Workflow Efficiency Research Articles

Feasibility and Safety of the “One-Week Breast Radiation Therapy” Program

AimsFAST-Forward and UK-FAST-trials have demonstrated the safety and efficacy of five-fraction breast adjuvant radiation therapy (RT) and have become the standard of care for selected early breast cancer patients. In response to the additional burden caused by the COVID-19 pandemic, we implemented “One-Week Breast RT,” an innovative program delivering five-fraction whole breast RT in a complete 5-day workflow. The primary objective of this study was to demonstrate the feasibility and safety of our program. The secondary objective was to evaluate cosmetic results. Material and methodsA total of 120 patients treated from February 2021 to March 2022, received whole breast RT without lymph node irradiation nor boost, with 26 Gy in five fractions over one week. Inverse planning with restricted optimization parameters offers systematic deep inspiration breath-hold aimed to provide treatment plans compliant with FAST-Forward recommendations. Toxicity and cosmetic evaluations were prospectively registered prior (pre-RT), at the end (end-RT), and 6 months after RT (6 months) based on Common Terminology Criteria for Adverse Events v. 4.03 and Harvard scale. ResultsWith a median age of 70 years (interquartile range (IQR): 66–74) and a median follow-up of 6 months (IQR: 6.01–6.25), most patients (93.3%) completed their RT in one week from baseline to the end of the treatment consultation. The most common acute toxicities (at end-RT) were skin-related: radio-dermatitis (72%), induration (35%), hyperpigmentation (8%), and breast edema (16%). The rate of radio-dermatitis decreased from end-RT to 6 months (71.7% vs 5.4%, P< 0.001). No patient experienced grade ≥3 toxicity. At 6 months, cosmetic results were generally good or excellent (94.1%). ConclusionThis study confirms the feasibility and acute safety of the “One-Week Breast RT” in real life. Favorable toxicity profiles and good cosmetic outcomes are in line with FAST-Forward results. A prospective national cohort, aimed at decreasing treatment burden, maintaining safety, efficacy, and improving RT workflow efficiency with longer follow-up is ongoing.

Selecting the Best Radiology Workflow Efficiency Applications.

In the rapidly evolving digital radiology landscape, a surge in solutions has emerged including more than 500 artificial intelligence applications that have received 510k clearance by the FDA. Moreover, there is an extensive number of non-regulated applications, specifically designed to enhance workflow efficiency within radiology departments. These efficiency applications offer tremendous opportunities to resolve operational pain points and improve efficiency for radiology practices worldwide. However, selecting the most effective workflow efficiency applications presents a major challenge due to the multitude of available solutions and unclear evaluation criteria. In this article, we share our perspective on how to structure the broad field of workflow efficiency applications and how to objectively assess individual solutions. Along the different stages of the radiology workflow, we highlight 31 key operational pain points that radiology practices face and match them with features of workflow efficiency apps aiming to address them. A framework to guide practices in assessing and curating workflow efficiency applications is introduced, addressing key dimensions, including a solution's pain point coverage, efficiency claim strength, evidence and credibility, ease of integration, and usability. We apply this framework in a large-scale analysis of workflow efficiency applications in the market, differentiating comprehensive workflow efficiency ecosystems seeking to address a multitude of pain points through a unified solution from workflow efficiency niche apps following a targeted approach to address individual pain points. Furthermore, we propose an approach to quantify the financial benefits generated by different types of applications that can be leveraged for return-on-investment calculations.

High‐throughput in silico workflow for optimization and characterization of multimodal chromatographic processes

AbstractWhile high‐throughput (HT) experimentation and mechanistic modeling have long been employed in chromatographic process development, it remains unclear how these techniques should be used in concert within development workflows. In this work, a process development workflow based on HT experiments and mechanistic modeling was constructed. The integration of HT and modeling approaches offers improved workflow efficiency and speed. This high‐throughput in silico (HT‐IS) workflow was employed to develop a Capto MMC polishing step for mAb aggregate removal. High‐throughput batch isotherm data was first generated over a range of mobile phase conditions and a suite of analytics were employed. Parameters for the extended steric mass action (SMA) isotherm were regressed for the multicomponent system. Model validation was performed using the extended SMA isotherm in concert with the general rate model of chromatography using the CADET modeling software. Here, step elution profiles were predicted for eight RoboColumn runs across a range of ionic strength, pH, and load density. Optimized processes were generated through minimization of a complex objective function based on key process metrics. Processes were evaluated at lab‐scale using two feedstocks, differing in composition. The results confirmed that both processes obtained high monomer yield (&gt;85%) and removed of aggregate species. Column simulations were then carried out to determine sensitivity to a wide range of process inputs. Elution buffer pH was found to be the most critical process parameter, followed by resin ionic capacity. Overall, this study demonstrated the utility of the HT‐IS workflow for rapid process development and characterization.

Improving Part Setup Workflow Efficiency in a Supply Chain using Share Point

Improving Part Setup Workflow Efficiency in a Supply Chain using Share Point

A Semi-Automated Workflow for the Cryopreservation of Coral Sperm to Support Biobanking and Aquaculture.

Coral reefs are facing a crisis as the frequency of bleaching events caused by ocean warming increases, resulting in the death of corals on reefs around the world. The subsequent loss of genetic diversity and biodiversity can diminish the ability of coral to adapt to the changing climate, so efforts to preserve existing diversity are essential to maximize the resources available for reef restoration now and in the future. The most effective approach to secure genetics long-term is cryopreservation and biobanking, which permits the frozen storage of living samples at cryogenic temperatures in liquid nitrogen indefinitely. Cryopreservation of coral sperm has been possible since 2012, but the seasonal nature of coral reproduction means that biobanking activities are restricted to just a few nights per year when spawning occurs. Improving the efficiency of coral sperm processing and cryopreservation workflows is therefore essential to maximizing these limited biobanking opportunities. To this end, we set out to optimize cryopreservation processing pathways for coral sperm by building on existing technologies and creating a semi-automated approach to streamline the assessment, handling, and cryopreservation of coral sperm. The process, which combines computer-assisted sperm analysis, barcoded cryovials, and a series of linked auto-datasheets for simultaneous editing by multiple users, improves the efficiency of both sample processing and metadata management in the field. Through integration with cross-cutting research programs such as the Reef Restoration and Adaptation Program in Australia, cryopreservation can play a crucial role in large-scale reef restoration programs by facilitating the genetic management of aquaculture populations, supporting research to enhance thermal tolerance, and preventing the extinction of coral species. The described procedures will be utilized for coral cryopreservation and biobanking practitioners on reefs worldwide and will provide a model for the transition of cryopreservation technologies from research laboratories to large-scale applications.

Performance Evaluation of a Commercial Automated Library Preparation System for Clinical Microbial Whole-Genome Sequencing Assays

Next-generation sequencing (NGS) has proven clinical utility on disease management and serves as an important tool for genomic surveillance. Currently, hurdles surrounding its implementation, namely the complex and demanding analytical workflows, have impeded its widespread use in many laboratories. To address this challenge, the UCLA Molecular Microbiology and Pathogen Genomics Laboratory evaluated the performance of the Tecan MagicPrep NGS system, a commercial automated solution for library preparation for clinical whole-genome sequencing assays, against the Illumina Nextera DNA Flex Library Prep. Using 35 unique organisms (28 bacteria and 7 fungi) for various clinical applications, including microbial identification and genomic characterization, we compared the quantity and quality of the prepared libraries and the resulting sequences, and concordance of the overall results. We also assessed the impact of its implementation on laboratory workflow. The MagicPrep NGS produced higher library concentrations with smaller sizes, and correspondingly, higher molarity. Quality metrics of the sequences, however, demonstrated no significant impact on the overall results, producing 100% concordance with the reference method. Importantly, workflow analysis showed 5 hours less hands-on time per run with more flexibility. This evaluation study indicates that performance of the MagicPrep NGS is comparable to the Nextera DNA Flex with the added benefit of improving workflow efficiency and reducing labor for performing routine clinical microbial whole-genome sequencing tests.

Biological attributes required for epidermal regeneration: Evaluation of the next-generation autologous cell harvesting device.

Early wound intervention and closure is critical for reducing infection and improving aesthetic and functional outcomes for patients with acute burn wounds and nonthermal full-thickness skin defects. Treatment of partial-thickness burns or full-thickness injuries with autologous skin cell suspension (ASCS) achieves robust wound closure while limiting the amount of donor skin compared with standard autografting. A Next Generation Autologous Cell Harvesting Device (NG-ACHD) was developed to standardize the preparation process for ASCS to ensure biological attributes are obtained known to correlate with well-established safety and performance data. This study compared ASCS prepared using the NG-ACHD and ACHD following the manufacturer's guidance, evaluating cellular yields, viability, apoptotic activity, aggregates, phenotypes and functional capacity. Non-inferiority was established for all biological attributes tested and comparable healing trajectories were demonstrated using an in vitro skin regeneration model. In addition to standardization, the NG-ACHD also provides workflow efficiencies with the potential to decrease training requirements and increase the ease of incorporation and utilization of ASCS in clinical practice.

Exploring the Influence of Cloud Computing on Workflow Efficiency in Public Administration

This review examines the impact of cloud computing on public administration workflow efficiency, highlighting its potential to improve service delivery, optimize costs, and promote innovation. It provides a comprehensive evaluation framework for assessing efficiency, highlighting case studies of the UK Government Digital Service and Estonia's e-government initiatives. Despite challenges like security and data privacy, the study suggests promising future prospects, including hybrid cloud solutions and AI integration.

Deep Learning for Histopathological Assessment of Esophageal Adenocarcinoma Precursor Lesions

Histopathological assessment of esophageal biopsies is a key part in the management of patients with Barrett esophagus (BE) but prone to observer variability and reliable diagnostic methods are needed. Artificial intelligence (AI) is emerging as a powerful tool for aided diagnosis but often relies on abstract test and validation sets while real-world behavior is unknown. In this study, we developed a 2-stage AI system for histopathological assessment of BE-related dysplasia using deep learning to enhance the efficiency and accuracy of the pathology workflow. The AI system was developed and trained on 290 whole-slide images (WSIs) that were annotated at glandular and tissue levels. The system was designed to identify individual glands, grade dysplasia, and assign a WSI-level diagnosis. The proposed method was evaluated by comparing the performance of our AI system with that of a large international and heterogeneous group of 55 gastrointestinal pathologists assessing 55 digitized biopsies spanning the complete spectrum of BE-related dysplasia. The AI system correctly graded 76.4% of the WSIs, surpassing the performance of 53 out of the 55 participating pathologists. Furthermore, the receiver-operating characteristic analysis showed that the system’s ability to predict the absence (nondysplastic BE) versus the presence of any dysplasia was with an area under the curve of 0.94 and a sensitivity of 0.92 at a specificity of 0.94. These findings demonstrate that this AI system has the potential to assist pathologists in assessment of BE-related dysplasia. The system’s outputs could provide a reliable and consistent secondary diagnosis in challenging cases or be used for triaging low-risk nondysplastic biopsies, thereby reducing the workload of pathologists and increasing throughput.

The potential of artificial intelligence to revolutionize health care delivery, research, and education in cardiac electrophysiology.

The field of electrophysiology (EP) has benefited from numerous seminal innovations and discoveries that have enabled clinicians to deliver therapies and interventions that save lives and promote quality of life. The rapid pace of innovation in EP may be hindered by several challenges including the aging population with increasing morbidity, the availability of multiple costly therapies that, in many instances, confer minor incremental benefit, the limitations of healthcare reimbursement, the lack of response to therapies by some patients, and the complications of the invasive procedures performed. To overcome these challenges and continue on a steadfast path of transformative innovation, the EP community must comprehensively explore how artificial intelligence (AI) can be applied to healthcare delivery, research, and education and consider all opportunities in which AI can catalyze innovation; create workflow, research, and education efficiencies; and improve patient outcomes at a lower cost. In this white paper, we define AI and discuss the potential of AI to revolutionize the EP field. We also address the requirements for implementing, maintaining, and enhancing quality when using AI and consider ethical, operational, and regulatory aspects of AI implementation. This manuscript will be followed by several perspective papers that will expand on some of these topics.

Improving Workflow Efficiency at an Outpatient MRI Imaging Facility: A Case Study

Improving Workflow Efficiency at an Outpatient MRI Imaging Facility: A Case Study

Synergizing BIM and RIBA in architectural practice – technology workflow efficiencies, challenges, and insights

ABSTRACT Productivity improvements are constrained by insufficient consideration of technology adoption within professions. This study clarifies the limited knowledge between BIM and RIBA architectural workflow synergies and proposes a framework to improve identified inefficiencies. Consequently, a qualitative approach consisting of 10 interviews with architects was conducted to understand their personal experiences and opportunities for improvement. The findings revealed that multidisciplinary design cooperation and risk reduction are major possibilities for workflow improvements in the design stage, while site communication and error detection increase the need for an architect’s presence onsite during the construction stage. Additionally, this study identifies interoperability, barriers to change, and design inflexibility as professional drawbacks of using BIM. These findings provide hierarchical data to form an architect’s BIM matrix, which aims to maximise opportunities and reduce challenges in BIM implementation. This study highlights the importance of considering both technical and human factors in BIM adoption and uses management theories to explain how architects can better utilise technology to enhance their practice.

Clinical application of novel mobile AI solution for real-time detection and differential diagnosis in breast ultrasound: The first prospective feasibility study.

574 Background: Existing artificial intelligence (AI) solutions for breast ultrasound faced limitations due to their non-real-time detection and server dependency. However, novel mobile real-time AI solution now enable on-device detection and differential diagnosis, aiding in immediate decision-making. This study aims to evaluate the feasibility and effectiveness of such a mobile AI solution in clinical settings, by comparing it against expert breast imaging diagnoses. Methods: From August to December 2023, a feasibility study was conducted in tertiary medical center of Taiwan using mobile AI solution (CadAI-B for breast, BeamWorks Inc., Korea). CadAI-B is connected via HDMI or DVI to an ultrasound equipment using tablet PC. It supports healthcare providers to detect suspicious breast lesions by highlighting suspicious area during breast ultrasound scanning, and to make a differential diagnosis by providing BI-RADS categories and malignancy score (0-100%). The rate of real-time detection in malignancies and area under the receiver operating characteristic curve (AUC) was calculated. Distribution of BI-RADS categorized by CadAI-B were compared with those by breast imaging experts on independent review. Results: The analysis included 33 patients with an average age of 47 years (IQR, 44 – 62). There were 14 malignancies, 17 benign lesions, and 2 normal cases, and 30 patients (90.9%) underwent biopsy. For patients with malignancies, CadAI-B successfully identified all malignancies. Overall diagnostic performance, as AUCs calculated by the malignancy score and BI-RADS, was 0.835 and 0.850, respectively. The per-patient sensitivity of CadAI-B was 100.0%, while the specificity was 52.6%. The BI-RADS distribution between CadAI-B and expert was the same in malignant cases. In benign cases, CadAI-B categorized 9 cases (50.0%) as C4A or C4B, while experts classified 13 cases (72.2%), indicating a potential reduction in the need for biopsy. Conclusions: This study demonstrates that real-time processing capabilities of CadAI-B enable the identification of critical lesions during breast ultrasound, helping to reduce unnecessary biopsy. These findings may be helpful in diagnostic decisions and training of non-experts in clinical settings and offer the potential to improve workflow efficiency. [Table: see text]

Enhancing Workflow Efficiency in Yocto Project: A Build Tool for Fetch Error Detection and Fixing

Enhancing Workflow Efficiency in Yocto Project: A Build Tool for Fetch Error Detection and Fixing

Improving assessment of lesions in longitudinal CT scans: a bi-institutional reader study on an AI-assisted registration and volumetric segmentation workflow

PurposeAI-assisted techniques for lesion registration and segmentation have the potential to make CT-based tumor follow-up assessment faster and less reader-dependent. However, empirical evidence on the advantages of AI-assisted volumetric segmentation for lymph node and soft tissue metastases in follow-up CT scans is lacking. The aim of this study was to assess the efficiency, quality, and inter-reader variability of an AI-assisted workflow for volumetric segmentation of lymph node and soft tissue metastases in follow-up CT scans. Three hypotheses were tested: (H1) Assessment time for follow-up lesion segmentation is reduced using an AI-assisted workflow. (H2) The quality of the AI-assisted segmentation is non-inferior to the quality of fully manual segmentation. (H3) The inter-reader variability of the resulting segmentations is reduced with AI assistance.Materials and methodsThe study retrospectively analyzed 126 lymph nodes and 135 soft tissue metastases from 55 patients with stage IV melanoma. Three radiologists from two institutions performed both AI-assisted and manual segmentation, and the results were statistically analyzed and compared to a manual segmentation reference standard.ResultsAI-assisted segmentation reduced user interaction time significantly by 33% (222 s vs. 336 s), achieved similar Dice scores (0.80–0.84 vs. 0.81–0.82) and decreased inter-reader variability (median Dice 0.85–1.0 vs. 0.80–0.82; ICC 0.84 vs. 0.80), compared to manual segmentation.ConclusionThe findings of this study support the use of AI-assisted registration and volumetric segmentation for lymph node and soft tissue metastases in follow-up CT scans. The AI-assisted workflow achieved significant time savings, similar segmentation quality, and reduced inter-reader variability compared to manual segmentation.

Advancements in AI-driven diagnostic radiology: Enhancing accuracy and efficiency

Background: Healthcare delivery has transformed significantly with the integration of clinical decision support systems (CDS) and medical imaging. Convolutional neural networks (CNNs), a type of artificial intelligence (AI) algorithm, have exhibited remarkable accuracy in discerning intricate patterns and anomalies within medical images, surpassing human capability. Aim: This study aims to explore the impact of AI augmentation on diagnostic tasks, focusing on enhancing sensitivity, accuracy, and interrater agreement across various medical conditions. Additionally, it seeks to investigate how AI simplifies complex processes and integrates with existing technologies, extending its role in CDS systems beyond diagnostic accuracy. Methods: The research examines the effectiveness of AI in interpreting CT imaging and diagnosis. Furthermore, it assesses the integration of AI with radiology to enhance the detection of cerebral hemorrhages on head CT scans in time-pressed clinical settings. The research was performed using search engines such as google scholar and Pubmed. Results: The findings indicate that AI augmentation significantly enhances diagnostic capabilities, improves physician confidence, reduces interpretation time, and optimizes workflow efficiency. AI not only improves accuracy but also simplifies processes, thereby revolutionizing healthcare delivery. Conclusion: As artificial intelligence continues to evolve, its revolutionary potential in healthcare becomes increasingly evident.

P.103 Automated pituitary adenoma segmentation for radiosurgery with deep learning-based model

Background: Pituitary adenomas are treated with endoscopic surgery, while stereotactic radiosurgery addresses complex cases. Our study highlights AI’s role in accurate segmentation, improving treatment planning workflow efficiency Methods: In a retrospective study at Na Homolce Hospital (January 2010 to October 2022), SRS for pituitary adenomas was analyzed. Data were split 80:20 for training and validation. Using nnU-net, a medical image segmentation tool, a model predicted precise tumor, optic nerve, and pituitary gland segmentation. Accuracy was evaluated quantitatively with Dice similarity coefficient and qualitatively by human experts. The study explored the impact of tumor volume and hormonal activity status on segmentation accuracy. Results: The study comprised 582 and 146 patients in training and validation sets, respectively. The model achieved Dice similarity coefficients of 83.1% (tumor), 62.9% (normal gland), and 78.0% (optic nerve). Expert assessments deemed 41% directly applicable, 31.5% needing minor adjustments, and 27.4% unsuitable for clinical use. Larger tumor volume and non-functioning adenomas correlated with higher accuracy. Including T2 weighted scans improved DSC for optic nerve and normal gland. Conclusions: The study showcases deep learning’s potential in automating pituitary adenoma segmentation from MRI data, particularly excelling in large, hormonally inactive macroadenomas. Encourages collaborative use with clinicians for improved neurosurgical patient care.

Maximise workflow efficiency and safety in your practice

Maximise workflow efficiency and safety in your practice

Prospective phase II trial of preoperative hypofractionated proton therapy for extremity and truncal soft tissue sarcoma: the PRONTO study rationale and design

BackgroundOncologic surgical resection is the standard of care for extremity and truncal soft tissue sarcoma (STS), often accompanied by the addition of pre- or postoperative radiation therapy (RT). Preoperative RT may decrease the risk of joint stiffness and fibrosis at the cost of higher rates of wound complications. Hypofractionated, preoperative RT has been shown to provide acceptable outcomes in prospective trials. Proton beam therapy (PBT) provides the means to decrease dose to surrounding organs at risk, such as the skin, bone, soft tissues, and adjacent joint(s), and has not yet been studied in patients with extremity and truncal sarcoma.MethodsOur study titled “PROspective phase II trial of preoperative hypofractionated protoN therapy for extremity and Truncal soft tissue sarcOma (PRONTO)” is a non-randomized, prospective phase II trial evaluating the safety and efficacy of preoperative, hypofractionated PBT for patients with STS of the extremity and trunk planned for surgical resection. Adult patients with Eastern Cooperative Group Performance Status ≤ 2 with resectable extremity and truncal STS will be included, with the aim to accrue 40 patients. Treatment will consist of 30 Gy radiobiological equivalent of PBT in 5 fractions delivered every other day, followed by surgical resection 2–12 weeks later. The primary outcome is rate of major wound complications as defined according to the National Cancer Institute of Canada Sarcoma2 (NCIC-SR2) Multicenter Trial. Secondary objectives include rate of late grade ≥ 2 toxicity, local recurrence-free survival and distant metastasis-free survival at 1- and 2-years, functional outcomes, quality of life, and pathologic response.DiscussionPRONTO represents the first trial evaluating the use of hypofractionated PBT for STS. We aim to prove the safety and efficacy of this approach and to compare our results to historical outcomes established by previous trials. Given the low number of proton centers and limited availability, the short course of PBT may provide the opportunity to treat patients who would otherwise be limited when treating with daily RT over several weeks. We hope that this trial will lead to increased referral patterns, offer benefits towards patient convenience and clinic workflow efficiency, and provide evidence supporting the use of PBT in this setting.Trial registration: NCT05917301 (registered 23/6/2023).

Implementation Barriers and Recommendations for a Multisite Community Health Worker Intervention in Palliative Care for African American Oncology Patients: A Qualitative Study.

Background: Palliative care remains underutilized by African American patients with advanced cancer. Community health workers (CHWs) may help improve palliative care outcomes among this patient population. Objectives: To explore barriers to success of a proposed CHW intervention and synthesize design and implementation recommendations to both optimize our intervention and inform others working to alleviate palliative care disparities. Design: Semi-structured qualitative interviews. Setting/Subjects: Key informants were health care professionals across clinical, leadership, and community health fields. Participants were recruited through purposive sampling from Baltimore, Maryland; Birmingham, Alabama; and Salisbury, Maryland. Measurements: Interviewers used an interview guide grounded in established implementation science models. Data were analyzed through a combined abductive/deductive approach by independent coders. A framework methodology was used to facilitate thematic analysis. Results: In total, 25 professionals completed an interview. Key informants discussed multiple barriers, including at the patient level (lack of knowledge), clinician and facility level (decreased workflow efficiency), and health system level (limited funding). Recommendations related to the intervention's design included high quality preintervention CHW training and full integration of CHWs into the care team to "bridge" divides between outpatient, inpatient, and at-home settings. Intervention delivery recommendations included clearly defining care team roles and balancing flexibility and standardization in CHW support approaches. These recommendations were then used to adapt the planned intervention and its implementation process. Conclusions: Clinicians, cancer center leaders, and CHWs identified multilevel potential barriers to the intervention's success but also described recommendations that may mitigate these barriers. Key informant input represents an important step prior to initiating CHW-based interventions.