ResearchConnect.info: An Interactive Web-Based Platform for Building Academic Collaborations.

RATIONALE AND OBJECTIVES: Radiology is a rapidly evolving field that benefits from continuous innovation and research participation among trainees. Traditional methods for involving residents in research are often inefficient and limited, usually due to the absence of a standardized approach to identifying available research projects. A centralized online platform can enhance networking and offer equal opportunities for all residents. MATERIALS AND METHODS: Research Connect is an online platform built with PHP, SQL, and JavaScript. Features include project and collaboration listing as well as advertisement of project openings to medical/undergraduate students, residents, and fellows. The automated system maintains project data and sends notifications for new research opportunities when they meet user preference criteria. Both pre- and post-launch surveys were used to assess the platform's efficacy. RESULTS: Before the introduction of Research Connect, 69% of respondents used informal conversations as their primary method of discovering research opportunities. One year after its launch, Research Connect had 141 active users, comprising 63 residents and 41 faculty members, along with 85 projects encompassing various radiology subspecialties. The platform received a median satisfaction rating of 4 on a 1-5 scale, with 54% of users successfully locating projects of interest through the platform. CONCLUSION: Research Connect addresses the need for a standardized method and centralized platform with active research projects and is designed for scalability. Feedback suggests it has increased the visibility and accessibility of radiology research, promoting greater trainee involvement and academic collaboration.

Checklist for Reproducibility of Deep Learning in Medical Imaging.

The application of deep learning (DL) in medicine introduces transformative tools with the potential to enhance prognosis, diagnosis, and treatment planning. However, ensuring transparent documentation is essential for researchers to enhance reproducibility and refine techniques. Our study addresses the unique challenges presented by DL in medical imaging by developing a comprehensive checklist using the Delphi method to enhance reproducibility and reliability in this dynamic field. We compiled a preliminary checklist based on a comprehensive review of existing checklists and relevant literature. A panel of 11 experts in medical imaging and DL assessed these items using Likert scales, with two survey rounds to refine responses and gauge consensus. We also employed the content validity ratio with a cutoff of 0.59 to determine item face and content validity. Round 1 included a 27-item questionnaire, with 12 items demonstrating high consensus for face and content validity that were then left out of round 2. Round 2 involved refining the checklist, resulting in an additional 17 items. In the last round, 3 items were deemed non-essential or infeasible, while 2 newly suggested items received unanimous agreement for inclusion, resulting in a final 26-item DL model reporting checklist derived from the Delphi process. The 26-item checklist facilitates the reproducible reporting of DL tools and enables scientists to replicate the study's results.

Large-Scale Telemedicine Implementation for Outpatient Clinicians: Results From a Pandemic-Adapted Learning Collaborative.

Learning collaboratives are seldom used outside of health care quality improvement. We describe a condensed, 10-week learning collaborative ("Telemedicine Hack") that facilitated telemedicine implementation for outpatient clinicians early in the COVID-19 pandemic. Live attendance averaged 1688 participants per session. Of 1005 baseline survey respondents, 57% were clinicians with one-third identifying as from a racial/ethnic minoritized group. Practice characteristics included primary care (71%), rural settings (51%), and community health centers (28%). Of three surveys, a high of 438 (81%) of 540 clinicians had billed ≥1 video-based telemedicine visit. Our learning collaborative "sprint" is a promising model for scaling knowledge during emergencies and addressing health inequities.

Resource Document on Best Practices in Synchronous Videoconferencing-Based Telemental Health.

Background: This document represents an updated collaboration between the American Psychiatric Association (APA) and the American Telemedicine Association (ATA) to create a consolidated update of the previous APA and ATA official documents and resources in telemental health, to provide a single guide on clinical best practices for providing mental health services through synchronous videoconference. Methods: A joint writing committee drawn from the APA Committee on Telepsychiatry and the ATA TMH Special Interest Group (TMH SIG). was convened to draft and finalize the guidelines. This document draws directly from the 2018 APA/ATA guide and the ATA s previous guidelines, selecting from key statements/guidelines, consolidating them across documents, and then updating them where indicated. Guideline approval was provided following internal review by the APA, the ATA, the Board of Directors of the ATA, and the Joint Reference Committee of the APA. Results: The guidelines contain requirements, recommendations, and actions that are identified by text containing the keywords "shall," "should," or "may." Conclusions: Compliance with these recommendations will not guarantee accurate diagnoses or successful outcomes. The purpose of this guide is to assist providers in providing effective and safe medical care founded on expert consensus, research evidence, available resources, and patient needs.

Special Issue Editorial: Medical Image Perception and Observer Performance.

The editorial introduces the JMI Special Section on Medical Image Perception and Observer Performance.

Achieving Digital Health Equity by Personalizing the Patient Experience.

Background: COVID saw a significant increase in the use of virtual care, supporting its utility and its benefits. It also revealed that unfortunately there are limitations and gaps we still need to address, including inequitable access to digitally enabled health care tools. Methods: On November 8, 2022, the Mass General Brigham held the Third Annual Virtual Care Symposium: Demystifying Clinical Appropriateness in Virtual Care and What's Ahead for Pay Parity. One panel addressed digital health equity and key points are summarized here. Results: Four experts discussed the key domains of digital equity and inclusion in the session titled "Achieving Digital Health Equity: Is it a One-Size-Fits-All Approach or Personalized Patient Experience?" These included lessons from strategies and tactics being used by hospitals and health systems to address digital equity issues; and opportunities to achieve digital health equity for specific populations (e.g., Medicaid). Conclusions: Understanding the drivers of digital health disparities can help organizations and health care systems develop and test strategies to reduce them and improve access to quality health care through digitally enabled technologies and delivery channels.

Best Practices for Technology in Clinical Social Work and Mental Health Professions to Promote Well-being and Prevent Fatigue.

The shift to communication technologies during the pandemic has had positive and negative effects on clinical social worker practice. Best practices are identified for clinical social workers to maintain emotional well-being, prevent fatigue, and avoid burnout when using technology. A scoping review from 2000 to 21 of 15 databases focused on communication technologies for mental health care within four areas: (1) behavioral, cognitive, emotional, and physical impact; (2) individual, clinic, hospital, and system/organizational levels; (3) well-being, burnout, and stress; and (4) clinician technology perceptions. Out of 4795 potential literature references, full text review of 201 papers revealed 37 were related to technology impact on engagement, therapeutic alliance, fatigue and well-being. Studies assessed behavioral (67.5%), emotional (43.2%), cognitive (57.8%), and physical (10.8%) impact at the individual (78.4%), clinic (54.1%), hospital (37.8%) and system/organizational (45.9%) levels. Participants were clinicians, social workers, psychologists, and other providers. Clinicians can build a therapeutic alliance via video, but this requires additional skill, effort, and monitoring. Use of video and electronic health records were associated with clinician physical and emotional problems due to barriers, effort, cognitive demands, and additional workflow steps. Studies also found high user ratings on data quality, accuracy, and processing, but low satisfaction with clerical tasks, effort required and interruptions. Studies have overlooked the impact of justice, equity, diversity and inclusion related to technology, fatigue and well-being, for the populations served and the clinicians providing care. Clinical social workers and health care systems must evaluate the impact of technology in order to support well-being and prevent workload burden, fatigue, and burnout. Multi-level evaluation and clinical, human factor, training/professional development and administrative best practices are suggested.

Decoding radiology reports: Potential application of OpenAI ChatGPT to enhance patient understanding of diagnostic reports.

PURPOSE: To evaluate the complexity of diagnostic radiology reports across major imaging modalities and the ability of ChatGPT (Early March 2023 Version, OpenAI, California, USA) to simplify these reports to the 8th grade reading level of the average U.S. adult. METHODS: We randomly sampled 100 radiographs (XR), 100 ultrasound (US), 100 CT, and 100 MRI radiology reports from our institution's database dated between 2022 and 2023 (N = 400). These were processed by ChatGPT using the prompt "Explain this radiology report to a patient in layman's terms in second person: ". Mean report length, Flesch reading ease score (FRES), and Flesch-Kincaid reading level (FKRL) were calculated for each report and ChatGPT output. T-tests were used to determine significance. RESULTS: Mean report length was 164 ± 117 words, FRES was 38.0 ± 11.8, and FKRL was 10.4 ± 1.9. FKRL was significantly higher for CT and MRI than for US and XR. Only 60/400 (15%) had a FKRL <8.5. The mean simplified ChatGPT output length was 103 ± 36 words, FRES was 83.5 ± 5.6, and FKRL was 5.8 ± 1.1. This reflects a mean decrease of 61 words (p < 0.01), increase in FRES of 45.5 (p < 0.01), and decrease in FKRL of 4.6 (p < 0.01). All simplified outputs had FKRL <8.5. DISCUSSION: Our study demonstrates the effective use of ChatGPT when tasked with simplifying radiology reports to below the 8th grade reading level. We report significant improvements in FRES, FKRL, and word count, the last of which requires modality-specific context.

Image quality assessment of advanced reconstruction algorithm for point-of-care MRI scanner.

Purpose: Portable magnetic resonance imaging (pMRI) has potential to rapidly acquire images at the patients' bedside to improve access in locations lacking MRI devices. The scanner under consideration has a magnetic field strength of 0.064 T, thus image-processing algorithms to improve image quality are required. Our study evaluated pMRI images produced using a deep learning (DL)-based advanced reconstruction scheme to improve image quality by reducing image blurring and noise to determine if diagnostic performance was similar to images acquired at 1.5 T. Approach: Six radiologists viewed 90 brain MRI cases (30 acute ischemic stroke (AIS), 30 hemorrhage, 30 no lesion) with T1, T2, and fluid attenuated inversion recovery sequences, once using standard of care (SOC) images (1.5 T) and once using pMRI DL-based advanced reconstruction images. Observers provided a diagnosis and decision confidence. Time to review each image was recorded. Results: Receiver operating characteristic area under the curve revealed overall no significant difference (p=0.0636) between pMRI and SOC images. Examining each abnormality, for acute ischemic stroke, there was a significant difference (p=0.0042) with SOC better than pMRI; but for hemorrhage, there was no significant difference (p=0.1950). There was no significant difference in viewing time for pMRI versus SOC (p=0.0766) or abnormality (p=0.3601). Conclusions: The deep learning (DL)-based reconstruction scheme to improve pMRI was successful for hemorrhage, but for acute ischemic stroke the scheme could still be improved. For neurocritical care especially in remote and/or resource poor locations, pMRI has significant clinical utility, although radiologists should be aware of limitations of low-field MRI devices in overall quality and take that into account when diagnosing. As an initial triage to aid in the decision of whether to transport or keep patients on site, pMRI images likely provide enough information.

Institutional Strategies to Maintain and Grow Imaging Research During the COVID-19 Pandemic.

Understanding imaging research experiences, challenges, and strategies for academic radiology departments during and after COVID-19 is critical to prepare for future disruptive events. We summarize key insights and programmatic initiatives at major academic hospitals across the world, based on literature review and meetings of the Radiological Society of North America Vice Chairs of Research (RSNA VCR) group. Through expert discussion and case studies, we provide suggested guidelines to maintain and grow radiology research in the postpandemic era.

Eye-tracking differences between free text and template radiology reports: a pilot study.

Purpose: One possible limitation of structured template radiology reports is that radiologists look back and forth between viewing and dictation monitors, thereby impacting the length of time spent reviewing images and generating a report. We hypothesize that the total time spent viewing case images is diminished and/or the total time spent creating a report is prolonged when the report is generated using a structured template compared with free text format. Approach: Three neuroradiologists and three senior residents viewed five brain magnetic resonance imaging cases with unique findings while eye position was recorded. Participants generated reports for each case utilizing both structured templates and free text dictation. The time spent viewing images was compared with the time spent looking at the dictation screen. Results: The two main hypotheses were confirmed: the total time viewing images diminished with templates versus free text dictation and the total time to create a report was prolonged with templates. The mean time (s) spent on the "image" region of interest approached statistical significance as a function of the report type [free: attendings = 236.79 (154.43), residents = 223.55 (77.79); template: attendings = 163.40 (73.42), residents = 182.48 (77.47)] and was overall lower with the template reporting for both attendings and residents ( F = 3.77 , p = 0.0623 ), but it did not differ as a function of seniority ( F = 0.017 , p = 0.8977 ). Conclusions: Template-based radiology reports have significant potential to alter the way radiologists view images and report on them, spending more time viewing the report monitor rather than diagnostic images compared with free text dictation. Many radiologists prefer templates for reporting as the structured format may aid in conducting a more systematic or thorough search for findings, although prior work on this assumption is mixed. Future eye-tracking studies could further elucidate whether and how templates and free reports impact the detection and classification of radiographic findings.

Quality Frameworks for Virtual Care: Expert Panel Recommendations.

Given the significant advance of virtual care in the past year and half, it seems timely to focus on quality frameworks and how they have evolved collaboratively across health care organizations. Massachusetts General Hospital's (MGH) Center for TeleHealth and Mass General Brigham's (MGB) Virtual Care Program are committed to hosting annual symposia on key topics related to virtual care. Subject matter experts across the country, health care organizations, and academic medical centers are invited to participate. The inaugural MGH/MGB Virtual Care Symposium, which focused on rethinking curriculum, competency, and culture in the virtual care era, was held on September 2, 2020. The second MGH/MGB Virtual Care Symposium was held on November 2, 2021, and focused on virtual care quality frameworks. Resultant topics were (1) guiding principles necessary for the future of virtual care measurement; (2) best practices deployed to measure quality of virtual care and how they compare and align with in-person frameworks; (3) evolution of quality frameworks over time; (4) how increased adoption of virtual care has impacted patient access and experience and how it has been measured; (5) the pitfalls and barriers which have been encountered by organizations in developing virtual care quality frameworks; and (6) examples of how quality frameworks have been applied in various use cases. Common elements of a quality framework for virtual care programs among symposium participants included improving the patient and provider experience, a focus on achieving health equity, monitoring success rates and uptime of the technical elements of virtual care, financial stewardship, and clinical outcomes. Virtual care represents an evolution in the access to care paradigm that helps keep health care aligned with other modern industries in digital technology and systems adoption. With advances in health care delivery models, it is vitally important that the quality measurement systems be adapted to include virtual care encounters. New methods may be necessary for asynchronous transactions, but synchronous virtual visits and consults can likely be accommodated in traditional quality frameworks with minimal adjustments. Ultimately, quality frameworks for health care will adapt to hybrid in-person and virtual care practices.

Artificial Intelligence/Machine Learning Education in Radiology: Multi-institutional Survey of Radiology Residents in the United States.

RATIONALE AND OBJECTIVES: To evaluate radiology residents' perspectives regarding inclusion of artificial intelligence/ machine learning (AI/ML) education in the residency curriculum. MATERIALS AND METHODS: An online anonymous survey was sent to 759 residents at 21 US radiology residency programs. Resident demographics, sub-specialty interests, educational background and research experiences, as well as the awareness, availability, and usefulness of various resources for AI/ML education were collected. RESULTS: The survey response rate was 27% (209/759). A total of 74% of respondents were male, 80% were training at large university programs, and only a minority (<20) had formal education or research experience in AI/ML. All four years of training were represented (range: 20%-38%). The majority of the residents agreed or strongly agreed (83%) that AI/ML education should be a part of the radiology residency curriculum and that such education should equip them with the knowledge to troubleshoot an AI tool in practice / determine whether a tool is working as intended (82%). Among the residency programs that offer AI/ML education, the most common resources were lecture series (43%), national informatics courses (28%), and in-house/institutional courses (26%). About 24% of the residents reported no AI/ML educational offerings in their residency curriculum. Hands on AI/ML laboratory (67%) and lecture series (61%) were reported as the most beneficial or effective. The majority of the residents preferred AI/ML education offered as a continuous course spanning the radiology residency (R1 to R4) (76%), followed by mini fellowship during R4 (32%) and as a course during PGY1 (21%). CONCLUSION: Residents largely favor the inclusion of formal AI/ML education in the radiology residency curriculum, prefer hands-on learning and lectures as learning tools, and prefer a continuous AI/ML course spanning R1-R4.

Prevalence of Novel Pedagogical Methods in the Radiology Education of Medical Students.

OBJECTIVES: Radiology education is essential in medical school; however, developing an integrated and comprehensive curriculum remains a challenge. Many novel methods have been implemented with varying outcomes. In this study, the authors sought to examine published pedagogical methods of radiology instruction and query US academic faculty members on their current use within radiology education. METHODS: A literature search for current and novel pedagogical methods of radiology instruction was performed and studies were assessed for positive educational outcomes. Educational approaches were grouped according to encountered themes. A survey was distributed to faculty members of the Alliance of Medical Student Educators in Radiology to ascertain the prevalence of these pedagogical methods in the radiology education of medical students. RESULTS: The following themes were encountered: supplemental instruction of anatomy and pathology; radiology-clinical correlation electives; flipped classrooms; hands-on and simulation training; peer-to-peer learning; e-learning; adaptive tutorials; and asynchronous learning. Of the survey respondents, 90% reported that their institution offers a formal radiology clerkship. The majority of respondents reported the use of flipped classrooms (70%) and e-learning (78%); however, few reported offering hands-on clinical experiences (31%) and simulation-based training (36%). Only 5% reported use of adaptive tutorials. CONCLUSIONS: In the review of the literature, a combination of hands-on, case-based, team-based, and didactic training, in addition to other forms of active learning within an integrated curriculum, was found to be highly effective and preferred by students and faculty. Virtual and in-person learning incorporating modern technology was found to either increase knowledge and skills or yield similar outcomes as traditional in-person instruction. These methods are currently heterogeneously used across the US medical schools represented by survey respondents, with utilization ranging from 5% to 78%.

Fifty years of SPIE Medical Imaging proceedings papers.

Purpose: To commemorate the 50th anniversary of the first SPIE Medical Imaging meeting, we highlight some of the important publications published in the conference proceedings. Approach: We determined the top cited and downloaded papers. We also asked members of the editorial board of the Journal of Medical Imaging to select their favorite papers. Results: There was very little overlap between the three methods of highlighting papers. The downloads were mostly recent papers, whereas the favorite papers were mostly older papers. Conclusions: The three different methods combined provide an overview of the highlights of the papers published in the SPIE Medical Imaging conference proceedings over the last 50 years.

Mandating Limits on Workload, Duty, and Speed in Radiology.

Research has not yet quantified the effects of workload or duty hours on the accuracy of radiologists. With the exception of a brief reduction in imaging studies during the 2020 peak of the COVID-19 pandemic, the workload of radiologists in the United States has seen relentless growth in recent years. One concern is that this increased demand could lead to reduced accuracy. Behavioral studies in species ranging from insects to humans have shown that decision speed is inversely correlated to decision accuracy. A potential solution is to institute workload and duty limits to optimize radiologist performance and patient safety. The concern, however, is that any prescribed mandated limits would be arbitrary and thus no more advantageous than allowing radiologists to self-regulate. Specific studies have been proposed to determine whether limits reduce error, and if so, to provide a principled basis for such limits. This could determine the precise susceptibility of individual radiologists to medical error as a function of speed during image viewing, the maximum number of studies that could be read during a work shift, and the appropriate shift duration as a function of time of day. Before principled recommendations for restrictions are made, however, it is important to understand how radiologists function both optimally and at the margins of adequate performance. This study examines the relationship between interpretation speed and error rates in radiology, the potential influence of artificial intelligence on reading speed and error rates, and the possible outcomes of imposed limits on both caseload and duty hours. This review concludes that the scientific evidence needed to make meaningful rules is lacking and notes that regulating workloads without scientific principles can be more harmful than not regulating at all.

Findings and Guidelines on Provider Technology, Fatigue, and Well-being: Scoping Review.

BACKGROUND: Video and other technologies are reshaping the delivery of health care, yet barriers related to workflow and possible provider fatigue suggest that a thorough evaluation is needed for quality and process improvement. OBJECTIVE: This scoping review explored the relationship among technology, fatigue, and health care to improve the conditions for providers. METHODS: A 6-stage scoping review of literature (from 10 databases) published from 2000 to 2020 that focused on technology, health care, and fatigue was conducted. Technologies included synchronous video, telephone, informatics systems, asynchronous wearable sensors, and mobile health devices for health care in 4 concept areas related to provider experience: behavioral, cognitive, emotional, and physical impact; workplace at the individual, clinic, hospital, and system or organizational levels; well-being, burnout, and stress; and perceptions regarding technology. Qualitative content, discourse, and framework analyses were used to thematically analyze data for developing a spectrum of health to risk of fatigue to manifestations of burnout. RESULTS: Of the 4221 potential literature references, 202 (4.79%) were duplicates, and our review of the titles and abstracts of 4019 (95.21%) found that 3837 (90.9%) were irrelevant. A full-text review of 182 studies revealed that 12 (6.6%) studies met all the criteria related to technology, health care, and fatigue, and these studied the behavioral, emotional, cognitive, and physical impact of workflow at the individual, hospital, and system or organizational levels. Video and electronic health record use has been associated with physical eye fatigue; neck pain; stress; tiredness; and behavioral impacts related to additional effort owing to barriers, trouble with engagement, emotional wear and tear and exhaustion, cognitive inattention, effort, expecting problems, multitasking and workload, and emotional experiences (eg, anger, irritability, stress, and concern about well-being). An additional 14 studies that evaluated behavioral, emotional, and cognitive impacts without focusing on fatigue found high user ratings on data quality, accuracy, and processing but low satisfaction with clerical tasks, the effort required in work, and interruptions costing time, resulting in more errors, stress, and frustration. Our qualitative analysis suggests a spectrum from health to risk and provides an outline of organizational approaches to human factors and technology in health care. Business, occupational health, human factors, and well-being literature have not studied technology fatigue and burnout; however, their findings help contextualize technology-based fatigue to suggest guidelines. Few studies were found to contextually evaluate differences according to health professions and practice contexts. CONCLUSIONS: Health care systems need to evaluate the impact of technology in accordance with the Quadruple Aim to support providers' well-being and prevent workload burden, fatigue, and burnout. Implementation and effectiveness approaches and a multilevel approach with objective measures for clinical, human factors, training, professional development, and administrative workflow are suggested. This requires institutional strategies and competencies to integrate health care quality, technology and well-being outcomes.

Overview of Noninterpretive Artificial Intelligence Models for Safety, Quality, Workflow, and Education Applications in Radiology Practice.

Artificial intelligence has become a ubiquitous term in radiology over the past several years, and much attention has been given to applications that aid radiologists in the detection of abnormalities and diagnosis of diseases. However, there are many potential applications related to radiologic image quality, safety, and workflow improvements that present equal, if not greater, value propositions to radiology practices, insurance companies, and hospital systems. This review focuses on six major categories for artificial intelligence applications: study selection and protocoling, image acquisition, worklist prioritization, study reporting, business applications, and resident education. All of these categories can substantially affect different aspects of radiology practices and workflows. Each of these categories has different value propositions in terms of whether they could be used to increase efficiency, improve patient safety, increase revenue, or save costs. Each application is covered in depth in the context of both current and future areas of work. Keywords: Use of AI in Education, Application Domain, Supervised Learning, Safety © RSNA, 2022.