Workflow Efficiency Research Articles

Enhancing medical imaging education: integrating computing technologies, digital image processing and artificial intelligence

AbstractThe rapid advancement of technology has brought significant changes to various fields, including medical imaging (MI). This discussion paper explores the integration of computing technologies (e.g. Python and MATLAB), digital image processing (e.g. image enhancement, segmentation and three‐dimensional reconstruction) and artificial intelligence (AI) into the undergraduate MI curriculum. By examining current educational practices, gaps and limitations that hinder the development of future‐ready MI professionals are identified. A comprehensive curriculum framework is proposed, incorporating essential computational skills, advanced image processing techniques and state‐of‐the‐art AI tools, such as large language models like ChatGPT. The proposed curriculum framework aims to improve the quality of MI education significantly and better equip students for future professional practice and challenges while enhancing diagnostic accuracy, improving workflow efficiency and preparing students for the evolving demands of the MI field.

Automatic delineation of cervical cancer target volumes in small samples based on multi-decoder and semi-supervised learning and clinical application.

Radiotherapy has been demonstrated to be one of the most significant treatments for cervical cancer, during which accurate and efficient delineation of target volumes is critical. To alleviate the data demand of deep learning and promote the establishment and promotion of auto-segmentation models in small and medium-sized oncology departments and single centres, we proposed an auto-segmentation algorithm to determine the cervical cancer target volume in small samples based on multi-decoder and semi-supervised learning (MDSSL), and we evaluated the accuracy via an independent test cohort. In this study, we retrospectively collected computed tomography (CT) datasets from 71 pelvic cervical cancer patients, and a 3:4 ratio was used for the training and testing sets. The clinical target volumes (CTVs) of the primary tumour area (CTV1) and pelvic lymph drainage area (CTV2) were delineated. For definitive radiotherapy (dRT), the primary gross target volume (GTVp) was simultaneously delineated. According to the data characteristics for small samples, the MDSSL network structure based on 3D U-Net was established to train the model by combining clinical anatomical information, which was compared with other segmentation methods, including supervised learning (SL) and transfer learning (TL). The dice similarity coefficient (DSC), 95% Hausdorff distance (HD95) and average surface distance (ASD) were used to evaluate the segmentation performance. The ability of the segmentation algorithm to improve the efficiency of online adaptive radiation therapy (ART) was assessed via geometric indicators and a subjective evaluation of radiation oncologists (ROs) in prospective clinical applications. Compared with the SL model and TL model, the proposed MDSSL model displayed the best DSC, HD95 and ASD overall, especially for the GTVp of dRT. We calculated the above geometric indicators in the range of the ground truth (head-foot direction). In the test set, the DSC, HD95 and ASD of the MDSSL model were 0.80/5.85mm/0.95mm for CTV1 of post-operative radiotherapy (pRT), 0.84/ 4.88mm/0.73mm for CTV2 of pRT, 0.84/6.58mm/0.89mm for GTVp of dRT, 0.85/5.36mm/1.35mm for CTV1 of dRT, and 0.84/4.09mm/0.73mm for CTV2 of dRT, respectively. In a prospective clinical study of online ART, the target volume modification time (MTime) was 3-5min for dRT and 2-4min for pRT, and the main duration of CTV1 modification was approximately 2min. The introduction of the MDSSL method successfully improved the accuracy of auto-segmentation for the cervical cancer target volume in small samples, showed good consistency with RO delineation and satisfied clinical requirements. In this prospective online ART study, the application of the segmentation model was demonstrated to be useful for reducing the target volume delineation time and improving the efficiency of the online ART workflow, which can contribute to the development and promotion of cervical cancer online ART.

AI AND MACHINE LEARNING IN BUSINESS PROCESS AUTOMATION: INNOVATING WAYS AI CAN ENHANCE OPERATIONAL EFFICIENCIES OR CUSTOMER EXPERIENCES IN U.S. ENTERPRISES

This study presents a comprehensive review of the transformative role of Artificial Intelligence (AI) and Machine Learning (ML) in enhancing business processes across various industries. By examining a total of 75 peer-reviewed articles, the review highlights key areas where AI and ML have demonstrated significant impact, including operational efficiency, customer engagement, and strategic decision-making. Findings indicate that AI-driven process optimizations, particularly through predictive maintenance and resource management, have led to substantial cost savings and improved productivity by minimizing downtime and enhancing workflow efficiencies. Additionally, AI’s ability to support personalized customer experiences—through recommendation systems, dynamic pricing, and chatbots—has proven instrumental in driving customer satisfaction, retention, and engagement, making it a critical tool in customer relationship management. Furthermore, the strategic adoption of AI and ML has enabled data-driven decision-making, allowing companies to respond more effectively to market changes and forecast business outcomes with greater accuracy. The study also explores the transition from Robotic Process Automation (RPA) to AI as a foundational step, illustrating how RPA provides a structured entry point for advanced AI applications, creating an automation-ready environment. However, challenges such as technical limitations, ethical concerns, and organizational resistance persist, underscoring the need for careful implementation strategies. Addressing these challenges will be vital for organizations aiming to maximize the benefits of AI-driven automation in an increasingly competitive digital landscape. This review underscores the significant potential of AI and ML in reshaping business models and emphasizes the importance of ongoing research and development in these fields to support sustainable and scalable innovations.

Revolutionizing JIRA Management with Artificial Intelligence: Streamlining Workflow Efficiency and Enhancing Project Outcomes

IT organizations are investing heavily on artificial intelligence (AI), though the value realization and areas of application of AI are still limited and not explored completely. Artificial intelligence applications are using large set of data to train the system to either generate new content or to predict the outcomes. Predictive analytics is used in agile management projects for budgeting, forecasting and capacity management. The article represents the use of cycle time and cumulative flow diagram metrics in ActionableAgile or JIRA to predict in agile development projects for forecasting of inflow of work and for the improvement of current work processes.

Revolutionizing Radiology With Artificial Intelligence.

Artificial intelligence (AI) is rapidly transforming the field of radiology, offering significant advancements in diagnostic accuracy, workflow efficiency, and patient care. This article explores AI's impact on various subfields of radiology, emphasizing its potential to improve clinical practices and enhance patient outcomes. AI-driven technologies such as machine learning, deep learning, and natural language processing (NLP) are playing a pivotal role in automating routine tasks, aiding in early disease detection, and supporting clinical decision-making, allowing radiologists to focus on more complex diagnostic challenges. Key applications of AI in radiology include improving image analysis through computer-aided diagnosis (CAD) systems, which enhance the detection of abnormalities in imaging, such as tumors. AI tools have demonstrated high accuracy in analyzing medical images, integrating data from multiple imaging modalities such as CT, MRI, and PET to provide comprehensive diagnostic insights. These advancements facilitate personalized treatment planning and complement radiologists' workflows. However, for AI to be fully integrated into radiology workflows, several challenges must be addressed, including ensuring transparency in how AI algorithms work, protecting patient data, and avoiding biases that could affect diverse populations. Developing explainable AI systems that can clearly show how decisions are made is crucial, as is ensuring AI tools can seamlessly fit into existing radiology systems. Collaboration between radiologists, AI developers, and policymakers, alongside strong ethical guidelines and regulatory oversight, will be key to ensuring AI is implemented safely and effectively in clinical practice. Overall, AI holds tremendous promise in revolutionizing radiology. Through its ability to automate complex tasks, enhance diagnostic capabilities, and streamline workflows, AI has the potential to significantly improve the quality and efficiency of radiology practices. Continued research, development, and collaboration will be crucial in unlocking AI's full potential and addressing the challenges that accompany its adoption.

The use of artificial intelligence in colonoscopic evaluations.

This review aims to highlight the transformative impact of artificial intelligence in the field of gastrointestinal endoscopy, particularly in the detection and characterization of colorectal polyps. Over the past decade, artificial intelligence has significantly advanced the medical industry, including gastrointestinal endoscopy. Computer aided diagnosis - detection (CADe) systems have shown notable success in increasing ADR. Recent meta-analyses of RCTs have demonstrated that patients undergoing colonoscopy with CADe assistance had a higher ADR compared with conventional methods. Similarly, computer aided diagnosis - characterization (CADx) systems have proven effective in distinguishing between adenomatous and nonadenomatous polyps, enhancing diagnostic confidence and supporting cost-saving measures like the resect-and-discard strategy. Despite the high performance of these systems, the variability in real-world adoption highlights the importance of integrating artificial intelligence as an assistive tool rather than a replacement for human expertise. Artificial intelligence integration in colonoscopy, through CADe and CADx systems, marks a significant advancement in gastroenterology. These systems enhance lesion detection and characterization, leading to improved diagnostic accuracy, training outcomes, and clinical workflow efficiency. While artificial intelligence offers substantial benefits, the optimal approach involves using artificial intelligence to augment the expertise of endoscopists, ensuring that clinical decisions remain under human oversight.

MaskAppendix: Backbone-Enriched Mask R-CNN Based on Grad-CAM for Automatic Appendix Segmentation

Background: A leading cause of emergency abdominal surgery, appendicitis is a common condition affecting millions of people worldwide. Automatic and accurate segmentation of the appendix from medical imaging is a challenging task, due to its small size, variability in shape, and proximity to other anatomical structures. Methods: In this study, we propose a backbone-enriched Mask R-CNN architecture (MaskAppendix) on the Detectron platform, enhanced with Gradient-weighted Class Activation Mapping (Grad-CAM), for precise appendix segmentation on computed tomography (CT) scans. In the proposed MaskAppendix deep learning model, ResNet101 network is used as the backbone. By integrating Grad-CAM into the MaskAppendix network, our model improves feature localization, allowing it to better capture subtle variations in appendix morphology. Results: We conduct extensive experiments on a dataset of abdominal CT scans, demonstrating that our method achieves state-of-the-art performance in appendix segmentation, outperforming traditional segmentation techniques in terms of both accuracy and robustness. In the automatic segmentation of the appendix region in CT slices, a DSC score of 87.17% was achieved with the proposed approach, and the results obtained have the potential to improve clinical diagnostic accuracy. Conclusions: This framework provides an effective tool for aiding clinicians in the diagnosis of appendicitis and other related conditions, reducing the potential for diagnostic errors and enhancing clinical workflow efficiency.

Enhancing workflow efficiency with a modified Firefly Algorithm for hybrid cloud edge environments

Efficient scheduling of scientific workflows in hybrid cloud-edge environments is crucial for optimizing resource utilization and minimizing completion time. In this study, we evaluate various scheduling algorithms, emphasizing the Modified Firefly Optimization Algorithm (ModFOA) and comparing it with established methods such as Ant Colony Optimization (ACO), Genetic Algorithm (GA), and Particle Swarm Optimization (PSO). We investigate key performance metrics, including makespan, resource utilization, and energy consumption, across both cloud and edge configurations. Scientific workflows often involve complex tasks with dependencies, which can challenge traditional scheduling algorithms. While existing methods show promise, they may not fully address the unique demands of hybrid cloud-edge environments, potentially leading to suboptimal outcomes. Our proposed ModFOA integrates cloud and edge computing resources, offering an effective solution for scheduling workflows in these hybrid environments. Through comparative analysis, ModFOA demonstrates improved performance in reducing makespan and completion times, while maintaining competitive resource utilization and energy efficiency. This study highlights the importance of incorporating cloud-edge integration in scheduling algorithms and showcases ModFOA’s potential to enhance workflow efficiency and resource management across hybrid environments. Future research should focus on refining ModFOA’s parameters and validating its effectiveness in practical hybrid cloud-edge scenarios.

Cluster randomized trial of a team communication training implementation strategy for depression screening in a pediatric healthcare system: a study protocol

BackgroundPediatric depression is a global concern that has fueled efforts for enhanced detection and treatment engagement. As one example, the US Preventive Services Task Force recommends depression screening for adolescents ages 12–18 years. While many health systems have implemented components of depression screening protocols, there is limited evidence of effective follow-up for pediatric depression. A key barrier is timely team communication and coordination across clinicians and staff within and across service areas for prompt service linkage. However, team effectiveness interventions have been shown to improve team processes and outcomes and can be applied in healthcare settings.MethodsThis project aims to refine and test a team communication training implementation strategy to improve implementation of an existing pediatric depression screening protocol in a large pediatric healthcare system. The team will be defined as part of the study but is expected to include medical assistants, nurses, physicians, and behavioral health clinicians within and across departments. The implementation strategy will target team mechanisms at the team-level (i.e., intra-organizational alignment and implementation climate) and team member-level (i.e., communication, coordination, psychological safety, and shared cognition). First, the project will use mixed methods to refine the team training strategy to fit the organizational context and workflows. Next, a hybrid type 3 implementation-effectiveness pilot trial will assess the initial effectiveness of the team communication training (implementation strategy) paired with the current universal depression screening protocol (clinical intervention) on implementation outcomes (i.e., feasibility, acceptability, appropriateness, workflow efficiency) and clinical/services outcomes (increased frequency of needed screening and reduced time to service linkage). Finally, the study will assess mechanisms at the team and team member levels that may affect implementation outcomes.DiscussionTeam communication training is hypothesized to lead to improved, efficient, and effective decision-making to increase the compliance with depression screening and timely service linkage. Findings are expected to yield better understanding and examples of how to optimize team communication to improve efficiency and effectiveness in the pediatric depression screening-to-treatment cascade. This should also culminate in improved implementation outcomes including patient engagement critical to address the youth mental health crisis.Trial registrationNCT06527196.Trial Sponsor: University of California San Diego.

Critical Result Notifications: What Do Providers Really Think?

Abstract Introduction/Objective Timely communication of critical test results, those that are significantly outside expected ranges and potentially life-threatening, is crucial in patient care. The Joint Commission and other regulatory bodies, such as CMS, CAP, mandate that hospitals have an alert system for reporting critical values. Traditionally, our hospital system communicates these results through phone calls to the responsible licensed provider, a system that has remained unchanged despite the rise of electronic health records. To assess the perception of this system and providers’ readiness to utilize new communication methods, we conducted a survey among healthcare professionals in our institution. Methods/Case Report We sent a survey to all providers via broadcast email. It contained 10 questions, some with free-text input, others with categorical or multiple-choice responses. The survey was open for 4 weeks with 2 reminder emails. Of 544 providers who opened the survey, 191 completed it (35.1% completion rate). The majority (84%) of respondents were physicians, with a smaller portion (15%) identifying as advanced practitioners. Results (if a Case Study enter NA) Most providers reported using multiple modes to check critical results and did not rely solely on lab notifications, though a minority (7%) depended on phone calls. Only 2 respondents used the smartphone app (EPIC Haiku) for checking results. Notably, over 67% of providers were unfamiliar with different methods of critical result notification, yet nearly 82% indicated they were open to adopting a new system. A significant proportion (more than a third) felt that phone calls distracted from patient care, suggesting that alternate methods of communication could be beneficial. Despite this, 62% of respondents found these calls useful. However, more than a third did not find the calls helpful, indicating a possible need for improved communication methods. Conclusion The survey indicates that while the current system of critical result notifications is useful to many providers, there are clear opportunities for improvement. Implementing secure, smart systems and increasing awareness of these new communication modalities may help reduce distractions and streamline critical result communication. This could also offer providers a customizable approach to receiving notifications, enhancing both patient care and workflow efficiency.

Red Blood Cell (RBC) Antigen Confirmation Workflow – A Positive Impact at an Academic Medical Center

Abstract Introduction/Objective Introduction: Blood group antigens, present on the surface of red blood cells (RBCs), play a crucial role in transfusion medicine. These antigens are recognized serologically and form the basis of RBC phenotyping. Understanding the significance of antigen-negative RBCs is essential for mitigating the risk of hemolytic transfusion reactions and hemolytic disease of the fetus and newborn (HDFN). This study aims to assess the impact of implementing a new RBC antigen confirmation workflow on transfusion service efficiency and patient outcomes. Methods/Case Report Method: The study involved collaboration between our Transfusion Services Department (TS), the Blood Center supplier, and the Apheresis department. A comprehensive review of historical practices and FDA recommendations regarding RBC antigen labeling was conducted. Changes were implemented to streamline antigen confirmation testing and labeling processes. The impact on TS workload, turnaround time, and patient care metrics was evaluated. Results (if a Case Study enter NA) Results: Implementation of the new RBC antigen confirmation workflow led to a significant reduction in antigen confirmation tests performed by TS, with a 607% decrease observed from 2020 to 2023. Concurrently, the workload for reference lab workups increased by 337% over the same period. This shift in workload distribution allowed TS staff to focus more on patient reference workup cases and case review. The improved workflow efficiency resulted in enhanced turnaround times for providing patients with antigen-negative RBCs, ultimately improving the patient experience. Conclusion Conclusion: The adoption of the new RBC antigen confirmation workflow has resulted in compliance with FDA labeling requirements, efficient utilization of TS staff resources, and decreased turnaround times for blood delivery. These improvements have positively impacted patient care by ensuring timely access to compatible blood products. Future steps include developing reports to track turnaround time for patients with special blood product needs and continuing collaboration with the blood supplier and the Apheresis department to optimize inventory management and transfusion processes.

Artificial Intelligence in Uropathology.

The global population is currently at unprecedented levels, with an estimated 7.8 billion people inhabiting the planet. We are witnessing a rise in cancer cases, attributed to improved control of cardiovascular diseases and a growing elderly population. While this has resulted in an increased workload for pathologists, it also presents an opportunity for advancement. The accurate classification of tumors and identification of prognostic and predictive factors demand specialized expertise and attention. Fortunately, the rapid progression of artificial intelligence (AI) offers new prospects in medicine, particularly in diagnostics such as image and surgical pathology. This article explores the transformative impact of AI in the field of uropathology, with a particular focus on its application in diagnosing, grading, and prognosticating various urological cancers. AI, especially deep learning algorithms, has shown significant potential in improving the accuracy and efficiency of pathology workflows. This comprehensive review is dedicated to providing an insightful overview of the primary data concerning the utilization of AI in diagnosing, predicting prognosis, and determining drug responses for tumors of the urinary tract. By embracing these advancements, we can look forward to improved outcomes and better patient care.

Strategy for scheduled downtime of hospital information system utilizing third-party applications

BackgroundThe widespread adoption of Hospital Information Systems (HIS) has brought significant benefits in healthcare quality and workflow efficiency. However, downtimes for system maintenance are inevitable and pose a considerable challenge to continuous patient care. Existing strategies, including manual prescription methods, are no longer effective due to increasing reliance on digital systems.MethodThis study implemented two main strategies to mitigate the impact of scheduled downtimes. First, we created an “Emergency query program” that switches to a read-only backup server during downtimes, allowing clinicians to view essential patient data. Second, an “Emergency prescription system” was developed based on the Microsoft Power Platform and integrated into Microsoft Teams. This allows clinicians to perform digital prescriptions even during downtimes.ResultsDuring a planned 90-minute downtime, 282 users accessed the Emergency Prescription System, resulting in 22 prescriptions from various departments. Average times for prescription confirmation and completion were 8 min and 3 s, and 18 min and 40 s, respectively. A post-downtime evaluation revealed high user satisfaction.ConclusionEssential maintenance-induced HIS downtimes are inherently disruptive to patient care process. Our deployment of an emergency query program and a Microsoft Teams-integrated emergency prescription system demonstrated robust care continuity during HIS downtime.

Quality assurance for online adaptive radiotherapy: a secondary dose verification model with geometry-encoded U-Net

Abstract Background & Purpose: In online adaptive radiotherapy (ART), quick computation-based secondary dose verification is crucial for ensuring the quality of ART plans while the patient is positioned on the treatment couch. However, traditional dose verification algorithms are generally time-consuming, reducing the efficiency of ART workflow. This study aims to develop an ultra-fast deep-learning (DL) based secondary dose verification algorithm to accurately estimate dose distributions using CT and fluence maps (FMs). 
Methods: We integrated FMs into the CT image domain by explicitly resolving the geometry of treatment delivery. For each gantry angle, an FM was constructed based on the optimized multi-leaf collimator apertures and corresponding monitoring units (MUs). To effectively encode treatment beam configuration, the constructed FMs were back-projected to 30 cm away from the isocenter with respect to the exact geometry of the treatment machines. Then, a 3D U-Net was utilized to take the integrated CT and FM volume as input to estimate dose. Training and validation were performed on 381 prostate cancer cases, with an additional 40 testing cases for independent evaluation of model performance.
Results: The proposed model can estimate dose in ~15 ms for each patient. The average γ passing rate (3%/2mm, 10% threshold) for the estimated dose was 99.9% ± 0.15% on testing patients. The mean dose differences for the planning target volume (PTV) and organs at risk (OARs) were 0.07%±0.34% and 0.48%±0.72%, respectively.
Conclusion: We have developed a geometry-resolved DL framework for accurate dose estimation and demonstrated its potential in real-time online ART doses verification.

Management of adverse effects of construction digitalization on the quantity surveying key roles: a qualitative Delphi study

Purpose Digitalization has significantly impacted the job roles of construction professionals globally. This study aims to investigate how to manage the adverse effects of digitalization on roles of quantity surveyor (QS) by assessing the level of digitalization of quantity surveying key roles identifying the adverse effects of digitalization on key roles and proposing mitigation strategies to manage adverse effects. It addresses a critical gap by providing a nuanced understanding of adverse effects faced by QSs due to digital advancement. Design/methodology/approach Adopting an interpretive stance, the research involved three rounds of 16 semistructured interviews with experts selected through heterogeneous purposive sampling. The data were analyzed using code-based content analysis with NVivo12. Findings The study identified ten quantity surveying key roles adopting digital technologies. While digitalization has enhanced efficiency and accuracy, it has also introduced adverse effects such as increased workload, potential burnout due to skills shortages, resistance to digital tools and data security concerns. Effective mitigation strategies include task prioritization, balancing traditional and digital skills and investing in continuous training. The study underscores the need for ongoing professional development for QSs and organizational restructuring to support digital integration, enhance workflow efficiency and reduce resistance to technological changes. Research limitations/implications This study’s novel contribution lies in its exploration of the adverse effects of digitalization on the QSs, usage and the level of adaptation of digital tools by QSs and strategies to manage them. Therefore, the findings of this study serve as precious steerage for QSs to have an in-depth understanding of digitalization’s usage and level of adaptation, its adverse effects and mitigation of the adverse effects. Originality/value This study’s novel contribution lies in its exploration of the adverse effects of digitalization on the QSs, usage and the level of adaptation of digital tools by QSs and strategies to manage them. Therefore, the findings of this study serve as precious steerage for QSs to have an in-depth understanding of digitalization’s usage and level of adaptation, its adverse effects and mitigation of the adverse effects.

Systematic Layout Planning and Analytical Hierarchy Process for Laboratory Layout Optimization: A Case Study of DESPRIN

The Product Design and Innovation Laboratory (DESPRIN) at Del Institute of Technology plays a crucial role in supporting innovation. Yet, its current layout struggles to meet its users' diverse and evolving needs. This study addresses the problem of inadequate spatial configuration, which hampers workflow efficiency, ergonomics, and the lab's overall capacity to accommodate various activities. To resolve this issue, a flexible layout is designed using the Systematic Layout Planning (SLP) method, emphasizing optimizing space utilization, workflow, and spatial adaptability. Data collected from literature reviews, interviews, statistical analyses, and anthropometric measurements inform the design process to enhance the lab’s quality and spatial efficiency. The Analytical Hierarchy Process (AHP) is employed to prioritize key layout design factors, identifying capacity as the most critical element (0.4930), followed by facilities (0.1688), accessibility (0.1414), security (0.1270), and environment (0.0708). The study results in 13 new layout configurations that can accommodate various activities within DESPRIN, providing a more dynamic and responsive user environment.

B-008 An Evaluation of the Analytical Performance of the New Beckman Coulter DxC 500i Clinical Analyzer

Abstract Background The DxC 500i Clinical Analyzer* is Beckman Coulter’s latest integrated Clinical Chemistry and Immunoassay analyzer. It provides reliability, scalability, and workflow efficiency via a flexible independent maintenance mode, increased uptime, and a dynamic sample handler to optimize sample management. Standard Beckman Coulter Chemistry and Immunoassay consumables and reagents ensure network standardization and commutability. The purpose of this study was to evaluate the analytical performance of the new DxC 500i analyzer and to compare the performance against the Beckman Coulter stand-alone Clinical Chemistry and Immunoassay analyzers on the market. Methods To assess performance over a range of assay methodologies, several DxC 500i Beckman Coulter Immunoassays and Chemistry assays were evaluated. Precision was assessed using human samples following CLSI EP05-A3 guidelines. Linear range was assessed following CLSI EP06-A guidelines. Method Comparison and Bias Estimation studies compared the DxC 500i Clinical Analyzer against the Access 2 Immunoassay System, the AU480 and the DxC 700 AU Clinical Chemistry analyzers, following CLSI EP09c-ED3 guidelines. To investigate carryover potential as samples are processed across the integrated system, a carryover study was designed. This focused on evaluating potential sample-to-sample carryover caused by the AU sample probe and its effects on immunoassay samples during reflex scenarios. Results DxC 500i Chemistry results: Glucose Serum (OSR6x21) Assessments of repeatability and within laboratory precision at multiple critical analyte concentrations showed total imprecision of <3% and within-run imprecision of <3%. The Glucose serum assay demonstrated acceptable linearity throughout the 10-800 mg/dL analytical measuring range. Method comparison studies compared the DxC 500i analyzer against the AU480 and DxC 700 AU systems. A Weighted Deming regression of serum patient samples (n>100 measured across the analytical range) yielded a 0.986 slope, 0.227 mg/dL intercept, and R=0.9999 correlation coefficient against the DxC 700 AU and a 0.995 slope, 0.356 mg/dL intercept, and R=0.9999 correlation coefficient against the AU480. DxC 500i Immunoassay results: Cortisol (33600) Beckman Coutler’s Cortisol assay exhibited total imprecision of <12%CV at <5 μg/dL or <10CV% at ≥5 μg/dL for serum. The Cortisol assay demonstrated acceptable linearity throughout the 0.4-60 μg/dL analytical measuring range. A method comparison study compared the DxC 500i against the Access 2 system. A Weighted Deming regression of serum patient samples (n>100 measured across the analytical range) yielded a 0.9785 slope, 0.2764 μg/dL intercept, and R=0.9932 correlation coefficient. DxC 500i Carryover Assessment The Total βhCG (5th IS) assay was used to evaluate the potential of chemistry analyzer to immunoassay analyzer sample carryover. The study used High hCG serum samples (>1,000,000 mIU/mL) and Low hCG serum samples (4-6 mIU/mL). The DxC 500i exhibited a cumulative carryover of ≤2 parts per million (PPM) for serum. Conclusions These studies demonstrate excellent analytical performance for the new Beckman Coulter DxC 500i Clinical Analyzer* and confirm comparable performance to current Beckman Coulter standalone Clinical Chemistry and Immunoassay analyzers on the market. *Pending clearance by the United States Food and Drug Administration; not yet available for in vitro diagnostic use in the U.S. Product availability and regulatory status depends on country registration per applicable regulations.

B-018 Design and Development of MASTM Diabetes Max Controls, Ready-to-Use Plastic Tube Format

Abstract Background The Thermo Scientific™ MAS™ Controls are assayed controls to monitor assay performance within clinical laboratory settings. The user can compare observed results of controls with their expected ranges as a means of assuring consistent performance of both reagent and instrument. The objective of developing MAS™ Ready-to-Use Tube Controls is to provide MAS™ controls in a new automation-friendly plastic tube format, as an alternative to the current glass vials. Diabetes Max Control is the first MAS™ product designed and developed in this ready-to-use format to be placed directly on the analyzer. The new format will increase efficiency and allow for on-board refrigerated storage, and it is expected to reduce material loss and contamination. It contains Hemoglobin A1c formulated in whole blood matrix to mimic patient specimens. In this presentation we summarize the Feasibility, Development, Verification, and Validation results of MAS™ Diabetes Max Controls. Methods The performance was assessed by conducting the following studies for MAS™ Diabetes Max Tube Controls. The analyte Hemoglobin A1c was measured in functional studies to evaluate and verify the product performance. Other studies were also designed and performed to assess the product usability. Results MAS™ Diabetes Max Control results demonstrated comparable performance and Fit/Form/Function criterion on specified platforms, particularly the TOSOH™ platform. All feasibility studies provided very promising results passing per protocol criterion. Design Verification and Validation studies ensured product integrity and passed specifications for proposed claims. Process Validation is in progress with data being collected for both Value Assignments and Real Time Stability. Conclusions MAS™ Diabetes Max Controls provided in ready-to-use tubes met the design specification criteria. We believe that MAS™ Diabetes Max Controls will contribute to the increased efficiency of core lab workflow by allowing the analyzer(s) to aspirate controls directly from the tube and allow for control storage in an on-board refrigeration unit.

A Collection of Components to Design Clinical Dashboards Incorporating Patient-Reported Outcome Measures: Qualitative Study.

A clinical dashboard is a data-driven clinical decision support tool visualizing multiple key performance indicators in a single report while minimizing time and effort for data gathering. Studies have shown that including patient-reported outcome measures (PROMs) in clinical dashboards supports the clinician's understanding of how treatments impact patients' health status, helps identify changes in health-related quality of life at an early stage, and strengthens patient-physician communication. This study aims to determine design components for clinical dashboards incorporating PROMs to inform software producers and users (ie, physicians). We conducted interviews with software producers and users to test preselected design components. Furthermore, the interviews allowed us to derive additional components that are not outlined in existing literature. Finally, we used inductive and deductive coding to derive a guide on which design components need to be considered when building a clinical dashboard incorporating PROMs. A total of 25 design components were identified, of which 16 were already surfaced during the literature search. Furthermore, 9 additional components were derived inductively during our interviews. The design components are clustered in a generic dashboard, PROM-related, adjacent information, and requirements for adoption components. Both software producers and users agreed on the primary purpose of a clinical dashboard incorporating PROMs to enhance patient communication in outpatient settings. Dashboard benefits include enhanced data visualization and improved workflow efficiency, while interoperability and data collection were named as adoption challenges. Consistency in dashboard design components is preferred across different episodes of care, with adaptations only for disease-specific PROMs. Clinical dashboards have the potential to facilitate informed treatment decisions if certain design components are followed. This study establishes a comprehensive framework of design components to guide the development of effective clinical dashboards incorporating PROMs in health care practice.

B-031 A new Ready to use Bio-Rad Quality Control to Streamline Daily Workflow

Abstract Background A comprehensive Quality Management System starts with performing a daily Quality Control (Q.C.) process. The daily Q.C. process monitors the Instrument’s optimal performance and detects shifts and trends. However, the existing traditional daily Q.C. process is not only prone to manual errors, but also time consuming, costly and labor intensive. We evaluated a Bio-Rad InteliQTM Load and go Quality Control that comes in a barcoded tube ready to be loaded. We monitored the workflow efficiency gained in comparison to the traditional quality control material that comes in glass vials. Methods We evaluated the new quality control material ready to load on the instrument. This new Q.C. (Bio-Rad InteliQ Assayed Multiqual Control - 3 levels) were processed in parallel with our routine Q.C. (Bio-Rad Assayed Chemistry Lyphocheck vials - 2 levels). We performed the evaluation on Abbott Architect c8000 Chemistry analyzers in our central Laboratory (NGHA-Jeddah). The Q.C. was stored frozen. Before testing it was thawed at room temperature as per the insert instruction. Over a 5 days period, in parallel with our routine quality control, we run the new Q.C. Two times per day for 33 analytes. We monitored the processing time difference between the new and current approaches. We monitored the stability of all the analytes in the tube after Two days and again after Five days. We also monitored the cost effectiveness, labor intensiveness, probability of operator error and the overall improvement in workflow and Turn Around Time. Results Over the evaluation period, we observed the following: - Saving 40 minutes in average per week only for chemistry Q.C (time spent in reconstituting, mixing and aliquoting old Q.C) - 100% reduction in waste of plastic consumables such as pipette and plastic tubes. - Save an average of 1.0 ml dead volume in every 5.0 ml old Q.C. vial (20% of the volume, hence saving 20% of the total Q.C. cost). The dead volume wasted due to aliquoting step using the old Q.C. - 100% Eliminated delays caused by operator error such as wrong reconstitution, inadequate mixing and wrong Q.C. aliquoting. Not needed for the InteliQ. - High score of staff satisfaction. - Stability of the 32 analytes was excellent except that of Co2, which deteriorated by 14% after Two days as indicated in the insert package. Co2 day five result deteriorated by 34%. Conclusions Bio-Rad InteliQ is easy to use. It saves time and cost. When combined with Unity Q.C. data management solution it can streamline Q.C. workflow, eliminate operator errors, reduce T.A.T. and increase the overall laboratory performance. It may also work optimally with Instruments that have on-board fridge for Q.C.