Data Warehouse Research Articles

United States Veterans with comorbid chronic fatigue syndrome and chronic pain: do women differ from men?

ABSTRACT Objective Examine group differences among women and men United States Veterans with chronic fatigue syndrome (CFS) and chronic pain (CP). Method Administrative data from the VA Corporate Data Warehouse were derived from a cohort that met criteria for CP in 2018. Differences across sociodemographic characteristics, psychiatric comorbidities, and pain-related healthcare utilization were examined. Analyses included group comparisons (chi-square tests for independence or independent samples t-tests) and effect size differences (Cohen’s d or Cramer’s V). Results The period prevalence for comorbid CFS and CP was greater among women Veterans (1.20% vs. 0.61%). Women Veterans with CFS and CP were younger (d = 0.80), more likely to be Black, and less likely to be White (V = 0.13). Women Veterans with CFS and CP were more likely to have fibromyalgia (V = 0.23), headache (V = 0.24), or urogenital, pelvic, and menstrual pain (V = 0.14), and less likely to have neuropathy relative to men; they were also more likely to have depression (V = 0.15) or anxiety (V = 0.13). Conclusions Comorbid CFS and CP are more prevalent among women Veterans relative to men. Group differences in pain clusters and psychiatric comorbidities may have implications for clinical offerings to women Veterans with these conditions.

The prevalence of human papillomavirus in ocular surface squamous neoplasia in HIV positive and negative patients in a South African population.

To assess the prevalence and subtypes of Human Papillomavirus (HPV) in Ocular Surface Squamous Neoplasia (OSSN) in Human Immunodeficiency Virus (HIV) positive and negative patients in South Africa. This study was a single center retrospective cross-sectional study, conducted at Tygerberg Hospital, Western Cape, South Africa. We assessed 63 histopathologically confirmed OSSN formalin-fixed paraffin-embedded (FFPE) tissue blocks from 2015-2023. The presence of HPV was determined using the Hybrispot Direct Flow Chip Kit. Corresponding clinical data was retrieved from the National Health Laboratory Service (NHLS) central data warehouse. Of the confirmed OSSN samples, 66.7% tested positive for HPV (95% confidence interval [CI] 54-77.3%). Of the 42 HPV positive samples, 38 (90.5%) had one or more known genotypes detected and 4 had unknown genotypes. The most prevalent subtypes were HPV 11, 16 and 18 (found in 61.9%, 52.4% and 33.3% of HPV positive samples respectively). 88.9% of the lesions biopsied were from HIV positive patients, of whom 56.4% had a CD4 + count of < 200 cells/µL. A lower median CD4 + count was detected among HIV positive patients with invasive squamous cell carcinoma compared to those with moderate dysplasia (p < 0.0198). There is a high prevalence of HPV in OSSN in South Africa. Certain subtypes namely, 11, 16, 18, 31, 33 and 35 may be more carcinogenic. HIV with HPV co-infection may be linked as a causative factor in the development of OSSN.

Disease burden of Barrett's esophagus across the Paris metropolitan area

IntroductionThe prevalence of Barrett's esophagus (BE) in France is unknown. However, the management of dysplastic BE in expert centers is recommended and reduces the risk of developing invasive adenocarcinoma. Our aim was to determine the burden of BE patients in the Paris Region. MethodsWe performed a retrospective study using the data from electronic medical records from the data warehouse of the 39 Greater Paris public hospitals (Entrepôt de données de santé de l’ Assistance Publique- Hôpitaux de Paris) for the year 2018, and used natural language processing to search for occurrences of Barrett's esophagus in endoscopy and pathology reports. Resultswe observed a 2.2 % prevalence of Barrett's esophagus. Patients with Barrett's esophagus were older, more frequently males, with a hiatal hernia, proton pump inhibitor users, and less frequently infected by H. Pylori. Gastro-esophageal reflux symptoms were not more frequently encountered in Barrett's patients. Eleven percent of patients with Barrett's esophagus had dysplasia or adenocarcinoma. DiscussionOver 200 000 patients with Barrett's esophagus are expected in the Paris Region, of which 11 % harbor dysplasia or adenocarcinoma. This data should be taken into account to tailor healthcare offer in France.

Computable phenotype for diagnostic error: developing the data schema for application of symptom-disease pair analysis of diagnostic error (SPADE).

Diagnostic errors are the leading cause of preventable harm in clinical practice. Implementable tools to quantify and target this problem are needed. To address this gap, we aimed to generalize the Symptom-Disease Pair Analysis of Diagnostic Error (SPADE) framework by developing its computable phenotype and then demonstrated how that schema could be applied in multiple clinical contexts. We created an information model for the SPADE processes, then mapped data fields from electronic health records (EHR) and claims data in use to that model to create the SPADE information model (intention) and the SPADE computable phenotype (extension). Later we validated the computable phenotype and tested it in four case studies in three different health systems to demonstrate its utility. We mapped and tested the SPADE computable phenotype in three different sites using four different case studies. We showed that data fields to compute an SPADE base measure are fully available in the EHR Data Warehouse for extraction and can operationalize the SPADE framework from provider and/or insurer perspective, and they could be implemented on numerous health systems for future work in monitor misdiagnosis-related harms. Data for the SPADE base measure is readily available in EHR and administrative claims. The method of data extraction is potentially universally applicable, and the data extracted is conveniently available within a network system. Further study is needed to validate the computable phenotype across different settings with different data infrastructures.

A STUDY ON IMPROVING SUPPLY CHAIN USING BUSINESS INTELLIGENCE CONCERNING DHARA LOGISTICS

Business intelligence (BI) is software that ingests business data and presents it in user-friendly views such as reports, dashboards, charts and graphs. BI tools enable business users to access different types of data — historical and current, third-party and in-house, as well as semi-structured data and unstructured data like social media. Users can analyze this information to gain insights into how the business is performing. According to CIO magazine: “Although business intelligence does not tell business users what to do or what will happen if they take a certain course, neither is BI only about generating reports. Rather, BI offers a way for people to examine data to understand trends and derive insights.” Organizations can use the insights gained from business intelligence and data analysis to improve business decisions, identify problems or issues, spot market trends, and find new revenue or business opportunities. BI platforms traditionally rely on data warehouses for their baseline information. A data warehouse aggregates data from multiple data sources into one central system to support business analytics and reporting. Business intelligence software queries the warehouse and presents the results to the user in the form of reports, charts and maps. Data warehouses can include an online analytical processing (OLAP) engine to support multidimensional queries. “OLAP provides powerful technology for data discovery, facilitating business intelligence, complex analytic calculations and predictive analytics,” says IBM offering manager Doug Dailey in his data warehousing blog. “One of the main benefits of OLAP is the consistency of information and calculations it uses to drive data to improve product quality, customer interactions and process improvements.” Some newer business intelligence solutions can extract and ingest raw data directly using technology such as Hadoop, but data warehouses are still the data source of choice in many cases.

Design and Implementation of an Intelligent Metro Project Investment Decision Support System

The construction of subway projects involves tight engineering cycles, multiple technical challenges, and complex coordination among various stakeholders. Due to the influence of uncertain factors during the construction process, the investment in subway project construction exhibits non-linear changes over time. Investment decision-making is the process through which the investment entity determines its investment activities. For typical investment entities, project investment decision-making primarily entails analyzing and evaluating proposed engineering projects based on investigation, analysis, and argumentation, ultimately deciding whether to invest. With the widespread application of information technology (IT) across various fields, decision support systems (DSS) have emerged to enhance the decision-making capabilities of enterprise management. This article designs an intelligent subway project investment DSS, leveraging data mining (DM) technology to integrate DSS with a data warehouse (DW).

Compliance Considerations in the Geo-enrichment of an EHR Data Warehouse with Social &amp; Environmental Determinants of Health

Compliance Considerations in the Geo-enrichment of an EHR Data Warehouse with Social &amp; Environmental Determinants of Health

Health Care Utilization by Patients With Chronic Pelvic Pain.

To describe the patterns of health care utilization among patients with chronic pelvic pain. Deidentified administrative claims data from the OptumLabs Data Warehouse were used. Adult female patients who had their first medical claim for chronic pelvic pain between January 1, 2016, and December 31, 2019, were included. Utilization was examined for 12 months after the index diagnosis. The greedy nearest neighbor matching method was used to identify a control group of individuals without chronic pelvic pain. Comparisons were made between those with and those without chronic pelvic pain using χ2 tests for categorical data and Wilcoxon rank-sum tests for continuous data. In total, 18,400 patients were analyzed in the chronic pelvic pain cohort. Patients with chronic pelvic pain had a higher rate of chronic overlapping pain conditions. Patients with chronic pelvic pain had higher rates of health care utilization across all queried indices. They had more outpatient office visits; 55.5% had 10 or more office visits. Patients with chronic pelvic pain showed higher utilization of the emergency department (ED) (6.3 visits vs 1.9 visits; P<.001). Urine culture and pelvic ultrasonography were the most utilized tests. One-third of patients with chronic pelvic pain utilized physical therapy (PT), and 13% utilized psychological or behavioral therapy. Patients with chronic pelvic pain had higher rates of hysterectomy (8.9% vs 0.6%). The average total health care costs per patient with chronic pelvic pain per year was $12,254. Patients with chronic pelvic pain have higher rates of chronic overlapping pain conditions and undergo more ED visits, imaging tests, and hysterectomies than patients without chronic pelvic pain. Improving access to multidisciplinary care, increasing utilization of interventions such as PT and psychological or behavioral therapy, and reducing ED utilization may be possible targets to help reduce overall health care costs and improve patient care.

Costs and health care resource utilization among Medicare beneficiaries diagnosed with chronic lymphocytic leukemia.

Chronic lymphocytic leukemia (CLL) is the most common type of leukemia. However, published studies of CLL have either only focused on costs among individuals diagnosed with CLL without a non-CLL comparator group or focused on costs associated with specific CLL treatments. An examination of utilization and costs across different care settings provides a holistic view of utilization associated with CLL. To quantify the health care costs and resource utilization types attributable to CLL among Medicare beneficiaries and identify predictors associated with each of the economic outcomes among beneficiaries diagnosed with CLL. This retrospective study used a random 20% sample of the Medicare Chronic Conditions Data Warehouse (CCW) database covering the 2017-2019 period. The study population consisted of individuals with and without CLL. The CLL cohort and non-CLL cohort were matched using a 1:5 hard match based on baseline categorical variables. We characterized economic outcomes over 360 days across cost categories and places of services. We estimated average marginal effects using multivariable generalized linear regression models of total costs and across type of services. Total cost was compared between CLL and non-CLL cohorts using the matched sample. We used generalized linear models appropriate for the count or binary outcome to identify factors associated with various categories of health care resource utilization, such as inpatient admissions, emergency department (ED) visits, and oncologist/hematologist visits. A total of 2,736 beneficiaries in the CLL cohort and 13,571 beneficiaries in the non-CLL matched cohort were identified. Compared with the non-CLL cohort, the annual cost for the CLL cohort was higher (CLL vs non-CLL, mean [SD]: $22,781 [$37,592] vs $13,901 [$24,725]), mainly driven by health care provider costs ($6,535 vs $3,915) and Part D prescription drug costs ($5,916 vs $2,556). The main categories of health care resource utilization were physician evaluation/management visits, oncologist/hematologist visits, and laboratory services. Compared with beneficiaries aged 65-74 years, beneficiaries aged 85 years or older had lower use and cost in maintenance services (ie, oncologist visits, hospital outpatient costs, and prescription drug cost) but higher use and cost in acute services (ie, ED). Compared with residency in a metropolitan area, living in a nonmetropolitan area was associated with fewer physician visits but higher ED visits and hospitalizations. The cooccurrence of lower utilization of routine care services, along with higher utilization of acute care services among some individuals, has implications for patient burden and warrants further study.

Extracting Electronic Health Record Neuroblastoma Treatment Data With High Fidelity Using the REDCap Clinical Data Interoperability Services Module.

Although the International Neuroblastoma Risk Group Data Commons (INRGdc) has enabled seminal large cohort studies, the research is limited by the lack of real-world, electronic health record (EHR) treatment data. To address this limitation, we evaluated the feasibility of extracting treatment data directly from EHRs using the REDCap Clinical Data Interoperability Services (CDIS) module for future submission to the INRGdc. Patients enrolled on the Children's Oncology Group neuroblastoma biology study ANBL00B1 (ClinicalTrials.gov identifier: NCT00904241) who received care at the University of Chicago (UChicago) or the Vanderbilt University Medical Center (VUMC) after the go-live dates for the Fast Healthcare Interoperability Resources (FHIR)-compliant EHRs were identified. Antineoplastic drug orders were extracted using the CDIS module. To validate the CDIS output, antineoplastic agents extracted through FHIR were compared with those queried through EHR relational databases (UChicago's Clinical Research Data Warehouse and VUMC's Epic Clarity database) and manual chart review. The analytic cohort consisted of 41 patients at UChicago and 32 VUMC patients. Antineoplastic drug orders were identified in the extracted EHR records of 39 (95.1%) UChicago patients and 26 (81.3%) VUMC patients. Manual chart review confirmed that patients with missing (n = 8) or discontinued (n = 1) orders in the CDIS output did not receive antineoplastic agents during the timeframe of the study. More than 99% of the antineoplastic drug orders in the EHR relational databases were identified in the corresponding CDIS output. Our results demonstrate the feasibility of extracting EHR treatment data with high fidelity using HL7-FHIR via REDCap CDIS for future submission to the INRGdc.

Antimicrobial Resistance as Risk Factor for Recurrent Bacteremia after Staphylococcus aureus, Escherichia coli, or Klebsiella spp. Community-Onset Bacteremia.

We investigated links between antimicrobial resistance in community-onset bacteremia and 1-year bacteremia recurrence by using the clinical data warehouse of Europe's largest university hospital group in France. We included adult patients hospitalized with an incident community-onset Staphylococcus aureus, Escherichia coli, or Klebsiella spp. bacteremia during 2017-2019. We assessed risk factors of 1-year recurrence using Fine-Gray regression models. Of the 3,617 patients included, 291 (8.0%) had >1 recurrence episode. Third-generation cephalosporin (3GC)-resistance was significantly associated with increased recurrence risk after incident Klebsiella spp. (hazard ratio 3.91 [95% CI 2.32-6.59]) or E. coli (hazard ratio 2.35 [95% CI 1.50-3.68]) bacteremia. Methicillin resistance in S. aureus bacteremia had no effect on recurrence risk. Although several underlying conditions and infection sources increased recurrence risk, 3GC-resistant Klebsiella spp. was associated with the greatest increase. These results demonstrate a new facet to illness induced by 3GC-resistant Klebsiella spp. and E. coli in the community setting.

Análisis de modelos algorítmicos de aprendizaje automático para la predicción del estado vital a los seis meses tras fractura de cadera en pacientes mayores de 74 años

Background and objectiveThe objective is to develop a model that predicts vital status six months after fracture as accurately as possible. For this purpose we will use five different data sources obtained through the National Hip Fracture Registry, the Health Management Unit and the Economic Management Department. Material and methodsThe study population is a cohort of patients over 74 years of age who suffered a hip fracture between May 2020 and December 2022. A warehouse is created from five different data sources with the necessary variables. An analysis of missing values and outliers as well as unbalanced classes of the target variable («vital status») is performed. Fourteen different algorithmic models are trained with the training. The model with the best performance is selected and a fine tuning is performed. Finally, the performance of the selected model is analyzed with test data. ResultsA data warehouse is created with 502 patients and 144 variables. The best performing model is Linear Regression. Sixteen of the 24 cases of deceased patients are classified as live, and 14 live patients are classified as deceased. A sensitivity of 31%, an accuracy of 34% and an area under the curve of 0.65 is achieved. ConclusionsWe have not been able to generate a model for the prediction of six-month survival in the current cohort. However, we believe that the method used for the generation of algorithms based on machine learning can serve as a reference for future works.

Effectiveness, utilisation and cost associated with implantable loop recorders versus external monitors after ischaemic or cryptogenic stroke

ObjectiveImplantable loop recorders (ILRs) are increasingly used for long-term rhythm monitoring after ischaemic and cryptogenic stroke, with the goal of detecting atrial fibrillation (AF) and subsequent initiation of oral anticoagulation...

Mastering Data Product Development: Strategies, Architectures, and Best Practices

Abstract: The importance of data products for organizations cannot be overstated, as they enable valuable insights and informed decision-making. This article offers a thorough guide to constructing data products, with a specific emphasis on data warehouse architecture and data product management. Key concepts in data product management are explored, such as treating data as a product, understanding the data product lifecycle, and defining roles and responsibilities. The article explores the key elements of a data warehouse, including data integration and ETL processes, data modeling, and storage and retrieval techniques. This approach outlines a systematic process for developing data products, covering everything from identifying opportunities and defining requirements to design, implementation, testing, deployment, and maintenance. Emphasizing the significance of data governance, quality assurance, security, privacy, and regulatory compliance. Additionally, we delve into performance metrics, monitoring, and strategies for continuous improvement of data products. The article showcases case studies, such as Our World in Data [1], to demonstrate real-world applications and best practices. Finally, we will explore future trends, challenges, and opportunities in data p

IMPLEMENTASI NINE STEP METHODOLOGY DALAM PERANCANGAN DATA WAREHOUSE

Rapid technological developments require companies to create information systems that can organize and process company information effectively and efficiently. PT. Universal Karya Jaya is a company engaged in metal cutting services. The company does not have a running Information System yet. Therefore, it is necessary to have a good information system to support its business processes through data warehouse design. The design of the data warehouse uses the nine- step methodology from Kimball and uses star modeling so that PT. Universal Karya Jaya can make their company data integrated and help superiors analyze data for fast and accurate decision making. The goal to achieve from this research is to design a data warehouse model that suits the needs of the company and it is hoped that this design can be implemented at PT. Universal Karya Jaya to support their business processes. Meanwhile, the research method used for this research was literature study and interviews. With this data warehouse, it is hoped that it can assist companies in handling large amounts of data so that it can become a helping hand on inputs for stakeholders in the decision-making process.

Use of Electronic Patient Messaging by Pregnant Patients Receiving Prenatal Care at an Academic Health System: Retrospective Cohort Study.

The COVID-19 pandemic accelerated telemedicine and mobile app use, potentially changing our historic model of maternity care. MyChart is a widely adopted mobile app used in health care settings specifically for its role in facilitating communication between health care providers and patients with its messaging function in a secure patient portal. However, previous studies analyzing portal use in obstetric populations have demonstrated significant sociodemographic disparities in portal enrollment and messaging, specifically showing that patients who have a low income and are non-Hispanic Black, Hispanic, and uninsured are less likely to use patient portals. The study aimed to estimate changes in patient portal use and intensity in prenatal care before and during the pandemic period and to identify sociodemographic and clinical disparities that continued during the pandemic. This retrospective cohort study used electronic medical record (EMR) and administrative data from our health system's Enterprise Data Warehouse. Records were obtained for the first pregnancy episode of all patients who received antenatal care at 8 academically affiliated practices and delivered at a large urban academic medical center from January 1, 2018, to July 22, 2021, in Chicago, Illinois. All patients were aged 18 years or older and attended ≥3 clinical encounters during pregnancy at the practices that used the EMR portal. Patients were categorized by the number of secure messages sent during pregnancy as nonusers or as infrequent (≤5 messages), moderate (6-14 messages), or frequent (≥15 messages) users. Monthly portal use and intensity rates were computed over 43 months from 2018 to 2021 before, during, and after the COVID-19 pandemic shutdown. A logistic regression model was estimated to identify patient sociodemographic and clinical subgroups with the highest portal nonuse. Among 12,380 patients, 2681 (21.7%) never used the portal, and 2680 (21.6%), 3754 (30.3%), and 3265 (26.4%) were infrequent, moderate, and frequent users, respectively. Portal use and intensity increased significantly over the study period, particularly after the pandemic. The number of nonusing patients decreased between 2018 and 2021, from 996 of 3522 (28.3%) in 2018 to only 227 of 1743 (13%) in the first 7 months of 2021. Conversely, the number of patients with 15 or more messages doubled, from 642 of 3522 (18.2%) in 2018 to 654 of 1743 (37.5%) in 2021. The youngest patients, non-Hispanic Black and Hispanic patients, and, particularly, non-English-speaking patients had significantly higher odds of continued nonuse. Patients with preexisting comorbidities, hypertensive disorders of pregnancy, diabetes, and a history of mental health conditions were all significantly associated with higher portal use and intensity. Reducing disparities in messaging use will require outreach and assistance to low-use patient groups, including education addressing health literacy and encouraging appropriate and effective use of messaging.

Insomnia and risk of mortality in older adults.

Existing evidence linking insomnia to all-cause mortality in older individuals remains inconclusive. We conducted a retrospective study of a large cohort of veterans aged 65-80 years old identified from the Corporate Data Warehouse, a large data repository derived from the Veterans Health Administration integrated medical records. Veterans' enrollees with and without International Classification of Diseases, Ninth and Tenth Revision, codes corresponding to insomnia diagnosis between 1 January 2010 and 30 March 2019 were assessed for eligibility. The primary outcome was all-cause mortality. A total of 36,269 veterans, 9584 with insomnia and 26,685 without insomnia, were included in the analysis. Baseline mean (SD) age was 72.6 (4.2) years. During a mean follow-up of 6.0 (2.9) years of the propensity score matched sample, the mortality rate was 34.8 [95% confidence interval: 33.2-36.6] deaths per 1000 person-years among patients with insomnia compared with 27.8 [95% confidence interval: 26.6-29.1] among patients without insomnia. In a Cox proportional hazards model, insomnia was significantly associated with higher mortality (hazard ratio: 1.39; [95% confidence interval: 1.27-1.52]). Patients with insomnia also had a higher risk of non-fatal cardiovascular events (hazard ratio: 1.21; [95% confidence interval: 1.06-1.37]). Secondary stratified analyses by sex, race, ethnicity and hypertension showed no evidence of effect modification. A higher risk of mortality (hazard ratio: 1.51; [95% confidence interval: 1.33-1.71]) was observed when depression was present compared with absent (hazard ratio: 1.26; [95% confidence interval: 1.12-1.44]; p = 0.02). In this cohort study, insomnia was associated with increased risk-adjusted mortality and non-fatal cardiovascular events in older individuals.

Predictive modeling of initiation and delayed mental health contact for depression

BackgroundDepression is prevalent among Operation Enduring Freedom and Operation Iraqi Freedom (OEF/OIF) Veterans, yet rates of Veteran mental health care utilization remain modest. The current study examined: factors in electronic health records (EHR) associated with lack of treatment initiation and treatment delay; the accuracy of regression and machine learning models to predict initiation of treatment.MethodsWe obtained data from the VA Corporate Data Warehouse (CDW). EHR data were extracted for 127,423 Veterans who deployed to Iraq/Afghanistan after 9/11 with a positive depression screen and a first depression diagnosis between 2001 and 2021. We also obtained 12-month pre-diagnosis and post-diagnosis patient data. Retrospective cohort analysis was employed to test if predictors can reliably differentiate patients who initiated, delayed, or received no mental health treatment associated with their depression diagnosis.Results108,457 Veterans with depression, initiated depression-related care (55,492 Veterans delayed treatment beyond one month). Those who were male, without VA disability benefits, with a mild depression diagnosis, and had a history of psychotherapy were less likely to initiate treatment. Among those who initiated care, those with single and mild depression episodes at baseline, with either PTSD or who lacked comorbidities were more likely to delay treatment for depression. A history of mental health treatment, of an anxiety disorder, and a positive depression screen were each related to faster treatment initiation. Classification of patients was modest (ROC AUC = 0.59 95%CI = 0.586–0.602; machine learning F-measure = 0.46).ConclusionsHaving VA disability benefits was the strongest predictor of treatment initiation after a depression diagnosis and a history of mental health treatment was the strongest predictor of delayed initiation of treatment. The complexity of the relationship between VA benefits and history of mental health care with treatment initiation after a depression diagnosis is further discussed. Modest classification accuracy with currently known predictors suggests the need to identify additional predictors of successful depression management.

INTERFACES OF VIRTUAL DATA STORAGE IN THE CONDITIONS OF A FAST-CHANGING INFORMATION ENVIRONMENT

The article analyzes data storage architectures, explores the main approaches to cloud technologies, and highlights the features of virtual data storage architecture formation. In the process of analysis, positive and negative features of individual data storage architectures were highlighted. Cloud information technologies are considered from the point of view of their use for data storage and distribution. When considering cloud data storage, attention is focused on their architectural features from the point of view of efficiency and availability for users. Storage efficiency is an important characteristic of cloud storage infrastructure, especially given its emphasis on overall cost savings. Performance has many aspects, but the main task of a cloud storage system is to move data between the user and the remote cloud service provider. The system architecture was designed based on the proposed and modeled methods of organizing access to the cloud storage. According to the designed architecture, the data flows in the system were analyzed and investigated. The developed algorithm for the collection and integration of disparate data based on virtual data warehouses, in contrast to the Inmon and Kimball models, makes it possible to process NoSQL temporal (InfluxDB), graph (Neo4j) and document-oriented (MongoDB) data with a significantly lower value of capacity complexity and the possibility execution of recursive queries. The developed interface should be able to work with relational, temporal, graph and document-oriented data sources. To do this, it must provide the ability to perform SQL-like queries against relational databases, as well as specialized queries for each type of non-relational data. It allows to increase the efficiency of process0ng both relational and non-relational data, which makes it a powerful tool for analyzing and processing information from various sources. The development of virtual data warehouses may be associated with the introduction of new technologies, such as artificial intelligence, blockchain, quantum computing, etc. In addition, further improvements in metadata management and data security are expected.

The Impact of the Global Pandemic on Veterans with Serious Mental Illness (SMI): Healthcare Utilization and Mortality.

Individuals with serious mental illness (SMI) experience barriers to accessing and engaging with healthcare, which may have been exacerbated during the emergence of the global pandemic and the rapid shift to telemedicine platforms, substantially decreasing healthcare utilization for non-COVID-19 disorders. Important repercussions on morbidity and mortality may be seen in the months and years to come, which may disproportionately affect high-risk populations, such as patients with SMI, with reduced access to technology platforms. In this study, we explored the impact of the pandemic on healthcare utilization and all-cause mortality rate in SMI compared to non-SMI individuals for the months of March-September 2020 and the same two quarters in 2019. Data were obtained from the VA Corporate Data Warehouse (CDW), a data repository from clinical and administrative VA systems. The sample included veterans with ≥1 outpatient clinical encounter nationally between 1 January 2019 and 31 December 2020. The cohort for this study included 1,018,047 veterans receiving care through the Veterans Health Administration between 2019 and 2020. Of those, 339,349 had a diagnosis of SMI. Patients with SMI had a significantly larger pre-post-pandemic decrease in outpatient (49.7%, p < 0.001), inpatient (14.4%, p < 0.001), and ED (14.5%, p < 0.001) visits compared to non-SMI patients. Overall, 3752 (1.59%) veterans without SMI and 4562 (1.93%) veterans with SMI died during our observation period. Veterans without SMI who died during the observation period were more likely to have had a positive COVID-19 test compared to veterans with SMI. Unadjusted analyses showed that veterans with SMI were approximately 2.5 times more likely to die than veterans without SMI during the first 6 months of the pandemic, compared to the same two quarters of the previous year. However, after adjustment by pertinent covariates, the predictors associated with an increased risk of death from SMI were older age, being male, a higher CAN score, more inpatient stays in the pre period compared to post, and a positive COVID-19 test. Consistent with our initial hypothesis, all the indices of healthcare utilization, namely the number of outpatient, inpatient, and ED visits, significantly decreased between pre- and post-pandemic and did more so for veterans with SMI, despite having more chronic medical illnesses and being prescribed more medications than veterans without SMI. On the other hand, while mortality was greater post-pandemic, factors such as age, morbidity, and having a positive COVID-19 test predicted mortality above and beyond having an SMI diagnosis.