基于病例的学习模式促进实习医师神经解剖定位诊断学习

目的 探讨传统教学、基于病例的学习（CBL）及微信辅助教学对神经内科实习医生定位诊断临床思维的影响，寻找最适合的临床教学方式。方法 对2017年1月至2018年2月期间在我科进行轮转的实习医师按随机分组方式进行传统教学、CBL及微信辅助教学不同顺序组合的床边教学培训，并在培训结束后进行问卷调查及访谈了解不同教学方法在神经系统疾病定位诊断思维、技能、对神经病学的态度三方面对受训者的影响。结果 纳入实习生37名，共回收有效问卷33份；教师12名，有效问卷12份。除在促进体格检查技能领域外（P=0.145），利用CBL进行教学在神经解剖定位诊断思维、教学安排、激发兴趣及帮助应试的领域中的得分均显著高于微信辅助教学和传统教学，各组之间的差异有统计学意义（P<0.05）。结论 相对于传统教学和微信辅助教学，CBL学习模式更能实习医师掌握神经内科的定位诊断思维，激发对神经科学的兴趣。     

北京协和医院超声医学科医患沟通现状及培训需求调查

目的调查北京协和医院超声医学科医师对医患沟通相关培训的教学需求。方法北京协和医院超声医学科对2013~2014年期间在本科室工作的128名医师发放调查问卷,对医患沟通中是否遇到负面经历、对建立和谐医患关系的信心、对相关培训的需求程度进行评分。结果共获得125份有效问卷。超声医学科医师44.8%曾经历医患沟通负面经历,仅36.0%对自己的医患沟通能力感到相当自信或非常自信,83.2%认为应当开展医患沟通的相关培训。结论医患沟通培训对于超声医师非常重要。     

检验医师培养的实践探讨

采取临床科室轮转学习、临床教学查房、临床会诊和病例讨论、检验项目和质量控制宣传教育、定期学术汇报、规范考核制度以及加强科研创新能力培养等多种形式进行检验医师规范化培训,积极探索检验医师临床工作模式,并对我国检验医师培养面临的问题进行剖析与思考。     

日常病例报告评价及反馈系统在超声住院医师培训中的应用

目的探讨日常病例报告评价及反馈系统在超声住院医师规范化培训中的应用价值。方法按照规范化操作、规范留图、体表标记、彩色血流、语言表达及报告书写等方面制定住院医师考核评分评估的方法;按照评分方法对培训后的住院医师的日常病例报告进行评分,并对病例进行定期点评反馈;以往届未经过培训的住院医师同时期的诊断报告为对照,在以上方面及常规定期考核成绩进行配对t检验,判断同期水平住院医师报告诊断水平有无差别。结果经过日常病例报告评价及反馈系统培训后的住院医师临床报告各方面的评分(总分46.70)水平均较对照组的评分(总分40.70)有提高(P0.05);住院医师在超声诊断及报告水平提高方面的自我评价结果满意率为90%。结论日常病例报告评价与反馈系统在临床教学实践中具有较高的可行性,可以有效地提高医师培训的质量。     

麻省大学医学院神经内科住院医师轮转设计及课程设置的启示

目前中国全国统一的神经内科住院医师规培体系已初步建立,但住院医师具体轮转设计和课程设置尚有改善空间。美国的住院医师规范化培训以保证住院医师规范化培训质量为前提,对其轮转设计和课程设置进行统筹规划。在赴美3个月的交流中,作者复习了美国毕业后医学教育委员会(ACGME)的有关文件和麻省大学(UMASS)医学院神经内科住院医师规范化培训细则,得到以下启示:轮转设计要满足住院医师能力培养渐行式上升的要求;增加门诊轮转弥补单纯病房轮转的不足;课程设置方面,除临床知识和技能外,还要满足其他核心胜任力培养;要合理应用多种不同的教学形式培养住院医师多方面能力。     

外科医师麻醉科轮转带教体会

临床医师轮转培训是住院医师正规化培训体系的重要组成部分.因为麻醉工作在危重急症监护处理、疼痛治疗和外科疾病手术治疗中的重要性、特殊性和密切相关性,外科医师在麻醉科的轮转培训显得尤为重要.如何使轮转医师对临床常用危重急救技术和专业相关疾病的临床麻醉,以及围麻醉期可能出现的各种并发症的诊断、处理等情况有所了解和掌握,是带教工作的重点.我科对多年进行麻醉学带教的教学体会进行总结,现报告如下.     

“寓教于练”在肌肉骨骼超声影像教学中的应用探索

目的讨论在肌肉骨骼超声影像诊断培训中应用"寓教于练"方法的教学效果。方法通过对30名参加培训的进修医师进行一个月的"寓教于法"教学,其培养效果通过客观考题和问卷调查进行评估。结果 30名进修医师在培训后试卷的正确率较培训前显著提高(P0.05)。进修医生对于整个培训内容方面的综合评价,满意度为100%,其中非常满意为70%,满意为30%。对于自己在肌骨超声诊断水平提高方面的自我评价结果为非常满意45%,满意为40%,不满意为15%。结论通过"寓教于练"方法的实施,能够让进修医师尽快掌握肌肉骨骼的超声解剖理论知识,具备常见肌肉骨骼系统疾病的独立诊断能力。     

PET/CT诊断报告书写规范探讨

根据解放军总医院核医学科PET/CT中心多年的临床工作和教学经验以及同道的带教精华,对PET/CT诊断报告的要素加以总结归纳。从PET/CT诊断报告的临床作用到报告正确书写的各细节中提出了一套有序、详细及规范的PET/CT诊断报告的书写方法和要求。我科PET/CT诊断报告书写规范对本专业或临床医师、研究生、进修生及规培生都有不同的参考价值。规范的PET/CT诊断报告还将成为医疗、教学、科研及法律有关的客观资料。     

医院信息管理在核医学科中的应用

目的构建笔者所在医院核医学影像图像获取与传输系统（PACS）,实现影像科室的数字化和网络化。方法从科室的实际情况出发,结合笔者医院放射科、CT、MRI等产生的图像的采集、显示、储存、传送和管理的成功经验．阐述了Medex系统在核医学科的建立及应用。结果PACS的实施提高了工作效率及管理水平,推动了医生工作模式的变革：方便了工作、科研和学习;提高了教学质量。规范化、计算机化的诊断报告质量优于人工书写报告。结论Medex系统的建立及应用促进核医学科工作的改观。     

医院影像管理软件系统的设计和应用

医院影像中心诊断报告（以下简称报告）的书写及相关统计工作是医院影像诊断科室日常工作的重要组成部分。如何快速、准确、方便地书写标准规范的影像报告,既减轻医师的工作强度,又满足影像科室科学化管理的需要,成为当前医院影像诊断科室工作的一个重要方面。为此,我...     

Clinical impact of picture archiving and communication systems: Evaluation of a prototype system

Opinion surveys were gathered before and 6 months after installation of a prototype picture archiving and communication system (PACS) (PACS/1, Siemens Medical Systems, Iselin, NJ). Median turnaround times and the percent of delayed or missing reports were calculated for 1,026 baseline and 8,438 follow-up studies at 6 months. Neuroradiological (neuro) computed tomography (CT) used PACS, while neuro magnetic resonance (MR), body CT, and body MR served as controls. The opinion surveys showed improved service in all categories, including those not directly affected by PACS. PACS images favorably impressed 86% of respondents, but most considered the system too slow, unreliable, and the storage capacity too low. A majority of 81% recommended against purchase of PACS now. There was an overall increase in the median report turnaround time for both neuro CT and the controls. Neuro CT showed a 41% decrease in delayed or missing reports, but controls also showed similar decreases. The effects of this prototype PACS on turnaround time or on report delivery could not be distinguished from section-wide changes in CT and MR services. Future improvements in PACS should vigorously address increased speed, reliability, and storage capacity.     

Who PACS a Punch? The Role of the Picture Archiving and Communication System/Radiology Information System (PACS/RIS) in Quantifying Experiential Learning in Radiology Residency

The clinical logbook is the currently accepted tool for evaluating experiential learning (EL) in postgraduate radiology training programs internationally. The role of the picture archiving and communication system/radiology information system (PACS/RIS) in defining the complete EL portfolio of radiology residents has not been explored. To conduct a PACS/RIS-based analysis of the comprehensive clinical outputs of radiology residents, and to correlate outputs with residency recruitment criteria and exit examination performance. Retrospective, customized searches of the institutional PACS/RIS were conducted to determine the clinical outputs of radiology residents completing a standardized 4-year training program at a single institution in a middle-income country. The association between outputs and prior-to-residency radiology experience, prior-to residency completion of the primary examination, and performance in the exit examination were determined. Fifteen residents were included. Average clinical output was 8286 cases, with a wide range (6268-10460). Total output was not associated with first-time exit examination success (p=0.16). Residents with prior radiology experience tended to greater success at first exit examination attempt (10/11, 91% versus 2/4, 50%; p=0.09), despite lower average outputs (8138 versus 8695). Outputs were not associated with prior completion of the radiology primary examination (8263 versus 8378; p=0.87). This first PACS/RIS-based analysis of the complete clinical outputs of any radiology residency training program provides important baseline educational data, with the potential to inform discourses on specialty training internationally. It demonstrates the potential for the modern PACS/RIS to supersede the traditional logbook and to serve as a comprehensive EL portfolio for radiology residents.     

QRSE: a Novel Metric for the Evaluation of Trainee Radiologist Reporting Skills

Diagnostic radiology training programs must produce highly skilled diagnostic radiologists capable of interpreting radiological examinations and communicating results to clinicians. Established training performance tools evaluate interpretive skills, but trainees' competency in reporting skills is also essential. Our semi-automated passive electronic tool entitled the Quantitative Reporting Skills Evaluation (QRSE) allows radiology training programs to evaluate the quantity of edits made to trainee preliminary reports by attending physicians as a metric to evaluate trainee reporting performance. Consecutive report pairs and metadata extracted from the radiology information system were anonymized and exported to a MySQL database. To perform the QRSE, for each report pair, open source software was first utilized to calculate the Levenshtein Percent (LP), the percent of character changes required to convert each preliminary report to its corresponding final report. The average LP (ALP), ALP for each trainee, and standard deviations were calculated. Eighty-four trainees and 56 attending radiologists interpreted 228,543 radiological examinations during the study period. The overall ALP was 6.38 %. Trainee-specific ALPs ranged from 1.1 to 15.3 %. Among trainee-specific ALPs, the standard deviation was 3.7 %. Our analysis identified five trainees with trainee-specific ALPs above 2 standard deviations from the mean and 14 trainees with trainee-specific ALPs less than 1 standard deviation below the mean. The QRSE methodology allows for the passive, quantitative, and longitudinal evaluation of the reporting skills of trainees during diagnostic radiology residency training. The QRSE identifies trainees with high and low levels of edits to their preliminary reports, as a marker for trainee overall reporting skills, and thus represents a novel performance metric for radiology training programs.     

A Qualitative Analysis of the Needs and Experiences of Hospital-based Clinicians when Accessing Medical Imaging

As digital imaging is now a common and essential tool in the clinical workflow, it is important to understand the experiences of clinicians with medical imaging systems in order to guide future development. The objective of this paper was to explore health professionals’ experiences, practices and preferences when using Picture Archiving and Communications Systems (PACS), to identify shortcomings in the existing technology and inform future developments. Semi-structured interviews are reported with 35 hospital-based healthcare professionals (3 interns, 11 senior health officers, 6 specialist registrars, 6 consultants, 2 clinical specialists, 5 radiographers, 1 sonographer, 1 radiation safety officer). Data collection took place between February 2019 and December 2020 and all data are analyzed thematically. A majority of clinicians report using PACS frequently (6+ times per day), both through dedicated PACS workstations, and through general-purpose desktop computers. Most clinicians report using basic features of PACS to view imaging and reports, and also to compare current with previous imaging, noting that they rarely use more advanced features, such as measuring. Usability is seen as a problem, including issues related to data privacy. More sustained training would help clinicians gain more value from PACS, particularly less experienced users. While the majority of clinicians report being unconcerned about sterility when accessing digital imaging, clinicians were open to the possibility of touchless operation using voice, and the ability to execute multiple commands with a single voice command would be welcomed.     

Quantitative Computed Tomography (QCT) as a Radiology Reporting Tool by Using Optical Character Recognition (OCR) and Macro Program

The objectives are (1) to introduce a new concept of making a quantitative computed tomography (QCT) reporting system by using optical character recognition (OCR) and macro program and (2) to illustrate the practical usages of the QCT reporting system in radiology reading environment. This reporting system was created as a development tool by using an open-source OCR software and an open-source macro program. The main module was designed for OCR to report QCT images in radiology reading process. The principal processes are as follows: (1) to save a QCT report as a graphic file, (2) to recognize the characters from an image as a text, (3) to extract the T scores from the text, (4) to perform error correction, (5) to reformat the values into QCT radiology reporting template, and (6) to paste the reports into the electronic medical record (EMR) or picture archiving and communicating system (PACS). The accuracy test of OCR was performed on randomly selected QCTs. QCT as a radiology reporting tool successfully acted as OCR of QCT. The diagnosis of normal, osteopenia, or osteoporosis is also determined. Error correction of OCR is done with AutoHotkey-coded module. The results of T scores of femoral neck and lumbar vertebrae had an accuracy of 100 and 95.4 %, respectively. A convenient QCT reporting system could be established by utilizing open-source OCR software and open-source macro program. This method can be easily adapted for other QCT applications and PACS/EMR.     

The iPad Tablet Computer for Mobile On-Call Radiology Diagnosis? Auditing Discrepancy in CT and MRI Reporting

Tablet computers such as the iPad, which have a large format, improved graphic display resolution and a touch screen interface, may have an advantage compared to existing mobile devices such as smartphones and laptops for viewing radiological images. We assessed their potential for emergency radiology teleconsultation by reviewing multi-image CT and MRI studies on iPad tablet computers compared to Picture Archival and Communication Systems (PACS) workstations. Annonymised DICOM images of 79 CT and nine MRI studies comprising a range of common on-call conditions, reported on full-featured diagnostic PACS workstation by one Reporting Radiologist, were transferred from PACS to three iPad tablet computers running OsiriX HD v 2.02 DICOM software and viewed independently by three reviewing radiologists. Structured documentation was made of major findings (primary diagnosis or other clinically important findings), minor findings (incidental findings), and user feedback. Two hundred and sixty four readings (88 studies read by three reviewing radiologists) were compared, with 3.4 % (nine of 264) major discrepancies and 5.6 % (15 of 264) minor discrepancies. All reviewing radiologists reported favorable user experience but noted issues with software stability and limitations of image manipulation tools. Our results suggest that emergency conditions commonly encountered on CT and MRI can be diagnosed using tablet computers with good agreement with dedicated PACS workstations. Shortcomings in software and application design should be addressed if the potential of tablet computers for mobile teleradiology is to be fully realized.     

Identifying Radiological Needs of Referring Clinicians

To provide prospective information about quality- and satisfaction-related product features in radiology, a customer-centered approach for acquiring clinicians' requirements and their prioritizations is essential. We introduced the Kano model for the first time in radiology to obtain such information. A Kano questionnaire, consisting of pairs of questions regarding 13 clinician requirements related to computed tomography (CT), magnetic resonance imaging (MRI) access and report turnaround time (RTT), was developed and administered. Each requirement was assigned a Kano category, and its satisfaction and dissatisfaction coefficients were calculated and presented in a Kano diagram. The data were stratified based on different clinics and on staff and resident clinicians. The time interval was evaluated between the completion of an examination and the first attempt to access the report by a clinician. Consultation for modality selection and scheduling and access to CT within 24 h and RTT within 8 to 24 h were considered as must-be requirements. Access to CT within 4 h and within 8 h, access to MRI within 8 h and within 24 h, and access to RTT within 4 h were one-dimensional requirements. The extension of operation time for CT or MRI, as well as MRI access within 4 h, was considered attractive. Eight out of nine clinics considered RTT within 8 h as a must-be requirement. There were differences in responses both among different clinics and between staff and resident clinicians. Access attempts to reports by clinicians in the first 4 h after the examination completion accounted for 65 % of CTs and 49 % of MRIs.     

Automatic detection of patients with invasive fungal disease from free-text computed tomography (CT) scans

BackgroundInvasive fungal diseases (IFDs) are associated with considerable health and economic costs. Surveillance of the more diagnostically challenging invasive fungal diseases, specifically of the sino-pulmonary system, is not feasible for many hospitals because case finding is a costly and labour intensive exercise. We developed text classifiers for detecting such IFDs from free-text radiology (CT) reports, using machine-learning techniques.MethodWe obtained free-text reports of CT scans performed over a specific hospitalisation period (2003–2011), for 264 IFD and 289 control patients from three tertiary hospitals. We analysed IFD evidence at patient, report, and sentence levels. Three infectious disease experts annotated the reports of 73 IFD-positive patients for language suggestive of IFD at sentence level, and graded the sentences as to whether they suggested or excluded the presence of IFD. Reliable agreement between annotators was obtained and this was used as training data for our classifiers. We tested a variety of Machine Learning (ML), rule based, and hybrid systems, with feature types including bags of words, bags of phrases, and bags of concepts, as well as report-level structured features. Evaluation was carried out over a robust framework with separate Development and Held-Out datasets.ResultsThe best systems (using Support Vector Machines) achieved very high recall at report- and patient-levels over unseen data: 95% and 100% respectively. Precision at report-level over held-out data was 71%; however, most of the associated false-positive reports (53%) belonged to patients who had a previous positive report appropriately flagged by the classifier, reducing negative impact in practice.ConclusionsOur machine learning application holds the potential for developing systematic IFD surveillance systems for hospital populations.