Standard Workflow Research Articles

Impact of reporting rapid susceptibility results in Gram negative bloodstream infections: a real world prospective study.

Mortality and morbidity of patients with bloodstream infection (BSI) remain high despite advances in diagnostic methods and efforts to speed up reporting. This study investigated the impact of reporting rapid Minimum Inhibitory Concentration (MIC)-results in Gram negative BSIs with the ASTar system (Q-linea, Uppsala, Sweden) on the adaptation of empirically started antimicrobial therapy. We performed a real-world study during which antimicrobial susceptibility testing (AST) results were instantly reported to the treating physician in an established multidisciplinary antimicrobial stewardship setting. Consecutive patients with Gram negative bacteremia were included in the study (monomicrobial Gram stain, life expectancy of at least 48h and flagging positive before 2 PM). Rapid AST (RAST) reporting with ASTar was added on top of the standard workflow. Technical performance of the system was evaluated as well as the impact on antimicrobial treatment and timelines of achieving effective and optimal antimicrobial therapy. A total of 79 analyses were performed in 77 patients, of which 68 episodes were eligible for analysis. A categorical agreement was observed in 97,5% of 1160 MIC results without false susceptible results. All patients on ineffective empirical therapy (12/68) were switched after a median time of approximately one hour (5min - 15h) after communication of the result. Furthermore, 20/55 non-optimal therapies were adapted within a median period of 3h after communication. The implementation of rapid MICs, measured by the ASTar system, in our low antimicrobial resistance setting with elaborate antimicrobial guidelines, was easy and led to early adaptation of empirical treatment in 32/55 instances (12 ineffective and 20 non-optimal therapy). NCT06218277 (date of registration: 18-12-2023).

A Quality Improvement Initiative to Decrease Behavioral Health Unit Readmission Rates.

Youth behavioral health inpatient beds are limited during a time of crisis. Around one-third of youth admitted to a behavioral health unit (BHU) will be readmitted within 1year of discharge, with 8% to 13% being admitted within 30days. In one study, they found that more than one-third of patients initially admitted for suicidal ideation or attempt were readmitted within 7days. Our objective was to decrease 7-day and 30-day readmission rates to our BHU by 20% by May of 2023. We collected baseline data through medical record review for our pediatric BHU readmissions from July 2020 until July 2021. Interventions, such as standardized workflows and checklists, were trialed with Plan-Do-Study-Act (PDSA) cycles beginning October 2021 until November 2022. Performance was analyzed using statistical process control charts (U-charts). Sustainment was tracked through December 2023. Length of stay (LOS) was tracked as a balancing measure. Compliance with our readmission checklist was tracked as a process measure. Both 7-day and 30-day readmission rates to the pediatric BHU decreased as interventions were initiated and adopted. The rates of patients readmitted within 7 and 30days decreased from a baseline mean of 5.54 to 2.83 (49%) and 11.52 to 7.38 (36%) per 100 hospitalizations, respectively. The LOS for the BHU decreased from 5.58 to 5.09days. The readmission checklist was used for 81 out of 83 patients, or 97.5%. Adoption of multiple interventions produced a decrease in readmissions to a pediatric BHU.

Development of a Deep Learning Tool to Support the Assessment of Thyroid Follicular Cell Hypertrophy in the Rat.

Thyroid tissue is sensitive to the effects of endocrine disrupting substances, and this represents a significant health concern. Histopathological analysis of tissue sections of the rat thyroid gland remains the gold standard for the evaluation for agrochemical effects on the thyroid. However, there is a high degree of variability in the appearance of the rat thyroid gland, and toxicologic pathologists often struggle to decide on and consistently apply a threshold for recording low-grade thyroid follicular hypertrophy. This research project developed a deep learning image analysis solution that provides a quantitative score based on the morphological measurements of individual follicles that can be integrated into the standard pathology workflow. To achieve this, a U-Net convolutional deep learning neural network was used that not just identifies the various tissue components but also delineates individual follicles. Further steps to process the raw individual follicle data were developed using empirical models optimized to produce thyroid activity scores that were shown to be superior to the mean epithelial area approach when compared with pathologists' scores. These scores can be used for pathologist decision support using appropriate statistical methods to assess the presence or absence of low-grade thyroid hypertrophy at the group level.

Shape-matching-based fracture reduction aid concept exemplified on the proximal humerus-a pilot study.

Optimizing fracture reduction quality is key to achieve successful osteosynthesis, especially for epimetaphyseal regions such as the proximal humerus (PH), but can be challenging, partly due to the lack of a clear endpoint. We aimed to develop the prototype for a novel intraoperative C-arm-based aid to facilitate true anatomical reduction of fractures of the PH. The proposed method designates the reduced endpoint position of fragments by superimposing the outer boundary of the premorbid bone shape on intraoperative C-arm images, taking the mirrored intact contralateral PH from the preoperative CT scan as a surrogate. The accuracy of the algorithm was tested on 60 synthetic C-arm images created from the preoperative CT images of 20 complex PH fracture cases (Dataset A) and on 12 real C-arm images of a prefractured human anatomical specimen (Dataset B). The predicted outer boundary shape was compared with the known exact solution by (1) a calculated matching error and (2) two experienced shoulder trauma surgeons. A prediction accuracy of 88% (with 73% 'good') was achieved according to the calculation method and an 87% accuracy (68% 'good') by surgeon assessment in Dataset A. Accuracy was 100% by both assessments for Dataset B. By seamlessly integrating into the standard perioperative workflow and imaging, the intuitive shape-matching-based aid, once developed as a medical device, has the potential to optimize the accuracy of the reduction of PH fractures while reducing the number of X-rays and surgery time. Further studies are required to demonstrate the applicability and efficacy of this method in optimizing fracture reduction quality.

High-throughput Experimentation Enables the Development of a Nickel-catalyzed Cyanation Platform for (Hetero)aryl Halides.

A novel screening platform for the nickel-catalyzed cyanation of (hetero)aryl halides relying on the use of air-stable Ni(COD)DQ at low loading is reported. Through high-throughput experimentation (HTE), various ligand and solvent combinations are systematically explored, allowing the fast identification of suitable conditions. This standardized workflow serves as an excellent starting point for selecting other competent nickel precatalysts and for further optimization of reluctant substrates. The transformation exhibits broad functional group tolerance and can be readily scaled up to gram-scale.

MetaboLabPy-An Open-Source Software Package for Metabolomics NMR Data Processing and Metabolic Tracer Data Analysis.

Introduction: NMR spectroscopy is a powerful technique for studying metabolism, either in metabolomics settings or through tracing with stable isotope-enriched metabolic precursors. MetaboLabPy (version 0.9.66) is a free and open-source software package used to process 1D- and 2D-NMR spectra. The software implements a complete workflow for NMR data pre-processing to prepare a series of 1D-NMR spectra for multi-variate statistical data analysis. This includes a choice of algorithms for automated phase correction, segmental alignment, spectral scaling, variance stabilisation, export to various software platforms, and analysis of metabolic tracing data. The software has an integrated help system with tutorials that demonstrate standard workflows and explain the capabilities of MetaboLabPy. Materials and Methods: The software is implemented in Python and uses numerous Python toolboxes, such as numpy, scipy, pandas, etc. The software is implemented in three different packages: metabolabpy, qtmetabolabpy, and metabolabpytools. The metabolabpy package contains classes to handle NMR data and all the numerical routines necessary to process and pre-process 1D NMR data and perform multiplet analysis on 2D-1H, 13C HSQC NMR data. The qtmetabolabpy package contains routines related to the graphical user interface. Results: PySide6 is used to produce a modern and user-friendly graphical user interface. The metabolabpytools package contains routines which are not specific to just handling NMR data, for example, routines to derive isotopomer distributions from the combination of NMR multiplet and GC-MS data. A deep-learning approach for the latter is currently under development. MetaboLabPy is available via the Python Package Index or via GitHub.

Automated Incidental Findings Notification Through the Electronic Health Record Utilizing Dictation Macros.

The objective of this study is to implement an actionable incidental findings (AIFs) communication workflow integrated into the electronic health record (EHR) using dictation macros to improve the quality of radiology reports and facilitate delivery of findings to clinicians. The workflow was implemented across an academic multi-hospital health system and used by over 100 radiologists from 12 divisions. Standardized macros were created for different organ systems including the thyroid, lungs, liver, pancreas, spleen, kidney, female reproductive, and others, designed based on the ACR Novel Quality Measure Set. All macros contained special codes enabling automated notification of clinicians in Epic EHR and unique codes to allow for tracking. When notified, clinicians can fast track ordering of follow-up imaging exams. All alerts were monitored by radiology operations who ensured messages were acknowledged within 73h. From September 2023 to March 2024, 12,919 AIFs alerts were filed for 10,766 patients. Median age was 65years, and 63.6% were female and 36.4% were male. Most alerts were submitted for outpatients (73.5%), and a majority originated from CT exams (57.3%) followed by radiographs (12.2%) and ultrasound (11.5%). Number of submissions per radiologist ranged from 0 to 930 with a median of 62. Median time to alert acknowledgment was 8.1h, and 93.9% were acknowledged within 73h. Follow-up orders were placed for 62.3% of patients. A standardized AIFs communication workflow utilizing dictation macros can help facilitate delivery of findings and follow-up recommendations to clinicians.

Optimizing hip MRI: enhancing image quality and elevating inter-observer consistency using deep learning-powered reconstruction

BackgroundConventional hip joint MRI scans necessitate lengthy scan durations, posing challenges for patient comfort and clinical efficiency. Previously, accelerated imaging techniques were constrained by a trade-off between noise and resolution. Leveraging deep learning-based reconstruction (DLR) holds the potential to mitigate scan time without compromising image quality.MethodsWe enrolled a cohort of sixty patients who underwent DL-MRI, conventional MRI, and No-DL MRI examinations to evaluate image quality. Key metrics considered in the assessment included scan duration, overall image quality, quantitative assessments of Relative Signal-to-Noise Ratio (rSNR), Relative Contrast-to-Noise Ratio (rCNR), and diagnostic efficacy. Two experienced radiologists independently assessed image quality using a 5-point scale (5 indicating the highest quality). To gauge interobserver agreement for the assessed pathologies across image sets, we employed weighted kappa statistics. Additionally, the Wilcoxon signed rank test was employed to compare image quality and quantitative rSNR and rCNR measurements.ResultsScan time was significantly reduced with DL-MRI and represented an approximate 66.5% reduction. DL-MRI consistently exhibited superior image quality in both coronal T2WI and axial T2WI when compared to both conventional MRI (p < 0.01) and No-DL-MRI (p < 0.01). Interobserver agreement was robust, with kappa values exceeding 0.735. For rSNR data, coronal fat-saturated(FS) T2WI and axial FS T2WI in DL-MRI consistently outperformed No-DL-MRI, with statistical significance (p < 0.01) observed in all cases. Similarly, rCNR data revealed significant improvements (p < 0.01) in coronal FS T2WI of DL-MRI when compared to No-DL-MRI. Importantly, our findings indicated that DL-MRI demonstrated diagnostic performance comparable to conventional MRI.ConclusionIntegrating deep learning-based reconstruction methods into standard clinical workflows has the potential to the promise of accelerating image acquisition, enhancing image clarity, and increasing patient throughput, thereby optimizing diagnostic efficiency.Trial registrationRetrospectively registered.

A Framework for Metadata Workflows

Designing metadata workflows is a core competency for cataloging and metadata practitioners. Unlike many other competencies, metadata workflow design lacks a conceptual framework, hindering understanding and skill development. This article presents a framework for metadata workflows that describes processes for adding, enhancing, and removing metadata from library systems. The framework describes standard and custom workflows, including original and copy cataloging, withdrawals, system migrations, metadata creation, and record enhancement. A better understanding of metadata workflows facilitates the implementation of automation and batch processing and can inform staff training, professional development offerings, and library and information science curricula.

Range and accuracy of in-plane anisotropic thermal conductivity measurement using the laser-based Ångstrom method.

High heat fluxes in electronic devices must be effectively dissipated to prevent local hotspots, which are critical for long-term device reliability. In particular, advanced semiconductor packaging trends toward thin form factor products increase the need for understanding and improving in-plane conduction heat spreading in anisotropic materials. The 2D laser-based Ångstrom method, an extension of traditional Ångstrom and lock-in thermography techniques, measures in-plane thermal properties of anisotropic sheet-like materials. This method uses non-contact infrared temperature mapping to measure the thermal response to periodic laser heating at the center of a suspended sample. The spatiotemporal temperature data are analyzed via an inverse fitting algorithm to extract thermal conductivities in the in-plane orthotropic directions that best adhere to the governing heat conduction equation. Using this algorithm, we present an approach to simultaneously fit data across multiple heating frequencies, which improves measurement sensitivity because the thermal penetration depth varies with frequency. The accuracy of this technique is assessed by tuning experimental parameters such as sample dimensions and heating frequency. A standardized workflow is proposed for measuring unknown materials and for processing the data, including filtering out regions influenced by laser absorption and heat sink boundary effects. Numerical simulations validate the method across a wide range of thermal conductivities (0.1-1000W m-1 K-1) and material thicknesses (0.1-10mm), with accuracy demonstrated for anisotropy ratios up to 1000:1. Experimental measurements on isotropic and anisotropic materials agree well with the benchmark values. Ultimately, standardization of this technique supports the development of engineered anisotropic heat-spreading materials for thermal management and packaging applications.

Golden Gate Cloning for the Standardized Assembly of Gene Elements with Modular Cloning in Chlamydomonas.

Modern synthetic biology requires fast and efficient cloning strategies for the assembly of new transcription units or entire pathways. Modular Cloning (MoClo) is a standardized synthetic biology workflow, which has tremendously simplified the assembly of genetic elements for transgene expression. MoClo is based on Golden Gate Assembly and allows to combine genetic elements of a library through a hierarchical syntax-driven pipeline. Here we describe the assembly of a genetic cassette for transgene expression in the single-celled model alga Chlamydomonas reinhardtii.

PTN secreted by cardiac fibroblasts promotes myocardial fibrosis and inflammation of pressure overload-induced hypertrophic cardiomyopathy through the PTN-SDC4 pathway.

Hypertrophic cardiomyopathy (HCM) is characterized by unexplained left ventricular hypertrophy (LVH) with key pathologic processes including myocardial necrosis, fibrosis, inflammation, and hypertrophy, which are involved in heart failure (HF), stroke, and even sudden death. Our aim was to explore the communication network among various cells in the heart of transverse aortic constriction (TAC) surgery induced HCM mice. Single-cell RNA-seq data of GSE137167 was downloaded from the Gene Expression Omnibus (GEO) database. Seurat was used to perform the standard workflow. CellChat was utilized to compute the cell-cell interaction network and analyze the ligand-receptor pairs. Weighted gene co-expression network analysis (WGCNA) was conducted to identify gene co-expression modules. In vitro and in vivo studies were performed to verify bioinformatic analysis findings through real-time quantitative PCR (RT-qPCR), Edu staining, transwell assay, western blot, immunofluorescence assay, CCK-8, hematoxylin and eosin (H&E) staining, and echocardiography based on TAC mouse model. Our results showed that after TAC surgery, the interaction between cardiac fibroblasts and macrophages was very common, and the increasing pleiotrophin (PTN) ligand secreted by cardiac fibroblasts could promote the self-proliferation or invasion for myocardial fibrosis as well as stimulate the inflammatory response of macrophages to contribute TAC surgery induced HCM through acting on Syndecan 4 (SDC4) receptor. Our study demonstrates that PTN derived from cardiac fibroblasts may play potential role in pressure overload-induced HCM through activating the PTN-SDC4 pathway in cardiac fibroblasts and macrophages, which may be a potential therapeutic target for pressure overload-induced HCM patients.

AI-Powered Coding Tools: A Study of Advancements, Challenges, and Future Directions

This review examines the effects of AI-assisted programming in contemporary software development, paying particular attention to tools driven by Large Language Models (LLMs), such as GPT-4o and GitHub Copilot. Starting with data processing and ending with model deployment, the review describes the standard workflow for training machine learning models and how programmers use these models to improve their coding processes. GitHub Copilot, an AI-powered code generator, and GPT-4o, a general-purpose LLM, are compared in terms of accuracy, usability, and efficiency when assisting with programming tasks. The results show that although both tools greatly facilitate coding, they each have particular advantages and disadvantages. GitHub Copilot is excellent at integrating with IDEs, providing contextual code recommendations and streamlining processes. In contrast, GPT-4o shows better accuracy when creating code from scratch, but it does not have Copilot’s seamless IDE integration. The review also identifies some of the current drawbacks of AI-powered coding tools, including the possibility of producing faulty or vulnerable code as a result of training on unreliable datasets and the inability to comprehend context, which can occasionally result in functionally correct but practically incorrect code. In order to filter and fix problematic code before training, the review recommends using advanced algorithms for data pre-processing. It also suggests improving the interpretability of code generated by AI to help developers better comprehend and trust the results.

Setting the foundation for a national collaborative learning health system in acute TBI rehabilitation: CARE4TBI Year 1 experience

AbstractIntroductionA learning health system (LHS) approach is a collaborative model that continuously examines, evaluates, and re‐evaluates data eventually transforming it into knowledge. High quantity of high‐quality data are needed to establish this model. The purpose of this article is to describe the collaborative discovery process used to identify and standardize clinical data documented during daily multidisciplinary inpatient rehabilitation that would then allow access to these data to conduct comparative effectiveness research.MethodsCARE4TBI is a prospective observational research study designed to capture clinical data within the standard inpatient rehabilitation documentation workflow at 15 TBI Model Systems Centers in the US. Three groups of stakeholders guided project development: therapy representative work group (TRWG) consisting of frontline therapists from occupational, physical, speech‐language, and recreational therapies; rehabilitation leader representative group (RLRG); and informatics and information technology team (IIT). Over a 12‐month period, the three work groups and research leadership team identified the therapeutic components captured within daily documentation throughout the duration of inpatient TBI rehabilitation.ResultsData brainstorming among the groups created 98 distinct categories of data with each containing a range of data elements comprising a total of 850 discrete data elements. The free‐form data were sorted into three large categories and through review and discussion, reduced to two categories of prospective data collection—session‐level and therapy activity‐level data. Twelve session data elements were identified, and 54 therapy activities were identified, with each activity containing discrete sub‐categories for activity components, method of delivery, and equipment or supplies. A total of 561 distinct meaningful data elements were identified across the 54 activities.DiscussionThe CARE4TBI data discovery process demonstrated feasibility in identifying and capturing meaningful high quantity and high‐quality treatment data across multiple disciplines and rehabilitation sites, setting the foundation for a LHS coalition for acute traumatic brain injury rehabilitation.

Reproducible protein quantitation of 270 human proteins at increased depth using nanoparticle-based fractionation and multiple reaction monitoring mass spectrometry with stable isotope-labelled internal standards.

Here we show that when using a mix of 274 light synthetic peptide standards (NAT) as surrogates for 270 human plasma proteins, as well as stable isotope-labelled standards (SIS) as normalizers (both from MRM Proteomics Inc.) for targeted quantitative analysis by liquid chromatography multiple reaction monitoring mass spectrometry (LC/MRM-MS), the Seer Proteograph™ platform allowed for the enrichment and absolute quantitation of up to an additional 62 targets (median) compared to two standard proteomic workflows without enrichment, representing an increase of 44%. The nanoparticle-based fractionation workflow resulted in improved reproducibility compared to a traditional proteomic workflow with no fractionation (median 8.3% vs. 13.1% CV). As expected, the protein concentrations in nanoparticle coronas were higher and had more compressed dynamic range in comparison to the concentrations determined either by a 3-hour Trypsin/LysC or overnight tryptic digestion methods. As the nanoparticle-based fractionation technology gains popularity, additional steps such as establishing technique-specific protein reference ranges across plasma samples and comparisons to well-established protein quantitation methods like enzyme-linked immunosorbent assay (ELISA) and LC/MRM-MS may be explored to enable absolute quantification of plasma proteins based on nanoparticle-based fractionation data.

Subsampling Blood Swabs as an Efficient and Good Practice for RapidHIT ID® Analyses

Background/Objectives: Rapid DNA instruments are gaining interest in the forensic community as a means of generating DNA profile information more quickly than standard laboratory workflows, and they have the potential to be carried out in decentralized structures of the main laboratory, such as in French overseas territories, where no laboratory facilities are installed. Although this system has been shown to be effective in analyzing rich traces (such as blood) sampled on a swab, it has the disadvantage of consuming the entire swab for analysis. Since it is impossible to double the sampling from very few blood stains, we designed a subsampling protocol of the main swab for analysis on RapidHIT ID® instruments. Methods: Two volumes of blood (10 µL and 150 µL) from four individuals were applied to a regular Copan flocked swab and then subsampled by three different operators using a Copan mini (subungual) flocked swab to recover a portion of the biological material. Results:The analysis of the mini swabs allows us to obtain a complete genetic profile regardless of the initial volume, with a better genetic profile intensity associated with a higher initial volume deposited on the regular swab. Conclusions: Therefore, this study presents a novel, effective, and adaptable subsampling method that has the potential to significantly improve forensic DNA analysis, particularly in remote or decentralized settings. It offers a flexible solution to current limitations of RapidHIT ID® technology and paves the way for future advancements in genetic forensic science.

A multimodal machine learning model for the stratification of breast cancer risk.

Machine learning models for the diagnosis of breast cancer can facilitate the prediction of cancer risk and subsequent patient management among other clinical tasks. For the models to impact clinical practice, they ought to follow standard workflows, help interpret mammography and ultrasound data, evaluate clinical contextual information, handle incomplete data and be validated in prospective settings. Here we report the development and testing of a multimodal model leveraging mammography and ultrasound modules for the stratification of breast cancer risk based on clinical metadata, mammography and trimodal ultrasound (19,360 images of 5,216 breasts) from 5,025 patients with surgically confirmed pathology across medical centres and scanner manufacturers. Compared with the performance of experienced radiologists, the model performed similarly at classifying tumours as benign or malignant and was superior at pathology-level differential diagnosis. With a prospectively collected dataset of 191 breasts from 187 patients, the overall accuracies of the multimodal model and of preliminary pathologist-level assessments of biopsied breast specimens were similar (90.1% vs 92.7%, respectively). Multimodal models may assist diagnosis in oncology.

Robust self-supervised denoising of voltage imaging data using CellMincer

Voltage imaging is a powerful technique for studying neuronal activity, but its effectiveness is often constrained by low signal-to-noise ratios (SNR). Traditional denoising methods, such as matrix factorization, impose rigid assumptions about noise and signal structures, while existing deep learning approaches fail to fully capture the rapid dynamics and complex dependencies inherent in voltage imaging data. Here, we introduce CellMincer, a novel self-supervised deep learning method specifically developed for denoising voltage imaging datasets. CellMincer operates by masking and predicting sparse pixel sets across short temporal windows and conditions the denoiser on precomputed spatiotemporal auto-correlations to effectively model long-range dependencies without large temporal contexts. We developed and utilized a physics-based simulation framework to generate realistic synthetic datasets, enabling rigorous hyperparameter optimization and ablation studies. This approach highlighted the critical role of conditioning on spatiotemporal auto-correlations, resulting in an additional 3-fold SNR gain. Comprehensive benchmarking on both simulated and real datasets, including those validated with patch-clamp electrophysiology (EP), demonstrates CellMincer’s state-of-the-art performance, with substantial noise reduction across the frequency spectrum, enhanced subthreshold event detection, and high-fidelity recovery of EP signals. CellMincer consistently outperforms existing methods in SNR gain (0.5–2.9 dB) and reduces SNR variability by 17–55%. Incorporating CellMincer into standard workflows significantly improves neuronal segmentation, peak detection, and functional phenotype identification, consistently surpassing current methods in both SNR gain and consistency.

Challenges in clinical translation of cardiac magnetic resonance imaging radiomics in non-ischemic cardiomyopathy: a narrative review.

Radiomics is an emerging technology that facilitates the quantitative analysis of multi-modal cardiac magnetic resonance imaging (MRI). This study aims to introduce a standardized workflow for applying radiomics to non-ischemic cardiomyopathies, enabling clinicians to comprehensively understand and implement this technology in clinical practice. A computerized literature search (up to August 1, 2024) was conducted using PubMed to identify relevant studies on the roles and workflows of radiomics in non-ischemic cardiomyopathy. Expert discussions were also held to ensure the accuracy and relevance of the findings. Only English-language publications were reviewed. The paper details the essential processes of radiomics, including feature extraction, feature engineering, model construction, and data analysis. It emphasizes the role of MRI in assessing cardiac structure and function and demonstrates how MRI-based radiomics can aid in diagnosing and differentiating non-ischemic cardiomyopathies such as hypertrophic cardiomyopathy, dilated cardiomyopathy, and myocarditis. The study also investigates various cardiac MRI sequences to enhance the clinical application of radiomics. The standardized radiomics workflow presented in this study aims to assist clinicians in effectively utilizing MRI-based radiomics for the diagnosis and management of non-ischemic cardiomyopathies, thereby improving clinical decision-making.

Effect of digital cataract workflow on time and resource efficiencies in cataract surgery: time and motion study.

To compare time and resource saving with integration of digital cataract workflow to the existing workflow in high-volume cataract surgery clinics. L V Prasad Eye Institute, Hyderabad, India (site 1), and Narayana Nethralaya, Bengaluru, India (site 2). Prospective, time and motion. The total time to complete each step (preoperative measurements, surgical planning, and surgical procedures) of the cataract workflow, number of data fields entered, and support staff required for both workflows were recorded. All study measurements were determined first for existing electronic medical record (EMR) cataract workflow followed by digital workflow (integrated data management system with data reviewer, surgical planner, and data transfer to operating room) at both sites. A total of 85 (site 1, 44; site 2, 41) cataract workflows were analyzed. The integration of digital workflow into the site's existing EMR workflow reduced the mean time for preoperative measurements by 25.3% ( P = .006), surgical planning by 55.1% ( P = .008), and surgical procedures by 22.6% ( P = .002). The mean ± SD overall time for the surgery was significantly shorter in the digital group (887.3 ± 103.3 vs 1271.3 ± 300.7 seconds; P < .0001). For both sites, the number of data fields recorded and number of support staff needed were significantly lesser for the digital workflow ( P < .0001, for both). Integration of digital workflow significantly reduced the overall cataract surgery time, variability of overall time, number of data fields recorded, and resource utilization. Complete digitalization has important implications for improving the efficiency and standardization of cataract surgery workflow.