Metagenome quality metrics and taxonomical annotation visualization through the integration of MAGFlow and BIgMAG.

Background: Building Metagenome-Assembled Genomes (MAGs) from highly complex metagenomics datasets encompasses a series of steps covering from cleaning the sequences, assembling them to finally group them into bins. Along the process, multiple tools aimed to assess the quality and integrity of each MAG are implemented. Nonetheless, even when incorporated within end-to-end pipelines, the outputs of these pieces of software must be visualized and analyzed manually lacking integration in a complete framework. Methods: We developed a Nextflow pipeline (MAGFlow) for estimating the quality of MAGs through a wide variety of approaches (BUSCO, CheckM2, GUNC and QUAST), as well as for annotating taxonomically the metagenomes using GTDB-Tk2. MAGFlow is coupled to a Python-Dash application (BIgMAG) that displays the concatenated outcomes from the tools included by MAGFlow, highlighting the most important metrics in a single interactive environment along with a comparison/clustering of the input data. Results: By using MAGFlow/BIgMAG, the user will be able to benchmark the MAGs obtained through different workflows or establish the quality of the MAGs belonging to different samples following the divide and rule methodology. Conclusions: MAGFlow/BIgMAG represents a unique tool that integrates state-of-the-art tools to study different quality metrics and extract visually as much information as possible from a wide range of genome features.

Hospital-acquired malnutrition: point prevalence, risk identifiers and utility of a digital Dashboard to identify high-risk, long-stay patients in five Australian facilities.

BACKGROUND: There are limited hospital-acquired malnutrition (HAM) studies among the plethora of malnutrition literature, and a few studies utilise electronic medical records to assist with malnutrition care. This study therefore aimed to determine the point prevalence of HAM in long-stay adult patients across five facilities, whether any descriptors could assist in identifying these patients and whether a digital Dashboard accurately reflected 'real-time' patient nutritional status. METHODS: HAM was defined as malnutrition first diagnosed >14 days after hospital admission. Eligible patients were consenting adult (≥18 years) inpatients with a length of stay (LOS) >14 days. Palliative, mental health and intensive care patients were excluded. Descriptive, clinical and nutritional data were collected, including nutritional status, and whether a patient had hospital-acquired malnutrition to determine point prevalence. Descriptive Fisher's exact and analysis of variance (ANOVA) tests were used. RESULTS: Eligible patients (n = 134) were aged 68 ± 16 years, 52% were female and 92% were acute admissions. HAM and malnutrition point prevalence were 4.5% (n = 6/134) and 19% (n = 26/134), respectively. Patients with HAM had 72 days greater LOS than those with malnutrition present on admission (p < 0.001). A high proportion of HAM patients were inpatients at a tertiary facility and longer-stay wards. The Dashboard correctly reflected recent ward dietitian assessments in 94% of patients at one facility (n = 29/31). CONCLUSIONS: HAM point prevalence was 4.5% among adult long-stay patients. Several descriptors may be suitable to screen for at-risk patients in future studies. Digital Dashboards have the potential to explore factors related to HAM.

Creation of an Automated and Comprehensive Resident Progress System for Residents and to Save Hours of Faculty Time: Mixed Methods Study.

BACKGROUND: It is vital for residents to have a longitudinal view of their educational progression, and it is crucial for the medical education team to have a clear way to track resident progress over time. Current tools for aggregating resident data are difficult to use and do not provide a comprehensive way to evaluate and display resident educational advancement. OBJECTIVE: This study aims to describe the creation and assessment of a system designed to improve the longitudinal presentation, quality, and synthesis of educational progress for trainees. We created a new system for residency progress management with 3 goals in mind, that are (1) a long-term and centralized location for residency education data, (2) a clear and intuitive interface that is easy to access for both the residents and faculty involved in medical education, and (3) automated data input, transformation, and analysis. We present evaluations regarding whether residents find the system useful, and whether faculty like the system and perceive that it helps them save time with administrative duties. METHODS: The system was created using a suite of Google Workspace tools including Forms, Sheets, Gmail, and a collection of Apps Scripts triggered at various times and events. To assess whether the system had an effect on the residents, we surveyed and asked them to self-report on how often they accessed the system and interviewed them as to whether they found it useful. To understand what the faculty thought of the system, we conducted a 14-person focus group and asked the faculty to self-report their time spent preparing for residency progress meetings before and after the system debut. RESULTS: The system went live in February 2022 as a quality improvement project, evolving through multiple iterations of feedback. The authors found that the system was accessed differently by different postgraduate years (PGY), with the most usage reported in the PGY1 class (weekly), and the least amount of usage in the PGY3 class (once or twice). However, all of the residents reported finding the system useful, specifically for aggregating all of their evaluations in the same place. Faculty members felt that the system enabled a more high-quality biannual clinical competency committee meeting and they reported a combined time savings of 8 hours in preparation for each clinical competency committee as a result of reviewing resident data through the system. CONCLUSIONS: Our study reports on the creation of an automated, instantaneous, and comprehensive resident progress management system. The system has been shown to be well-liked by both residents and faculty. Younger PGY classes reported more frequent system usage than older PGY classes. Faculty reported that it helped facilitate more meaningful discussion of training progression and reduced the administrative burden by 8 hours per biannual session.

Public deliberation on health gain measures.

Researchers and decision-makers use health gain measures to assess the value of health interventions. However, our current understanding of how these measures are understandable and accessible to the community is limited. This study examined a diverse group of stakeholders' attitudes and preferences for 9 commonly used health gain measures. We recruited 20 stakeholders, including patients, caregivers, pharmacists, allied health professionals, and citizens. We conducted 2 in-person deliberative meetings in which participants learned, discussed, deliberated on, and ranked 9 health gain measures. The final ranking conducted after unified deliberation showed the quality-adjusted life year (QALY) as the top-ranked measure, followed by the clinical benefit rating method used by the U.S. Preventive Services Task Force, and multicriteria decision analysis (MCDA). We identified 3 themes during deliberations: the importance of using patient values in population-based health gain measures, examining complementary measures together, and choosing measures that are intuitive and easy to understand. Future policymaking should consider incorporating the QALY, clinical benefit rating, and MCDA into prioritization decisions.

Community-Engaged Research and the Use of Open Access ToxVal/ToxRef In Vivo Databases and New Approach Methodologies (NAM) to Address Human Health Risks From Environmental Contaminants.

BACKGROUND: The paper analyzes opportunities for integrating Open access resources (Abstract Sifter, US EPA and NTP Toxicity Value and Toxicity Reference [ToxVal/ToxRefDB]) and New Approach Methodologies (NAM) integration into Community Engaged Research (CEnR). METHODS: CompTox Chemicals Dashboard and Integrated Chemical Environment with in vivo ToxVal/ToxRef and NAMs (in vitro) databases are presented in three case studies to show how these resources could be used in Pilot Projects involving Community Engaged Research (CEnR) from the University of California, Davis, Environmental Health Sciences Center. RESULTS: Case #1 developed a novel assay methodology for testing pesticide toxicity. Case #2 involved detection of water contaminants from wildfire ash and Case #3 involved contaminants on Tribal Lands. Abstract Sifter/ToxVal/ToxRefDB regulatory data and NAMs could be used to screen/prioritize risks from exposure to metals, PAHs and PFAS from wildfire ash leached into water and to investigate activities of environmental toxins (e.g., pesticides) on Tribal lands. Open access NAMs and computational tools can apply to detection of sensitive biological activities in potential or known adverse outcome pathways to predict points of departure (POD) for comparison with regulatory values for hazard identification. Open access Systematic Empirical Evaluation of Models or biomonitoring exposures are available for human subpopulations and can be used to determine bioactivity (POD) to exposure ratio to facilitate mitigation. CONCLUSIONS: These resources help prioritize chemical toxicity and facilitate regulatory decisions and health protective policies that can aid stakeholders in deciding on needed research. Insights into exposure risks can aid environmental justice and health equity advocates.

Quality, Usability, and Trust Challenges to Effective Data Use in the Deployment and Use of the Bangladesh Nutrition Information System Dashboard: Qualitative Study.

BACKGROUND: Evidence-based decision-making is essential to improve public health benefits and resources, especially in low- and middle-income countries (LMICs), but the mechanisms of its implementation remain less straightforward. The availability of high-quality, reliable, and sufficient data in LMICs can be challenging due to issues such as a lack of human resource capacity and weak digital infrastructure, among others. Health information systems (HISs) have been critical for aggregating and integrating health-related data from different sources to support evidence-based decision-making. Nutrition information systems (NISs), which are nutrition-focused HISs, collect and report on nutrition-related indicators to improve issues related to malnutrition and food security-and can assist in improving populations' nutritional statuses and the integration of nutrition programming into routine health services. Data visualization tools (DVTs) such as dashboards have been recommended to support evidence-based decision-making, leveraging data from HISs or NISs. The use of such DVTs to support decision-making has largely been unexplored within LMIC contexts. In Bangladesh, the Mukto dashboard was developed to display and visualize nutrition-related performance indicators at the national and subnational levels. However, despite this effort, the current use of nutrition data to guide priorities and decisions remains relatively nascent and underused. OBJECTIVE: The goal of this study is to better understand how Bangladesh's NIS, including the Mukto dashboard, has been used and areas for improvement to facilitate its use for evidence-based decision-making toward ameliorating nutrition-related service delivery and the health status of communities in Bangladesh. METHODS: Primary data collection was conducted through qualitative semistructured interviews with key policy-level stakeholders (n=24). Key informants were identified through purposive sampling and were asked questions about the experiences and challenges with the NIS and related nutrition dashboards. RESULTS: Main themes such as trust, data usability, personal power, and data use for decision-making emerged from the data. Trust in both data collection and quality was lacking among many stakeholders. Poor data usability stemmed from unstandardized indicators, irregular data collection, and differences between rural and urban data. Insufficient personal power and staff training coupled with infrastructural challenges can negatively affect data at the input stage. While stakeholders understood and expressed the importance of evidence-based decision-making, ultimately, they noted that the data were not being used to their maximum potential. CONCLUSIONS: Leveraging DVTs can improve the use of data for evidence-based decision-making, but decision makers must trust that the data are believable, credible, timely, and responsive. The results support the significance of a tailored data ecosystem, which has not reached its full potential in Bangladesh. Recommendations to reach this potential include ensuring a clear intended user base and accountable stakeholders are present. Systems should also have the capacity to ensure data credibility and support ongoing personal power requirements.

The Effects of Layout Order on Interface Complexity: An Eye-Tracking Study for Dashboard Design.

This study investigated the effect of layout order on the complexity of the dashboard interface based on screen-based eye trackers. By simplifying and abstracting dashboard interfaces and incorporating subjective ratings (symmetry and unity calculations), we successfully manipulated the levels of complexity and layout order of the interface materials. Using four types of eye movement data (total fixation count, total gaze duration, scanning paths, and hotspot maps) and behavioral data, we compared participants' visual search behavior on interfaces with different layout orders and complexity levels. Experiment 1 revealed a significant interaction between layout order and interface complexity, with participants performing significantly better in the high-level layout order condition. Experiment 2 confirmed that the position of the core chart plays a crucial role in users' visual search behavior and that the optimal layout order for the dashboard is to place the core chart on the left side of the interface's horizontal axis, with partial symmetry in the no-core chart areas. This study highlights the effectiveness of eye-tracking techniques in user interface design research and provides valuable insights into optimizing dashboard interface design. Designers should adopt the design principle of "order is more" in addition to "less is more" and consider designing the core chart in the left-center position.

Toward Model-Informed Precision Dosing for Remimazolam: A Population Pharmacokinetic-Pharmacodynamic Analysis.

Remimazolam, widely used for procedural sedation and general anesthesia, is a new ultra short-acting benzodiazepine for intravenous sedation and anesthesia. We aim to characterize the pharmacokinetics/pharmacodynamics (PK/PD) of remimazolam and its metabolite CNS 7054 in healthy Chinese volunteers using population analysis and suggest an optimal dosing regimen for sedation therapy. Data were collected from a single-center, placebo-controlled, randomized, and dose-escalation clinical pharmacology study. Forty-six healthy volunteers received a single infusion dose of remimazolam, while nine healthy subjects received a continuous infusion of remimazolam. A population PK/PD model was established and RxODE and Shiny in R were used to design the remimazolam dosing regimens. A three-compartment model best described the PK of remimazolam and a two-compartment model with one transit compartment was adopted for CNS 7054. The relationship between exposure and the bispectral index was best described using an effect compartment model with an inhibitory sigmoid model. Additionally, a web-based dashboard was developed to provide individualized dosing regimens, complemented by a graphical illustration of the PK/PD profiles of the proposed dosing regimen. The established population PK/PD model characterized the dose-exposure-response relationship of remimazolam well, which could be applied to optimize individual dosing regimens.

An expert rule-based approach for identifying infantile-onset Pompe disease patients using retrospective electronic health records.

Pompe disease (OMIM #232300), a rare genetic disorder, leads to glycogen buildup in the body due to an enzyme deficiency, particularly harming the heart and muscles. Infantile-onset Pompe disease (IOPD) requires urgent treatment to prevent mortality, but the unavailability of these methods often delays diagnosis. Our study aims to streamline IOPD diagnosis in the UAE using electronic health records (EHRs) for faster, more accurate detection and timely treatment initiation. This study utilized electronic health records from the Abu Dhabi Healthcare Company (SEHA) healthcare network in the UAE to develop an expert rule-based screening approach operationalized through a dashboard. The study encompassed six diagnosed IOPD patients and screened 93,365 subjects. Expert rules were formulated to identify potential high-risk IOPD patients based on their age, particular symptoms, and creatine kinase levels. The proposed approach was evaluated using accuracy, sensitivity, and specificity. The proposed approach accurately identified five true positives, one false negative, and four false positive IOPD cases. The false negative case involved a patient with both Pompe disease and congenital heart disease. The focus on CHD led to the overlooking of Pompe disease, exacerbated by no measurement of creatine kinase. The false positive cases were diagnosed with Mitochondrial DNA depletion syndrome 12-A (SLC25A4 gene), Immunodeficiency-71 (ARPC1B mutation), Niemann-Pick disease type C (NPC1 gene mutation leading to frameshift), and Group B Streptococcus meningitis. The proposed approach of integrating expert rules with a dashboard facilitated efficient data visualization and automated patient screening, which aids in the early detection of Pompe disease. Future studies are encouraged to investigate the application of machine learning methodologies to enhance further the precision and efficiency of identifying patients with IOPD.

Implementation and impact of a surgical dashboard on pediatric tonsillectomy outcomes: A quality improvement study.

Introduction: In pediatric tonsillectomy management, the consistent tracking of surgical outcomes and adherence to guidelines are vital. This study explores how a surgical dashboard can serve as a tool in research analysis, translating AAO-HNSF guidelines into measurable performance improvements. Methods: Using a prospective registry from three pediatric hospitals, a Tableau dashboard was constructed to graphically visualize key demographic and postoperative outcomes (including intensive care unit [ICU] utilization, 30-day emergency department (ED) visits, and postoperative bleed rates) in children undergoing tonsillectomy from 2020 to 2024. From the dashboard data, a retrospective cohort study analyzing 6767 tonsillectomies was conducted from January 2, 2020, to June 20, 2023. Patients were categorized into low-risk, OSA-only (by ICD-10 codes), and high-risk groups based on comorbidities. Logistic regression identified factors influencing ED revisits and unplanned nursing calls. Three quality initiatives were assessed: preoperative school absence notes, perioperative dexamethasone recording, and post-tonsillectomy parental education. Results: A total of 2122 (31%) were low-risk, 2648 (39%) were OSA-only, and 1997 (30%) high risk. Risk factors that increased the likelihood of ED visits were high-risk comorbidities (OR = 1.46; 95% CI = 1.24-1.74; p < 0.001) and older age (OR = 1.05; 95% CI = 1.03-1.08; p < 0.001). Risk factors that increased the likelihood of an unplanned nursing communication were high-risk comorbidities (OR = 1.53; 95% CI = 1.34-1.75; p < 0.001), older age (OR = 1.03, 95% CI = 1.01-1.04; p = 0.001), and Medicaid insurance (OR = 1.25; 95% CI = 1.09-1.43; p = 0.002). Postoperative bleed control was generally comparable between the groups, at 2.8% (low risk), 2.7% (OSA), 3.2 (high risk) (p = 0.651). Conclusion: The dashboard aided in data collection, data visualization, and data analysis of quality improvement initiatives, effectively translating guidelines into tangible measures to enhance care. Level of evidence: NA.

Public Maternal Health Dashboards in the United States: Descriptive Assessment.

BACKGROUND: Data dashboards have become more widely used for the public communication of health-related data, including in maternal health. OBJECTIVE: We aimed to evaluate the content and features of existing publicly available maternal health dashboards in the United States. METHODS: Through systematic searches, we identified 80 publicly available, interactive dashboards presenting US maternal health data. We abstracted and descriptively analyzed the technical features and content of identified dashboards across four areas: (1) scope and origins, (2) technical capabilities, (3) data sources and indicators, and (4) disaggregation capabilities. Where present, we abstracted and qualitatively analyzed dashboard text describing the purpose and intended audience. RESULTS: Most reviewed dashboards reported state-level data (58/80, 72%) and were hosted on a state health department website (48/80, 60%). Most dashboards reported data from only 1 (33/80, 41%) or 2 (23/80, 29%) data sources. Key indicators, such as the maternal mortality rate (10/80, 12%) and severe maternal morbidity rate (12/80, 15%), were absent from most dashboards. Included dashboards used a range of data visualizations, and most allowed some disaggregation by time (65/80, 81%), geography (65/80, 81%), and race or ethnicity (55/80, 69%). Among dashboards that identified their audience (30/80, 38%), legislators or policy makers and public health agencies or organizations were the most common audiences. CONCLUSIONS: While maternal health dashboards have proliferated, their designs and features are not standard. This assessment of maternal health dashboards in the United States found substantial variation among dashboards, including inconsistent data sources, health indicators, and disaggregation capabilities. Opportunities to strengthen dashboards include integrating a greater number of data sources, increasing disaggregation capabilities, and considering end-user needs in dashboard design.

Narrowing the Digital Divide: Framework for Creating Telehealth Equity Dashboards.

Telehealth presents both the potential to improve access to care and to widen the digital divide contributing to health care disparities and obliging health care systems to standardize approaches to measure and display telehealth disparities. Based on a literature review and the operational experience of clinicians, informaticists, and researchers in the Supporting Pediatric Research on Outcomes and Utilization of Telehealth (SPROUT)-Clinical and Translational Science Awards (CTSA) Network, we outline a strategic framework for health systems to develop and optimally use a telehealth equity dashboard through a 3-phased approach of (1) defining data sources and key equity-related metrics of interest; (2) designing a dynamic and user-friendly dashboard; and (3) deploying the dashboard to maximize engagement among clinical staff, investigators, and administrators.

Advancing Public Health Surveillance in Child Care Centers: Stakeholder-Informed Redesign and User Satisfaction Evaluation of the MCRISP Network.

Unlabelled: Leveraging user feedback, we redesigned a novel disease monitoring utility to allow for bidirectional data flow and in this letter offer insights into that process as well as lessons learned.

Developing the DIGIFOOD Dashboard to Monitor the Digitalization of Local Food Environments: Interdisciplinary Approach.

BACKGROUND: Online food delivery services (OFDS) enable individuals to conveniently access foods from any deliverable location. The increased accessibility to foods may have implications on the consumption of healthful or unhealthful foods. Concerningly, previous research suggests that OFDS offer an abundance of energy-dense and nutrient-poor foods, which are heavily promoted through deals or discounts. OBJECTIVE: In this paper, we describe the development of the DIGIFOOD dashboard to monitor the digitalization of local food environments in New South Wales, Australia, resulting from the proliferation of OFDS. METHODS: Together with a team of data scientists, we designed a purpose-built dashboard using Microsoft Power BI. The development process involved three main stages: (1) data acquisition of food outlets via web scraping, (2) data cleaning and processing, and (3) visualization of food outlets on the dashboard. We also describe the categorization process of food outlets to characterize the healthfulness of local, online, and hybrid food environments. These categories included takeaway franchises, independent takeaways, independent restaurants and cafes, supermarkets or groceries, bakeries, alcohol retailers, convenience stores, and sandwich or salad shops. RESULTS: To date, the DIGIFOOD dashboard has mapped 36,967 unique local food outlets (locally accessible and scraped from Google Maps) and 16,158 unique online food outlets (accessible online and scraped from Uber Eats) across New South Wales, Australia. In 2023, the market-leading OFDS operated in 1061 unique suburbs or localities in New South Wales. The Sydney-Parramatta region, a major urban area in New South Wales accounting for 28 postcodes, recorded the highest number of online food outlets (n=4221). In contrast, the Far West and Orana region, a rural area in New South Wales with only 2 postcodes, recorded the lowest number of food outlets accessible online (n=7). Urban areas appeared to have the greatest increase in total food outlets accessible via online food delivery. In both local and online food environments, it was evident that independent restaurants and cafes comprised the largest proportion of food outlets at 47.2% (17,437/36,967) and 51.8% (8369/16,158), respectively. However, compared to local food environments, the online food environment has relatively more takeaway franchises (2734/16,158, 16.9% compared to 3273/36,967, 8.9%) and independent takeaway outlets (2416/16,158, 14.9% compared to 4026/36,967, 10.9%). CONCLUSIONS: The DIGIFOOD dashboard leverages the current rich data landscape to display and contrast the availability and healthfulness of food outlets that are locally accessible versus accessible online. The DIGIFOOD dashboard can be a useful monitoring tool for the evolving digital food environment at a regional scale and has the potential to be scaled up at a national level. Future iterations of the dashboard, including data from additional prominent OFDS, can be used by policy makers to identify high-priority areas with limited access to healthful foods both online and locally.

Advancing Equitable Ambulatory Telehealth Through Dashboard Development.

Telehealth has the potential to improve access to health care by mitigating barriers related to geography, time, and finances. However, the increased adoption of ambulatory telehealth has inadvertently widened access gaps for socially disadvantaged and marginalized populations. Quality improvement approaches are a valuable strategy to address health care access inequities and disparities, involving data-driven implementation, assessment, and adaptation of tests of change over time. Because these iterative changes and interventions are data-driven, a critical element of quality improvement requires ongoing data collection and monitoring. This perspective describes the development and validation processes of a telehealth equity dashboard. This dashboard is currently available for use by our health system leaders, providers, and clinic staff. The overall objective of this dashboard is to identify and track inequities and to improve equitable ambulatory telehealth access across diverse patient groups. Lessons learned from creating this dashboard can inform other health care systems of how to develop and validate telehealth data feedback systems to promote quality improvement efforts to advance telehealth equity and accessibility.

Electronic health record modification and dashboard development to improve clinical care in pediatric rheumatology.

Objective: This report describes our experience in electronic health record (EHR) note modification and creation of an external dashboard to create a local learning health system that contributes to quality improvement and patient care within our pediatric rheumatology clinic. Methods: We applied quality improvement methodology to develop a more reliable and accurate system to identify patients with juvenile idiopathic arthritis and track important measures that aide in improving patient care and performance outcomes. From 2019 to 2021, we iteratively modified our outpatient clinic EHR note to include structured data elements to improve longitudinal monitoring. We then validated data transferred to an electronic dashboard external to the EHR and demonstrated utility for identifying an accurate patient population and tracking quality improvement initiatives. Results: Creation of the structured data elements improved the identification of patients with JIA with >99% accuracy and without requiring manual review of the chart. Using the dashboard to monitor performance, we improved documentation of critical disease activity measures that resulted in improvement in those scores across the local population of patients with JIA. The structured data elements also enabled us to automate electronic data transfer to a multicenter learning network registry. Conclusion: The structured data element modifications made to our outpatient EHR note populate a local dashboard that allows real time access to critical information for patient care, population management, and improvement in quality metrics. The collection and monitoring of structured data can be scaled to other quality improvement initiatives in our clinic and shared with other centers.

Monitoring Variability of Laboratory Results in a Clinical Data Warehouse Using Automatic Dashboard.

Hospital laboratory results are a significant data source in Clinical Data Ware-houses (CDW). To ensure comparability across healthcare organizations and for use in research studies, the results need to be interoperable. The LOINC (Logical Observation Identifiers, Names, and Codes) terminology provides a unique identifier for local codes for lab tests, enabling interoperability. However, in real-world, events occur over time and can disrupt the distribution of lab result values. For example, new equipment may be added to the analysis pipeline, a machine may be replaced, formulas may evolve due to new scientific knowledge, and legacy terminologies may be adopted. This article proposes a pipeline for creating an automated dashboard to monitor these events and data quality. We used automatic change point detection methods such as PELT for event detection in lab results. For a given LOINC code, we create a dashboard that summarizes the number of local codes mapped, and the number of patients (by sex, age, and hospital service) associated with the code. Finally, the dashboard enables the visualization of time events that disrupt the signal distribution. The biologists were able to explain to us the changes for several biological assays.

Intensive Care Quality Indicators Dashboard Using Observational Medical Outcomes Partnership Common Data Model.

This paper presents the development of a visualization dashboard for quality indicators in intensive care units (ICUs), using the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM). The dashboard enables the user to visualize quality indicator data using histograms, pie charts and tables. Our project uses the OMOP CDM, ensuring a seamless implementation of our dashboard across various hospitals. Future directions for our research include expanding the dashboard to incorporate additional quality indicators and evaluating clinicians' feedback on its effectiveness.

Integrating Automation, Interactive Visualization, and Unsupervised Learning for Enhanced Diabetes Management.

Effective management of diabetes necessitates efficient data handling, insightful analytics, and personalized interventions. In this study, we present a comprehensive system that automates the extraction, transformation, and loading of continuous glucose monitoring data. Data is integrated into an interactive dashboard with dual access levels: one for healthcare management professionals and another for patients for clinical management. The dashboard provides real-time updates and customizable visualization options, empowering users with actionable insights into their glucose levels. Furthermore, a clustering model to categorize patients into distinct groups based on their glucose profiles was developed. Through this model, three clusters representing different patterns of glucose control are identified. Healthcare professionals can utilize these insights to tailor treatment strategies, allocate resources effectively, and identify high-risk patients.