scholarly journals Stages of Milestones Implementation: A Template Analysis of 16 Programs Across 4 Specialties

2021 ◽  
Vol 13 (2s) ◽  
pp. 14-44
Author(s):  
Nicholas A. Yaghmour ◽  
Lauren J. Poulin ◽  
Elizabeth C. Bernabeo ◽  
Andem Ekpenyong ◽  
Su-Ting T. Li ◽  
...  
Keyword(s):  
Stakeholder Engagement ◽  
Early Stage ◽  
Implementation Strategies ◽  
Fine Tuning ◽  
Residency Programs ◽  
Program Leaders ◽  
Process Implementation ◽  
Template Analysis ◽  
Competency Based ◽  
Clinical Competency Committee

ABSTRACT Background Since 2013, US residency programs have used the competency-based framework of the Milestones to report resident progress and to provide feedback to residents. The implementation of Milestones-based assessments, clinical competency committee (CCC) meetings, and processes for providing feedback varies among programs and warrants systematic examination across specialties. Objective We sought to determine how varying assessment, CCC, and feedback implementation strategies result in different outcomes in resource expenditure and stakeholder engagement, and to explore the contextual forces that moderate these outcomes. Methods From 2017 to 2018, interviews were conducted of program directors, CCC chairs, and residents in emergency medicine (EM), internal medicine (IM), pediatrics, and family medicine (FM), querying their experiences with Milestone processes in their respective programs. Interview transcripts were coded using template analysis, with the initial template derived from previous research. The research team conducted iterative consensus meetings to ensure that the evolving template accurately represented phenomena described by interviewees. Results Forty-four individuals were interviewed across 16 programs (5 EM, 4 IM, 5 pediatrics, 3 FM). We identified 3 stages of Milestone-process implementation, including a resource-intensive early stage, an increasingly efficient transition stage, and a final stage for fine-tuning. Conclusions Residency program leaders can use these findings to place their programs along an implementation continuum and gain an understanding of the strategies that have enabled their peers to progress to improved efficiency and increased resident and faculty engagement.

scholarly journals Seven ways to get a grip on implementing Competency-Based Medical Education at the program level

2020 ◽  
Author(s):  
Jeffery Damon Dagnone ◽  
Laura McEwen ◽  
David Taylor ◽  
Amy Acker ◽  
Mary Bouchard ◽  
...  
Keyword(s):  
Medical Education ◽  
Change Management ◽  
Educational Outcomes ◽  
Management Theory ◽  
Competency Based Medical Education ◽  
Residency Programs ◽  
Program Leaders ◽  
Short Summary ◽  
Collective Experience ◽  
Competency Based

Competency-based medical education (CBME) curricula are becoming increasingly common in graduate medical education. Put simply, CBME is focused on educational outcomes, is independent of methods and time, and is composed of achievable competencies.1 In spite of widespread uptake, there remains much to learn about implementing CBME at the program level. Leveraging the collective experience of program leaders at Queen’s University, where CBME simultaneously launched across 29 specialty programs in 2017, this paper leverages change management theory to provide a short summary of how program leaders can navigate the successful preparation, launch, and initial implementation of CBME within their residency programs.

scholarly journals New polyp image classification technique using transfer learning of network-in-network structure in endoscopic images

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Young Jae Kim ◽  
Jang Pyo Bae ◽  
Jun-Won Chung ◽  
Dong Kyun Park ◽  
Kwang Gi Kim ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Transfer Learning ◽  
Test Data ◽  
State Of The Art ◽  
Early Stage ◽  
Statistical Significance ◽  
Recall Rate ◽  
Training Data ◽  
Fine Tuning ◽  
Accuracy Evaluation

AbstractWhile colorectal cancer is known to occur in the gastrointestinal tract. It is the third most common form of cancer of 27 major types of cancer in South Korea and worldwide. Colorectal polyps are known to increase the potential of developing colorectal cancer. Detected polyps need to be resected to reduce the risk of developing cancer. This research improved the performance of polyp classification through the fine-tuning of Network-in-Network (NIN) after applying a pre-trained model of the ImageNet database. Random shuffling is performed 20 times on 1000 colonoscopy images. Each set of data are divided into 800 images of training data and 200 images of test data. An accuracy evaluation is performed on 200 images of test data in 20 experiments. Three compared methods were constructed from AlexNet by transferring the weights trained by three different state-of-the-art databases. A normal AlexNet based method without transfer learning was also compared. The accuracy of the proposed method was higher in statistical significance than the accuracy of four other state-of-the-art methods, and showed an 18.9% improvement over the normal AlexNet based method. The area under the curve was approximately 0.930 ± 0.020, and the recall rate was 0.929 ± 0.029. An automatic algorithm can assist endoscopists in identifying polyps that are adenomatous by considering a high recall rate and accuracy. This system can enable the timely resection of polyps at an early stage.

Deep Learning Representation from Electroencephalography of Early-Stage Creutzfeldt-Jakob Disease and Features for Differentiation from Rapidly Progressive Dementia

2016 ◽  
Vol 27 (02) ◽  
pp. 1650039 ◽  
Author(s):  
Francesco Carlo Morabito ◽  
Maurizio Campolo ◽  
Nadia Mammone ◽  
Mario Versaci ◽  
Silvana Franceschetti ◽  
...  
Keyword(s):  
Deep Learning ◽  
Supervised Learning ◽  
Early Stage ◽  
Processing System ◽  
Fine Tuning ◽  
Permutation Entropy ◽  
Support Vector ◽  
Progressive Dementia ◽  
Time Frequency ◽  
Jakob Disease

A novel technique of quantitative EEG for differentiating patients with early-stage Creutzfeldt–Jakob disease (CJD) from other forms of rapidly progressive dementia (RPD) is proposed. The discrimination is based on the extraction of suitable features from the time-frequency representation of the EEG signals through continuous wavelet transform (CWT). An average measure of complexity of the EEG signal obtained by permutation entropy (PE) is also included. The dimensionality of the feature space is reduced through a multilayer processing system based on the recently emerged deep learning (DL) concept. The DL processor includes a stacked auto-encoder, trained by unsupervised learning techniques, and a classifier whose parameters are determined in a supervised way by associating the known category labels to the reduced vector of high-level features generated by the previous processing blocks. The supervised learning step is carried out by using either support vector machines (SVM) or multilayer neural networks (MLP-NN). A subset of EEG from patients suffering from Alzheimer’s Disease (AD) and healthy controls (HC) is considered for differentiating CJD patients. When fine-tuning the parameters of the global processing system by a supervised learning procedure, the proposed system is able to achieve an average accuracy of 89%, an average sensitivity of 92%, and an average specificity of 89% in differentiating CJD from RPD. Similar results are obtained for CJD versus AD and CJD versus HC.

Identifying Strategies for Robust Survivorship Program Implementation: A Qualitative Analysis of Cancer Programs

2021 ◽  
Author(s):  
Soohyun Hwang ◽  
Burcu Bozkurt ◽  
Tamara Huson ◽  
Sarah Asad ◽  
Lauren Richardson ◽  
...  
Keyword(s):  
Program Implementation ◽  
Health Care Professionals ◽  
Implementation Strategies ◽  
The United States ◽  
Survivorship Care ◽  
Team Members ◽  
Care Plans ◽  
Care Processes ◽  
Key Stakeholders ◽  
Template Analysis

PURPOSE The Commission on Cancer seeks to promote robust survivorship programs among accredited cancer programs. In practice, cancer programs' survivorship programs range from cursory (eg, developing care plans without robust services) to robust (eg, facilitating follow-up care). To inform cancer programs' future efforts, in this study, we identified the implementation strategies that cancer programs used to achieve robust survivorship programs, distinguishing them from cursory programs. METHODS We sampled 39 cancer programs across the United States with approaches to survivorship program implementation ranging from cursory to robust on the basis of LIVESTRONG survivorship care consensus elements. Within sampled cancer programs, we conducted in-depth semistructured interviews with a total of 42 health care professionals. We used template analysis to distinguish implementation strategies used in cancer programs with robust survivorship programs from strategies that yielded cursory survivorship programs. RESULTS Cancer programs with robust survivorship programs established clear systems survivorship care and formal committees to improve the survivorship care processes. They sought buy-in from multiple stakeholders to leverage cancer program resources and defined clear roles with shared accountability among multidisciplinary groups. By contrast, cancer programs with cursory survivorship programs reported less consistency in survivorship care processes and lacked buy-in from key stakeholders. They had limited resources, faced persistent structural concerns, and had insufficient clarity in roles among team members. CONCLUSION Accrediting bodies may consider incorporating the implementation strategies that robust survivorship programs have used as guidance for supporting cancer programs in operationalizing survivorship care and evaluating the use of these strategies during the accreditation and review process.

scholarly journals Mitochondrial OXPHOS Biogenesis: Co-Regulation of Protein Synthesis, Import, and Assembly Pathways

2020 ◽  
Vol 21 (11) ◽  
pp. 3820 ◽  
Author(s):  
Jia Xin Tang ◽  
Kyle Thompson ◽  
Robert W. Taylor ◽  
Monika Oláhová
Keyword(s):  
Gene Expression ◽  
Protein Synthesis ◽  
Protein Import ◽  
Acyl Carrier Protein ◽  
Early Stage ◽  
Mitochondrial Gene ◽  
Mitochondrial Protein ◽  
Fine Tuning ◽  
Mitochondrial Translation ◽  
Oxphos Complex

The assembly of mitochondrial oxidative phosphorylation (OXPHOS) complexes is an intricate process, which—given their dual-genetic control—requires tight co-regulation of two evolutionarily distinct gene expression machineries. Moreover, fine-tuning protein synthesis to the nascent assembly of OXPHOS complexes requires regulatory mechanisms such as translational plasticity and translational activators that can coordinate mitochondrial translation with the import of nuclear-encoded mitochondrial proteins. The intricacy of OXPHOS complex biogenesis is further evidenced by the requirement of many tightly orchestrated steps and ancillary factors. Early-stage ancillary chaperones have essential roles in coordinating OXPHOS assembly, whilst late-stage assembly factors—also known as the LYRM (leucine–tyrosine–arginine motif) proteins—together with the mitochondrial acyl carrier protein (ACP)—regulate the incorporation and activation of late-incorporating OXPHOS subunits and/or co-factors. In this review, we describe recent discoveries providing insights into the mechanisms required for optimal OXPHOS biogenesis, including the coordination of mitochondrial gene expression with the availability of nuclear-encoded factors entering via mitochondrial protein import systems.

scholarly journals A Technical Review of Convolutional Neural Network-Based Mammographic Breast Cancer Diagnosis

2019 ◽  
Vol 2019 ◽  
pp. 1-16 ◽  
Author(s):  
Lian Zou ◽  
Shaode Yu ◽  
Tiebao Meng ◽  
Zhicheng Zhang ◽  
Xiaokun Liang ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Convolutional Neural Network ◽  
Cancer Diagnosis ◽  
Early Stage ◽  
Breast Cancer Diagnosis ◽  
Fine Tuning ◽  
Learning Tools ◽  
Pros And Cons ◽  
Future Work

This study reviews the technique of convolutional neural network (CNN) applied in a specific field of mammographic breast cancer diagnosis (MBCD). It aims to provide several clues on how to use CNN for related tasks. MBCD is a long-standing problem, and massive computer-aided diagnosis models have been proposed. The models of CNN-based MBCD can be broadly categorized into three groups. One is to design shallow or to modify existing models to decrease the time cost as well as the number of instances for training; another is to make the best use of a pretrained CNN by transfer learning and fine-tuning; the third is to take advantage of CNN models for feature extraction, and the differentiation of malignant lesions from benign ones is fulfilled by using machine learning classifiers. This study enrolls peer-reviewed journal publications and presents technical details and pros and cons of each model. Furthermore, the findings, challenges and limitations are summarized and some clues on the future work are also given. Conclusively, CNN-based MBCD is at its early stage, and there is still a long way ahead in achieving the ultimate goal of using deep learning tools to facilitate clinical practice. This review benefits scientific researchers, industrial engineers, and those who are devoted to intelligent cancer diagnosis.

scholarly journals Examining the Functioning and Reliability of the Family Medicine Milestones

2017 ◽  
Vol 9 (1) ◽  
pp. 46-53 ◽  
Author(s):  
Michael R. Peabody ◽  
Thomas R. O'Neill ◽  
Lars E. Peterson
Keyword(s):  
Family Medicine ◽  
Item Difficulty ◽  
High Reliability ◽  
Latent Trait ◽  
Training Sequence ◽  
Partial Credit Model ◽  
Residency Programs ◽  
Program Leaders ◽  
The Family ◽  
Key Dimensions

ABSTRACT Background  The Family Medicine (FM) Milestones are a framework designed to assess development of residents in key dimensions of physician competency. Residency programs use the milestones in semiannual reviews of resident performance from entry toward graduation. Objective  To examine the functioning and reliability of the FM Milestones and to determine whether they measure the amount of a latent trait (eg, knowledge or ability) possessed by a resident or simply indicate where a resident falls along the training sequence. Methods  This study utilized the Rasch Partial Credit model to examine academic year 2014–2015 ratings for 10 563 residents from 476 residency programs (postgraduate year [PGY] 1 = 3639; PGY-2 = 3562; PGY-3 = 3351; PGY-4 = 11). Results  Reliability was exceptionally high at 0.99. Mean scores were 3.2 (SD = 1.3) for PGY-1; 5.0 (SD = 1.3) for PGY-2; 6.7 (SD = 1.2) for PGY-3; and 7.4 (SD = 1.0) for PGY-4. Keyform analysis showed a rating on 1 item was likely to be similar for all other items. Conclusions  Our findings suggest that FM Milestones seem to largely function as intended. Lack of spread in item difficulty and lack of variation in category probabilities show that FM Milestones do not measure the amount of a latent trait possessed by a resident, but rather describe where a resident falls along the training sequence. High reliability indicates residents are being rated in a stable manner as they progress through residency, and individual residents deviating from this rating structure warrant consideration by program leaders.

Competency-Based Medical Education in Radiology: A Survey of Medical Student Perceptions

2020 ◽  
pp. 084653711989366
Author(s):  
Joseph Yang ◽  
Danny Jomaa ◽  
Omar Islam ◽  
Benedetto Mussari ◽  
Corinne Laverty ◽  
...  
Keyword(s):  
Medical Education ◽  
Medical Student ◽  
Medical Students ◽  
Student Perceptions ◽  
Diagnostic Radiology ◽  
Competency Based Medical Education ◽  
Residency Programs ◽  
Radiology Residency ◽  
Competency Based ◽  
Almost All

Purpose: Implementing competency-based medical education in diagnostic radiology residencies will change the paradigm of learning and assessment for residents. The objective of this study is to evaluate medical student perceptions of competency-based medical education in diagnostic radiology programs and how this may affect their decision to pursue a career in diagnostic radiology. Methods: First-, second-, and third-year medical students at a Canadian university were invited to complete a 14-question survey containing a mix of multiple choice, yes/no, Likert scale, and open-ended questions. This aimed to collect information on students’ understanding and perceptions of competency-based medical education and how the transition to competency-based medical education would factor into their decision to enter a career in diagnostic radiology. Results: The survey was distributed to 300 medical students and received 63 responses (21%). Thirty-seven percent of students had an interest in pursuing diagnostic radiology that ranged from interested to committed and 46% reported an understanding of competency-based medical education and its learning approach. The implementation of competency-based medical education in diagnostic radiology programs was reported to be a positive factor by 70% of students and almost all reported that breaking down residency into measurable milestones and required case exposure was beneficial. Conclusions: This study demonstrates that medical students perceive competency-based medical education to be a beneficial change to diagnostic radiology residency programs. The changes accompanying the transition to competency-based medical education were favored by students and factored into their residency decision-making.

scholarly journals A Further Step to Develop Patient-Friendly Implementation Strategies for Virtual Reality–Based Rehabilitation in Patients With Acute Stroke

2016 ◽  
Vol 96 (10) ◽  
pp. 1554-1564 ◽  
Author(s):  
Minyoung Lee ◽  
Sung-Bom Pyun ◽  
Jinjoo Chung ◽  
Jungjin Kim ◽  
Seon-Deok Eun ◽  
...  
Keyword(s):  
Virtual Reality ◽  
Acute Stroke ◽  
Early Stage ◽  
Implementation Strategies ◽  
Early Mobilization ◽  
Factors Affecting ◽  
Perceived Difficulty ◽  
Training Mode ◽  
And Performance ◽  
Major Factors

AbstractBackgroundVirtual reality (VR)–based rehabilitation is gaining attention as a way to promote early mobilization in patients with acute stroke. However, given the motor weakness and cognitive impairment associated with acute stroke, implementation strategies for overcoming patient-perceived difficulty need to be developed to enhance their motivation for training.ObjectiveThe purpose of this study was to explore patient-perceived difficulty and enjoyment during VR-based rehabilitation and the factors affecting those experiences.DesignAn exploratory mixed-method design was used in this study.MethodsEight individuals with acute stroke participated in 2 training modes of VR-based rehabilitation (ie, workout and game modes) 20 to 30 minutes per day for 5 to 8 sessions. A visual analog scale was used to assess patient-perceived difficulty and enjoyment at every session. Then semistructured interviews were conducted to explore the factors affecting those experiences.ResultsLevels of difficulty and enjoyment varied depending on the training mode and participants' phases of recovery. Five major factors were identified as affecting those varied experiences: (1) ease of following the directions, (2) experience of pain, (3) scores achieved, (4) novelty and immediate feedback, and (5) self-perceived effectiveness.ConclusionsLevels of difficulty and enjoyment during VR-based rehabilitation differed depending on the phases of recovery and training mode. Therefore, graded implementation strategies for VR-based rehabilitation are necessary for overcoming patient-perceived difficulty and enhancing enjoyment. Ease of following the directions might be best considered in the very early stage, whereas multisensory feedback may be more necessary in the later stage. Health professionals also should find a way for patients to avoid pain during training. Feedback, such as knowledge of results and performance, should be used appropriately.

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