Exploring Opportunities for Clinical Data Warehouse Enhancement Through Data Catalog Integration.

Secondary use of clinical health data implies a prior integration of mostly heterogenous and multidimensional data sets. A clinical data warehouse addresses the technological and organizational framework conditions required for this, by making any data available for analysis. However, users of a data warehouse often do not have a comprehensive overview of all available data and only know about their own data in their own systems - a situation which is also referred to as 'data siloed state'. This problem can be addressed and ultimately solved by implementation of a data catalog. Its core function is a search engine, which allows for searching the metadata collected from different data sources and thereby accessing all data there is. With this in mind, we conducted an explorative online market survey followed by vendor comparison as a pre-requisite for system selection of a data catalog. Assessment of vendor performance was based on seven predetermined and weighted selection criteria. Although three vendors achieved the highest score, results were lying closely together. Detailed investigations and test installations are needed for further narrowing down the selection process.

Understanding IT in Healthcare: Relevance and Training Needs of IT in Private Medical Practice.

BACKGROUND: The integration of Information Technology (IT) into private medical practice is crucial in modern healthcare. Physicians managing office-related IT without proper knowledge risk operational inefficiencies and security. OBJECTIVES: This study determines the relevance of specific IT topics in medical practice and identifies the training needs of physicians for enhancing IT competencies in healthcare. METHODS: In March 2023 a cross-sectional online survey was conducted with physicians comprising nine IT-related topics in Tyrol, Austria. RESULTS: The survey results highlighted a strong perceived relevance and high demand for IT education among physicians working in their medical practice, especially in areas of core medical IT and security. The majority of responses indicated high relevance (76.7%) and high demand (69.7%) for IT topics in medical practice. CONCLUSION: The findings underscore a significant need for targeted IT training and support in medical practices, particularly in areas related to the medical practice and security. Addressing these needs could lead to improved healthcare delivery and better management of technological resources in the healthcare sector.

Quality and Effectiveness of AI Tools for Students and Researchers for Scientific Literature Review and Analysis.

This study scrutinizes free AI tools tailored for supporting literature review and analysis in academic research, emphasizing their response to direct inquiries. Through a targeted keyword search, we cataloged relevant AI tools and evaluated their output variation and source validity. Our results reveal a spectrum of response qualities, with some tools integrating non-academic sources and others depending on outdated information. Notably, most tools showed a lack of transparency in source selection. Our study highlights two key limitations: the exclusion of commercial AI tools and the focus solely on tools that accept direct research queries. This raises questions about the potential capabilities of paid tools and the efficacy of combining various AI tools for enhanced research outcomes. Future research should explore the integration of diverse AI tools, assess the impact of commercial tools, and investigate the algorithms behind response variability. This study contributes to a better understanding of AI's role in academic research, emphasizing the importance of careful selection and critical evaluation of these tools in academic endeavors.

Unveiling the Surge: A Comprehensive Analysis of the Stimulating Impact of the Covid-19 Pandemic on Telemedicine and Its Academic Landscape.

The Covid-19 pandemic spurred an unprecedented shift towards digitalization, prompting a surge in telehealth practices. This paper explores the impact of the pandemic on telemedicine through a comprehensive analysis of scientific publications. Utilizing a bibliometric approach, the study examines trends in telemedicine research before and after the onset of Covid-19. The systematic search in PubMed yielded 8,454 pre-Covid-19 publications (2016-2019) and 16,633 post-Covid-19 publications (2020-2023). A total of 21,989 distinct keywords were extracted. Co-occurrence maps reveal evolving thematic clusters, with "mhealth" and "ehealth" dominating pre-Covid-19, while "Covid-19" emerges as a top keyword post-pandemic. The Top-10 keywords shift post-Covid-19, reflecting dynamic research priorities. The bibliometric approach illuminates a heightened exploration of telehealth solutions post-pandemic, emphasizing the enduring impact of the crisis on academic discourse. Changes in key terms and shifts in key term ranking indicate dynamic research priorities and a broader consideration of multidimensional healthcare challenges. Acknowledging study limitations, the analysis offers a high-level perspective, focusing on authors' keywords. Despite challenges, the study provides a systematic overview, revealing the emergence of new telemedicine application domains and the need for further in-depth analyses. Future research directions may explore the ecological impact of telemedicine applications and other intriguing aspects, contributing to a comprehensive understanding of telemedicine's scholarly trajectory.

Transforming Clinical Information Systems: Empowering Healthcare through Telemedicine, Data Science, and Artificial Intelligence Applications.

OBJECTIVE: In this synopsis, the editors of the Clinical Information Systems (CIS) section of the IMIA Yearbook of Medical Informatics overview recent research and propose a selection of best papers published in 2022 in the CIS field. METHODS: The editors follow a systematic approach to gather relevant articles and select the best papers for the section. This year, they updated the query to incorporate the topic of telemedicine and removed search terms related to geographic information systems. The revised query resulted in a larger number of identified papers, necessitating the appointment of a third section editor to handle the increased workload. The editors narrowed the initial pool of articles to 15 candidate papers through a multi-stage selection process. At least seven independent reviews were collected for each candidate paper, and a selection meeting with the IMIA Yearbook editorial board led to the final selection of the best papers for the CIS section. RESULTS: The query was carried out in mid-January 2023 and retrieved a deduplicated result set of 5,206 articles from 1,500 journals. This year, 15 papers were nominated as candidates, and four were finally selected as the best papers in the CIS section.Including telemedicine in the query resulted in a substantial increase in the number of papers found. The analysis highlights the growing convergence between clinical information systems and telemedicine, with mobile health (mHealth) technologies and data science applications gaining prominence. The selected candidate papers emphasize the practical impact of research efforts, focusing on patient-centric outcomes and benefits, including intelligent mobile health monitoring systems and AI-assisted decision-making in healthcare. CONCLUSIONS: Looking ahead, the field of CIS is expected to continue evolving, driven by advances in telemedicine, mHealth technologies, data science, and AI integration, leading to more efficient, patient-oriented, and intelligent healthcare systems and overall improvement of global healthcare outcomes.

The Surviral Epidemiological Early Warning System Tyrol Dashboard - System, Functionality and Findings.

BACKGROUND: Dashboards provide a good retrospective view of the development of the disease. Yet, current COVID-related dashboards typically lack the capability to predict future trends. However, this is important for health policy makers and health care providers in order to adopt meaningful containment strategies. OBJECTIVES: The aim of this paper is to present the Surviral dashboard, which allows the effective monitoring of infectious disease dynamics. METHODS: The presented dashboard comprises a wide range of information, including retrospective and prognostic data based on an agent-based simulation framework. It served as the basis for informed decision-making and planning of disease control strategies within the federal state of Tyrol. RESULTS: By visualizing the information in an understandable format, the dashboard provided a comprehensive overview of the COVID-19 situation in Tyrol and allowed for the identification of trends and patterns. CONCLUSION: The presented dashboard is a valuable tool for managing pandemics such as COVID-19. It provides a convenient and efficient way to monitor the spread of a disease and identify potential areas for intervention.

Making a Virtue of Necessity - A Highly Structured Clinical Data Warehouse as the Source of Assured Truth in a Hospital.

Data-driven decision-making in health care is becoming increasingly important in daily clinical use. A data warehouse, storing all the clinically relevant information in a highly structured way, is a primary basis for achieving this goal. We are developing a clinical data warehouse where more than 20 years of clinical data can be persisted, and newly generated data from different sources can be integrated. A back room was created to store all hospital information system data in a PostgreSQL database. Due to the enormous number of diverse forms in the hospital information system, a broker service was developed that integrates the individual data sources into the data warehouse as soon as they are released for storage. The front room represents the interface from the infrastructure to the targeted analysis. Database query and visualization tools or business intelligence tools can display and analyze processed and interleaved data. In all areas of business and medicine, structured and quality-adjusted data is of major importance. With the help of a clinical data warehouse system, it is possible to perform patient-centered analyses and thus realize optimal therapy. Furthermore, it is possible to provide staff and management with dashboards for control purposes.

Telemedicine Research from Big Bang to 2022.

The Clinical Information Systems (CIS) section of the IMIA Yearbook of Medical Informatics systematically screens about 2,500 publications from more than 1,000 journals annually to find the best CIS publications. The editors of the CIS section have noticed a trend toward patient-centered care supported by AI and machine learning and increased research in cross-institutional data sharing, particularly in telemedicine. As a result, they adjusted their search query to include the MeSH term "telemedicine." As a preliminary step and to get a sense of the historical development of telemedicine research activity, they performed a bibliometric analysis of all previously published papers in PubMed indexed with the tag "Telemedicine" as MeSH Major Topic. They retrieved 29,289 publications from 1976 to 2022 and used their titles and abstracts to create a bibliometric network that visualizes the most relevant terms, their frequency and relationship to each other, and the chronological sequence of their publication. The development over time also shows a clear move toward patient-centeredness. Interestingly, the term "Covid," which has only recently come into use, takes on a central role in the network.

"All Quiet on the Western Front" - Clinical Information Systems Research in the Year 2021.

OBJECTIVES: In this synopsis, we give an overview of recent research and propose a selection of best papers published in 2021 in the field of Clinical Information Systems (CIS). METHOD: As CIS section editors, we annually apply a systematic process to retrieve articles for the IMIA Yearbook of Medical Informatics. For eight years now, we use the same query to find relevant publications in the CIS field. Each year we retrieve more than 2,400 papers which we categorize in a multi-pass review to distill a preselection of up to 15 candidate papers. External reviewers and yearbook editors then assess the selected candidate papers. Based on the review results, the IMIA Yearbook editorial board chooses up to four best publications for the section at a selection meeting. To get a comprehensive overview of the content of the retrieved articles, we use text mining and term co-occurrence mapping techniques. RESULTS: We carried out the query in mid-January 2022 and retrieved a deduplicated result set of 2,688 articles from 1,062 different journals. This year, we nominated ten papers as candidates and finally selected two of them as the best papers in the CIS section. As in the previous years, the content analysis of the articles revealed the broad spectrum of topics covered by CIS research, but - on the other side - no real innovations or new upcoming research trends. However, the significant impact of COVID-19 on CIS research was observable also this year. CONCLUSIONS: The trends in CIS research, as seen in recent years, continue to be observable. The content analysis revealed nothing really new in the CIS domain. What was very visible was the impact of the COVID-19 pandemic, which still effects our lives and also CIS.

Medical informatics and digital health multilingual ontology (MIMO): A tool to improve international collaborations.

BACKGROUND: Even if English is the leading language for international communication, it is essential to keep in mind that research runs at the local level by local teams generally communicating in their local/national language, especially in Europe among European projects. OBJECTIVE: Therefore, the European Federation for Medical Informatics - Working Group on Health Informatics for Inter-regional Cooperation" has one objective: To develop a multilingual ontology focusing on Health Informatics and Digital Health as a collaboration tool that improves international and, in particular, European collaborations. RESULTS: We have developed the Medical Informatics and Digital Health Multilingual Ontology (MIMO). Hosted on the Health Terminology/Ontology Portal (HeTOP), MIMO contains around 1,000 concepts, 460 MeSH Descriptors, 220 MeSH Concepts, and more than 300 newly created concepts. MIMO is continuously updated to comprise as recent as possible concepts and their translations in more than 30 languages. Moreover, the MIMO's development team constantly improves MIMO content and supporting information. Thus, during workshop discussions and one-on-one exchanges, the MIMO team has collected domain experts' opinions about the community's interests and suggestions for future enhancements. Moreover, MIMO will be integrated to support the annotation and categorization of research products into the HosmartAI European project involving more than 20 countries around Europe and worldwide. CONCLUSION: MIMO is hosted by HeTOP (Health Terminology/Ontology Portal), which integrates 100 terminologies and ontologies in 55 languages. MIMO is freely available online. MIMO is portable to other knowledge platforms as part of MIMO's main aims to facilitate communication between medical librarians, translators, and researchers as well as to support students' self-learning.

The effect of the volemic and cardiac status on brain oxygenation in patients with subarachnoid hemorrhage: a bi-center cohort study.

BACKGROUND: Fluid management in patients after subarachnoid hemorrhage (SAH) aims at the optimization of cerebral blood flow and brain oxygenation. In this study, we investigated the effects of hemodynamic management on brain oxygenation by integrating advanced hemodynamic and invasive neuromonitoring. METHODS: This observational cohort bi-center study included data of consecutive poor-grade SAH patients who underwent pulse contour cardiac output (PiCCO) monitoring and invasive neuromonitoring. Fluid management was guided by the transpulmonary thermodilution system and aimed at euvolemia (cardiac index, CI ≥ 3.0 L/min/m2; global end-diastolic index, GEDI 680-800 mL/m2; stroke volume variation, SVV < 10%). Patients were managed using a brain tissue oxygenation (PbtO2) targeted protocol to prevent brain tissue hypoxia (BTH, PbtO2 < 20 mmHg). To assess the association between CI and PbtO2 and the effect of fluid challenges on CI and PbtO2, we used generalized estimating equations to account for repeated measurements. RESULTS: Among a total of 60 included patients (median age 56 [IQRs 47-65] years), BTH occurred in 23% of  the monitoring time during the first 10 days since admission. Overall, mean CI was within normal ranges (ranging from 3.1 ± 1.3 on day 0 to 4.1 ± 1.1 L/min/m2 on day 4). Higher CI levels were associated with higher PbtO2 levels (Wald = 14.2; p < 0.001). Neither daily fluid input nor fluid balance was associated with absolute PbtO2 levels (p = 0.94 and p = 0.85, respectively) or the occurrence of BTH (p = 0.68 and p = 0.71, respectively). PbtO2 levels were not significantly different in preload dependent patients compared to episodes of euvolemia. PbtO2 increased as a response to fluid boluses only if BTH was present at baseline (from 13 ± 6 to 16 ± 11 mmHg, OR = 13.3 [95% CI 2.6-67.4], p = 0.002), but not when all boluses were considered (p = 0.154). CONCLUSIONS: In this study a moderate association between increased cardiac output and brain oxygenation was observed. Fluid challenges may improve PbtO2 only in the presence of baseline BTH. Individualized hemodynamic management requires advanced cardiac and brain monitoring in critically ill SAH patients.

Visual Analytics in Delirium Management.

BACKGROUND: Delirium is a patient safety issue that often occurs within the population of elderly people. As delirium may be characterized by fluctuating progress, the aim of this work is to find methods to visualize the occurrence of delirium over time in different patient stays in gerontopsychatric settings. METHODS: We analyzed current data mining visualization techniques for clinical research using a delirium data set collected in a gerontopsychatric setting. RESULTS: We identified heatmaps and dendrograms resulting from hierarchical clustering as a suitable visualization method. CONCLUSION: Heat maps with hierarchical clustering are a suitable data mining tool or visualization technique to study delirium cases in the time course of patient stays.

IT-Assisted Process Management in Healthcare.

Clinical processes need to be well understood before a new health IT tool can be introduced. Observations, interviews, surveys, or documentation analysis are carried out to systematically collect information to better understand a clinical process. To aggregate and visualize the collected information about a clinical process, use case diagrams can build a basis. Formal process models such as process chain diagrams or BPMN diagrams are well suited to model the process in detail. The objective of this chapter is to discuss these methods for analyzing and modeling clinical processes, as this is an important precondition for systematic process management in health care.

Learning Analytics and the Community of Inquiry: Indicators to Analyze and Visualize Online-Based Learning.

BACKGROUND: The Community of Inquiry (CoI) describes success factors for online-based learning. OBJECTIVES: To develop approaches for automatic analysis of CoI to be visualized within student and teacher dashboards. METHODS: Extending indicators from social network analysis and linguistics; evaluation within a case study. RESULTS: The project is just starting. CONCLUSION: Results will help to better understand and improve cooperative online-based learning in higher education.

FHIR Analytics - Pragmatic Review of Recent Studies.

BACKGROUND: Big Data Analytics is changing the way medical information is processed. FHIR aims to provide a robust infrastructure to enable the interoperability of clinical information. OBJECTIVES: Analyze the ongoing development of analytical implementations of FHIR. METHODS: Pragmatic Review of current research and implementation projects. RESULTS: US healthcare providers have implemented FHIR in their products, enabling scalable analytical tools. Europe demonstrated interest in FHIR. CONCLUSION: The potential of FHIR analytics is a value-added use case of the implementation of FHIR.

Evaluating Performance and Interpretability of Machine Learning Methods for Predicting Delirium in Gerontopsychiatric Patients.

Delirium is an acute mental disturbance that particularly occurs during hospital stay. Current clinical assessment instruments include the Delirium Observation Screening Scale (DOSS) or the Confusion Assessment Method (CAM). The aim of this work is to analyze the performance of machine learning approaches to detect delirium based on DOSS and CAM information obtained from two geropsychiatric wards in Tyrol. From a machine learning perspective, the questions of these two assessment instruments represent the features and the ICD 10 diagnoses of delirium (yes/no) is the corresponding class variable. We compare seven popular classification methods and analyze the performance and interpretability of the learning models. As our dataset is highly imbalanced, we also evaluate the effect of common sampling methods including down- and up-sampling methods as well as hybrid methods. Our results indicate a high predictive ability of advanced methods such as Random Forest that can handle even unbalanced datasets. Overall, combining a good performance of a prediction model with the ability of users to understand the prediction is challenging. However, for clinical application in fully electronic settings, a good performance seems to be more important than an easy interpretation of the prediction by the user. On the other hand, explanations of decisions are often needed to assess other criteria such as safety.

Implementing National Electronic Health Records in Nursing Homes in Tyrol: A Nursing Management Perspective.

BACKGROUND: Health information technologies as electronic health records (EHR) have the potential to improve the quality and efficiency of health care. Implementing national EHR in nursing homes in Tyrol is a required task within the next years. OBJECTIVES: To explore and analyze the current situation of information exchange in nursing homes in Tyrol as well as expected potentials and challenges of an ELGA implementation from a nursing management perspective. METHODS: Semi-structured interviews with nine nursing managers of nursing homes in Tyrol were conducted and analyzed using qualitative content analysis. RESULTS: The explorative interviews offered a glance at the actual information exchange with other institutions and at the perception of nursing managers regarding potentials (e.g. decreasing organizational effort) and challenges (e.g. incompleteness of available patient information) of the nationally mandated implementation of ELGA in the nursing homes in Tyrol. CONCLUSION: The interviews with the nine nursing managers of nursing homes in Tyrol revealed some hopes (e.g. the reduction of double documentation) that ELGA will probably not be able to fulfill.

Automated Mapping of LEP Nursing Data to Nursing Minimum Data Sets.

Nursing Minimum Data Sets (NMDS) intend to systematically describe nursing care. Until now NMDS have been populated with nursing data by manual data ascertainment which is inefficient. The objective of this work was to evaluate an automated mapping pipeline for transforming nursing data into an NMDS. We used LEP Nursing 3 data as source data and the Austrian and German NMDS as target formats. Based on a human expert mapping between LEP and NMDS, an automated data mapping algorithm was developed and implemented in an automatic mapping pipeline. The results show that most LEP nursing interventions can be matched to the NMDS-AT and G-NMDS and that a fully automated mapping process from LEP Nursing 3 data to NMDS-AT performs effectively and very efficiently. The shown approach can also be used to map different nursing classifications and to automatically transform point-of-care nursing data into nursing minimum data sets.

An Observational Study on the Use of Intravenous Non-Opioid Analgesics and Antipyretics in Poor-Grade Subarachnoid Hemorrhage: Effects on Hemodynamics and Systemic and Brain Temperature.

Intravenous nonsteroidal anti-inflammatory drugs and nonopioid analgesics are used to achieve normothermia or relieve pain in patients with aneurysmal subarachnoid hemorrhage (aSAH). We investigated the effects of paracetamol (1 g), diclofenac (75 mg) and metamizole (1 g) on systemic and cerebral hemodynamics and temperature during febrile and nonfebrile episodes after aSAH. Prospectively collected data from 77 consecutive poor-grade aSAH patients with invasive neuromonitoring were included. The burden and occurrence of hypotension (mean arterial pressure <70 mmHg), brain tissue hypoxia (PbtO2 < 20 mmHg), high intracranial pressure (>22 mmHg), low cerebral perfusion pressure (CPP <70 mmHg), and cerebral autoregulation pressure (pressure reactivity index [PRx]) during baseline (1 hour before) and 6 hours after medication were analyzed in febrile (core temperature; Tcore ≥ 38.3°C) and nonfebrile episodes. Nine hundred eighty-nine infusions (278 paracetamol, 542 diclofenac, and 169 metamizole) were administered resulting in significant reduction of core and brain temperature during febrile (49%) and nonfebrile (51%) episodes (p < 0.001). In febrile cases, temperature decreased for >1 hour below 37.5°C in 36% of interventions and ≤37°C in 11%. Hemodynamic side effects with hypotension and low CPP occurred in both febrile and nonfebrile episodes (p < 0.001) prompting increased vasopressor support in 31% of cases, even more pronounced during the vasospasm period (4-12 days postictus) (OR 5.4, 95% CI 1.8-16). The magnitude of PbtO2-decrease is directly correlated with the decrease in Tcore (p = 0.002) and higher baseline PbtO2 (p < 0.001). PRx decreased in febrile and nonfebrile episodes (p < 0.001), indicating improvement of cerebrovascular autoregulation. Antipyretics were insufficient to achieve sustained normothermia in poor-grade aSAH patients. Hemodynamic side effects were common even when given as analgesic drugs. Further studies are needed to weigh hemodynamic side effects to benefits (inter alia improved cerebral autoregulation).

Towards a Systematic Construction of a Minimum Data Set for Delirium to Support Secondary Use of Clinical Routine Data.

Patient safety is an important topic but non-trivial to measure, as it comprises very different phenomena. One important example of patient safety issues is delirium. Many approaches for the detection and prediction of delirium are described in the literature. However, additional effort is often needed for a comprehensive data collection and for the avoidance of potential biases. To systematize a process for the detection and prediction of delirium reusing available clinical routine data, we aim to develop a minimum data set (MDS) for delirium. By combining a top-down and bottom-up approach, we compiled a comprehensive delirium map containing potential delirium elements based on evidence. The alignment with clinical routine data led to a specific version of an MDS for delirium and revealed that most of the delirium elements could be identified within available nursing routine data.