Clinical Data Flow in Botswana Clinics: Protocol for a Mixed-Methods Assessment.

BACKGROUND: Botswana has made significant investments in its health care information infrastructure, including vertical programs for child health and nutrition, HIV care, and tuberculosis. However, effectively integrating the more than 18 systems in place for data collection and reporting has proved to be challenging. The Botswana Health Data Collaborative Roadmap Strategy (2020-24) states that "there exists parallel reporting systems and data is not integrated into the mainstream reports at the national level," seconded by the Botswana National eLearning strategy (2020), which states that "there is inadequate information flow at all levels, proliferation of systems, reporting tools are not synthesized; hence too many systems are not communicating." OBJECTIVE: The objectives of this study are to (1) create a visual representation of how data are processed and the inputs and outputs through each health care system level; (2) understand how frontline workers perceive health care data sharing across existing platforms and the impact of data on health care service delivery. METHODS: The setting included a varied range of 30 health care facilities across Botswana, aiming to capture insights from multiple perspectives into data flow and system integration challenges. The study design combined qualitative and quantitative methodologies, informed by the rapid assessment process and the technology assessment model for resource limited settings. The study used a participatory research approach to ensure comprehensive stakeholder engagement from its inception. Survey instruments were designed to capture the intricacies of data processing, sharing, and integration among health care workers. A purposive sampling strategy was used to ensure a wide representation of participants across different health care roles and settings. Data collection used both digital surveys and in-depth interviews. Preliminary themes for analysis include perceptions of the value of health care data and experiences in data collection and sharing. Ethical approvals were comprehensively obtained, reflecting the commitment to uphold research integrity and participant welfare throughout the study. RESULTS: The study recruited almost 44 health care facilities, spanning a variety of health care facilities. Of the 44 recruited facilities, 27 responded to the surveys and participated in the interviews. A total of 75% (112/150) of health care professionals participating came from clinics, 20% (30/150) from hospitals, and 5% (8/150) from health posts and mobile clinics. As of October 10, 2023, the study had collected over 200 quantitative surveys and conducted 90 semistructured interviews. CONCLUSIONS: This study has so far shown enthusiastic engagement from the health care community, underscoring the relevance and necessity of this study's objectives. We believe the methodology, centered around extensive community engagement, is pivotal in capturing a nuanced understanding of the health care data ecosystem. The focus will now shift to the analysis phase of the study, with the aim of developing comprehensive recommendations for improving data flow within Botswana's health care system. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/52411.

High-risk spatiotemporal patterns of leprosy in the southeastern region of Yunnan province from 2010 to 2022: an analysis at the township level.

BACKGROUND: Despite being preventable and curable, leprosy remains endemic in some undeveloped regions, including China. Wenshan Zhuang and Miao Autonomous Prefecture (Wenshan prefecture) currently bears the highest leprosy burden in China. In this ecological study, we aimed to analyze the epidemiological characteristics as well as identify and visualize the high-risk townships of Wenshan prefecture using the most updated leprosy data from 2010 to 2022. METHODS: Geographical information system combined with spatial scan statistics was used for newly detected leprosy cases abstracted from the Leprosy Management Information System in China. Global Moran's I index was used to uncover the spatial pattern of leprosy at the township level. Spatial scan statistics, encompassing purely temporal, purely spatial, spatial variation in temporal trends, and space-time analysis, were implemented for detecting the risk clusters. RESULTS: Between 2010 and 2022, Wenshan prefecture detected 532 new leprosy cases, comprising 352 (66.17%) males and 180 (33.83%) females. The aggregated time primarily occurred between October 2010 and March 2014. The distribution pattern of newly detected leprosy cases was spatially clustered. We identified four high-risk spatial clusters encompassing 54.51% of the new cases. Furthermore, spatial variation in temporal trends highlighted one cluster as a potential high-risk area. Finally, two space-time clusters were detected, and the most likely cluster was predominantly located in the central and northwest regions of Wenshan prefecture, spanning from January 2010 to September 2013. CONCLUSIONS: In this ecology study, we characterized the epidemiological features and temporal and spatial patterns of leprosy in Wenshan prefecture using the most recent leprosy data between 2010 and 2022. Our findings offer scientific insights into the epidemiological profiles and spatiotemporal dynamics of leprosy in Wenshan prefecture. Clinicians and policymakers should pay particular attention to the identified clusters for the prevention and control of leprosy.

The Automatic Context Measurement Tool (ACMT) to Compile Participant-Specific Built and Social Environment Measures for Health Research: Development and Usability Study.

BACKGROUND: The environment shapes health behaviors and outcomes. Studies exploring this influence have been limited to research groups with the geographic information systems expertise required to develop built and social environment measures (eg, groups that include a researcher with geographic information system expertise). OBJECTIVE: The goal of this study was to develop an open-source, user-friendly, and privacy-preserving tool for conveniently linking built, social, and natural environmental variables to study participant addresses. METHODS: We built the automatic context measurement tool (ACMT). The ACMT comprises two components: (1) a geocoder, which identifies a latitude and longitude given an address (currently limited to the United States), and (2) a context measure assembler, which computes measures from publicly available data sources linked to a latitude and longitude. ACMT users access both of these components using an RStudio/RShiny-based web interface that is hosted within a Docker container, which runs on a local computer and keeps user data stored in local to protect sensitive data. We illustrate ACMT with 2 use cases: one comparing population density patterns within several major US cities, and one identifying correlates of cannabis licensure status in Washington State. RESULTS: In the population density analysis, we created a line plot showing the population density (x-axis) in relation to distance from the center of the city (y-axis, using city hall location as a proxy) for Seattle, Los Angeles, Chicago, New York City, Nashville, Houston, and Boston with the distances being 1000, 2000, 3000, 4000, and 5000 m. We found the population density tended to decrease as distance from city hall increased except for Nashville and Houston, 2 cities that are notably more sprawling than the others. New York City had a significantly higher population density than the others. We also observed that Los Angeles and Seattle had similarly low population densities within up to 2500 m of City Hall. In the cannabis licensure status analysis, we gathered neighborhood measures such as age, sex, commute time, and education. We found the strongest predictive characteristic of cannabis license approval to be the count of female children aged 5 to 9 years and the proportion of females aged 62 to 64 years who were not in the labor force. However, after accounting for Bonferroni error correction, none of the measures were significantly associated with cannabis retail license approval status. CONCLUSIONS: The ACMT can be used to compile environmental measures to study the influence of environmental context on population health. The portable and flexible nature of ACMT makes it optimal for neighborhood study research seeking to attribute environmental data to specific locations within the United States.

Mapping new psychoactive substances: Leveraging GIS technology for advanced global surveillance and policy support.

The escalating challenge of New Psychoactive Substances (NPS) necessitates enhanced global monitoring and analysis capabilities. This study introduces an advanced interactive visualization tool that employs Geographic Information System (GIS) technologies to improve the functionality of the UNODC's Early Warning Advisory. The tool enables dynamic observation and analysis of NPS's geographical and temporal distribution, thereby facilitating a comprehensive understanding of their public health impacts. By incorporating detailed choropleth maps and annual and cumulative bar charts, the tool allows policymakers and researchers to visually track and analyze trends in NPS usage and control efforts across different regions. The results demonstrate the tool's effectiveness in providing actionable insights, which support the strategic development of public health policies and interventions to curb the global rise in NPS usage. This initiative illustrates the essential role of digital tools in enhancing public health strategies and responses to emerging drug trends. This rising challenge underscores the urgent need for innovative solutions in monitoring drug trends, a theme explored in this paper. The web tool is available at https://nps-vis.cmdm.tw, and the code is available at https://github.com/CMDM-Lab/nps-vis.

The Effects of Electronic Health Records on Medical Error Reduction: Extension of the DeLone and McLean Information System Success Model.

BACKGROUND: Medical errors are becoming a major problem for health care providers and those who design health policies. These errors cause patients' illnesses to worsen over time and can make recovery impossible. For the benefit of patients and the welfare of health care providers, a decrease in these errors is required to maintain safe, high-quality patient care. OBJECTIVE: This study aimed to improve the ability of health care professionals to diagnose diseases and reduce medical errors. METHODS: Data collection was performed at Dr George Mukhari Academic Hospital using convenience sampling. In total, 300 health care professionals were given a self-administered questionnaire, including doctors, dentists, pharmacists, physiologists, and nurses. To test the study hypotheses, multiple linear regression was used to evaluate empirical data. RESULTS: In the sample of 300 health care professionals, no significant correlation was found between medical error reduction (MER) and knowledge quality (KQ) (ß=.043, P=.48). A nonsignificant negative relationship existed between MER and information quality (IQ) (ß=-.080, P=.19). However, a significant positive relationship was observed between MER and electronic health records (EHR; ß=.125, 95% CI 0.005-0.245, P=.042). CONCLUSIONS: Increasing patient access to medical records for health care professionals may significantly improve patient health and well-being. The effectiveness of health care organizations' operations can also be increased through better health information systems. To lower medical errors and enhance patient outcomes, policy makers should provide financing and support for EHR adoption as a top priority. Health care administrators should also concentrate on providing staff with the training they need to operate these systems efficiently. Empirical surveys in other public and private hospitals can be used to further test the validated survey instrument.

A virtual assistant for primary care's food and nutrition surveillance system: Development and validation study in Brazil.

OBJECTIVE: The study aimed to develop and validate a conversational agent (chatbot) designed to support Food and Nutrition Surveillance (FNS) practices in primary health care settings. METHODS: This mixed-methods research was conducted in three stages. Initially, the study identified barriers and challenges in FNS practices through a literature review and feedback from 655 health professionals and FNS experts across Brazil. Following this, a participatory design approach was employed to develop and validate the chatbot's content. The final stage involved evaluating the chatbot's user experience with FNS experts. RESULTS: The chatbot could accurately understand and respond to 60 different intents or keywords related to FNS. Themes such as training, guidance, and access emerged as crucial for guiding FNS initiatives and addressing implementation challenges, primarily related to human resources. The chatbot achieved a Global Content Validation Index of 0.88. CONCLUSION: The developed chatbot represents a significant advancement in supporting FNS practices within primary health care. PRACTICE IMPLICATION: By providing an innovative, interactive, educational tool that is both accessible and reliable, this digital assistant has the potential to facilitate the operationalization of FNS practices, addressing the critical need for effective training and counseling in developing countries.

Causes of Pediatric Deaths in Lusaka, Zambia: A Quantitative Geographic Information Systems Approach.

Background: While childhood mortality has been declining in Zambia, it remains high at 58 per 1000 live births. Importantly, many leading causes of mortality in Zambia are preventable. This study was conducted to identify clusters of childhood mortality, causes of death of recently deceased children, barriers to care, and risk factors for mortality in Lusaka, Zambia. Methods: This study was conducted as a prospective cohort study. Family members or lawfully authorized representatives (LARs) were interviewed when they came to pick up death certificates for recently deceased children from Lusaka Children's Hospital. Each interview included a verbal autopsy, determination of the child's location of residence, and collection of demographic information. Demographic data was also collected from a healthy control group. Quantitative Geographic Information Systems was used to visualize mortality and evaluate for clustering. Results: Leading primary causes of death included malnutrition (21%), complications of chronic illnesses (16%), and central nervous system infections (13%), while the leading barriers to care were cost (58%) and difficulties with travel (53%). Compared to controls, recently deceased children came from families with significantly lower incomes (1905 Kwacha vs. 2412 Kwacha, p = 0.03) and were significantly more likely to have a history of malnutrition (16.7% vs. 1.4%, p = 0.005). Mortality was clustered in two high-population density, low-income neighborhoods in Lusaka. Conclusions: Systems to reduce financial barriers to care and improve access to transportation could reduce childhood mortality in Lusaka. The aforementioned neighborhoods are ideal locations for public health interventions or improved healthcare services.

Data quality in hospital information systems: Lessons learned from analyzing 30 years of patient data in a regional German hospital.

BACKGROUND: The integration of Hospital Information Systems (HIS) into healthcare delivery has significantly enhanced patient care and operational efficiency. Nonetheless, the rapid acceleration of digital transformation has led to a substantial increase in the volume of data managed by these systems. This emphasizes the need for robust mechanisms for data management and quality assurance. OBJECTIVE: This study addresses data quality issues related to patient identifiers within the Hospital Information System (HIS) of a regional German hospital, focusing on improving the accuracy and consistency of these administrative data entries. METHODS: Employing a combination of data analysis and expert interviews, this study reviews and programmatically cleanses a dataset with over 2,000,000 patient data entries extracted from the HIS. The areas of investigation are patient admissions, discharges, and geographical data. RESULTS: The analysis revealed that roughly 25% of the dataset was rendered unusable by errors and inconsistencies. By implementing a thorough data cleansing process, we significantly enhanced the utility of the dataset. In doing so, we identified the primary issues affecting data quality, including ambiguities among similar variables and a gap between the intended and actual use of the system. CONCLUSION: The findings highlight the critical importance of enhancing data quality in healthcare information systems. This study shows the necessity of a careful review of data extracted from the HIS before it can be reliably utilized for machine learning tasks, thereby rendering the data more usable for both clinical and analytical purposes.

Applications of ChatGPT in the diagnosis, management, education, and research of retinal diseases: a scoping review.

PURPOSE: This scoping review aims to explore the current applications of ChatGPT in the retina field, highlighting its potential, challenges, and limitations. METHODS: A comprehensive literature search was conducted across multiple databases, including PubMed, Scopus, MEDLINE, and Embase, to identify relevant articles published from 2022 onwards. The inclusion criteria focused on studies evaluating the use of ChatGPT in retinal healthcare. Data were extracted and synthesized to map the scope of ChatGPT's applications in retinal care, categorizing articles into various practical application areas such as academic research, charting, coding, diagnosis, disease management, and patient counseling. RESULTS: A total of 68 articles were included in the review, distributed across several categories: 8 related to academics and research, 5 to charting, 1 to coding and billing, 44 to diagnosis, 49 to disease management, 2 to literature consulting, 23 to medical education, and 33 to patient counseling. Many articles were classified into multiple categories due to overlapping topics. The findings indicate that while ChatGPT shows significant promise in areas such as medical education and diagnostic support, concerns regarding accuracy, reliability, and the potential for misinformation remain prevalent. CONCLUSION: ChatGPT offers substantial potential in advancing retinal healthcare by supporting clinical decision-making, enhancing patient education, and automating administrative tasks. However, its current limitations, particularly in clinical accuracy and the risk of generating misinformation, necessitate cautious integration into practice, with continuous oversight from healthcare professionals. Future developments should focus on improving accuracy, incorporating up-to-date medical guidelines, and minimizing the risks associated with AI-driven healthcare tools.

Application of Spatial Analysis on Electronic Health Records to Characterize Patient Phenotypes: Systematic Review.

BACKGROUND: Electronic health records (EHRs) commonly contain patient addresses that provide valuable data for geocoding and spatial analysis, enabling more comprehensive descriptions of individual patients for clinical purposes. Despite the widespread use of EHRs in clinical decision support and interventions, no systematic review has examined the extent to which spatial analysis is used to characterize patient phenotypes. OBJECTIVE: This study reviews advanced spatial analyses that used individual-level health data from EHRs within the United States to characterize patient phenotypes. METHODS: We systematically evaluated English-language, peer-reviewed studies from the PubMed/MEDLINE, Scopus, Web of Science, and Google Scholar databases from inception to August 20, 2023, without imposing constraints on study design or specific health domains. RESULTS: A substantial proportion of studies (>85%) were limited to geocoding or basic mapping without implementing advanced spatial statistical analysis, leaving only 49 studies that met the eligibility criteria. These studies used diverse spatial methods, with a predominant focus on clustering techniques, while spatiotemporal analysis (frequentist and Bayesian) and modeling were less common. A noteworthy surge (n=42, 86%) in publications was observed after 2017. The publications investigated a variety of adult and pediatric clinical areas, including infectious disease, endocrinology, and cardiology, using phenotypes defined over a range of data domains such as demographics, diagnoses, and visits. The primary health outcomes investigated were asthma, hypertension, and diabetes. Notably, patient phenotypes involving genomics, imaging, and notes were limited. CONCLUSIONS: This review underscores the growing interest in spatial analysis of EHR-derived data and highlights knowledge gaps in clinical health, phenotype domains, and spatial methodologies. We suggest that future research should focus on addressing these gaps and harnessing spatial analysis to enhance individual patient contexts and clinical decision support.

Spatial analysis to identify unauthorized municipal solid waste disposal sites in rural areas of southern Mexico.

The management of solid waste in rural areas of developing countries faces significant challenges due to economic constraints and irregular human settlements. These factors often lead to the creation of unauthorized disposal sites, which pose risks to human health, ecosystems and the economy. Remote sensing and geographic information system techniques provide a means to understand the complex issues associated with inadequate municipal solid waste (MSW) disposal. This study aimed to identify unauthorized disposal sites in the rural areas of southern Quintana Roo, Mexico, by examining land surface temperature (LST) and vegetation indices as potential indicators of unauthorized final disposal sites (FDSs). The findings reveal that 13% of the study areas have a high, moderate or low probability of hosting unauthorized disposal sites. Additionally, 3 authorized final disposal sites (FDSs) were confirmed, and 20 unauthorized sites were identified. LST and the normalized difference vegetation index were effective in detecting unauthorized sites, as these areas exhibited higher temperatures and less vigorous vegetation compared to adjacent areas. The results provide valuable insights into the issues associated with inadequate waste disposal in rural areas and offer information that can help optimize MSW management and mitigate its environmental and health impacts.

Perspectives of Decision Support System TeleRehab in the Management of Post-Stroke Telerehabilitation.

Stroke is the main cause of disability among adults. Decision-making in stroke rehabilitation is increasingly complex; therefore, the use of decision support systems by healthcare providers is becoming a necessity. However, there is a significant lack of software for the management of post-stroke telerehabilitation (TR). This paper presents the results of the developed software "TeleRehab" to support the decision-making of clinicians and healthcare providers in post-stroke TR. We designed a Python-based software with a graphical user interface to manage post-stroke TR. We searched Scopus, ScienceDirect, and PubMed databases to obtain research papers with results of clinical trials for post-stroke TR and to form the knowledge base of the software. The findings show that TeleRehab suggests recommendations for TR to provide practitioners with optimal and real-time support. We observed feasible outcomes of the software based on synthetic data of patients with balance problems, spatial neglect, and upper and lower extremities dysfunctions. Also, the software demonstrated excellent usability and acceptability scores among healthcare professionals.

Search for medical information for chronic rhinosinusitis through an artificial intelligence ChatBot.

Objectives: Artificial intelligence is evolving and significantly impacting health care, promising to transform access to medical information. With the rise of medical misinformation and frequent internet searches for health-related advice, there is a growing demand for reliable patient information. This study assesses the effectiveness of ChatGPT in providing information and treatment options for chronic rhinosinusitis (CRS). Methods: Six inputs were entered into ChatGPT regarding the definition, prevalence, causes, symptoms, treatment options, and postoperative complications of CRS. International Consensus Statement on Allergy and Rhinology guidelines for Rhinosinusitis was the gold standard for evaluating the answers. The inputs were categorized into three categories and Flesch-Kincaid readability, ANOVA and trend analysis tests were used to assess them. Results: Although some discrepancies were found regarding CRS, ChatGPT's answers were largely in line with existing literature. Mean Flesch Reading Ease, Flesch-Kincaid Grade Level and passive voice percentage were (40.7%, 12.15%, 22.5%) for basic information and prevalence category, (47.5%, 11.2%, 11.1%) for causes and symptoms category, (33.05%, 13.05%, 22.25%) for treatment and complications, and (40.42%, 12.13%, 18.62%) across all categories. ANOVA indicated no statistically significant differences in readability across the categories (p-values: Flesch Reading Ease = 0.385, Flesch-Kincaid Grade Level = 0.555, Passive Sentences = 0.601). Trend analysis revealed readability varied slightly, with a general increase in complexity. Conclusion: ChatGPT is a developing tool potentially useful for patients and medical professionals to access medical information. However, caution is advised as its answers may not be fully accurate compared to clinical guidelines or suitable for patients with varying educational backgrounds.Level of evidence: 4.

Mapping eating disorders in adolescents and young adults: an investigation of geographic distribution and access to care in Ontario, Canada.

BACKGROUND: There is limited research on the spatial distribution of eating disorders and the proximity to available eating disorder services. Therefore, this study investigates the distribution of eating disorders among adolescents and young adults in Ontario, Canada, with a specific focus on geographic disparities and access to publicly-funded specialized eating disorder services. METHODS: A community sample of 1,377 adolescents and young adults ages 16-30 across Ontario between November and December 2021 participated in this study and completed the Eating Disorder Examination Questionnaire. Utilizing Geographic Information System (GIS) technology, we mapped the geographic prevalence of eating disorders and examined proximity to specialized eating disorder services. Multiple linear and logistic regression analyses were utilized to determine the association between geographic region and eating disorder symptomatology. Additionally, t-tests were utilized to examine differences between time/distance to specialized services and clinical risk for eating disorders. RESULTS: Applying geospatial analysis techniques, we detected significant spatial clusters denoting higher eating disorder scores in rural areas and areas with fewer specialized services. Likewise, our findings report disparities between rural and urban areas, suggesting that rural regions exhibit elevated rates of eating disorders. There were no associations between distance/time to services and eating disorder symptomology. CONCLUSIONS: The discrepancies in eating disorder symptomology between urban/rural may stem from stigma and unique socio-cultural contexts in rural communities. The study underscores the need for targeted intervention, including telehealth, in addressing the eating disorder challenges faced by adolescents and young adults in rural regions.

This study explores how common eating disorders are among adolescents and young adults in Ontario, Canada, with a specific focus on the geographic disparities of eating disorders. This study uses mapping technology to assess where eating disorders were more common and how close these areas were to specialized eating disorder treatment services. The findings showed that places with fewer services, especially rural areas, had higher rates of eating disorders. However, there wasn't a clear link between how far people lived from these services and the severity of their eating disorders. This may suggest that those in rural areas might struggle more with eating disorders due to greater stigma and different social and cultural factors compared to urban areas. This study emphasizes the need for targeted interventions, like telehealth, to address these disparities. This research is pivotal in guiding equitable healthcare solutions for eating disorders, particularly in underserved rural communities.

System interoperability and data linkage in the era of health information management: A bibliometric analysis.

Background: Across the world, health data generation is growing exponentially. The continuous rise of new and diversified technology to obtain and handle health data places health information management and governance under pressure. Lack of data linkage and interoperability between systems undermines best efforts to optimise integrated health information technology solutions. Objective: This research aimed to provide a bibliometric overview of the role of interoperability and linkage in health data management and governance. Method: Data were acquired by entering selected search queries into Google Scholar, PubMed, and Web of Science databases and bibliometric data obtained were then imported to Endnote and checked for duplicates. The refined data were exported to Excel, where several levels of filtration were applied to obtain the final sample. These sample data were analysed using Microsoft Excel (Microsoft Corporation, Washington, USA), WORDSTAT (Provalis Research, Montreal, Canada) and VOSviewer software (Leiden University, Leiden, Netherlands). Results: The literature sample was retrieved from 3799 unique results and consisted of 63 articles, present in 45 different publications, both evaluated by two specific in-house global impact rankings. Through VOSviewer, three main clusters were identified: (i) e-health information stakeholder needs; (ii) e-health information quality assessment; and (iii) e-health information technological governance trends. A residual correlation between interoperability and linkage studies in the sample was also found. Conclusion: Assessing stakeholders' needs is crucial for establishing an efficient and effective health information system. Further and diversified research is needed to assess the integrated placement of interoperability and linkage in health information management and governance. Implications: This research has provided valuable managerial and theoretical contributions to optimise system interoperability and data linkage within health information research and information technology solutions.

[Geriatric assessment goes digital-Possible applications in the emergency department setting]. / Geriatrisches Assessment goes digital ­ Einsatzmöglichkeiten im Setting der Notaufnahme.

The geriatric assessment is a basic requirement and a key quality parameter in geriatric care. An increasing number of older patients are presenting to emergency or central admission departments and discharge units in hospitals. For this reason, and in view of the time-critical decision-making requirements in this setting, digital applications of basic geriatric assessment data are becoming increasingly more important for the high-quality follow-up care of geriatric patients.

You cannot spell risk without "I-S": The disclosure of information systems risks by Fortune 1000 firms.

Cybersecurity events can cause business disruptions, health and safety repercussions, financial costs, and negative publicity for large firms, and executives rank cybersecurity as a top operational concern. Although cybersecurity may be the most publicized information systems (IS) risk, large firms face a range of IS risks. Over the past three decades, researchers developed frameworks to categorize and evaluate IS risks. However, there have been few updates to these frameworks despite numerous technological advances, and we are not aware of any research that uses empirical data to map actual IS risks cited by large firms to these frameworks. To address this gap, we coded and analyzed text data from Item 1A (Risk Factors) of the fiscal year 2020 Securities and Exchange Commission Forms 10-K for all Fortune 1000 firms. We build on prior research to develop a framework that places 25 IS risks into four quadrants and 10 categories, and we record the number and type of IS risks cited by each firm. The risk of cyberattack is cited by virtually all Fortune 1000 firms, and the risk of software/hardware failure is cited by 90% of Fortune 1000 firms. Risks associated with data privacy law compliance are cited by 70% of Fortune 1000 firms, and risks associated with internet/telecommunications/power outage, human error, and natural disasters/terrorism are cited by 60% of Fortune 1000 firms. We perform additional analysis to identify differences in risk citation based on industry and financial measures.

A Pipeline for the Usage of the Core Data Set of the Medical Informatics Initiative for Process Mining - A Technical Case Report.

INTRODUCTION: Process Mining (PM) has emerged as a transformative tool in healthcare, facilitating the enhancement of process models and predicting potential anomalies. However, the widespread application of PM in healthcare is hindered by the lack of structured event logs and specific data privacy regulations. CONCEPT: This paper introduces a pipeline that converts routine healthcare data into PM-compatible event logs, leveraging the newly available permissions under the Health Data Utilization Act to use healthcare data. IMPLEMENTATION: Our system exploits the Core Data Sets (CDS) provided by Data Integration Centers (DICs). It involves converting routine data into Fast Healthcare Interoperable Resources (FHIR), storing it locally, and subsequently transforming it into standardized PM event logs through FHIR queries applicable on any DIC. This facilitates the extraction of detailed, actionable insights across various healthcare settings without altering existing DIC infrastructures. LESSONS LEARNED: Challenges encountered include handling the variability and quality of data, and overcoming network and computational constraints. Our pipeline demonstrates how PM can be applied even in complex systems like healthcare, by allowing for a standardized yet flexible analysis pipeline which is widely applicable.The successful application emphasize the critical role of tailored event log generation and data querying capabilities in enabling effective PM applications, thus enabling evidence-based improvements in healthcare processes.

Development of a SNOMED CT Mapping Process and Tool at a Data Integration Centre - Lessons Learned.

INTRODUCTION: 16 million German-language free-text laboratory test results are the basis of the daily diagnostic routine of 17 laboratories within the University Hospital Erlangen. As part of the Medical Informatics Initiative, the local data integration centre is responsible for the accessibility of routine care data for medical research. Following the core data set, international interoperability standards such as FHIR and the English-language medical terminology SNOMED CT are used to create harmonised data. To represent each non-numeric laboratory test result within the base module profile ObservationLab, the need for a map and supporting tooling arose. STATE OF THE ART: Due to the requirement of a n:n map and a data safety-compliant local instance, publicly available tools (e.g., SNAP2SNOMED) were insufficient. Concept and Implementation: Therefore, we developed (1) an incremental mapping-validation process with different iteration cycles and (2) a customised mapping tool via Microsoft Access. Time, labour, and cost efficiency played a decisive role. First iterations were used to define requirements (e.g., multiple user access). LESSONS LEARNED: The successful process and tool implementation and the described lessons learned (e.g., cheat sheet) will assist other German hospitals in creating local maps for inter-consortia data exchange and research. In the future, qualitative and quantitative analysis results will be published.

Design, develop, and implement the strategic information system on health, dependence, and healthy aging: an analysis of the Mexican experience.

BACKGROUND: The Decade of Healthy Aging (2021-2030) emerges as a 10 years strategy to improve the lives of older adults, their families, and the communities in which they live. One of the actions defined in this framework is related to improving the measurement, monitoring, and understanding of characteristics, factors, and needs related to aging and health. The aim was to analyze and assess the process of construction and development of the Strategic Information System on Health, Funcional Dependence and Aging (SIESDE, for its acronym in Spanish). SIESDE will provide strategic information in Mexico at the municipal, state, and national levels that support the public policies on healthy aging. METHODS: The system processes and analyzes the data sources of the Health Information Systems and the National System of Statistical and Geographical Information. SIESDE comprises three components: (1) Design, construction, and evaluation of the indicators; (2) storage, management, and visualization, and (3) diffusion and translation of information. RESULTS: A total of 135 indicators were built on seven themes: (1) demographic, socioeconomic, and aging conditions, (2) health, (3) functional dependence, (4) healthy aging, (5) health services, (6) social and physical environments, and (7) complex indicators. CONCLUSIONS: SIESDE is an effective system for providing an overall view of health, aging, and functional dependence.