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TITLE: Physicians as “Patients”- Use of immersive simulated patient experiences to foster physician empathy and compassion AUTHORS: Aaron M. Lee, DO; Sean Kenmore, MD; Supraja Thota, MD; Constance Chance, MD; Anand Jagannath, MDINSTITUTION: Internal Medicine, University of California-San Diego Medical Center   BACKGROUND The importance of fostering physician empathy has become increasingly recognized as a critical aspect of physician training; among many things, increased physician empathy has been shown to lead to improved clinical outcomes, higher patient satisfaction, and decreased physician burnout. Despite this, there remains a paucity of interventions to effectively promote compassion and empathy in medical education. To address this void, we propose a set of novel immersive role-reversal simulation exercises which place resident physicians into patient roles to simulate the inpatient experience. We propose that increased appreciation of the patient experience through simulation can lead to improved physician empathy and compassion and thereby improved delivery of patient-centered care.   PILOT INTERVENTION While the ultimate goal is to develop a formal curriculum involving numerous simulation didactics, we developed a pilot program to study the initial feasibility and effectiveness of this intervention.Aim: Use of a 1-hour noon conference to expose end-of-year interns to numerous aspects of the patient experience to increase appreciate of the inpatient experienceMethods: Several different stations were developed that each highlighted a single aspect of the patient experience. At each station, a prompt provided a simulated patient context/perspective associated with a physical item. These stations included a hospital bed, patient foods, glucometers, bedpans, common patient foods, urinals, nasal cannulas/facemasks, incentive spirometer, oral secretion device, foley and urine leg bag. Participants were given patient gowns, telemetry leads, pulse oximet

Category: Quality/Systems Improvement | QI/Research Title: Getting Mammograms Shouldn’t Be So Bumpy- Improving Mammogram Workflow in VA Community CarePrimary Author: Aaron M. Lee, DO, MS (Internal Medicine, Chief Resident) Co-Authors and Specialty Program Affiliations: Jack Temple, MD (Internal Medicine), Susan Macdonald, RN (VA Community Care), Shayne Ovalles, RN (VA Community Care)  Background The interplay between Community Care (CC) and VA-based care has long been important for continuity of patient care, though transitioning care between the two is often fraught with problems. Mammograms ordered at VA San Diego (VASD) are often deferred to community care; while this process should be smooth and seamless, the process is instead convoluted and ripe for errors. The lack of standardization, disconnect between the interface between VASD and CC, and splintered tracking systems has led to delays in care, delays in results, and at worst, patient harm and delays in diagnosis. VA mammograms completed in the community have inconsistent rates of completion, with errors ranging from unscheduled appointments, delayed provider notification, and loss to follow up leading to late diagnoses of breast cancer. To reduce patient harm and increase fluidity of VA mammograms in the community, we applied extensive QI methodology towards overhauling the VA Community Care Mammogram workflow. Methods We evaluated the process of CC mammograms at VA San Diego. An A3 was used to process map the VA CC Mammogram process, beginning with VA provider entering a mammogram order to the community and ending with mammogram result release to the patient. A virtual Gemba walk was performed, during which the author evaluated how the CC team processed consults and results. Using Lean Six Sigma process improvement strategies, we identified several problematic areas within the CC Mammogram workflow that are prone to errors and can lead to patient safety issues. Weekly meetings and interviews were set

Title: I’ll make a “Patient” out of you: An Update to “Physicians as ‘Patients’”- Design, Implementation, and Challenges of Novel Immersive Simulated Patient Experiences to Foster Physician Empathy and Compassion Author: Aaron M. Lee, DO, MS (Internal Medicine, Chief Resident) Co-Authors and Specialty Program Affiliations:  Khanh Hoang Nicholas Le, MD (Internal Medicine, PGY-1), Preetham Suresh, MD (Anesthesia, faculty), Ricardo Wood (UCSD Simulation Center), Sean Kenmore, MD (Internal Medicine, faculty)  Issues Addressed/ Background The importance of fostering physician empathy and compassion has become increasingly recognized as a critical aspect of physician training, with the ACGME and AAMC both acknowledging empathy as a key component of professionalism, with recommendations to incorporate empathy education into core medical education goals. The challenge remains how best to teach physician empathy and compassion in medical education, and to create long term effective educational interventions. Empathy curriculum in graduate medical education remains limited, as there are few standardized methodologies for teaching empathy.  Herein lies an opportunity for growth and development of novel and new methodologies to deliver patient-centered and empathy/compassion education to physicians in training. We hypothesize that lack of appreciation or understanding of the patient experience is much to blame in the deficits in teaching physician empathy and compassion. To address this void, we propose a set of novel immersive simulation exercises to place resident physicians in the role of a patient with space for guided reflection. We propose that a standardized longitudinal curriculum based on high-fidelity immersive simulation exercises throughout medical training will improve physician empathy and compassion, and the delivery of this form of education can be effective, easily disseminated, cost effective, and enjoyable to the learner, ultimately l

Convolutional neural network (CNN) based segmentation methods provide an efficient and automated way for clinicians to assess the structure and function of the heart in cardiac MR images. While CNNs can generally perform the segmentation tasks with high accuracy when training and test images come from the same domain (e.g. same scanner or site), their performance often degrades dramatically on images from different scanners or clinical sites. We propose a simple yet effective way for improving the network generalization ability by carefully designing data normalization and augmentation strategies to accommodate common scenarios in multi-site, multi-scanner clinical imaging data sets. We demonstrate that a neural network trained on a single-site single-scanner dataset from the UK Biobank can be successfully applied to segmenting cardiac MR images across different sites and different scanners without substantial loss of accuracy. Specifically, the method was trained on a large set of 3,975 subjects from the UK Biobank. It was then directly tested on 600 different subjects from the UK Biobank for intra-domain testing and two other sets for cross-domain testing: the ACDC dataset (100 subjects, 1 site, 2 scanners) and the BSCMR-AS dataset (599 subjects, 6 sites, 9 scanners). The proposed method produces promising segmentation results on the UK Biobank test set which are comparable to previously reported values in the literature, while also performing well on cross-domain test sets, achieving a mean Dice metric of 0.90 for the left ventricle, 0.81 for the myocardium and 0.82 for the right ventricle on the ACDC dataset; and 0.89 for the left ventricle, 0.83 for the myocardium on the BSCMR-AS dataset. The proposed method offers a potential solution to improve CNN-based model generalizability for the cross-scanner and cross-site cardiac MR image segmentation task., Comment: 15 pages, 8 figures

Background: The trend towards large-scale studies including population imaging poses new challenges in terms of quality control (QC). This is a particular issue when automatic processing tools, e.g. image segmentation methods, are employed to derive quantitative measures or biomarkers for later analyses. Manual inspection and visual QC of each segmentation isn't feasible at large scale. However, it's important to be able to automatically detect when a segmentation method fails so as to avoid inclusion of wrong measurements into subsequent analyses which could lead to incorrect conclusions. Methods: To overcome this challenge, we explore an approach for predicting segmentation quality based on Reverse Classification Accuracy, which enables us to discriminate between successful and failed segmentations on a per-cases basis. We validate this approach on a new, large-scale manually-annotated set of 4,800 cardiac magnetic resonance scans. We then apply our method to a large cohort of 7,250 cardiac MRI on which we have performed manual QC. Results: We report results used for predicting segmentation quality metrics including Dice Similarity Coefficient (DSC) and surface-distance measures. As initial validation, we present data for 400 scans demonstrating 99% accuracy for classifying low and high quality segmentations using predicted DSC scores. As further validation we show high correlation between real and predicted scores and 95% classification accuracy on 4,800 scans for which manual segmentations were available. We mimic real-world application of the method on 7,250 cardiac MRI where we show good agreement between predicted quality metrics and manual visual QC scores. Conclusions: We show that RCA has the potential for accurate and fully automatic segmentation QC on a per-case basis in the context of large-scale population imaging as in the UK Biobank Imaging Study., Comment: 14 pages, 7 figures, Journal of Cardiovascular Magnetic Resonance

Cardiovascular magnetic resonance (CMR) imaging is a standard imaging modality for assessing cardiovascular diseases (CVDs), the leading cause of death globally. CMR enables accurate quantification of the cardiac chamber volume, ejection fraction and myocardial mass, providing information for diagnosis and monitoring of CVDs. However, for years, clinicians have been relying on manual approaches for CMR image analysis, which is time consuming and prone to subjective errors. It is a major clinical challenge to automatically derive quantitative and clinically relevant information from CMR images. Deep neural networks have shown a great potential in image pattern recognition and segmentation for a variety of tasks. Here we demonstrate an automated analysis method for CMR images, which is based on a fully convolutional network (FCN). The network is trained and evaluated on a large-scale dataset from the UK Biobank, consisting of 4,875 subjects with 93,500 pixelwise annotated images. The performance of the method has been evaluated using a number of technical metrics, including the Dice metric, mean contour distance and Hausdorff distance, as well as clinically relevant measures, including left ventricle (LV) end-diastolic volume (LVEDV) and end-systolic volume (LVESV), LV mass (LVM); right ventricle (RV) end-diastolic volume (RVEDV) and end-systolic volume (RVESV). By combining FCN with a large-scale annotated dataset, the proposed automated method achieves a high performance on par with human experts in segmenting the LV and RV on short-axis CMR images and the left atrium (LA) and right atrium (RA) on long-axis CMR images., Comment: Accepted for publication by Journal of Cardiovascular Magnetic Resonance