Improve Data Collection Research Articles

Healthcare Disparities among Socioeconomic Groups

Purpose: This study sought to explore the socioeconomic factors contributing to disparities in healthcare access and outcomes. Methodology: The study adopted a desktop research methodology. Desk research refers to secondary data or that which can be collected without fieldwork. Desk research is basically involved in collecting data from existing resources hence it is often considered a low cost technique as compared to field research, as the main cost is involved in executive’s time, telephone charges and directories. Thus, the study relied on already published studies, reports and statistics. This secondary data was easily accessed through the online journals and library. Findings: The findings reveal that there exists a contextual and methodological gap relating to healthcare disparities among socioeconomic groups. Preliminary empirical review revealed that individuals from lower socioeconomic backgrounds faced multiple barriers to healthcare access, leading to inequalities in health outcomes. The study highlighted the complex interplay between social factors such as income, education, and wealth, emphasizing the need for comprehensive, multisectoral approaches to address healthcare disparities. Despite progress, gaps in research, policy, and practice remained, underscoring the importance of continued efforts to achieve health equity through collaborative initiatives and evidence-based interventions. Unique Contribution to Theory, Practice and Policy: The Social Determinants of Health theory, Health Benefit model and Critical Medical Anthropology theory may be used to anchor future studies on healthcare disparities among socioeconomic groups. The study provided comprehensive recommendations to advance theory, practice, and policy. It contributed to theoretical frameworks by highlighting the complex interplay between socioeconomic factors and healthcare disparities. In practice, it emphasized the importance of patient-centered care and collaborative models to address social determinants of health. Policy recommendations focused on structural interventions to improve social and economic conditions and expand healthcare coverage. Capacity building efforts aimed to enhance skills and competencies, while improved data collection and monitoring systems were advocated for tracking progress. Finally, a research agenda prioritized understanding the mechanisms underlying disparities and evaluating intervention effectiveness. These recommendations aimed to promote health equity and ensure equitable access to healthcare services.

DWSP-MT: Discovery of workers sensing preferences to match tasks for improving data collection quality in MCS

Mobile CrowdSensing (MCS) is a new sensing paradigm that leverages the widespread use of mobile devices to collect data quickly and enable various key applications in the Internet of Things (IoT). Collecting high-quality data is essential for MCS to design and provide a high-quality service. However, it is difficult for the platform to detect dishonest workers who submit fake data. Therefore, MCS applications face a significant challenge. Although there are a number of studies on truth discovery, these studies mainly failed to consider the sensing preferences of workers, resulting in poor data quality enhancement. To overcome these issues, a Discovery of Workers Sensing Preferences to Match Tasks (DWSP-MT) scheme is proposed to improve the quality of MCS data collection. The DWSP-MT scheme primarily includes three components: 1) Unmanned Aerial Vehicles (UAVs) are employed to sense data as ground truth data and an unsupervised anomaly detection algorithm is adopted to identify the data truth of tasks submitted by unidentified workers. 2) The data truth of unidentified workers in historical tasks is evaluated to calculate their sensing preferences, which are used to identify trusted workers. 3) To improve the quality of data collection, a task assignment model based on the sensing preferences of trusted workers is proposed for the first time, which can ensure that tasks are assigned to their areas of most excellent expertise. At last, two open-access datasets were used to conduct experiments comparing the DWSP-MT scheme with existing truth discovery methods. The outcomes of these experiments indicate that our approach outperforms the state-of-the-art research and improves F1-score by 65.74 %, improves accuracy by 36.62 %, and reduces data bias by 60.61 %

Comprehensive Outreach: Reach Every Child Initiative Expanded Program on Immunization Khyber Pakhtunkhwa, Pakistan

Background: Vaccination is one of the most effective public health interventions for preventing infectious diseases. In regions like Khyber Pakhtunkhwa, Pakistan, where access to healthcare is limited, immunization coverage often remains below optimal levels. Targeted outreach activities can play a pivotal role in improving vaccine uptake, particularly in underserved populations. Objective: This study aimed to assess the effectiveness of immunization outreach activities in increasing vaccine coverage in Khyber Pakhtunkhwa over a defined period. Methods: A comprehensive methodological approach was utilized involving predefined Excel sheets for daily data collection by trained personnel. Data validation procedures were implemented to ensure accuracy, with regular feedback sessions to improve data collection processes. Monthly reports were compiled and analyzed using SPSS version 25 to summarize immunization activities and coverage rates. The outreach strategy included community announcements and door-to-door campaigns by Local Health Workers (LHWs), designed to identify and vaccinate zero-dose and defaulting children. Results: The outreach campaign significantly improved immunization coverage, with zero-dose vaccinations achieving 100% coverage. Penta I, II, and III vaccines saw coverage rates of 78.99%, 72.48%, and 78.02%, respectively. Measles vaccines MCV-I and MCV-II achieved coverage rates of 83.01% and 86.11%. The overall strategy effectively reduced gaps in vaccine coverage across various districts. Conclusion: The study confirms that structured and well-coordinated outreach activities can effectively increase immunization coverage in areas with limited healthcare access. Continuous effort and strategic planning are essential to sustain and build upon these gains to ensure comprehensive vaccine coverage.

Combining impact goal and impact descriptor frameworks to elucidate the societal impacts of research: a pilot study

Universities, researchers and funders are increasingly asking how research contributes to positive changes in society and the environment, and seeking ways to document and describe impacts consistently across diverse disciplines and organisational scales. The societal impacts framework presented in this pilot study uses a combination of impact goal and impact descriptor frameworks to elucidate the societal impacts of research. The framework blends elements of assessment-driven and mission-driven reporting frameworks, and was administered online to volunteers from one interdisciplinary environmental research institute. The 12 projects in the pilot study addressed 15 of the 17 UN Sustainable Development Goals, and all 12 projects reported impacts in two or more of six impact descriptor categories. We also identified an impact subcategory dealing with changes to higher education practice. Combining two types of impacts frameworks – societal goals and descriptors of changes – allowed us to understand how the research projects contributed to broad societal goals, not just that they addressed the goals. Responses from study participants indicated a good fit between the framework and their research efforts. However, we found that the online reporting tool, in its current form, was not effective in eliciting full and accurate reports from all participants. We reflect upon how to improve data collection in the future, as well as on opportunities for additional tests of the framework in new contexts.

PREDICTIVE MODELING FOR DISEASE OUTBREAKS: A REVIEW OF DATA SOURCES AND ACCURACY

This review explores the dynamic field of predictive modelling for disease outbreaks, focusing on the data sources, modelling techniques, accuracy, challenges, and future directions integral to its advancement. It underscores the significance of diverse data sources, including epidemiological, environmental, social media, and mobility data. It also discusses various modelling approaches, from statistical models to advanced machine learning algorithms and network analysis. The review highlights the critical role of accuracy and validation in predictive models, alongside the challenges posed by data quality, model complexity, and the ethical use of personal data. It outlines promising research avenues, such as improving data collection methods, integrating novel data sources like genomic data, and leveraging emerging technologies such as AI and IoT to enhance predictive capabilities. This comprehensive overview emphasizes the importance of predictive modelling in informing public health decisions and the continuous need for innovation and collaboration to address the complex dynamics of disease outbreaks.
 Keywords: Predictive Modeling, Disease Outbreaks, Data Sources, Machine Learning, Public Health, Emerging Technologies.

IoT-enabled Smart Weather Stations: Innovations, Challenges, and Future Directions

The evolution of the Internet of Things (IoT) has ushered in innovative approaches facilitated by breakthrough technologies. This paper presents a comprehensive review of recent advancements in smart weather stations, focusing on IoT-enabled solutions. The integration of Internet of Things (IoT) technologies has revolutionized traditional weather monitoring systems, enabling seamless data collection, analysis, and dissemination. Commercially available automated weather stations offer cost-effective solutions for comprehensive meteorological data collection. However, challenges such as limited local deployment and reliance on expensive options persist, hindering comprehensive monitoring efforts. The critical need for improved data collection methods is underscored to enhance the accuracy of weather forecasts and address evolving climatic conditions. Climate change impacts, including shifts in weather patterns and rising temperatures, highlight the importance of effective weather monitoring for agriculture, infrastructure, and national security. Additionally, the dependence on non-renewable energy sources for electricity generation emphasizes the environmental and economic implications of energy production. In response to these challenges, numerous IoT based smart weather station systems have been proposed by earlier researchers. The introduction of IoT-enabled smart weather stations represents a significant advancement in weather monitoring technology. These stations leverage IoT technologies to collect, analyse, and visualize meteorological data in real time. In order to identify the challenges and prospects in this area of technology, the purpose of this study is to present a thorough analysis of the suggested designs as well as to compare, evaluate, and assess the outcomes, contributing to the development of robust and efficient weather monitoring systems.

THE RECEPTIVENESS PUZZLE: UNDERSTANDING WHY SMALL FAMILY BUSINESSES STRUGGLE WITH ACTIVITY-BASED COSTING ADOPTION

This study examines the challenges associated with the adoption of activity-based costing (ABC) in a small family business in a less developed country. Through a case study of PT XYZ, a logistic company, the study found that technical factors, including unpredictable customer requests, varying duration of activities, and uncertainty around distance and fuel consumption for each truck, make adopting the ABC method challenging. Organizational factors, such as a top-down hierarchy and the negative perception of the owner affecting the rest of the hierarchy, also contribute to the non-acceptance of ABC. Additionally, key agents within the company did not demonstrate enthusiasm toward changes. The cultural and contextual factors and unique characteristics of small family businesses further make them less receptive to adopting new ideas or changes, including the ABC method. This study offers practical implications for introducing changes, such as improving data collection processes, establishing better communication channels with family business owners, and seeking external expertise or advice from professionals with experience in family business management. The study expands the current discussion on ABC implementation and contributes to the family business literature by linking family business characteristics with the adoption of the ABC method.

The National Healthcare Safety Network's digital quality measures: CDC's automated measures for surveillance of patient safety.

This article presents the National Healthcare Safety Network (NHSN)'s approach to automation for public health surveillance using digital quality measures (dQMs) via an open-source tool (NHSNLink) and piloting of this approach using real-world data in a newly established collaborative program (NHSNCoLab). The approach leverages Health Level Seven Fast Healthcare Interoperability Resources (FHIR) application programming interfaces to improve data collection and reporting for public health and patient safety beginning with common, clinically significant, and preventable patient harms, such as medication-related hypoglycemia, healthcare facility-onset Clostridioides difficile infection, and healthcare-associated venous thromboembolism. The NHSN's FHIR dQMs hold the promise of minimizing the burden of reporting, improving accuracy, quality, and validity of data collected by NHSN, and increasing speed and efficiency of public health surveillance.

Dementia-Related Disparities in Adult Day Centers: Results of a Bivariate Analysis.

Adult day services (ADS) are a valuable resource for people living with Alzheimer's disease and Alzheimer's disease and related dementias (AD/ADRD) and serve a large population of late-life immigrants, often with limited English proficiency (LEP). This secondary data analysis examined potential disparities in diagnosis, dementia severity, medical complexity, and dementia-related behavioral problems in persons with AD/ADRD with LEP within the ADS setting. The current study used data from TurboTAR, the electronic health record for ADS in California. Bivariate analyses were conducted to examine differences in clinical management for those with and without LEP. Of 3,053 participants included in the study, 42.3% had LEP. Participants with LEP had higher rates of emergency department use and medication mismanagement. However, due to non-standard data collection, there was a significant amount of missing data on language preference (38.1%) and race/ethnicity (46.5%). Although these findings suggest LEP may play a role in the clinical management of persons with AD/ADRD in ADS, missing data caused by lack of standardized collection compromise the results. It is essential to improve data collection practices in ADS on language, race, and ethnicity to help identify health disparities and promote equitable care for marginalized older adults. [Journal of Gerontological Nursing, 50(4), 42-47.].

Using lessons learned from a multidecadal intercept survey of Puerto Rico spear fishers to improve data collection in the U.S. Caribbean

AbstractObjectiveSpearfishing is a common activity used to harvest coastal marine species worldwide and can be a significant contributor to landings of ecologically and economically relevant species. As such, it is important to monitor spearfishing catch and effort. Spearfishing is commonly practiced throughout the Caribbean, yet characteristics of this gear's fishery remain understudied in the region. Thus, the objectives of this study were to provide a description of the Puerto Rico spearfishing fishery utilizing a long‐term MRIP APAIS data set, examine the data set quality of this survey, and provide suggestions to improve MRIP implementation in the U.S. Caribbean.MethodsWe described the Puerto Rico recreational spearfishing fishery using a multidecadal (2000–2015) Marine Recreational Information Program (MRIP) Access Point Angler Intercept Survey conducted along the coast of Puerto Rico and surrounding territories. Data issues were identified in the survey results, including possible errors in measurement and identification; therefore, we limited analyses to species composition, enumeration, and mean catch rates.ResultThere were 89 species identified, with snappers (family Lutjanidae) being the most encountered family. Other economically relevant finfish frequently reported were Hogfish Lachnolaimus maximus (n = 121), Queen Triggerfish Balistes vetula (n = 115), and Red Hind Epinephelus guttatus (n = 90). Multiple reef fishes were landed during their spawning season, while prohibited by seasonal closures. Further, spearfishing accounted for 25.8% of the total harvest throughout the study period.ConclusionOur results suggest that spearfishing could be a considerable contributor to finfish landings in Puerto Rico, warranting its continued and improved monitoring in the region. Further, modifications in the design of fisheries surveys are warranted. We suggest increasing the allocation of resources, training, data quality assurance and quality control, and funding for the MRIP to maximize the accuracy of data obtained and meet MRIP U.S. Caribbean priorities. Implementation of a recreational fishing license would assist by creating a registry to monitor total recreational fishing effort and by generating funds to supplement the MRIP. The use of electronic reporting can help mitigate discrepancies with species identification and size measurements and enhance overall recreational fisheries data collection.

Generating trajectory data without behavior modeling: An online virtual reality method in wayfinding performance evaluation for buildings

Wayfinding performance is one of the key issues in pre- or post-occupancy evaluation for large-scale public buildings and such evaluation typically revolves around participants’ wayfinding trajectories. Traditional evaluation methods, including laboratory virtual reality (VR) experiments and computer simulations, have their respective advantages in accuracy or cost-efficiency. However, they either suffer from high costs and low efficiency or sacrifice the diversity of behavioral data for higher efficiency and lower costs. Therefore, this paper proposes a novel method named “sampling as simulation” (SaS) via an online VR wayfinding experiment. This method allows for the rapid collection of behavioral data at a lower cost to generate highly accurate wayfinding trajectory data for evaluation purposes. It utilizes a model-image integrated approach to create realistic online virtual environments and employs a spatiotemporal replay algorithm to generate a crowd of true-trajectory-driven avatars, significantly enhancing the immersion and realism of the online virtual scenes. This method was demonstrated and validated with a wayfinding performance post-occupancy evaluation (POE) project at the International Departure of Satellite Terminal One (S1) in Shanghai Pudong International Airport (PVG). In this example, a total of 13,712 original trajectories were collected within a 72-h time window, serving as the original dataset for evaluation. Based on this dataset, the study effectively simulated wayfinding behavior in three different scenarios and evaluated wayfinding performance. Compared to existing methods, the SaS method not only improves data collection efficiency and reduces costs but also maintains the diversity of behavior by generating transformed trajectories directly from human data without behavior modeling.

The Military-Civilian Partnership Quality Improvement Program Concept: A Process to Improve Data Collection and Outcomes Assessment.

Military-Civilian Partnerships (MCPs) are vital for maintaining the deployment readiness of military health care physicians. However, tracking their clinical activity has proven to be challenging. In this study, we introduce a locally driven process aimed at the passive collection of external clinical workload data. This process is designed to facilitate an assessment of MCP physicians' deployment readiness and the effectiveness of individual MCPs. From March 2020 to February 2023, we conducted a series of quality improvement projects at the Wright Patterson Medical Center (WPMC) to enhance our data collection efforts for MCP physicians. Our methodology encompassed several steps. First, we assessed our existing data collection processes and their outcomes to identify improvement areas. Next, we tested various data collection methods, including self-reporting, a web-based smart phone application, and an automated process based on billing or electronic health record data. Following this, we refined our data collection process, incorporating the identified improvements and systematically tracking outcomes. Finally, we evaluated the refined process in 2 different MCPs, with our primary outcome measure being the collection of monthly health care data. Our examination at the WPMC initially identified several weaknesses in our established data collection efforts. These included unclear responsibility for data collection within the Medical Group, an inadequate roster of participating MCP physicians, and underutilization of military and community resources for data collection. To address these issues, we implemented revisions to our data collection process. These revisions included establishing clear responsibility for data collection through the Office of Military-Civilian Partnerships, introducing a regular "roll call" to match physicians to MCP agreements, passively collecting data each month through civilian partner billing or information technology offices, and integrating Office of Military-Civilian Partnership efforts into regular executive committee meetings. As a result, we observed a 4-fold increase in monthly data capture at WPMC, with similar gains when the refined process was implemented at an Air Force Center for the Sustainment of Trauma and Readiness Skills site. The Military-Civilian Partnership Quality Improvement Program concept is an effective, locally driven process for enhancing the capture of external clinical workload data for military providers engaged in MCPs. Further examination of the Military-Civilian Partnership Quality Improvement Program process is needed at other institutions to validate its effectiveness and build a community of MCP champions.

A 5-Year Review of a Tracheostomy Quality Improvement Initiative: Reducing Adverse Event Frequency and Severity.

The number of tracheostomies performed annually in resource-rich countries is estimated at 250,000. While an essential procedure, approximately 20% to 30% of patients will experience at least 1 tracheostomy-related adverse event. Within tracheostomy careand across wider health care environments, quality improvement (QI) programs have been shown to reduce patient harm and improve outcomes. Herein we report on a 5-year long, tracheostomy QI initiative aimed at improving patient experience and reducing the frequency and severity of adverse events. A 5-year (ongoing) QI initiative led by the Cardiff and Vale University Health Board tracheostomy team, within a tertiary, 1000-bedded hospital in South Wales, United Kingdom. The QI initiative has focused on 3 main themes: (1) Education and training; (2) Clinical oversight and decision making; and (3) improved data collection. Data were collected from existing tracheostomy databases. Over the past 5 years, we have observed a sustained reduction in both the frequency and severity of adverse events, with less than 1 patient per 100 experiencing a moderate or severe adverse event. This has resulted in improvements in patient experience and a cost reduction of £GBP364,726 per annum. Our 5-year ongoing tracheostomy QI initiative has resulted in improved outcomes with increased achievement of tracheostomy weaning markers and sustained reductions in both the frequency and severity of adverse events. A continuous focus on QI is associated with improved patient and service outcomes. These improvements can be spread and scaled to benefit more patients and organizations.

The Society of Thoracic Surgeons Congenital Heart Surgery Database: 2023 Update on Outcomes and Research

The Society of Thoracic Surgeons (STS) Congenital Heart Surgery Database (CHSD) continues to be the most comprehensive database of congenital and pediatric cardiothoracic surgical procedures in the world and contains information on 664,210 operations as of June 30, 2023. The 35th harvest of the STS CHSD data was undertaken in Spring 2023, spanning the 4-year period January 1, 2019, through December 31, 2022, and included 144,919 operations performed at 114 participating sites in North America. The harvest analysis was successfully executed by the STS Research and Analytic Center. The overall unadjusted mortality rate was 2.68% and has remained stable over the 4 years included in the current harvest window. Mortality is highest in neonates (7.4%) and lowest in children (1.1%). As in prior analyses, observed mortality and postoperative length of stay in the database increase with an increase in STS-European Association for Cardio-Thoracic Surgery (STAT) Congenital Heart Surgery Mortality Categories. This quality report summarizes contemporary outcomes, provides the odds ratios for the CHSD risk model variables based on this analysis, and describes on-going efforts to improve data collection and augment analytical approaches. Lastly, 5 research publications completed in the last year using data from the CHSD are also summarized.

Twelve years after: The french national network on rare head and neck tumours (REFCOR)

BackgroundRare cancers constitute less than 10% of head and neck cancers and lack sufficient evidence for standardized care. The French Rare Head and Neck Cancer Expert Network (REFCOR) as established a national database to collect data on these rare cancers. This study aims to describe patient and tumour characteristics in this database. MethodsProspective data collection was conducted across multiple centers. Survival analyses were performed using Kaplan Meier method and Log Rank test. Odds ratios were used for comparing proportions. ResultsA total of 7208 patients were included over a period of 10 years. The most frequent histologies were: Not Otherwise Specified (NOS) adenocarcinoma 13 %, adenoid cystic carcinoma 12 %, squamous cell carcinoma of rare locations 10 %, mucoepidermoid carcinoma 9 %, intestinal-type adenocarcinoma (8 %). Tumours were located in sinonasal area (38 %); salivary glands (32 %); oral cavity / oropharynx / nasopharynx (16 %); larynx / hypopharynx (3 %); ears (1 %); others (3 %). Tumours were predominantly classified as T4 (23 %), N0 (54 %), and M0 (62 %). Primary treatment approach involved tumour resection (78 %) and / or radiotherapy (63 %). Patients with salivary gland cancers exhibited better 5-year overall survival (OS) rates (p < 0.05), and lower recurrence rates compared to patients with sinonasal, laryngeal/ hypopharyngeal cancers. No significant differences were observed in the other comparisons. Acinar cell carcinoma demonstrated the best OS while mucous melanoma had the poorest prognosis. ConclusionMelanoma, carcinoma NOS, and sinonasal undifferenciated carcinoma still have poor prognoses. Efforts are being made, including training and guidelines, to expand network coverage (REFCOR, EURACAN), improve data collection and contribute to personalized therapies.

Unified deep learning models for enhanced lung cancer prediction with ResNet-50-101 and EfficientNet-B3 using DICOM images.

Significant advancements in machine learning algorithms have the potential to aid in the early detection and prevention of cancer, a devastating disease. However, traditional research methods face obstacles, and the amount of cancer-related information is rapidly expanding. The authors have developed a helpful support system using three distinct deep-learning models, ResNet-50, EfficientNet-B3, and ResNet-101, along with transfer learning, to predict lung cancer, thereby contributing to health and reducing the mortality rate associated with this condition. This offer aims to address the issue effectively. Using a dataset of 1,000 DICOM lung cancer images from the LIDC-IDRI repository, each image is classified into four different categories. Although deep learning is still making progress in its ability to analyze and understand cancer data, this research marks a significant step forward in the fight against cancer, promoting better health outcomes and potentially lowering the mortality rate. The Fusion Model, like all other models, achieved 100% precision in classifying Squamous Cells. The Fusion Model and ResNet-50 achieved a precision of 90%, closely followed by EfficientNet-B3 and ResNet-101 with slightly lower precision. To prevent overfitting and improve data collection and planning, the authors implemented a data extension strategy. The relationship between acquiring knowledge and reaching specific scores was also connected to advancing and addressing the issue of imprecise accuracy, ultimately contributing to advancements in health and a reduction in the mortality rate associated with lung cancer.

Breaking barriers to agroforestry: FAO’s global capacity needs assessment

The FAO's Global Agroforestry Capacity Needs Assessment (CNA) revealed persistent challenges which are hindering widespread adoption of agroforestry despite its recognised benefits. Three primary action areas were identified: transforming agroforestry into an economically viable production system; strengthening enabling environments through agroforestry policies and strategies; and improving agroforestry extension for more biodiverse and agroecological systems. Stakeholders - governmental entities, NGOs, land users and community groups, researchers, private-sector entities and investors and donors - demonstrated expertise in agroforestry planning but identified gaps in socioeconomic aspects, and policy analysis and implementation. Challenges included insecure land tenure, limited market access and insufficient financial incentives for farmers. The assessment emphasised the need for collaborative efforts to address these challenges, recommending tailored approaches for the different stakeholder groups. Recommendations included improving data collection on socioeconomic aspects, developing innovative financing mechanisms and promoting peer-to-peer knowledge exchange. Efforts to strengthen policy frameworks, enhance extension services, and integrate local and Indigenous knowledge were also proposed. Concerted action is, therefore, necessary to unlock agroforestry's full potential in addressing global sustainability challenges to promote sustainable land management and food security worldwide.

Digital ecosystem for enhancing research and innovation activities of master's students

Introduction. The dynamic development of modern digital technologies emphasizes the need for the development of new tools to activate the research and innovation activities of master's students. A digital ecosystem holds significant pedagogical potential not only in the academic activities of students but also in research and innovation. However, its creation is complicated by a lack of understanding of the essence, structure, and emergent properties of the system. The purpose of the study is to identify the features, mechanisms, and functionality of the digital educational ecosystem to enhance the research and innovation activities of undergraduates. Materials and methods. Bibliographic databases such as Scopus, Web of Science, Lens, and other international information systems of peer-reviewed scientific literature were used for the analysis of publications. System analysis and analysis of internet resources were employed to address the research objectives. The results of the study. The functional capabilities of the digital educational ecosystem for activating the research and innovation activities of master's students were identified, including access to information systems and databases of scientific information, automation of research management, creation and administration of electronic surveys, access to tools for processing and analyzing large volumes of data, access to data visualization tools, collaboration systems for working on research projects, automation of planning, tracking, and organizing research or educational projects, access to electronic systems for applying for research grants and scholarships, access to specialized plagiarism detection systems, electronic portfolios, access to publication management systems, automation of the creation and formatting of scientific articles and presentations, automation of publication formatting, access to virtual laboratories and modeling environments. Conclusion. The presented analysis allowed for the identification and justification of the pedagogical potential of the digital ecosystem for the core research and project activities of master's students, specifically: providing research with information resources; improving data collection and analysis; optimizing collaborative and network interactions; automating project management; digitally supporting the research process; digital technologies for stimulating creativity; digital support for publication preparation; expanding research boundaries; self-development; supporting distance learning; supporting virtual laboratories and modeling environments; ensuring data visualization; instrumental support for research quality analysis.

Data Linkages for Wildfire Exposures and Human Health Studies: A Scoping Review.

Wildfires are increasing in frequency and intensity, with significant consequences that impact human health. A scoping review was conducted to: (a) understand wildfire-related health effects, (b) identify and describe environmental exposure and health outcome data sources used to research the impacts of wildfire exposures on health, and (c) identify gaps and opportunities to leverage exposure and health data to advance research. A literature search was conducted in PubMed and a sample of 83 articles met inclusion criteria. A majority of studies focused on respiratory and cardiovascular outcomes. Hospital administrative data was the most common health data source, followed by government data sources and health surveys. Wildfire smoke, specifically fine particulate matter (PM2.5), was the most common exposure measure and was predominantly estimated from monitoring networks and satellite data. Health data were not available in real-time, and they lacked spatial and temporal coverage to study health outcomes with longer latency periods. Exposure data were often available in real-time and provided better temporal and spatial coverage but did not capture the complex mixture of hazardous wildfire smoke pollutants nor exposures associated with non-air pathways such as soil, household dust, food, and water. This scoping review of the specific health and exposure data sources used to underpin these studies provides a framework for the research community to understand: (a) the use and value of various environmental and health data sources, and (b) the opportunities for improving data collection, integration, and accessibility to help inform our understanding of wildfires and other environmental exposures.

Real-Time Reporting of Complications in Hospitalized Surgical Patients by Surgical Team Members Using a Smartphone Application

The surgical morbidity and mortality (M&M) conference is a vital part of a resident's surgical education, but methods to collect and store M&M data are often rudimentary and unreliable. The authors propose a Health Insurance Portability and Accountability Act (HIPAA)-compliant, electronic health record (EHR)-connected application and database to report and store complication data. The app is linked to the patient's EHR, and as a result, basic data on each surgical case-including diagnosis, surgery type, and surgeon-are automatically uploaded to the app. In addition, all data are stored in a secure SQL database-with communications between the app and the database end-to-end encrypted for HIPAA compliance. The full surgical team has access to the app, democratizing complications reporting and allowing for reporting in both the inpatient and outpatient settings. This complication information can then be automatically pulled from the app with a premade presentation for the M&M conference. The data can also be accessed by a Power BI dashboard, allowing for easy quality improvement analyses. When implemented, the app improved data collection for the M&M conference while providing a database for institutional quality improvement use. The authors also identified additional utility of the app, including ensuring appropriate revenue capture. The general appearance of the app and the dashboard can be found in the article. The app developed in this project significantly improves on more common methods for M&M conference complication reporting-transforming M&M data into a valuable resource for resident education and quality improvement.