Workflow Process Research Articles

The Global Context Camera (CTX) Mosaic of Mars: A Product of Information‐Preserving Image Data Processing

AbstractThe Mars Reconnaissance Orbiter and its Context Camera (CTX) have acquired more than 100,000 separate panchromatic images that capture nearly the entire surface of Mars at ∼5–6 m/pixel. This paper describes a data processing workflow used to generate the first contiguous global mosaic of CTX data, which represents a large improvement in spatial resolution over existing 100 m/pixel contiguous global mosaics. We describe the overarching strategy for the mosaic's construction, which was to maximize the scientific utility of a continuous mosaic that is 5.7 trillion pixels in size. The pipeline used for data processing prioritized traceability and reproducibility of the final mosaic, such that the provenance of all pixels is reported, equipping scientists with information to differentiate mosaic artifacts from surface landforms and to incorporate critical image metadata into their analyses. The CTX data set synthesized into a global CTX mosaic facilitates ready analysis and provides a new capability in transitioning global studies of Mars from high‐resolution investigations of individual images to systematic studies of the entire Martian surface at outcrop‐resolving quality without regard to image boundaries.

Assessing RNA-Seq Workflow Methodologies Using Shannon Entropy.

RNA-seq faces persistent challenges due to the ongoing, expanding array of data processing workflows, none of which have yet achieved standardization to date. It is imperative to determine which method most effectively preserves biological facts. Here, we used Shannon entropy as a tool for depicting the biological status of a system. Thus, we assessed the measurement of Shannon entropy by several RNA-seq workflow approaches, such as DESeq2 and edgeR, but also by combining nine normalization methods with log2 fold change on paired samples of TCGA RNA-seq representing datasets of 515 patients and spanning 12 different cancer types with 5-year overall survival rates ranging from 20% to 98%. Our analysis revealed that TPM, RLE, and TMM normalization, coupled with a threshold of log2 fold change ≥1, for identifying differentially expressed genes, yielded the best results. We propose that Shannon entropy can serve as an objective metric for refining the optimization of RNA-seq workflows and mRNA sequencing technologies.

A Reliable System for Quantitative G-Protein Activation Imaging in Cancer Cells.

Fluorescence resonance energy transfer (FRET) biosensors have proven to be an indispensable tool in cell biology and, more specifically, in the study of G-protein signalling. The best method of measuring the activation status or FRET state of a biosensor is often fluorescence lifetime imaging microscopy (FLIM), as it does away with many disadvantages inherent to fluorescence intensity-based methods and is easily quantitated. Despite the significant potential, there is a lack of reliable FLIM-FRET biosensors, and the data processing and analysis workflows reported previously face reproducibility challenges. Here, we established a system in live primary mouse pancreatic ductal adenocarcinoma cells, where we can detect the activation of an mNeonGreen-Gαi3-mCherry-Gγ2 biosensor through the lysophosphatidic acid receptor (LPAR) with 2-photon time-correlated single-photon counting (TCSPC) FLIM. This combination gave a superior signal to the commonly used mTurquoise2-mVenus G-protein biosensor. This system has potential as a platform for drug screening, or to answer basic cell biology questions in the field of G-protein signalling.

Automatic uncovering of patient primary concerns in portal messages using a fusion framework of pretrained language models.

The surge in patient portal messages (PPMs) with increasing needs and workloads for efficient PPM triage in healthcare settings has spurred the exploration of AI-driven solutions to streamline the healthcare workflow processes, ensuring timely responses to patients to satisfy their healthcare needs. However, there has been less focus on isolating and understanding patient primary concerns in PPMs-a practice which holds the potential to yield more nuanced insights and enhances the quality of healthcare delivery and patient-centered care. We propose a fusion framework to leverage pretrained language models (LMs) with different language advantages via a Convolution Neural Network for precise identification of patient primary concerns via multi-class classification. We examined 3 traditional machine learning models, 9 BERT-based language models, 6 fusion models, and 2 ensemble models. The outcomes of our experimentation underscore the superior performance achieved by BERT-based models in comparison to traditional machine learning models. Remarkably, our fusion model emerges as the top-performing solution, delivering a notably improved accuracy score of 77.67 ± 2.74% and an F1 score of 74.37 ± 3.70% in macro-average. This study highlights the feasibility and effectiveness of multi-class classification for patient primary concern detection and the proposed fusion framework for enhancing primary concern detection. The use of multi-class classification enhanced by a fusion of multiple pretrained LMs not only improves the accuracy and efficiency of patient primary concern identification in PPMs but also aids in managing the rising volume of PPMs in healthcare, ensuring critical patient communications are addressed promptly and accurately.

ANALYSIS OF THE USE OF BLOCKCHAIN AND SMART CONTRACT TECHNOLOGIES FOR MANAGEMENT AUTOMATION AND ENSURING BUSINESS RESILIENCE

Increasing competition, the need to enhance efficiency and transparency of business processes, as well as the requirements to ensure the security and reliability of operations, demand new tools and approaches for automating management and ensuring the sustainability of the enterprise. Currently, it is blockchain and smart contract technologies that are opening up vast opportunities for this. Therefore, the research is aimed at a comprehensive analysis of the features and directions of using blockchain and smart contract technologies for automating management and ensuring the sustainability of the enterprise. Within the research, it has been proven that the use of blockchain and smart contract technologies creates a flexible and secure process for developing conditions and strategies that allow an enterprise to function effectively and remain capable of ensuring its own progressive development even in conditions of high stochasticity, economic turbulence, or crises. It is concluded that among the main directions where the use of blockchain technologies for ensuring enterprise resilience is worthwhile, the following should be highlighted: supply chain management, balanced management, security management, and basic risk management, as well as managing relationships with customers and partners. In unity, these outlined directions establish the principles of automating enterprise management, enhance its ability to function effectively, and direct its progressive development. It is concluded that among the main directions where the use of smart contracts for ensuring enterprise resilience is beneficial, the following should be highlighted: access and security management, remote tracking of resource utilization, automation of production and commercial processes, improvement of customer service, and management of data processing workflows. In unity, these outlined directions establish the principles of enterprise management automation, guiding and expanding the enterprise's capabilities for progressive development. In the context of ensuring enterprise resilience, the goal of applying smart contracts and blockchain technologies can vary. However, the use of these outlined technological solutions should guarantee the enterprise's ability to withstand external and internal influences.

A High-Precision Earthquake Catalog for Nevada

Abstract The state of Nevada is home to one of the most seismically active regions in the world, with crustal deformation associated with the Walker Lane transitioning into Basin and Range tectonics as one traverses from west to east across the state. Despite hosting numerous prominent earthquake sequences over the past century and beyond, at present, there exists no unified research-quality earthquake catalog for the state and its surrounding region. Here, we present a newly compiled, high-precision catalog of more than 180,000 earthquakes occurring around Nevada from 2008 to 2023. The data processing workflow to create this catalog includes an absolute location step that accounts for topography and 3D variations in subsurface wavespeed, and a relative relocation step that refines event positions using differential times measured from waveform cross-correlation. We also provide an update to the local magnitude scale that better accounts for the observed distance attenuation of waveform amplitudes as well as local site effects. We describe some fundamental insights that can be derived from the new catalog, including regional variations in event depth distributions and sequence clustering statistics, and publish the catalog to the wider community to facilitate future research efforts.

Optimizing a transformer-based network for a deep-learning seismic processing workflow

StorSeismic is a recently introduced model based on the transformer network to adapt to various seismic processing tasks through its pretraining and fine-tuning strategy. In the original implementation, StorSeismic uses a sinusoidal positional encoding (PE) and a conventional self-attention mechanism, borrowed from natural language processing applications. For seismic processing, they provide good results but also indicate limitations in efficiency and expressiveness. We develop modifications to these two key components, by using relative PE and low-rank attention matrices as replacements for the standard ones. Our changes are tested on processing tasks applied to realistic Marmousi and offshore field data as a sequential strategy, starting from denoising, direct-arrival removal, multiple attenuation, and finally root-mean-squared velocity ([Formula: see text]) prediction for normal moveout correction. We observe faster pretraining and competitive results on the fine-tuning tasks and, in addition, fewer parameters to train compared with the standard model.

Navigating the complexity of detrital rutile provenance: methodological insights from the Neotethys Orogen in Anatolia

Abstract. Sedimentary provenance is a powerful tool for reconstructing convergent margin evolution. However, single mineral approaches, like detrital zircon, have struggled to track sediment input from mafic and metamorphic sources. Detrital rutile complements detrital zircon datasets by offering a path forward in sedimentary provenance reconstructions where metamorphic terranes are potential source regions. However, U–Pb geochronology in rutile can be difficult due to low uranium concentrations and incorporation of common Pb, and multiple workflows are currently in use. Here, we investigate U–Pb and trace element data reduction, processing, and common Pb correction workflows using new detrital rutile U–Pb geochronology and trace element geochemistry results from the Late Cretaceous to Eocene Central Sakarya and Sarıcakaya basins in Anatolia. A significant number of analyses were rejected (54 %) due to signal intensity limitations, namely low U, low Pb, anomalous signal, and inclusions. We identify this as a universal limitation of large-n detrital rutile studies and recommend the systematic reporting of the amount of discarded analysis and the processes for rejection in all studies using detrital rutile U–Pb geochronology. Additionally, we show that (1) the 208Pb and 207Pb common Pb reduction schemes produce similar age distributions and can be used interchangeably, while (2) the Stacey–Kramers distance is a suitable metric for quantifying U–Pb discordance, but a discordance filter is not recommended. (3) Instead, filtering U–Pb data by a power law function based on the corrected date uncertainty is appropriate. (4) The exclusion of low uranium concentration rutile biases date distributions and favors pelitic-derived, higher Zr-in-rutile temperature, and higher U–Pb concordance grains. (5) Paired U–Pb and trace elements can be used to evaluate potential bias in U–Pb data rejection, which reveals that data rejection does not bias the provenance interpretations. Finally, (6) The signature of sediment recycling can be identified through U–Pb dates and Zr-in-rutile temperatures. To better navigate the complexity of detrital rutile datasets and to facilitate the standardization of data reporting approaches, we provide open-access code as Jupyter notebooks for data processing and analysis steps, including common Pb corrections, uncertainty filters, discordance calculations, and trace element analysis.

Close-range photogrammetry for analysis of rock relief details: An investigation of symbols purported to be Jewish Menorahs in Rough Cilicia

Close-range photogrammetry of certain rock reliefs in Rough Cilicia was used to investigate published claims that symbols in them represent Jewish menorahs, but with five branches instead of the usual seven. Details of ancient rock reliefs are difficult to assess because of mottled rock texture, color variation, wear or damage, and conditions of lighting. Thus, published photos of the rock reliefs in question are insufficient for evaluation of claims about them. Simple photogrammetry, however, produces 3D and digital elevation models that eliminate visual ambiguity and provide certainty of detail for analysis. This study describes the simple, non-invasive, and inexpensive data collection technique and the photogrammetry software processing workflow for creation of such models. Resulting models in various presentation formats provide factual data for reevaluation of the claimed menorahs themselves and their relationship to other elements of the reliefs. These findings contribute to informed discussion on interpretation of the symbols and their meaning. Adoption of the process described here is encouraged for subsequent publication, analysis, and interpretation of rock relief details in the region.

PINNslope: Seismic data interpolation and local slope estimation with physics informed neural networks

Interpolation of aliased seismic data constitutes a key step in a seismic processing workflow to obtain high-quality velocity models and seismic images. Building on the idea of describing seismic wavefields as a superposition of local plane waves, we propose to interpolate seismic data by using a physics informed neural network (PINN). In the proposed framework, two feed-forward neural networks are jointly trained using the local plane wave differential equation as well as the available data as two terms in the objective function: a primary network assisted by positional encoding is tasked with reconstructing the seismic data, whereas an auxiliary, smaller network estimates the associated local slopes. Results on synthetic and field data validate the effectiveness of the proposed method in handling aliased (coarsely sampled) data and data with large gaps. Our method compares favorably against a classic least-squares inversion approach regularized by the local plane-wave equation as well as a PINN-based approach with a single network and precomputed local slopes. We find that introducing a second network to estimate the local slopes, whereas at the same time interpolating the aliased data enhances the overall reconstruction capabilities and convergence behavior of the primary network. Moreover, an additional positional encoding layer embedded as the first layer of the wavefield network confers to the network the ability to converge faster, improving the accuracy of the data term.

Migration of House Banks in S/4HANA

SAP S/4HANA Cash Management’s one of the core functionalities is “Bank Relationship Management”. Finance Managers can centralize bank accounts using bank account master data. SAP S/4HANA Customers can also use workflow processes for opening, changing, closing bank account Master data. This provides companies relief from seeking approval thru emails and can get rid of the tedious manual approval processes when creating, changing, closing a bank account Master data. So, we don’t lose any information and achieve harmonization.

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.

Implementation of a LC-MS based multi-attribute method (MAM) and intact multi-attribute method (iMAM) workflow for the characterisation of a GLP-Fc fusion protein

Over the past few years, the implementation of mass spectrometry (MS) in QC laboratories has become a more common occurrence. The multi-attribute method (MAM), and emerging intact multi-attribute method (iMAM), are powerful analytical tools utilising liquid chromatography−mass spectrometry (LC-MS) methods that enable the monitoring of critical quality attributes (CQAs) in biotherapeutic proteins in compliant settings. Both MAM and iMAM are intended to replace or supplement several conventional assays with a single LC-MS method utilising MS data in combination with robust, semi-automated data processing workflows. MAM and iMAM workflows can also be implemented into current Good Manufacturing Practices environments due to the availability of CFR 11 compliant chromatography data system software. In this study, MAM and iMAM are employed for the analysis of 4 batches of a glucagon-like peptide-Fc fusion protein. MAM approach involved a first the discovery phase for the identification of CQAs and second, the target monitoring phase of the selected CQAs in other samples. New peak detection was performed on the data set to determine the appearance, absence or change of any peak. For native iMAM workflow both size exclusion and strong cation exchange chromatography were optimized for the identification and monitoring of CQAs at the intact level.

Mapping fault geomorphology with drone-based lidar

The advent of sub-meter resolution topographic surveying has revolutionized active fault mapping. Light detection and ranging (lidar) collected using crewed airborne laser scanning (ALS) can provide ground coverage of entire fault systems but is expensive, while Structure-from-Motion (SfM) photogrammetry from uncrewed aerial vehicles (UAVs) is popular for mapping smaller sites but cannot image beneath vegetation. Here, we present a new UAV laser scanning (ULS) system which overcomes these limitations to survey fault-related topography cost-effectively, at desirable spatial resolutions, and even beneath dense vegetation. In describing our system, data acquisition and processing workflows, we provide a practical guide for other researchers interested in developing their own ULS capabilities. We showcase ULS data collected over faults from a variety of terrain and vegetation types across the Canadian Cordillera and compare them to conventional ALS and SfM data. Due to the lower, slower UAV flights, ULS offers improved ground return density (~260 points/m2 for the capture of a paleoseismic trenching site and ~10–72 points/m2 for larger, multi-kilometer fault surveys) over conventional ALS (~3–9 points/m2) as well as better vegetation penetration than both ALS and SfM. The resulting ~20–50 cm-resolution ULS terrain models reveal fine-scale tectonic landforms that would otherwise be challenging to image.

Enhancing post-traumatic stress disorder patient assessment: leveraging natural language processing for research of domain criteria identification using electronic medical records

BackgroundExtracting research of domain criteria (RDoC) from high-risk populations like those with post-traumatic stress disorder (PTSD) is crucial for positive mental health improvements and policy enhancements. The intricacies of collecting, integrating, and effectively leveraging clinical notes for this purpose introduce complexities.MethodsIn our study, we created a natural language processing (NLP) workflow to analyze electronic medical record (EMR) data and identify and extract research of domain criteria using a pre-trained transformer-based natural language model, all-mpnet-base-v2. We subsequently built dictionaries from 100,000 clinical notes and analyzed 5.67 million clinical notes from 38,807 PTSD patients from the University of Pittsburgh Medical Center. Subsequently, we showcased the significance of our approach by extracting and visualizing RDoC information in two use cases: (i) across multiple patient populations and (ii) throughout various disease trajectories.ResultsThe sentence transformer model demonstrated high F1 macro scores across all RDoC domains, achieving the highest performance with a cosine similarity threshold value of 0.3. This ensured an F1 score of at least 80% across all RDoC domains. The study revealed consistent reductions in all six RDoC domains among PTSD patients after psychotherapy. We found that 60.6% of PTSD women have at least one abnormal instance of the six RDoC domains as compared to PTSD men (51.3%), with 45.1% of PTSD women with higher levels of sensorimotor disturbances compared to men (41.3%). We also found that 57.3% of PTSD patients have at least one abnormal instance of the six RDoC domains based on our records. Also, veterans had the higher abnormalities of negative and positive valence systems (60% and 51.9% of veterans respectively) compared to non-veterans (59.1% and 49.2% respectively). The domains following first diagnoses of PTSD were associated with heightened cue reactivity to trauma, suicide, alcohol, and substance consumption.ConclusionsThe findings provide initial insights into RDoC functioning in different populations and disease trajectories. Natural language processing proves valuable for capturing real-time, context dependent RDoC instances from extensive clinical notes.

Streamlining Document Processing: Basis foran Online Document Management System

This study addresses the critical need for an online document management system aimed at streamlining document submission, approval, and storing processes among the faculty and staff of Saint Michael College of Caraga (SMCC) located in Nasipit, Agusan del Norte, Philippines. By utilizing a mixed-methodology approach that integrates qualitative and quantitative techniques, the shortcomings and challenges of the current manual system were assessed through online survey and interviews. The research respondents, who were selected by convenience sampling, were primarily college instructors with 1 to 5 years of work experience. The investigation revealed significant inefficiencies in the current system, identifying key issues such as error-prone processes and delayed communication. The weighted mean scores indicated high level of agreement among the respondents for the proposed system that can potentially enhance process workflow, restructure communication channels, and promote transparency within the institution. The recommendations also include prioritizing primary functions such as user authentication, backup and recovery, and user training. Overall, it can be concluded the proposed online DMS can offer substantial improvements in document management practices, aligning with global trends and best practices in educational administration thus providing a potential model for other institutions facing similar challenges

Submarine cable positioning using a residual convolutional neural network based on magnetic features

Accurate positioning is important for improving the efficiency of repairing submarine cables and reducing the related repair costs. The magnetic anomaly produced by a submarine cable can be used to estimate its vertical and horizontal positions. A novel approach using magnetic data for estimating the position of submarine cables based on the 1D residual convolutional neural network (RCNN) is investigated. Infinitely long ferromagnetic cylinder models with different parameters are used to generate data sets for model training and testing. Tests on noisy synthetic data sets show that the developed 1D RCNN method can capture detailed features related to the magnetic source position information, which is more accurate than the conventional Euler method in estimating the position of submarine cables. The developed 1D RCNN method has also been successfully applied to processing field data. Furthermore, the processing workflow of our 1D RCNN method is less noise-sensitive compared with the conventional Euler method. The proposed 1D RCNN method and its workflow open a new window for estimating the position of submarine cables using magnetic data.

Navigating disparities: Insights from a colorectal cancer screening program in federally qualified community healthcare centers.

1537 Background: Colorectal cancer (CRC) is the second leading cause of cancer deaths in Georgia. Despite evidence for the effectiveness of CRC screening, disparities in access are prevalent in underserved populations. Georgia’s Federally Qualified Health Centers (FQHCs) struggle with low screening rates (42.8%), partly due to Georgia’s lack of Medicaid expansion, poor access to health services in rural areas, and deficiencies in coordinated strategies. The Georgia Colorectal Cancer Control Program (GCRCCP) was funded by the Centers for Disease Control and Prevention to implement evidence-based interventions (EBIs) in qualifying FQHCs across South Georgia. Here, we assess the program's impact on clinic-level screening rates (SR). Methods: Fourteen clinics with 12,159 CRC-eligible patients (ages 50-75) were enrolled from East Georgia Healthcare Center (EGHC) and Albany Area Primary Health Center (AAPHC). FQHC clinics had to have a SR below 60% to participate in the program. In addition to the EBIs (provider assessment and feedback, provider reminders, patient reminders, reducing structural barriers), the GCRCCP integrated provider education using the ECHO Model and navigation through a nationally recognized, evidence-based patient navigation program. We compared baseline (2020 and 2021) and follow-up data (2022) to determine the average change in SR over time, overall, and by demographic sub-groups. Results: The overall change in SR for the 14 clinics was +6.7%, from a baseline average of 45.3% to 52% in 2022. The average change in SR for EGHC clinics (n=9) was +11.5% (from 32.2 to 43.7%). Women had a higher SR (44.9%) than men (41.8%), but both realized an increase in SR, +10.5% and +9.1%. Non-Hispanic Black Americans (NHBA) experienced a higher increase in SR, +13.7% (from 37.2 to 50.9%) than their Non-Hispanic White (NHW) counterparts (+7.5% (from 29.8 to 37.3%)). The uninsured vs. insured followed a similar pattern (+15.3% (from 17.8 to 33.1%)) vs. +2.1 (from 45.9 to 48%). The average change in SR for AAPHC clinics (n=5) was +1.8% (from 58.4 to 60.2%). There was a decline in SR for men, -8.6% (from 64 to 55.4%), and an increase for women, +3.8% (from 58.2 to 62%). NHBA had a decrease in SR, -0.8% (from 61.2 to 61%), while NHW Americans had an increase in SR, +0.2% (from 53.8 to 54.2%). The uninsured and insured groups realized minimal increases of +0.6% (from 46 to 46.6%) and +0.8% (from 62 to 62.8%), respectively. Conclusions: The implementation of EBIs and patient navigation show varied potential to reduce disparities in accessing CRC screening in underserved populations. FQHC clinics that sustain implementation activities and workflow processes can benefit communities long-term and improve health equity.

The restoration, preservation and mastering of the Lantz Cartoons

This paper describes the workflow and approval process for bringing Lantz Cartoons from the Universal Pictures vault into a DolbyVision HDR workflow, and preserving film and audio elements as part of the Universal Pictures initiative to preserve and restore its 112-year-old library through digitisation. The paper describes the unique processes undertaken in accomplishing a request to master the films for a broadcast distribution deal and how the dedicated professionals used their technical knowledge, workflow expertise and understanding of library history to preserve, restore and master 139 short films to fulfil the sale. The paper details the challenges of creating a viable budget for the work and demonstrates the significance of technology in protecting historically essential films.

Timeliness of breast cancer care: Real-world experience at a tertiary care and a public safety-net hospital.

e13712 Background: Advances in breast cancer diagnostics and therapeutics have led to early cancer detection and an overall decrease in morbidity and mortality rates; nevertheless, cancer care disparity still exists and highlights areas of unmet need in cancer care delivery. Cancer navigator programs have been used as a strategy to reduce disparities arising from social determinants of health. We compared the timeliness of breast cancer care at UCLA Medical Center (UCLA), a tertiary care academic hospital, and at Olive View-UCLA Medical Center (OVMC), a safety-net hospital. This study highlights the benefits of cancer care navigation and streamlined workflow processes in bridging disparity gaps. Methods: In this retrospective cohort study, we examined patients diagnosed with breast cancer between 2018 and 2019 at UCLA and OVMC. A total of 100 patients were identified at each site. Demographics data, including age, race, ethnicity, pathology, and staging, were collected. For time interval analysis along the breast cancer care timeline, the mean number of days from the initial imaging date (i.e. either screening or diagnostic mammogram, with the latter if the patient presented with signs and symptoms of breast cancer) was compared with each diagnostic and therapeutic time point. These included time between screening and diagnostic mammograms (if available), time to biopsy, time to MRI, time to surgery, and time to treatment. Results: Mean age (years) at diagnosis was 62.0 and 57.5 at UCLA and OVMC, respectively (p=0.024). Racial distribution showed 60% White, 14% Asian, 7% Black, 7% Mixed, and 4% Other at UCLA, compared to 72%, 9%, 5%, 0%, and 0% at OVMC, respectively (p=0.01). Ethnicity comparison showed 11% Hispanic/Latino at UCLA versus 52% at OVMC (p<0.001). Breast cancer stage at diagnosis was not significantly different between sites. The time interval (days) from screening to diagnostic mammogram was significantly longer at OVMC compared to UCLA (35.1 versus 14.6, p=0.007); however, no statistically significant differences were observed from time to biopsy (27.3 versus 29.5, p=0.651), time to MRI (54.7 versus 49.9, p=0.703), time to neoadjuvant chemotherapy (73.4 versus 48.7, p=0.236), time to surgery (97.2 versus 106.2, p=0.523), time to adjuvant chemotherapy (158.6 versus 139.5, p=0.4), and time to radiation therapy (192.9 versus 198.6, p=0.796) between UCLA and OVMC, respectively. Conclusions: Our study shows that the timeliness of breast cancer care at UCLA and OVMC was overall comparable. This result can be attributed to the implementation of cancer navigator programs at OVMC as well as streamlined breast cancer detection workflow developed in multi-disciplinary collaboration across numerous departments. We believe this workflow can be expanded to all tumor types and be shared with other safety net facilities in order to bridge gaps in cancer care delivery for oncology patients.