: This study proposed the application of and assessed the use of user-specified and existing hypothesis testing methods in a novel setting—survey dropout data—to identify phases of higher or lower survey dropout. Methods: First, we proposed the application of user-specified thresholds to identify abrupt differences in the dropout rate. Second, we proposed the application of 2 existing hypothesis testing methods to detect significant differences in participant dropout. We assessed these methods through a simulation study and through application to a case study, featuring a questionnaire addressing decision-making surrounding cancer screening. Results: The user-specified method set to a low threshold performed best at accurately detecting phases of high attrition in both the simulation study and test case application, although all proposed methods were too sensitive. Conclusions: The user-specified method set to a low threshold correctly identified the attrition phases. Hypothesis testing methods, although sensitive at times, were unable to accurately identify the attrition phases. These results strengthen the case for further development of and research surrounding the science of attrition