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Computer imaging and workflow systems in the business office.

Computer imaging and workflow technology automates many business processes that currently are performed using paper processes. Documents are scanned into the imaging system and placed in electronic patient account folders. Authorized users throughout the organization, including preadmission, verification, admission, billing, cash posting, customer service, and financial counseling staff, have online access to the information they need when they need it. Such streamlining of business functions can increase collections and customer satisfaction while reducing labor, supply, and storage costs. Because the costs of a comprehensive computer imaging and workflow system can be considerable, healthcare organizations should consider implementing parts of such systems that can be cost-justified or include implementation as part of a larger strategic technology initiative.

Admitting Department, Hospital↗

Automated chromatin profiling with spa-ChIP-seq uncovers the impacts of condition variations.

Chromatin immunoprecipitation followed by sequencing (ChIP-seq) is widely used to study the genomic localization of DNA-associated proteins. However, conventional protocols include multiple manual steps that can introduce inconsistency and limit scalability, thereby restricting the inclusion of appropriate replicates and controls. Although the introduction of liquid handling platforms has improved reproducibility, most existing efforts have automated only a subset of the workflow, and extending automation to efficiently map non-histone proteins, such as chromatin regulators, remains challenging. Here, we present a fully automated implementation of our previously developed single-pot ChIP-seq protocol (Texari et al. 2021), named spa-ChIP-seq, which enables scalable processing of 8 to 96 ChIP-seq samples from crosslinked cells to sequencing-ready library in approximately three days with an estimated cost of $70 per sample. Benchmarking spa-ChIP-seq against manual ChIP-seq performed in parallel demonstrates comparable signal-to-noise ratio between the two workflows. Using spa-ChIP-seq, we systematically evaluate multiple parameters including shearing and crosslinking conditions, buffer compositions, and the ratio of antibody to cell-number. We find, for the first time to our knowledge, that weaker genomic localization signals are sensitive to changing the antibody to cell-number ratio, whereas the stronger signals remain unaffected. This finding underscores the importance of maintaining consistent antibody-to-cell-number ratio for comparative studies, such as treatment responses or chromatin-QTL mapping. The spa-ChIP-seq protocol is publicly available, including deck setups, operational parameters, and scripts. We envision that this robust, cost-efficient protocol will facilitate high-throughput, reproducible ChIP-seq analyses, supporting large-scale studies of antibody validation, compound screening, population genomics, and diagnostic frameworks.

Journal Article↗

Evaluation of multidimensional (ion-exchange/reversed-phase) protein separations using linear and step gradients in the first dimension.

The performance characteristics of multidimensional liquid chromatographic protein separations were evaluated using on-line electrospray mass detection, and a novel workflow for automated LC/MS data processing. Two-dimensional ion exchange/reversed-phase LC separations of Escherichia coli cytosol were conducted using either a continuous linear or discontinuous step gradient in the first dimension. Chromatographic profiles of the top 100 most abundant components were characterized to assess overall separation reproducibility within each mode, and to characterize differences in component distribution between the two modes of operation. Analysis of the resulting data indicates that multidimensional separations of complex protein mixtures can be done reproducibly. Furthermore, under the conditions employed within this study, a linear first dimension gradient was more effective at fractionating the protein mixture, distributing fewer major components to multiple second dimension cycles than an equivalent step gradient. The application of on line mass spectrometry, and automated processing of the resulting data, proved valuable for producing component level analysis of multidimensional protein separations.

Chromatography, Ion Exchange↗

PopGLen-a Snakemake pipeline for performing population genomic analyses using genotype likelihood-based methods.

SUMMARY: PopGLen is a Snakemake workflow for performing population genomic analyses within a genotype-likelihood framework, integrating steps for raw sequence processing of both historical and modern DNA, quality control, multiple filtering schemes, and population genomic analysis. Currently, the population genomic analyses included allow for estimating linkage disequilibrium, kinship, genetic diversity, genetic differentiation, population structure, inbreeding, and allele frequencies. Through Snakemake, it is highly scalable, and all steps of the workflow are automated, with results compiled into an HTML report. PopGLen provides an efficient, customizable, and reproducible option for analyzing population genomic datasets across a wide variety of organisms. AVAILABILITY AND IMPLEMENTATION: PopGLen is available under GPLv3 with code, documentation, and a tutorial at https://github.com/zjnolen/PopGLen. An example HTML report using the tutorial dataset is included in the Supplementary Material.

Software↗

Evaluation of sequencing reads at scale using rdeval.

MOTIVATION: Large sequencing datasets are being produced and deposited into public archives at unprecedented rates. The availability of tools that can reliably and efficiently generate and store sequencing read summary statistics has become critical. RESULTS: As part of the effort by the Vertebrate Genomes Project (VGP) to generate high-quality reference genomes at scale, we sought to address the community's need for efficient sequence data evaluation by developing rdeval, a standalone tool to quickly compute and interactively display sequencing read metrics. Rdeval can either run on the fly or store key sequence data metrics in tiny read 'snapshot' files. Statistics can then be efficiently recalled from snapshots for additional processing. Rdeval can convert fa*[.gz] files to and from other popular formats including BAM and CRAM for better compression. Overall, while CRAM achieves the best compression, the gain compared to BAM is marginal, and BAM achieves the best compromise between data compression and access speed. Rdeval also generates a detailed visual report with multiple data analytics that can be exported in various formats. We showcase rdeval's functionalities using long-read data from different sequencing platforms and species, including human. For PacBio long-read sequencing, our analysis shows dramatic improvements in both read length and quality over time, as well as the benefit of increased coverage for genome assembly, though the magnitude varies by taxa. AVAILABILITY AND IMPLEMENTATION: Rdeval is implemented in C++ for data processing and in R for data visualization. Precompiled releases (Linux, MacOS, Windows) and commented source code for rdeval are available under MIT license at https://github.com/vgl-hub/rdeval. Documentation is available on ReadTheDocs (https://rdeval-documentation.readthedocs.io). Rdeval is also available in Bioconda and in Galaxy (https://usegalaxy.org). An automated test workflow ensures the consistency of software updates.

Software↗

The caCORE Software Development Kit: streamlining construction of interoperable biomedical information services.

BACKGROUND: Robust, programmatically accessible biomedical information services that syntactically and semantically interoperate with other resources are challenging to construct. Such systems require the adoption of common information models, data representations and terminology standards as well as documented application programming interfaces (APIs). The National Cancer Institute (NCI) developed the cancer common ontologic representation environment (caCORE) to provide the infrastructure necessary to achieve interoperability across the systems it develops or sponsors. The caCORE Software Development Kit (SDK) was designed to provide developers both within and outside the NCI with the tools needed to construct such interoperable software systems. RESULTS: The caCORE SDK requires a Unified Modeling Language (UML) tool to begin the development workflow with the construction of a domain information model in the form of a UML Class Diagram. Models are annotated with concepts and definitions from a description logic terminology source using the Semantic Connector component. The annotated model is registered in the Cancer Data Standards Repository (caDSR) using the UML Loader component. System software is automatically generated using the Codegen component, which produces middleware that runs on an application server. The caCORE SDK was initially tested and validated using a seven-class UML model, and has been used to generate the caCORE production system, which includes models with dozens of classes. The deployed system supports access through object-oriented APIs with consistent syntax for retrieval of any type of data object across all classes in the original UML model. The caCORE SDK is currently being used by several development teams, including by participants in the cancer biomedical informatics grid (caBIG) program, to create compatible data services. caBIG compatibility standards are based upon caCORE resources, and thus the caCORE SDK has emerged as a key enabling technology for caBIG. CONCLUSION: The caCORE SDK substantially lowers the barrier to implementing systems that are syntactically and semantically interoperable by providing workflow and automation tools that standardize and expedite modeling, development, and deployment. It has gained acceptance among developers in the caBIG program, and is expected to provide a common mechanism for creating data service nodes on the data grid that is under development.

Humans↗

Scalable, open-access and multidisciplinary data integration pipeline for climate-sensitive diseases.

Climate-sensitive infectious diseases pose an important challenge for human, animal and environmental health and it has been estimated that over half of known human pathogenic diseases can be aggravated by climate change. While climatic and weather conditions are important drivers of transmission of vector-borne diseases, socio-economic, behavioural, and land-use factors as well as the interactions among them impact transmission dynamics. Analysis of drivers of climate-sensitive diseases require rapid integration of interdisciplinary data to be jointly analysed with epidemiological (including genomic and clinical) data. Current tools for the integration of multiple data sources are often limited to one data type or rely on proprietary data and software. To address this gap, we develop a scalable and open-access pipeline for the integration of multiple spatio-temporal datasets that requires only the declaration of the country and temporal range and resolution of the study. The tool is locally deployable and can easily be integrated into existing climate-disease-modelling applications. We demonstrate the utility of the tool for dengue modelling in Vietnam where epidemiological data are legally required to remain local. We include a pipeline for bias correction of climate data to enhance their quality for downstream modelling tasks. The Dengue Advanced Readiness Tools-Pipeline empowers users by simplifying complex download, correction, and aggregation steps, fostering data-driven discovery of relationships between infectious diseases and their drivers in space and time, and enhancing reproducibility in research. Additional modules and datasets can be added to the existing ones to make the pipeline extendable to use cases other than the ones presented here.

automated workflows↗

Hard to Halt: Automation Bias in Agent-Driven Sequencing Prior Authorization Workflows.

PURPOSE: Prior authorization (PA) for exome or genome sequencing is a time-consuming process that impedes timely rare disease diagnosis. Large language model-based browser agents offer potential for automating these workflows, but their clinical reliability remain uncharacterized. METHODS: We developed a sandbox compromising a simulated ES/GS PA submission payer portal and a synthetic EHR containing 836 patient records spanning compliant profiles and deficient profiles with different types of issues. Gemini 3 Pro, Gemini 3 Flash, and Claude Opus 4.5 were evaluated on task completion rate, form completion accuracy, and appropriate withholding for deficient profiles. RESULTS: Larger models achieved much higher task completion rates (Gemini 3 Pro 95.45%, Claude Opus 4.5 93.67%) compared to Gemini 3 Flash (56.05%), but nearly universally failed to withhold submission for deficient profiles whereas Gemini 3 Flash ironically demonstrated superior withholding performance (17.33%). In a non-agentic setting, Gemini 3 Pro correctly identified 91% of the issues in deficient profiles, indicating that withholding failure is attributable to the browser interaction rather than the model's reasoning limitations. CONCLUSION: Current LLM-based browser agents exhibit a systematic bias towards form submission that poses risks in PA workflows. A modular, multi-agent architecture with human supervision is necessary for a safe clinical deployment.

Journal Article↗

Towards an intelligent hospital environment: OR of the future.

Patients, providers, payers, and government demand more effective and efficient healthcare services, and the healthcare industry needs innovative ways to re-invent core processes. Business process reengineering (BPR) showed adopting new hospital information systems can leverage this transformation and workflow management technologies can automate process management. Our research indicates workflow technologies in healthcare require real time patient monitoring, detection of adverse events, and adaptive responses to breakdown in normal processes. Adaptive workflow systems are rarely implemented making current workflow implementations inappropriate for healthcare. The advent of evidence based medicine, guideline based practice, and better understanding of cognitive workflow combined with novel technologies including Radio Frequency Identification (RFID), mobile/wireless technologies, internet workflow, intelligent agents, and Service Oriented Architectures (SOA) opens up new and exciting ways of automating business processes. Total situational awareness of events, timing, and location of healthcare activities can generate self-organizing change in behaviors of humans and machines. A test bed of a novel approach towards continuous process management was designed for the new Weinburg Surgery Building at the University of Maryland Medical. Early results based on clinical process mapping and analysis of patient flow bottlenecks demonstrated 100% improvement in delivery of supplies and instruments at surgery start time. This work has been directly applied to the design of the DARPA Trauma Pod research program where robotic surgery will be performed on wounded soldiers on the battlefield.

Appointments and Schedules↗

SGXPro: a parallel workflow engine enabling optimization of program performance and automation of structure determination.

SGXPro consists of four components. (i) A parallel workflow engine that was designed to automatically manage communication between the different processes and build systematic searches of algorithm/program/parameter space to generate the best possible result for a given data set. This is performed by offering the user a palette of programs and techniques commonly used in X-ray structure determination in an environment that lets the user choose programs in a mix-and-match manner, without worrying about inter-program communication and file formats, during the structure-determination process. The current SGXPro program palette includes 3DSCALE, SHELXD, ISAS, SOLVE/RESOLVE, DM, SOLOMON, DMMULTI, BLAST, AMoRe, EPMR, XTALVIEW, ARP/wARP and MAID. (ii) A client/server architecture that allows the user to utilize the best computing facility available. (iii) Plug-in-and-play design, which allows easily integration of new programs into the system. (iv) User-friendly interface.

Crystallography, X-Ray↗

Virtual automation.

Total laboratory automation (TLA) can be substituted in mid-size laboratories by a computer sample workflow control (virtual automation). Such a solution has been implemented in our laboratory using PSM, software developed in cooperation with Roche Diagnostics (Barcelona, Spain), to this purpose. This software is connected to the online analyzers and to the laboratory information system and is able to control and direct the samples working as an intermediate station. The only difference with TLA is the replacement of transport belts by personnel of the laboratory. The implementation of this virtual automation system has allowed us the achievement of the main advantages of TLA: workload increase (64%) with reduction in the cost per test (43%), significant reduction in the number of biochemistry primary tubes (from 8 to 2), less aliquoting (from 600 to 100 samples/day), automation of functional testing, drastic reduction of preanalytical errors (from 11.7 to 0.4% of the tubes) and better total response time for both inpatients (from up to 48 hours to up to 4 hours) and outpatients (from up to 10 days to up to 48 hours). As an additional advantage, virtual automation could be implemented without hardware investment and significant headcount reduction (15% in our lab).

Autoanalysis↗

Hide and seek: de novo identification in sugar beet reveals impact of non-autonomous LTR retrotransposons.

Plant genomes are filled with retrotransposons and their derivatives, constantly undergoing sequence diversification and structural rearrangement. Among them, short, non-autonomous retrotransposons lack full coding capacity and often form subfamilies. As a result, non-autonomous retrotransposons are incompletely identified in most to all genome assemblies.Here, we capitalize on our comprehensive understanding of the transposable element (TE) landscape in sugar beet (Beta vulgaris) to assess the extent of the blind spot for non-autonomous long terminal repeat (LTR) retrotransposons. This use case serves to answer if all of these sequences are derivatives of easier-to-identify full-length elements or if there is more variability that is currently overlooked.For this we applied a semi-automated structural discovery workflow followed by in-depth manual verification to characterize non-autonomous LTR retrotransposons in sugar beet. We retrieve more than 100 non-autonomous LTR retrotransposon families that lack complete autonomous coding capacity, including canonical terminal-repeat retrotransposons in miniature (TRIMs), elongated non-coding derivatives and families retaining fragmented coding remnants. The identified families span a broad range, including elements exceeding 15,000 bp in length and display evidence for reshuffling and modular evolution. Only a subset of families could be confidently linked to autonomous retrotransposons, showing sequence diversification within the non-autonomous LTR retrotransposon fraction beyond the autonomous genomic templates.We highlight that a large fraction of non-autonomous LTR retrotransposons is incompletely recovered with the current TE identification workflows, even if the output is well-curated and condensed into TE libraries and suggest procedures to remedy this gap. This study gives a genome-wide view into the non-autonomous LTR retrotransposon landscape of a single plant genome and highlights the importance of structure-based approaches for their identification and classification.

LTR retrotransposons↗

Representing clinical guidelines in UMl: a comparative study.

Clinical guidelines can be represented using models, such as GLIF, specifically designed for healthcare guidelines. This paper demonstrates that they can also be modelled using a mainstream business modelling language such as UML. The paper presents a guideline in GLIF and as UML activity diagrams, and then presents a mapping of GLIF primitives to UML. The potential benefits of using a mainstream modelling language are outlined. These include availability of advanced modelling tools, transfer between modelling tools, and automation via business workflow technology.

Diabetes Mellitus↗

Accelerating natural product discovery, characterization and engineering by biofoundries.

Covering: From early developments to the presentNatural product (NP) discovery is increasingly constrained by low-throughput screening, repeated rediscovery, and challenges in scaling genome mining-guided validation workflows. This highlight examines how automated biofoundries are accelerating NP discovery, characterization, and engineering through integrated design-build-test-learn (DBTL) cycles. We discuss recent advances in phenotype-first and genome-first discovery strategies enabled by robotics, high-throughput pathway reconstitution, and automated screening platforms. We further highlight emerging technologies, including cell-free biosynthesis, automated culturomics, programmable chassis engineering, and AI-assisted workflow orchestration, that may enable increasingly autonomous biofoundries for scalable exploration of NP chemical space and therapeutic discovery.

Journal Article↗

Multidetector-row CT: economics and workflow.

With rapid evolution of multidetector-row CT (MDCT) technology and applications, several factors such as technology upgrade and turf battles for sharing cost and profitability affect MDCT workflow and economics. MDCT workflow optimization can enhance productivity and reduce unit costs as well as increase profitability, in spite of decrease in reimbursement rates. Strategies for workflow management include standardization, automation, and constant assessment of various steps involved in MDCT operations. In this review article, we describe issues related to MDCT economics and workflow.

Economics, Medical↗

A python based automated computational framework to classify and comparative genomics analysis of the global diversity of chili leaf curl virus (ChiLCV) strains to understand virus host interactions.

Chili leaf curl virus (ChiLCV) is a Begomovirus chillicapsici that is one of the most devastating viruses impacted on the production of chili in the world, especially in South Asia. In the present study, we combined high-throughput computational genomics with experimental analysis of global diversity. A workflow was created using automated Python scripts to download, curate and process ChiLCV genomes from public database. About 410 complete ChiLCV genomes download from public databases. Using a phylogenetic approach, these isolates were subdivided into 34 strains, belonging to 10 major clades, showing significant genetic diversity. Geographic analysis revealed that Pakistan (207 isolates) and India (148 isolates) were the main sources of ChiLCV diversity and the remainder of the isolates were from Oman, Bangladesh, Iran, Saudi Arabia and Sri Lanka. Recombination was observed as a major evolutionary force as more than twenty recombination events were detected. Analysis of cis-regulatory elements showed a complex structure of the viral promoter, including multiple binding sites for transcription factors, hormone-response elements, light-responsive elements, and stress-responsive elements, indicating a high number of interactions between viral regulatory elements and host signaling pathways. Pangenome analysis showed the presence of a highly dynamic open pangenome made up of strain-specific orthologous groups (species-specific orthogroups). Experimental inoculation of chili plants was also carried out to assess the biological effects of infection, along with phytochemical, FTIR, HPLC, and qPCR analyses.

Begomovirus↗