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Objectives To develop and validate a novel measure, action entropy, for assessing the cognitive effort associated with electronic health record (EHR)-based work activities. Materials and Methods EHR-based audit logs of attending physicians and advanced practice providers (APPs) from four surgical intensive care units in 2019 were included. Neural language models (LMs) were trained and validated separately for attendings' and APPs' action sequences. Action entropy was calculated as the cross-entropy associated with the predicted probability of the next action, based on prior actions. To validate the measure, a matched pairs study was conducted to assess the difference in action entropy during known high cognitive effort scenarios, namely, attention switching between patients and to or from the EHR inbox. Results Sixty-five clinicians performing 5 904 429 EHR-based audit log actions on 8956 unique patients were included. All attention switching scenarios were associated with a higher action entropy compared to non-switching scenarios (P  < .001), except for the from-inbox switching scenario among APPs. The highest difference among attendings was for the from-inbox attention switching: Action entropy was 1.288 (95% CI, 1.256-1.320) standard deviations (SDs) higher for switching compared to non-switching scenarios. For APPs, the highest difference was for the to-inbox switching, where action entropy was 2.354 (95% CI, 2.311-2.397) SDs higher for switching compared to non-switching scenarios. Discussion We developed a LM-based metric, action entropy, for assessing cognitive burden associated with EHR-based actions. The metric showed discriminant validity and statistical significance when evaluated against known situations of high cognitive effort (ie, attention switching). With additional validation, this metric can potentially be used as a screening tool for assessing behavioral action phenotypes that are associated with higher cognitive burden. Conclusion An LM-based action entropy metric—relying on sequences of EHR actions—offers opportunities for assessing cognitive effort in EHR-based workflows. [ABSTRACT FROM AUTHOR]

Tabletop simulation can be used to support and test system processes for clinical workflows. This paper will describe an innovative approach to testing change in clinical workflows for direct admissions to the operating room and interventional radiology departments in a 700-bed urban hospital in the Pacific Northwest and will highlight the specific benefits to nurse leaders. The simulation exercise involved key stakeholders and subject matter experts from various departments and aimed to evaluate the process workflow, provoke discussion on gaps, uncover improvement opportunities, and formulate recommendations. The simulation experience was broadly acknowledged by those involved as novel, engaging, suitable for complex process changes in health care settings and promoting valuable collaboration. [ABSTRACT FROM AUTHOR]

Purpose This article explores the potential of large language models (LLMs) to automate administrative tasks in healthcare, alleviating the burden on clinicians caused by electronic medical records. Potential LLMs offer opportunities in clinical documentation, prior authorization, patient education, and access to care. They can personalize patient scheduling, improve documentation accuracy, streamline insurance prior authorization, increase patient engagement, and address barriers to healthcare access. Caution However, integrating LLMs requires careful attention to security and privacy concerns, protecting patient data, and complying with regulations like the Health Insurance Portability and Accountability Act (HIPAA). It is crucial to acknowledge that LLMs should supplement, not replace, the human connection and care provided by healthcare professionals. Conclusion By prudently utilizing LLMs alongside human expertise, healthcare organizations can improve patient care and outcomes. Implementation should be approached with caution and consideration to ensure the safe and effective use of LLMs in the clinical setting. [ABSTRACT FROM AUTHOR]

Background and purpose: Implementing any radiopharmaceutical therapy (RPT) program requires a comprehensive review of system readiness, appropriate workflows, and training to ensure safe and efficient treatment delivery. A quantitative assessment of the dose delivered to targets and organs at risk (OAR) using RPT is possible by correlating the absorbed doses with the delivered radioactivity. Integrating dosimetry into an established RPT program demands a thorough analysis of the necessary components and system finetuning. This study aims to report an optimized workflow for molecular radiation therapy using 177Lu with a primary focus on integrating patient-specific dosimetry into an established radiopharmaceutical program in a radiation oncology setting. Materials and methods: We comprehensively reviewed using the Plan-Do-Check-Act (PDCA) cycle, including efficacy and accuracy of delivery and all aspects of radiation safety of the RPT program. The GE Discovery SPECT/CT 670DR™ system was calibrated per MIM protocol for dose calculation on MIM SurePlan™ MRT software. Jaszcak Phantom with 15-20 mCi of 177Lu DOTATATE with 2.5 µM EDTA solution was used, with the main energy window defined as 208 keV ±10% (187.6 to 229.2 keV); the upper scatter energy window was set to 240 keV ±5% (228 to 252 keV), while the lower scatter energy window was 177.8 keV ±5% (168.9 to 186.7 keV). Volumetric quality control tests and adjustments were performed to ensure the correct alignment of the table, NM, and CT gantry on SPECT/CT. A comprehensive end-to-end (E2E) test was performed to ensure workflow, functionality, and quantitative dose accuracy. Results: Workflow improvements and checklists are presented after systematically analyzing over 400 administrations of 177Lu-based RPT. Injected activity to each sphere in the NEMA Phantom scan was quantified, and the MIM Sureplan MRT reconstruction images calculated activities within ±12% of the injected activity. Image alignment tests on the SPECT/CT showed a discrepancy of more than the maximum tolerance of 2.2mmon any individual axis. As a result of servicing the machine and updating the VQC and COR corrections, the hybrid imaging system was adjusted to achieve an accuracy of <1 mm in all directions. Conclusion: Workflows and checklists, after analysis of system readiness and adequate training for staff and patients, are presented. Hardware and software components for patient-specific dosimetry are presented with a focus on hybrid image registration and correcting any errors that affect dosimetric quantification calculation. Moreover, this manuscript briefly overviews the necessary quality assurance requirements for converting diagnostic images into dosimetry measurement tools and integrating dosimetry for RPT based on 177Lu. [ABSTRACT FROM AUTHOR]

BACKGROUND: Timely angiographic embolization of abdominopelvic injuries is a hallmark of a high-functioning trauma center. Yet, the process depends on the timely mobilization of interventional radiology staff. Smartphone technology to notify and mobilize staff may be a viable option. OBJECTIVE: To describe the incorporation of a smartphone application into our trauma workflow process previously developed for stroke care. METHODS: In 2022, our Level I trauma center implemented a smartphone application with three simultaneously occurring functions: (a) high-definition image viewing on the phone; (b) text messaging thread for all parties; and (c) a single-call activation system for staff mobilization. The application was initially developed to notify interventional radiologists of large-vessel occlusions in victims of stroke and, at our request, was modified to fit our trauma workflow process. The smartphone application company developed a new program, installed the application on trauma service smartphones, and provided educational in-services over a 1-month period. The application was then integrated into our trauma workflow process. RESULTS: The trauma surgeon and the interventional radiologist can now simultaneously view high-definition images on their smartphones. Text messages are accessible to all team members. The staff is notified and mobilized with the singlecall smartphone application, preventing the placing and returning of phone calls. CONCLUSION: Smartphone technology enhances timely interventional radiology staff response for hemorrhagic patients requiring emergent angioembolization. [ABSTRACT FROM AUTHOR]

Introduction: In the case of the radiation oncology department, the large number of visits faced by patients illuminates the critical need for optimal time management. Focused on three central themes: daily waiting times, diagnosis-to-treatment waiting times, and appropriate staffing for the present workload, the research highlights the impact of inefficient time management on patient satisfaction and overall operational efficiency. The time and energy invested in a schedule are high and frequently many scheduling conflicts occur even after the schedule is made. The ability to schedule different employees in the most optimal manner would increase the productivity of the radiation oncology department. Methods: The scheduling software was constructed using Python language and importations of libraries from the Tkinker software for the Graphical User Interface. The software is a constraint-based algorithm that allocates staff to different sites based on each radiation therapy clinic’s staffing requirements. Results and Discussion: This work developed a basic software that creates a randomized schedule of employees. While this would benefit the team by curating a schedule that has no functional mistakes, the algorithm provides a foundation for the data collection that will facilitate the future incorporation of artificial intelligence (AI). This would allow for deeper learning overtime of the software to develop a schedule that is optimal for the success of the individual and, thus, the entire team. This pilot project aimed to generate interest regarding the introduction of AI to current scheduling software in the context of the radiation oncology department.

Ten years after the publication of a landmark article in AACN Advanced Critical Care, alarm fatigue continues to be an issue that researchers, clinicians, and organizations aim to remediate. Alarm fatigue contributes to missed alarms and medical errors that result in patient death, increased clinical workload and burnout, and interference with patient recovery. Led by the American Association of Critical-Care Nurses, national patient safety organizations continue to prioritize efforts to battle alarm fatigue and have proposed alarm management strategies to mitigate the effects of alarm fatigue. Similarly, clinical efforts now use simulation studies, individualized alarm thresholds, and interdisciplinary teams to optimize alarm use. Finally, engineering research efforts have innovated the standard alarm to convey information more effectively for medical users. By focusing on patient and provider safety, clinical workflow, and alarm technology, efforts to reduce alarm fatigue over the past 10 years have been grounded in an evidence-based and personnel-focused approach. [ABSTRACT FROM AUTHOR]

Abstract Purpose Integrating magnetic resonance (MR) into radiotherapy planning has several advantages. This report details the clinical implementation of an MR simulation (MR-planning) program for external beam radiotherapy (EBRT) in one of North America's largest radiotherapy programs. Methods and materials An MR radiotherapy planning program was developed and implemented at Sunnybrook Health Sciences Center in 2016 with two dedicated wide-bore MR platforms (1.5 and 3.0 Tesla). Planning MR was sequentially implemented every 3 months for separate treatment sites, including the central nervous system (CNS), gynecologic (GYN), head and neck (HN), genitourinary (GU), gastrointestinal (GI), breast, and brachial plexus. Essential protocols and processes were detailed in this report, including clinical workflow, optimized MR-image acquisition protocols, MR-adapted patient setup, strategies to overcome risks and challenges, and an MR-planning quality assurance program. This study retrospectively reviewed simulation site data for all MR-planning sessions performed for EBRT over the past 5 years. Results From July 2016 to December 2021, 8798 MR-planning sessions were carried out, which corresponds to 25% of all computer tomography (CT) simulations (CT-planning) performed during the same period at our institution. There was a progressive rise from 80 MR-planning sessions in 2016 to 1126 in 2017, 1492 in 2018, 1824 in 2019, 2040 in 2020, and 2236 in 2021. As a result, the relative number of planning MR/CT increased from 3% of all planning sessions in 2016 to 36% in 2021. The most common site of MR-planning was CNS (49%), HN (13%), GYN (12%), GU (12%), and others (8%). Conclusion Detailed clinical processes and protocols of our MR-planning program were presented, which have been improved over more than 5 years of robust experience. Strategies to overcome risks and challenges in the implementation process are highlighted. Our work provides details that can be used by institutions interested in implementing an MR-planning program.

Background and Purpose: The aim of this study was to analyze a magnetic resonance imaging (MRI)-only radiotherapy workflow from an economic perspective in terms of reduced time, costs and systematic uncertainties. Material/Methods: A documented Swedish clinical implementation of MRI-only radiotherapy was used as template for cost assessments compared to a combined computed tomography (CT)/MRI workflow. The costs were taken from official regional price lists from 2021. MRI-only specific quality assurance (QA) was assumed necessary in an initial phase. Treatment plans for target volumes with margins of 5–10 mm were created for ten prostate cancer patients prescribed 78 Gy in 39 fractions. The risk of Grade ≥ 2 rectal toxicity or rectal bleeding was calculated using the QUANTEC recommended NTCP model and costs estimated based on subsequent diagnostic examinations. Results: The exclusion of the CT-examination and faster target delineation were the main contributors to cost reductions. Additional QA procedures limited the initial cost reduction to 14 EUR/patient. Long-term MRI-only reduced the costs by 209 EUR/patient. Reducing margins resulted in Grade ≥ 2 rectal toxicity or rectal bleeding probability of 9.7 % for 7 mm margin and 6.0 % for 5 mm margin. This margin reduction resulted in an additional cost reduction of 46 EUR/patient. Conclusion: An MRI-only workflow implementation is associated with reduced costs when the workflow tasks are more time efficient and side effects are reduced as a result of margin reduction. The short-term economic benefits are limited due to extra costs of QA procedures. The economic benefits of MRI-only will make impact first when the workflow is well established, and margin reduction has been included.

Abstract Purpose Clinical efficiency is a key component of the value-based care model and a driver of patient satisfaction. The purpose of this study was to identify and address inefficiencies at a high-volume radiation oncology clinic. Methods and materials Patient flow analysis (PFA) was used to create process maps and optimize the workflow of consultation visits in a gastrointestinal radiation oncology clinic at a large academic cancer center. Metrics such as cycle times, waiting times, and rooming times were assessed by using a real-time patient status function in the electronic medical record for 556 consults and compared between before vs after implementation of the PFA recommendations. Results The initial PFA revealed four inefficiencies: (1) protracted rooming time, (2) inefficient communications, (3) duplicated tasks, and (4) ambiguous clinical roles. We analyzed 485 consult-visits before the PFA and 71 after the PFA. The PFA recommendations led to reductions in overall median cycle time by 21% (91 min vs 72 min, p 2 h vs. 0% after implementation of the recommendations (p

Autonomous Artificial Intelligence (AI) has the potential to reduce disparities, improve quality of care, and reduce cost by improving access to specialty diagnoses at the point-of-care. Diabetes and related complications represent a significant source of health disparities. Vision loss is a complication of diabetes, and there is extensive evidence supporting annual eye exams for prevention. Prior to the use of autonomous AI, store-and-forward imaging approaches using remote reading centers (asynchronous telemedicine) attempted to increase diabetes related eye exams with limited success. In 2018, after rigorous clinical validation, the first fully autonomous AI system [LumineticsCore™ (formerly IDx-DR), Digital Diagnostics Inc., Coralville, IA, United States] received U.S. Food and Drug Administration (FDA) De Novo authorization. The system diagnoses diabetic retinopathy (including macular edema) without specialist physician overread at the point-of-care. In addition to regulatory clearance, reimbursement, and quality measure updates, successful adoption requires local optimization of the clinical workflow. The general challenges of frontline care clinical workflow have been well documented in the literature. Because healthcare AI is so new, there remains a gap in the literature about challenges and opportunities to embed diagnostic AI into the clinical workflow. The goal of this review is to identify common workflow themes leading to successful adoption, measured as attainment number of exams per month using the autonomous AI system against targets set for each health center. We characterized the workflow in four different US health centers over a 12-month period. Health centers were geographically dispersed across the Midwest, Southwest, Northeast, and West Coast and varied distinctly in terms of size, staffing, resources, financing and demographics of patient populations. After 1 year, the aggregated number of diabetes-related exams per month increased from 89 after the first month of initial deployment to 174 across all sites. Across the diverse practice types, three primary determinants underscored sustainable adoption: (1) Inclusion of Executive and Clinical Champions; (2) Underlining Health Center Resources; and (3) Clinical workflows that contemplate patient identification (pre-visit), LumineticsCore Exam Capture and Provider Consult (patient visit), and Timely Referral Triage (post-visit). In addition to regulatory clearance, reimbursement and quality measures, our review shows that addressing the core determinants for workflow optimization is an essential part of large-scale adoption of innovation. These best practices can be generalizable to other autonomous AI systems in front-line care settings, thereby increasing patient access, improving quality of care, and addressing health disparities.

Precision medicine—of which precision prescribing is a core component—is becoming a new frontier in today's healthcare. Both artificial intelligence (AI) and machine learning (ML) have the potential to enhance our understanding of data and therefore our ability to accurately diagnose and treat patients. By leveraging these technologies and processes, we can uncover associations between a person's genomic makeup and their health, identify biomarkers associated with diseases, fine-tune patient selection for clinical trials, reduce costs, and accelerate drug discovery and vaccine development. Although real-world data pose challenges in terms of collection, representation, and missing or inaccurate data sets, the integration of precision medicine into healthcare is critical. Clearly, precision medicine can benefit from health information innovations that empower decision-making at the patient level. Healthcare fusion is an example of an innovative framework and process [K Zhai et al. ECKM 2022, 20(3), pp. 179–192]. Data science and process improvement are also expected to play a role in resource planning and operational efficiency for optimal patient-centered care. Driving this transformation are advances in 'omics' technologies, digital devices, and imaging capabilities, along with an arsenal of powerful analytics tools working across a multitude of institutions and stakeholders. Encompassing this entire ecosystem, medicine will be evidence-based and driven by three key components: (1) Data curation through clinical diagnostics and behavioral apps that capture health and disease states; (2) Individualized solutions driven by advanced data analytics and personalized therapies; and (3) Business models that deliver value and incentivize growth. The aim of this paper is to present a novel conceptual framework to leverage AI and enhance information flow to serve the patient as per components one and two. [ABSTRACT FROM AUTHOR]

Raw audit logs provide a comprehensive record of clinicians' activities on an electronic health record (EHR) and have considerable potential for studying clinician behaviors. However, research using raw audit logs is limited because they lack context for clinical tasks, leading to difficulties in interpretation. We describe a novel unsupervised approach using the comparison and visualization of EHR action embeddings to learn context and structure from raw audit log activities. Using a dataset of 15 767 634 raw audit log actions performed by 88 intern physicians over 6 months of EHR use across inpatient and outpatient settings, we demonstrated that embeddings can be used to learn the situated context for EHR-based work activities, identify discrete clinical workflows, and discern activities typically performed across diverse contexts. Our approach represents an important methodological advance in raw audit log research, facilitating the future development of metrics and predictive models to measure clinician behaviors at the macroscale. [ABSTRACT FROM AUTHOR]

Purpose: Integrating magnetic resonance (MR) into radiotherapy planning has several advantages. This report details the clinical implementation of an MR simulation (MR-planning) program for external beam radiotherapy (EBRT) in one of North America's largest radiotherapy programs. Methods and materials: An MR radiotherapy planning program was developed and implemented at Sunnybrook Health Sciences Center in 2016 with two dedicated wide-bore MR platforms (1.5 and 3.0 Tesla). Planning MR was sequentially implemented every 3 months for separate treatment sites, including the central nervous system (CNS), gynecologic (GYN), head and neck (HN), genitourinary (GU), gastrointestinal (GI), breast, and brachial plexus. Essential protocols and processes were detailed in this report, including clinical workflow, optimized MR-image acquisition protocols, MR-adapted patient setup, strategies to overcome risks and challenges, and an MR-planning quality assurance program. This study retrospectively reviewed simulation site data for all MR-planning sessions performed for EBRT over the past 5 years. Results: From July 2016 to December 2021, 8798 MR-planning sessions were carried out, which corresponds to 25% of all computer tomography (CT) simulations (CT-planning) performed during the same period at our institution. There was a progressive rise from 80 MR-planning sessions in 2016 to 1126 in 2017, 1492 in 2018, 1824 in 2019, 2040 in 2020, and 2236 in 2021. As a result, the relative number of planning MR/CT increased from 3% of all planning sessions in 2016 to 36% in 2021. The most common site of MR-planning was CNS (49%), HN (13%), GYN (12%), GU (12%), and others (8%). Conclusion: Detailed clinical processes and protocols of our MR-planning program were presented, which have been improved over more than 5 years of robust experience. Strategies to overcome risks and challenges in the implementation process are highlighted. Our work provides details that can be used by institutions interested in implementing an MR-planning program. [ABSTRACT FROM AUTHOR]

Abstract Background Accumulated electronic data from a wide variety of clinical settings has been processed using a range of informatics methods to determine the sequence of care activities experienced by patients. The “as is” or “de facto” care pathways derived can be analysed together with other data to yield clinical and operational information. It seems likely that the needs of both health systems and patients will lead to increasing application of such analyses. A comprehensive review of the literature is presented, with a focus on the study context, types of analysis undertaken, and the utility of the information gained. Methods A systematic review was conducted of literature abstracting sequential patient care activities (“de facto” care pathways) from care records. Broad coverage was achieved by initial screening of a Scopus search term, followed by screening of citations (forward snowball) and references (backwards snowball). Previous reviews of related topics were also considered. Studies were initially classified according to the perspective captured in the derived pathways. Concept matrices were then derived, classifying studies according to additional data used and subsequent analysis undertaken, with regard for the clinical domain examined and the knowledge gleaned. Results 254 publications were identified. The majority (n = 217) of these studies derived care pathways from data of an administrative/clinical type. 80% (n = 173) applied further analytical techniques, while 60% (n = 131) combined care pathways with enhancing data to gain insight into care processes. Discussion Classification of the objectives, analyses and complementary data used in data-driven care pathway mapping illustrates areas of greater and lesser focus in the literature. The increasing tendency for these methods to find practical application in service redesign is explored across the variety of contexts and research questions identified. A limitation of our approach is that the topic is broad, limiting discussion of methodological issues. Conclusion This review indicates that methods utilising data-driven determination of de facto patient care pathways can provide empirical information relevant to healthcare planning, management, and practice. It is clear that despite the number of publications found the topic reviewed is still in its infancy.

Coronary computed tomography angiography (CCTA) is increasingly the cornerstone in the management of patients with chronic coronary syndromes. This fact is reflected by current guidelines, which show a fundamental shift towards non-invasive imaging - especially CCTA. The guidelines for acute and stable coronary artery disease (CAD) of the European Society of Cardiology from 2019 and 2020 emphasize this shift. However, to fulfill this new role, a broader availability in adjunct with increased robustness of data acquisition and speed of data reporting of CCTA is needed. Artificial intelligence (AI) has made enormous progress for all imaging methodologies concerning (semi)-automatic tools for data acquisition and data post-processing, with outreach toward decision support systems. Besides onco- and neuroimaging, cardiac imaging is one of the main areas of application. Most current AI developments in the scenario of cardiac imaging are related to data postprocessing. However, AI applications (including radiomics) for CCTA also should enclose data acquisition (especially the fact of dose reduction) and data interpretation (presence and extent of CAD). The main effort will be to integrate these AI-driven processes into the clinical workflow, and to combine imaging data/results with further clinical data, thus - beyond the diagnosis of CAD- enabling prediction and forecast of morbidity and mortality. Furthermore, data fusing for therapy planning (e.g., invasive angiography/TAVI planning) will be warranted. The aim of this review is to present a holistic overview of AI applications in CCTA (including radiomics) under the umbrella of clinical workflows and clinical decision-making. The review first summarizes and analyzes applications for the main role of CCTA, i.e., to non-invasively rule out stable coronary artery disease. In the second step, AI applications for additional diagnostic purposes, i.e., to improve diagnostic power (CAC = coronary artery classifications), improve differential diagnosis (CT-FFR and CT perfusion), and finally improve prognosis (again CAC plus epi- and pericardial fat analysis) are reviewed.

Immunohistochemistry has long been held as the gold standard for understanding the expression patterns of therapeutically relevant proteins to identify prognostic and predictive biomarkers. Patient selection for targeted therapy in oncology has successfully relied upon standard microscopy-based methodologies, such as single-marker brightfield chromogenic immunohistochemistry. As promising as these results are, the analysis of one protein, with few exceptions, no longer provides enough information to draw effective conclusions about the probability of treatment response. More multifaceted scientific queries have driven the development of high-throughput and high-order technologies to interrogate biomarker expression patterns and spatial interactions between cell phenotypes in the tumor microenvironment. Such multi-parameter data analysis has been historically reserved for technologies that lack the spatial context that is provided by immunohistochemistry. Over the past decade, technical developments in multiplex fluorescence immunohistochemistry and discoveries made with improving image data analysis platforms have highlighted the importance of spatial relationships between certain biomarkers in understanding a patient’s likelihood to respond to, typically, immune checkpoint inhibitors. At the same time, personalized medicine has instigated changes in both clinical trial design and its conduct in a push to make drug development and cancer treatment more efficient, precise, and economical. Precision medicine in immuno-oncology is being steered by data-driven approaches to gain insight into the tumor and its dynamic interaction with the immune system. This is particularly necessary given the rapid growth in the number of trials involving more than one immune checkpoint drug, and/or using those in combination with conventional cancer treatments. As multiplex methods, like immunofluorescence, push the boundaries of immunohistochemistry, it becomes critical to understand the foundation of this technology and how it can be deployed for use as a regulated test to identify the prospect of response from mono- and combination therapies. To that end, this work will focus on: 1) the scientific, clinical, and economic requirements for developing clinical multiplex immunofluorescence assays; 2) the attributes of the Akoya Phenoptics workflow to support predictive tests, including design principles, verification, and validation needs; 3) regulatory, safety and quality considerations; 4) application of multiplex immunohistochemistry through lab-developed-tests and regulated in vitro diagnostic devices.

Purpose: Clinical efficiency is a key component of the value-based care model and a driver of patient satisfaction. The purpose of this study was to identify and address inefficiencies at a high-volume radiation oncology clinic. Methods and materials: Patient flow analysis (PFA) was used to create process maps and optimize the workflow of consultation visits in a gastrointestinal radiation oncology clinic at a large academic cancer center. Metrics such as cycle times, waiting times, and rooming times were assessed by using a real-time patient status function in the electronic medical record for 556 consults and compared between before vs after implementation of the PFA recommendations. Results: The initial PFA revealed four inefficiencies: (1) protracted rooming time, (2) inefficient communications, (3) duplicated tasks, and (4) ambiguous clinical roles. We analyzed 485 consult-visits before the PFA and 71 after the PFA. The PFA recommendations led to reductions in overall median cycle time by 21% (91 min vs 72 min, p < 0.001), in cumulative waiting times by 64% (45 min vs 16 min; p < 0.001), which included waiting room time (14 min vs 5 min; p < 0.001) and wait for physician (20 min vs. 6 min; p < 0.001). Slightly less than one-quarter (22%) of consult visits before the PFA lasted > 2 h vs. 0% after implementation of the recommendations (p < 0.001). Similarly, the proportion of visits requiring < 1 h was 16% before PFA vs 34% afterward (p < 0.001). Conclusions: PFA can be used to identify clinical inefficiencies and optimize workflows in radiation oncology consultation clinics, and implementing their findings can significantly improve cycle times and waiting times. Potential downstream effects of these interventions include improved patient experience, decreased staff burnout, financial savings, and opportunities for expanding clinical capacity. [ABSTRACT FROM AUTHOR]

Objective: Despite decades of obstetric scanning, the field of sonographer workflow remains largely unexplored. In the second trimester, sonographers use scan guidelines to guide their acquisition of standard planes and structures; however, the scan-acquisition order is not prescribed. Using deep-learning-based video analysis, the aim of this study was to develop a deeper understanding of the clinical workflow undertaken by sonographers during second-trimester anomaly scans.Methods: We collected prospectively full-length video recordings of routine second-trimester anomaly scans. Important scan events in the videos were identified by detecting automatically image freeze and image/clip save. The video immediately preceding and following the important event was extracted and labeled as one of 11 commonly acquired anatomical structures. We developed and used a purposely trained and tested deep-learning annotation model to label automatically the large number of scan events. Thus, anomaly scans were partitioned as a sequence of anatomical planes or fetal structures obtained over time.Results: A total of 496 anomaly scans performed by 14 sonographers were available for analysis. UK guidelines specify that an image or videoclip of five different anatomical regions must be stored and these were detected in the majority of scans: head/brain was detected in 97.2% of scans, coronal face view (nose/lips) in 86.1%, abdomen in 93.1%, spine in 95.0% and femur in 92.3%. Analyzing the clinical workflow, we observed that sonographers were most likely to begin their scan by capturing the head/brain (in 24.4% of scans), spine (in 23.2%) or thorax/heart (in 22.8%). The most commonly identified two-structure transitions were: placenta/amniotic fluid to maternal anatomy, occurring in 44.5% of scans; head/brain to coronal face (nose/lips) in 42.7%; abdomen to thorax/heart in 26.1%; and three-dimensional/four-dimensional face to sagittal face (profile) in 23.7%. Transitions between three or more consecutive structures in sequence were uncommon (up to 13% of scans). None of the captured anomaly scans shared an entirely identical sequence.Conclusions: We present a novel evaluation of the anomaly scan acquisition process using a deep-learning-based analysis of ultrasound video. We note wide variation in the number and sequence of structures obtained during routine second-trimester anomaly scans. Overall, each anomaly scan was found to be unique in its scanning sequence, suggesting that sonographers take advantage of the fetal position and acquire the standard planes according to their visibility rather than following a strict acquisition order. © 2022 The Authors. Ultrasound in Obstetrics & Gynecology published by John Wiley & Sons Ltd on behalf of International Society of Ultrasound in Obstetrics and Gynecology. [ABSTRACT FROM AUTHOR]

The integration of digital technologies into health care has significantly enhanced the efficiency and effectiveness of care coordination. Our perspective paper explores the digital information ecosystems in modern care coordination, focusing on the processes of information generation, updating, transmission, and exchange along a patient's care pathway. We identify several challenges within this ecosystem, including interoperability issues, information silos, hard-to-map patient care journeys, increased workload on health care professionals, coordination and communication gaps, and compliance with privacy regulations. These challenges are often associated with inefficiencies and diminished care quality. We also examine how emerging artificial intelligence (AI) tools have the potential to enhance the management of patient information flow. Specifically, AI can boost interoperability across diverse health systems; optimize and monitor patient care pathways; improve information retrieval and care transitions; humanize health care by integrating patients' desired outcomes and patient-reported outcome measures; and optimize clinical workflows, resource allocation, and digital tool usability and user experiences. By strategically leveraging AI, health care systems can establish a more robust and responsive digital information ecosystem, improving care coordination and patient outcomes. This perspective underscores the importance of continued research and investment in AI technologies in patient care pathways. We advocate for a thoughtful integration of AI into health care practices to fully realize its potential in revolutionizing care coordination., (©You Chen, Christoph U Lehmann, Bradley Malin. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 02.12.2024.)

Adaptive radiotherapy performed on the daily magnetic resonance imaging (MRI) is an option to improve the treatment quality. In the adapt-to-shape workflow of 1.5-T MR-Linac, the contours of structures are adjusted on the basis of patient daily MRI, and the adapted plan is recalculated on the MRI-based synthetic computed tomography (syCT) generated by bulk density assignment. Because dosimetric accuracy of this strategy is a priority and requires evaluation, this study aims to explore the usefulness of adding an assessment of dosimetric errors associated with recalculation on syCT to the clinical workflow. Sixty-one patients, with various tumor sites, treated using a 1.5-T MR-Linac were included in this study. In Monaco V5.4, the target and organs at risk (OARs) were contoured, and a reference CT plan that contains information about the outlined contours, their average electron density (ED), and the priority of ED assignment was generated. To evaluate the dosimetric error of syCT caused by the inherent approximation within bulk density assignment, the reference CT plan was recalculated on the syCT obtained from the reference CT by forcing all contoured structures to their mean ED defined on the reference plan. The dose–volume histogram (DVH) and dose distribution of the CT and syCT plan were compared. The causes of dosimetric discrepancies were investigated, and the reference plan was reworked to minimize errors if needed. For 54 patients, gamma analysis of the dose distribution on syCT and CT show a median pass rate of 99.7% and 98.5% with the criteria of 3%/3 mm and 2%/2 mm, respectively. DVH difference of targets and OARs remained less than 1.5% or 1 Gy. For the remaining patients, factors (i.e., inappropriate ED assignments) influenced the dosimetric agreement of the syCT vs. CT reference DVH by up to 21%. The causes of the errors were promptly identified, and the DVH dosimetry was realigned except for two lung treatments for which a significant discrepancy remained. The recalculation on the syCT obtained from the planning CT is a powerful tool to assess and decrease the minimal error committed during the adaptive plan on the MRI-based syCT. [ABSTRACT FROM AUTHOR]

Background and purpose: In radiation therapy (RT), significant improvements have been made recently particularly in the practices of planning imaging. This study aimed to conduct a cost evaluation between magnetic resonance imaging (MRI) -only and combined computed tomography (CT) and MRI workflows. Materials and methods: The time-driven activity-based costing (TDABC) model was used to conduct a cost evaluation between the two workflows in those steps, where cost differences were expected. Costs were divided into capital costs and operational costs. The former consisted of fixed, one-time expenses, e.g. the purchase of a scanner, whereas the latter were partially based on the amount of activity consumed i.e. time required for image acquisition, image registration and structure contouring. Results: In a review over a period of 10 years for 300 annual prostate cancer patients, the total cost of the workflow steps included in the study for an individual patient applying the MRI-only workflow was 903 € (100%), comprised of 537 € (59%) capital costs and 366 € (41%) operational costs. The corresponding total cost for an individual patient applying the CT + MRI workflow was 922 € (100%), comprised of 197 € (21%) capital costs and 726 € (79%) operational costs. In 10 years for 3000 patients, a total saving of 58,544 € (2%) was achieved with the MRI-only workflow compared with the dual imaging workflow. Conclusions: MRI-only workflow is a feasible and economic way to perform clinical RT for localized prostate cancer, in particular for medium- and large-sized departments treating a sufficient number of patients.

Purpose: This study aimed to evaluate the clinical need for an automated decision-support software platform for adaptive radiation therapy (ART) of head and neck cancer (HNC) patients. Methods: We tested RTapp (SegAna), a new ART software platform for deciding when a treatment replan is needed, to investigate a set of 27 HNC patients' data retrospectively. For each fraction, the software estimated key components of ART such as daily dose distribution and cumulative doses received by targets and organs at risk (OARs) from daily 3D imaging in real-time. RTapp also included a prediction algorithm that analyzed dosimetric parameter (DP) trends against user-specified thresholds to proactively trigger adaptive re-planning up to four fractions ahead. The DPs evaluated for ART were based on treatment planning dose constraints. Warning (V95<95%) and adaptation (V95<93%) thresholds were set for PTVs, while OAR adaptation dosimetric endpoints of +10% (DE10) were set for all Dmax and Dmean DPs. Any threshold violation at end of treatment (EOT) triggered a review of the DP trends to determine the threshold-crossing fraction Fx when the violations occurred. The prediction model accuracy was determined as the difference between calculated and predicted DP values with 95% confidence intervals (CI95). Results: RTapp was able to address the needs of treatment adaptation. Specifically, we identified 18/27 studies (67%) for violating PTV coverage or parotid Dmean at EOT. Twelve PTVs had V95<95% (mean coverage decrease of −6.8 ± 2.9%) including six flagged for adaptation at median Fx = 6 (range, 1–16). Seventeen parotids were flagged for exceeding Dmean dose constraints with a median increase of +2.60 Gy (range, 0.99–6.31 Gy) at EOT, including nine with DP>DE10. The differences between predicted and calculated PTV V95 and parotid Dmean was up to 7.6% (mean ± CI95, −2.7 ± 4.1%) and 5 Gy (mean ± CI95, 0.3 ± 1.6 Gy), respectively. The most accurate predictions were obtained closest to the threshold-crossing fraction. For parotids, the results showed that Fx ranged between fractions 1 and 23, with a lack of specific trend demonstrating that the need for treatment adaptation may be verified for every fraction. Conclusion: Integrated in an ART clinical workflow, RTapp aids in predicting whether specific treatment would require adaptation up to four fractions ahead of time. [ABSTRACT FROM AUTHOR]

Purpose: Today's hospitals are designed as collections of individual departments, with limited communication and collaboration between medical sub-specialties. Patients are constantly being moved between different places, which is detrimental for patient experience, overall efficiency and capacity. Instead, we argue that care should be brought to the patient, not vice versa, and thus propose a novel hospital architecture concept that we refer to as Patient Hub. It envisions a truly patient-centered, department-less facility, in which all critical functions occur in the same building and on the same floor. Methods: To demonstrate the feasibility and benefits of our concept, we selected an exemplary patient scenario and used 3D software to simulate resulting workflows for both the Patient Hub and a traditional hospital based on a generic hospital template by Kaiser-Permanente. Results: According to our workflow simulations, the Patient Hub model effectively eliminates waiting and transfer times, drastically simplifies wayfinding, reduces overall traveling distances by 54%, reduces elevator runs by 78% and improves access to quality views from 67 to 100% for patient rooms, from 0 to 100% for exam rooms and from 0 to 38% for corridors. In addition, the interaction of related medical fields is improved while maintaining the quality of care and the relationship between patients and caregivers. Conclusion: With the Patient Hub concept, we aim at rethinking traditional hospital layouts. We were able to demonstrate, alas on a proof-of-concept basis, that it is indeed feasible to place the patient at the very center of operations, while increasing overall efficiency and capacity at the same time and maintaining the quality of care. [ABSTRACT FROM AUTHOR]

Background: Digital health interventions have been shown to improve data quality and health services in low- and middle-income countries (LMICs). Nonetheless, in LMICs, systematic assessments of time saved with the use of digital tools are rare. We ran a set of cluster-randomized controlled trials as part of the implementation of a digital maternal and child health registry (eRegistry) in the West Bank, Palestine. Objective: In the eRegTime study, we compared time spent on health information management in clinics that use the eRegistry versus the existing paper-based documentation system. Methods: Intervention (eRegistry) and control (paper documentation) arms were defined by a stratified random subsample of primary health care clinics from the concurrent eRegQual trial. We used time-motion methodology to collect data on antenatal care service provision. Four observers used handheld tablets to record time-use data during one working day per clinic. We estimated relative time spent on health information management for booking and follow-up visits and on client care using mixed-effects linear regression. Results: In total, 22 of the 24 included clinics (12 intervention, 10 control) contributed data; no antenatal care visits occurred in the other two clinics during the study period. A total of 123 and 118 consultations of new pregnancy registrations and follow-up antenatal care visits were observed in the intervention and control groups, respectively. Average time spent on health information management for follow-up antenatal care visits in eRegistry clinics was 5.72 minutes versus 8.10 minutes in control clinics (adjusted relative time 0.69, 95% CI 0.60-0.79; P<.001), and 15.26 minutes versus 18.91 minutes (adjusted relative time 0.96, 95% CI 0.61-1.50; P=.85) for booking visits. The average time spent on documentation, a subcategory of health information management, was 5.50 minutes in eRegistry clinics versus 8.48 minutes in control clinics (adjusted relative time 0.68, 95% CI 0.56-0.83; P<.001). While the average time spent on client care was 5.01 minutes in eRegistry clinics versus 4.91 minutes in control clinics, some uncertainty remains, and the CI was consistent with eRegistry clinics using less, the same, or more time on client care compared to those that use paper (adjusted relative time 0.85, 95% CI 0.64-1.13; P=.27). Conclusions: The eRegistry captures digital data at point of care during client consultations and generates automated routine reports based on the clinical data entered. Markedly less time (plausibly a saving of at least 18%) was spent on health information management in eRegistry clinics compared to those that use paper-based documentation. This is likely explained by the fact that the eRegistry requires lesser repetitive documentation work than paper-based systems. Adoption of eRegistry-like systems in comparable settings may save valuable and scarce health care resources. [ABSTRACT FROM AUTHOR]

BACKGROUND: Despite the benefits of using electronic medical record (EMR) systems, existing studies show that many healthcare providers are uncertain regarding their usability. The usability issues of these systems decrease their efficiency, discourage clinicians, and cause dissatisfaction among patients, which may result in safety risks and harm. OBJECTIVE: The aim of this study was to collect and analyze EMR system usability problems from actual users. Practical user interface guidelines were presented based on the medical practices of these users. METHODS: Employing an online questionnaire with a seven-point Likert scale, usability issues of EMR systems were collected from 200 emergency department healthcare providers (103 physicians (medical doctors) and 97 nurses) from South Korea. RESULTS: The most common usability problem among the physicians and nurses was generating in-patient selection. This pertained to the difficulty in finding the required information on-screen because of poor visibility and a lack of distinctiveness. CONCLUSIONS: The major problems of EMR systems and their causes were identified. It is recommended that intensive visual enhancement of EMR system interfaces should be implemented to support user tasks. By providing a better understanding of the current usability problems among medical practitioners, the results of this study can be useful for developing EMR systems with increased effectiveness and efficiency. [ABSTRACT FROM AUTHOR]

PurposeThis study aimed to evaluate the clinical need for an automated decision-support software platform for adaptive radiation therapy (ART) of head and neck cancer (HNC) patients.MethodsWe tested RTapp (SegAna), a new ART software platform for deciding when a treatment replan is needed, to investigate a set of 27 HNC patients’ data retrospectively. For each fraction, the software estimated key components of ART such as daily dose distribution and cumulative doses received by targets and organs at risk (OARs) from daily 3D imaging in real-time. RTapp also included a prediction algorithm that analyzed dosimetric parameter (DP) trends against user-specified thresholds to proactively trigger adaptive re-planning up to four fractions ahead. The DPs evaluated for ART were based on treatment planning dose constraints. Warning (V95DE10. The differences between predicted and calculated PTV V95 and parotid Dmean was up to 7.6% (mean ± CI95, −2.7 ± 4.1%) and 5 Gy (mean ± CI95, 0.3 ± 1.6 Gy), respectively. The most accurate predictions were obtained closest to the threshold-crossing fraction. For parotids, the results showed that Fx ranged between fractions 1 and 23, with a lack of specific trend demonstrating that the need for treatment adaptation may be verified for every fraction.ConclusionIntegrated in an ART clinical workflow, RTapp aids in predicting whether specific treatment would require adaptation up to four fractions ahead of time.

Rationale and objectivesThe current paradigm of cancer diagnosis involves uncoordinated communication of findings from radiology and pathology to downstream physicians. Discordance between these findings can require additional time from downstream users to resolve, or given incorrect resolution, may adversely impact treatment decisions. To mitigate this problem, we developed a web-based system, called RadPath, for correlating and integrating radiology and pathology reporting.Materials and methodsRadPath includes interfaces to our institution's clinical information systems, which are used to retrieve reports, images, and test results that are structured into an interactive compendium for a diagnostic patient case. The system includes an editing interface for physicians, allowing for the inclusion of additional clinical data, as well as the ability to retrospectively correlate and contextualize imaging findings following pathology diagnosis.ResultsDuring pilot deployment and testing over the course of 1 year, physicians at our institution have completed 60 RadPath cases, requiring an average of 128 seconds from a radiologist and an average of 93 seconds from a pathologist per case. Several technical and workflow challenges were encountered during development, including interfacing with diverse clinical information systems, automatically structuring report contents, and determining the appropriate physicians to create RadPath summaries. Reaction to RadPath has been positive, with users valuing the system's ability to consolidate diagnostic information.ConclusionsWith the increasing complexity of medicine and the movement toward team-based disease management, there is a need for improved clinical communication and information exchange. RadPath provides a platform for generating coherent and correlated diagnostic summaries in cancer diagnosis with minimal additional effort from physicians.

Background: Accumulated electronic data from a wide variety of clinical settings has been processed using a range of informatics methods to determine the sequence of care activities experienced by patients. The "as is" or "de facto" care pathways derived can be analysed together with other data to yield clinical and operational information. It seems likely that the needs of both health systems and patients will lead to increasing application of such analyses. A comprehensive review of the literature is presented, with a focus on the study context, types of analysis undertaken, and the utility of the information gained.Methods: A systematic review was conducted of literature abstracting sequential patient care activities ("de facto" care pathways) from care records. Broad coverage was achieved by initial screening of a Scopus search term, followed by screening of citations (forward snowball) and references (backwards snowball). Previous reviews of related topics were also considered. Studies were initially classified according to the perspective captured in the derived pathways. Concept matrices were then derived, classifying studies according to additional data used and subsequent analysis undertaken, with regard for the clinical domain examined and the knowledge gleaned.Results: 254 publications were identified. The majority (n = 217) of these studies derived care pathways from data of an administrative/clinical type. 80% (n = 173) applied further analytical techniques, while 60% (n = 131) combined care pathways with enhancing data to gain insight into care processes.Discussion: Classification of the objectives, analyses and complementary data used in data-driven care pathway mapping illustrates areas of greater and lesser focus in the literature. The increasing tendency for these methods to find practical application in service redesign is explored across the variety of contexts and research questions identified. A limitation of our approach is that the topic is broad, limiting discussion of methodological issues.Conclusion: This review indicates that methods utilising data-driven determination of de facto patient care pathways can provide empirical information relevant to healthcare planning, management, and practice. It is clear that despite the number of publications found the topic reviewed is still in its infancy. [ABSTRACT FROM AUTHOR]

The quality of the healthcare provided in the context of cerebrovascular accident (stroke) is closely linked to the celerity of the diagnosis, so the action of health professionals must be coordinated, and all the processes should proceed as quickly, efficiently, and effectively as possible. In turn, the crowding of emergency services contributes for considerable high delay times. The research study reported by this paper aimed to identify features to include in the emergency services information systems to improve the contribution of the medical imaging within acute stroke diagnosis and treatment. Medical imaging processes related to Head Computed Tomography and Angio Computed Tomography in a medium-size Portuguese were analyzed to identify possible improvements. Moreover, semi-structured interviews were conducted involving a group of health care professionals with experience in pre- and intra-hospital emergency services to identify the needs that can be satisfied with innovative technological solutions and to assess the viability to introduce additional features in the information systems supporting the emergency services. The results show that additional features might be considered to improve acute stroke diagnosis and treatment and the health care professionals are receptive to these improvements. [ABSTRACT FROM AUTHOR]

Background: Treatment of pelvic injuries poses serious problems for surgeons due to the difficulties of the associated injuries. The objective of this research is to create a clinical workflow that integrates three-dimensional technologies in preoperative planning and performing surgery for the reduction and stabilization of associated acetabular fractures. Methods : The research methodology consisted of integrating the stages of virtual preoperative planning, physical preoperative planning, and performing the surgical intervention in a newly developed clinical workflow. The proposed model was validated in practice in a pilot surgical intervention. Results : On a complex pelvic injury case of a patient with an associated both-column acetabular fracture (AO/OTA-62C1g), we presented the results obtained in the six stages of the clinical workflow: acquisition of three-dimensional (3D) images, creation of the virtual model of the pelvis, creation of the physical model of the pelvis, preoperative physical simulation, orthopedic surgery, and imaging validation of the intervention. The life-size 3D model was fabricated based on computed tomography imagistics. To create the virtual model, the images were imported into Invesalius (version 3.1.1, CTI, Brazil), after which they were processed with MeshLab (version 2023.12, ISTI-CNR, Italy) and FreeCAD (version 0.21.2, LGPL, FSF, Boston, MA, USA). The physical model was printed in 21 h and 37 min using Ultimaker Cura software (version 5.7.2), on an Ultimaker 2+ printing machine through a Fused Deposition Modeling process. Using the physical model, osteosynthesis plate dimensions and fixation screw trajectories were tested to reduce the risk of neurovascular injury, after which they were adjusted and resterilized, which enhanced preoperative decision-making. Conclusions : The life-size physical model improved anatomical appreciation and preoperative planning, enabling accurate surgical simulation. The tools created demonstrated remarkable accuracy and cost-effectiveness that support the advancement and efficiency of clinical practice.

The emergence of generative artificial intelligence (AI) has revolutionized various fields. In ophthalmology, generative AI has the potential to enhance efficiency, accuracy, personalization and innovation in clinical practice and medical research, through processing data, streamlining medical documentation, facilitating patient-doctor communication, aiding in clinical decision-making, and simulating clinical trials. This review focuses on the development and integration of generative AI models into clinical workflows and scientific research of ophthalmology. It outlines the need for development of a standard framework for comprehensive assessments, robust evidence, and exploration of the potential of multimodal capabilities and intelligent agents. Additionally, the review addresses the risks in AI model development and application in clinical service and research of ophthalmology, including data privacy, data bias, adaptation friction, over interdependence, and job replacement, based on which we summarized a risk management framework to mitigate these concerns. This review highlights the transformative potential of generative AI in enhancing patient care, improving operational efficiency in the clinical service and research in ophthalmology. It also advocates for a balanced approach to its adoption., Competing Interests: Declaration of Competing Interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: co-author is the Editor-in-Chief of the Asia-Pacific Journal of Ophthalmology (APJO) - Dennis Shun-chiu Lam. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

This document is a corrigendum, or correction, to an article titled "A single-institution experience with 177Lu RPT workflow improvements and qualifying the SPECT/CT imaging for dosimetry." The correction updates the corresponding author's information and provides the correct email ID. The authors apologize for the error but state that it does not affect the scientific conclusions of the article. The document also includes a note from the publisher stating that the views expressed in the article are solely those of the authors and do not necessarily represent the views of their affiliated organizations or the publisher. The article is open access and was published in Frontiers in Oncology. [Extracted from the article]

As immuno-oncology (I/O) emerges as an effective approach in the fight against cancer, multispectral imaging of multiplex immunofluorescence (mIF) is maturing as an analytical platform. The timing is fortuitous. Due to health economic considerations surrounding the use of I/O, there is an urgent need for tests that accurately predict response to the growing list of available therapies. Multispectral mIF provides several advantages over other biomarker modalities by enabling deeper interrogation of the intricate biology within the tumor microenvironment, including detection of cell-to-cell spatial interactions that correlate with clinical outcomes. It also provides a practical path for generating reliable and reproducible results in a clinically suitable, high-throughput workflow. In this article, we (1) describe the principles behind multispectral mIF; (2) provide advice and recommendations on assay development and optimization and highlight characteristics of a well-performing assay; and (3) discuss the requirements for translating this approach into clinical practice.

Rapid ethnography and data mining approaches have been used individually to study clinical workflows, but have seldom been used together to overcome the limitations inherent in either type of method. For rapid ethnography, how reliable are the findings drawn from small samples? For data mining, how accurate are the discoveries drawn from automatic analysis of big data, when compared with observable data? This paper explores the combined use of rapid ethnography and process mining, aka ethno-mining, to study and compare metrics of a typical clinical documentation task, vital signs charting. The task was performed with different electronic health records (EHRs) used in three different hospital sites. The individual methods revealed substantial discrepancies in task duration between sites. Specifically, means of 159.6(78.55), 38.2(34.9), and 431.3(283.04) seconds were captured with rapid ethnography. When process mining was used, means of 518.6(3,808), 345.5(660.6), and 119.74(210.3) seconds were found. When ethno-mining was applied instead, outliers could be identified, explained and removed. Without outliers, mean task duration was similar between sites (78.1(66.7), 72.5(78.5), and 71.7(75) seconds). Results from this work suggest that integrating rapid ethnography and data mining into a single process may provide more meaningful results than a siloed approach when studying of workflow. [ABSTRACT FROM AUTHOR]

A CDSS generates a high number of inappropriate alerts that interrupt the clinical workflow. As a result, clinicians silence, disable, or ignore alerts, thereby undermining patient safety. Therefore, the effectiveness and appropriateness of CDSS alerts need to be evaluated. A systematic review was carried out to identify the factors that affect CDSS alert appropriateness in supporting clinical workflow. Seven electronic databases (PubMed, Scopus, ACM, Science Direct, IEEE, Ovid Medline, and Ebscohost) were searched for English language articles published between 1997 and 2018. Seventy six papers met the inclusion criteria, of which 26, 24, 15, and 11 papers are retrospective cohort, qualitative, quantitative, and mixed-method studies, respectively. The review highlights various factors influencing the appropriateness and efficiencies of CDSS alerts. These factors are categorized into technology, human, organization, and process aspects using a combination of approaches, including socio-technical framework, five rights of CDSS, and Lean. Most CDSS alerts were not properly designed based on human factor methods and principles, explaining high alert overrides in clinical practices. The identified factors and recommendations from the review may offer valuable insights into how CDSS alerts can be designed appropriately to support clinical workflow. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) is having a significant impact in medical imaging, advancing almost every aspect of the field, from image acquisition and postprocessing to automated image analysis with outreach toward supporting decision making. Noninvasive cardiac imaging is one of the main and most exciting fields for AI development. The aim of this review is to describe the main applications of AI in cardiac imaging, including CT and MR imaging, and provide an overview of recent advancements and available clinical applications that can improve clinical workflow, disease detection, and prognostication in cardiac disease., Competing Interests: Disclosure Dr C.N. De Cecco receives funding from Siemens Healthineers, Germany and is a consultant of Bayer and Xeos. Dr M. van Assen receives funding from Siemens Healthineers., (Copyright © 2024 Elsevier Inc. All rights reserved.)

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High-speed Rotational Angiography scans are prone to potential collisions between the C-arm X-ray system and the patient. A key factor during rotational angiography clinical workflow is thus the initial patient positioning to ensure a collision-free scan. The current practice for this involves multiple manual iterations of a low speed safety-run prior to the actual scan. Several iterations are often required before a scan is collision-free leading to a suboptimal clinical workflow. This work proposes a RGB-D camera based safety system which automates the patient positioning process. The camera determines collisions between the C-arm and patient and a re-positioning algorithm determines the movement of the patient table required to ensure a collisoin-free scan. A unique feature of the solution is the ability to warn clinical staff when a collision-free scan is not possible. The efficacy of the proposed system is illustrated experimentally for two major RA scan types. [ABSTRACT FROM AUTHOR]

Clinical decision support systems (CDSSs) provides vital information for managing patients by advising clinicians through an alert or reminders about adverse events and medication errors. Clinicians receive a high number of alerts, resulting in alert override and workflow disruptions. A systematic review was carried out to identify factors affecting CDSS alert appropriateness in supporting clinical workflows using a recently introduced framework. The review findings identified several influencing factors of CDSS alert appropriateness including: technology (usability, alert presentation, workload and data entry), human (training, knowledge and skills, attitude and behavior), organization (rules and regulation, privacy and security) and process (waste, delay, tuning and optimization). The findings can be used to guide the design of CDSS alert and minimise potential safety hazards associated with CDSS use. [ABSTRACT FROM AUTHOR]

Abstract Purpose To describe the details and experience of implementing a MR-only workflow in the clinic for simulation and planning of prostate cancer patients. Methods Forty-eight prostate cancer patients from June 2016 - Dec 2016 receiving external beam radiotherapy were scheduled to undergo MR-only simulation. MR images were acquired for contouring (T2w axial, coronal, sagittal), synthetic-CT generation (3D FFE-based) and fiducial identification (3D bFFE-based). The total acquisition time was 25 min. Syn-CT was generated at the console using commercial software called MRCAT. As part of acceptance testing of the MRCAT package, external laser positioning system QA (< 2 mm) and geometric fidelity QA (< 2 mm within 50 cm LR and 30 cm AP) were performed and baseline values were set. Our current combined CT + MR simulation process was modified to accommodate a MRCAT-based MR-only simulation workflow. An automated step-by-step process using a MIM™ workflow was created for contouring on the MR images. Patient setup for treatment was achieved by matching the MRCAT DRRs with the orthogonal KV radiographs based on either fiducial ROIs or bones. 3-D CBCTs were acquired and compared with the MR/syn-CT to assess the rectum and bladder filling compared to simulation conditions. Results Forty-two patients successfully underwent MR-only simulation and met all of our institutional dosimetric objectives that were developed based on a CT + MR-based workflow. The remaining six patients either had a hip prosthesis or their large body size fell outside of the geometric fidelity QA criteria and thus they were not candidates for MR-only simulation. A total time saving of ~15 min was achieved with MR-based simulation as compared to CT + MR-based simulation. An automated and organized MIM workflow made contouring on MR much easier, quicker and more accurate compared with combined CT + MR images because the temporal variations in normal structure was minimal. 2D and 3D treatment setup localization based on bones/fiducials using a MRCAT reference image was successfully achieved for all cases. Conclusions MR-only simulation and planning with equivalent or superior target delineation, planning and treatment setup localization accuracy is feasible in a clinical setting. Future work will focus on implementing a robust 3D isotropic acquisition for contouring.