Visualizing Nursing Narratives: An Evaluation of Latent Dirichlet Allocation Topic Modeling for Care Reports.

The increasing volume of unstructured textual data in healthcare, particularly in nursing care reports, presents both challenges and opportunities for enhancing patient care and operational efficiency. This study explores the application of Latent Dirichlet Allocation (LDA) topic modeling to analyze free-text nursing narratives from inpatient stays in three different clinics, aiming to uncover the latent thematic structures within. Utilizing the R programming environment and the visualization tool LDAvis, we identified three main themes: "Patient Well-being," "Patient Mobility and Care Activities," and "Treatment and Pain Management," the latter combining two closely related but initially distinct topics due to their overlapping content. Our findings demonstrate the potential of LDA topic modeling in extracting meaningful insights from nursing narratives, which could inform patient care strategies and healthcare practices. However, the study also highlights significant challenges associated with the method, including the sensitivity to parameter settings, the lack of updates for key software packages, and concerns about reproducibility. These issues highlight the need for meticulous parameter validation and the exploration of alternative text analysis methodologies for future research. By addressing these methodological challenges and emphasizing the importance of comparative method analysis, this study contributes to the advancement of text analytics in healthcare. It opens avenues for further research aimed at developing more robust, efficient, and accessible tools for analyzing free-text data, thereby enhancing the ability of healthcare professionals to use unstructured data to improve decision making and patient outcomes.

Smart FOX - Enabling Citizen-Based Donation of EHR-Standardised Data for Clinical Research in Austria.

Access to healthcare data for secondary use in clinical research is often restricted due to privacy concerns or business interests, hindering comprehensive analysis across patient pathways. The Smart FOX project seeks to address this challenge by developing concepts, methods, and tools to facilitate citizen/patient-driven donations of health data for clinical research. Leveraging the groundwork, laid by the national Electronic Health Record implementation in Austria (called ELGA), Smart FOX aims to harness structured datasets from ELGA for research purposes through an opt-in approach. With funding secured from the Austrian Research Promotion Agency, the project embarks on innovative solutions encompassing governance frameworks, community engagement, and technical infrastructure. The Smart FOX consortium, comprising key stakeholders across various healthcare-associated domains, will evaluate these efforts through demonstrators focusing on clinical registries, patient-generated data, and recruitment services. The project targets to accompany the development of future data donation infrastructure while ultimately advancing clinical research efficiency and bolstering Austria's preparedness for the European Health Data Space. This paper presents the first systematic evaluation of the technical concept and proposal for the federated system architecture of the Austrian Health Data Donation Space, which is the socio-technical goal of Smart FOX.

A Neuronal Network-Based Score Predicting Survival in Patients Undergoing Aortic Valve Intervention: The ABC-AS Score.

Background: Despite being the most commonly performed valvular intervention, risk prediction for aortic valve replacement in patients with severe aortic stenosis by currently used risk scores remains challenging. The study aim was to develop a biomarker-based risk score by means of a neuronal network. Methods: In this multicenter study, 3595 patients were divided into test and validation cohorts (70% to 30%) by random allocation. Input variables to develop the ABC-AS score were age, the cardiac biomarker high-sensitivity troponin T, and a patient history of cardiac decompensation. The validation cohort was used to verify the scores' value and for comparison with the Society of Thoracic Surgery Predictive Risk of Operative Mortality score. Results: Receiver operating curves demonstrated an improvement in prediction by using the ABC-AS score compared to the Society of Thoracic Surgery Predictive Risk of Operative Mortality (STS prom) score. Although the difference in predicting cardiovascular mortality was most notable at 30-day follow-up (area under the curve of 0.922 versus 0.678), ABC-AS also performed better in overall follow-up (0.839 versus 0.699). Furthermore, univariate analysis of ABC-AS tertiles yielded highly significant differences for all-cause (p < 0.0001) and cardiovascular mortality (p < 0.0001). Head-to-head comparison between both risk scores in a multivariable cox regression model underlined the potential of the ABC-AS score (HR per z-unit 2.633 (95% CI 2.156-3.216), p < 0.0001), while the STS prom score failed to reach statistical significance (p = 0.226). Conclusions: The newly developed ABC-AS score is an improved risk stratification tool to predict cardiovascular outcomes for patients undergoing aortic valve intervention.

Manual lymphatic drainage before and after total knee arthroplasty, a randomized controlled trial.

Purpose: Manual lymphatic drainage (MLD) is a routine therapeutic technique used to decrease peripheral oedema by activating lymphatic drainage. Evidence for its efficacy remains sparse. Therefore, the purpose of this study was to evaluate the effect of MLD before and after total knee arthroplasty (TKA). Methods: This was a single-centre randomized, controlled and observer-blinded trial. 112 patients were randomly assigned to one of three groups: group 1 underwent MLD for 30 min daily on five consecutive days both before and after TKA; group 2 underwent MLD for 30 min daily on five consecutive days only after TKA; and the control group did not undergo MLD. The Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) score, range of knee motion, swelling and pain were assessed before TKA, after two days, five days and six weeks. Results: The overall age of the patients was 69.4 years (SD = 9.8, range = 41-87). The groups were well matched in terms of sex, age, height, weight, and body mass index. There was no statistically significant difference with regard to any of the outcome measures between the groups. Conclusions: The present results indicate that MLD had no significant benefits when applied either before or early after TKA. Therefore, we do not recommend the routinely use of MLD in the early period before or after TKA. Further studies should evaluate the effect of MLD after arthroscopic surgery. Level of evidence: Therapeutic Level II, Lower quality RCT with follow up <80.

Towards an Electronic Health Prevention Record Based on HL7 FHIR and the OMOP Common Data Model.

BACKGROUND: Approximately 40% of all recorded deaths in Austria are due to behavioral risks. These risks could be avoided with appropriate measures. OBJECTIVES: Extension of the concept of EHR and EMR to an electronic prevention record, focusing on primary and secondary prevention. METHODS: The concept of a structured prevention pathway, based on the principles of P4 Medicine, was developed for a multidisciplinary prevention network. An IT infrastructure based on HL7 FHIR and the OHDSI OMOP common data model was designed. RESULTS: An IT solution supporting a structured and modular prevention pathway was conceptualized. It contained a personalized management of prevention, risk assessment, diagnostic and preventive measures supported by a modular, interoperable IT infrastructure including a health app, prevention record web-service, decision support modules and a smart prevention registry, separating primary and secondary use of data. CONCLUSION: A concept was created on how an electronic health prevention record based on HL7 FHIR and the OMOP common data model can be implemented.

CareNet Tyrol - Information System Success Assessment for Case & Care Management Service.

BACKGROUND: CareNet is the IT-based tool for Case and Care Management (CCM) in Tyrol, which facilitates standardised documentation of CCM activities. OBJECTIVES: Analysing the pilot usage of CareNet Tyrol. METHODS: Evaluation of the success and user experience of CareNet, expert interviews and a questionnaire-based assessment. RESULTS: Feedback from users in both phases indicated that the CareNet platform provides general benefits, but falls short of fully supporting the daily work of CCM experts and avoiding the need for parallel use of different documentation tools. CONCLUSION: This paper provides an insight into the ongoing transition to digital documentation for CCM at LIV Tyrol. While user feedback highlights areas for improvement, digital documentation is proved to be beneficial for the CCM team.

HerzMobil Tirol PreOp - A Multidisciplinary Telemonitoring Project for Heart Failure Patients Prior to Cardiac Surgery.

BACKGROUND: Patients with heart failure are at risk of perioperative complications with elective cardiac surgery. OBJECTIVES: Conception of a multidisciplinary telemedicine-assisted optimisation project for high-risk patients prior to elective cardiac surgery. METHODS: Multidisciplinary concept design. RESULTS: A pilot-project for 30 patients was developed. CONCLUSION: Design of the first preoperative telemonitoring-assisted optimisation project for high-risk patients undergoing cardiac surgery.

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.

Health data space nodes for privacy-preserving linkage of medical data to support collaborative secondary analyses.

Introduction: The potential for secondary use of health data to improve healthcare is currently not fully exploited. Health data is largely kept in isolated data silos and key infrastructure to aggregate these silos into standardized bodies of knowledge is underdeveloped. We describe the development, implementation, and evaluation of a federated infrastructure to facilitate versatile secondary use of health data based on Health Data Space nodes. Materials and methods: Our proposed nodes are self-contained units that digest data through an extract-transform-load framework that pseudonymizes and links data with privacy-preserving record linkage and harmonizes into a common data model (OMOP CDM). To support collaborative analyses a multi-level feature store is also implemented. A feasibility experiment was conducted to test the infrastructures potential for machine learning operations and deployment of other apps (e.g., visualization). Nodes can be operated in a network at different levels of sharing according to the level of trust within the network. Results: In a proof-of-concept study, a privacy-preserving registry for heart failure patients has been implemented as a real-world showcase for Health Data Space nodes at the highest trust level, linking multiple data sources including (a) electronical medical records from hospitals, (b) patient data from a telemonitoring system, and (c) data from Austria's national register of deaths. The registry is deployed at the tirol kliniken, a hospital carrier in the Austrian state of Tyrol, and currently includes 5,004 patients, with over 2.9 million measurements, over 574,000 observations, more than 63,000 clinical free text notes, and in total over 5.2 million data points. Data curation and harmonization processes are executed semi-automatically at each individual node according to data sharing policies to ensure data sovereignty, scalability, and privacy. As a feasibility test, a natural language processing model for classification of clinical notes was deployed and tested. Discussion: The presented Health Data Space node infrastructure has proven to be practicable in a real-world implementation in a live and productive registry for heart failure. The present work was inspired by the European Health Data Space initiative and its spirit to interconnect health data silos for versatile secondary use of health data.

Penetrance, cancer incidence and survival in HFE haemochromatosis-A population-based cohort study.

BACKGROUND AND AIMS: Haemochromatosis is characterized by progressive iron overload affecting the liver and can cause cirrhosis and hepatocellular carcinoma. Most haemochromatosis patients are homozygous for p.C282Y in HFE, but only a minority of individuals with this genotype will develop the disease. The aim was to assess the penetrance of iron overload, fibrosis, hepatocellular carcinoma and life expectancy. METHODS: A total of 8839 individuals from the Austrian region of Tyrol were genotyped for the p.C282Y variant between 1997 and 2021. Demographic, laboratory parameters and causes of death were assessed from health records. Penetrance, survival, and cancer incidence were ascertained from diagnosed cases, insurance- and cancer registry data. Outcomes were compared with a propensity score-matched control population. RESULTS: Median age at diagnosis in 542 p.C282Y homozygous individuals was 47.8 years (64% male). At genotyping, the prevalence of iron overload was 55%. The cumulative penetrance of haemochromatosis defined as the presence of provisional iron overload was 24.2% in males and 10.5% in females aged 60 years or younger. Among p.C282Y homozygotes of the same ages, the cumulative proportion of individuals without fibrosis (FIB-4 score < 1.3) was 92.8% in males and 96.7% in females. Median life expectancy was reduced by 6.8 years in individuals homozygous for p.C282Y when compared with population-matched controls (p = .001). Hepatocellular carcinoma incidence was not significantly higher in p.C282Y homozygotes than in controls matched for age and sex. CONCLUSION: Reduced survival and the observed age-dependent increase in penetrance among p.C282Y homozygotes call for earlier diagnosis of haemochromatosis to prevent complications.

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.

Can virtual care reduce treatment burden in pediatric leukemia? A retrospective cost analysis.

We conducted a retrospective analysis to determine the potential reduction in treatment burden through the expansion of virtual care among children with leukemia (n = 152). Patients living in urban areas traveled median distances of 1555 km compared with 7536 km for patients living in rural areas (p < .05). For the latter group, a median reduction in travel distance of 3560 km (interquartile range [IQR], 2136-5787 km), travel time of 51 h (IQR, 26-78 h), and CO2 emissions of 623 kg (IQR, 374-1013 kg) was estimated, if every second visit was replaced by video consultations.

Moonlight surgery: no influence of moon phase or Friday 13th on outcomes of total knee arthroplasty.

OBJECTIVES: The purpose of the present study was to investigate associations between revision-free survival and functional scores of total knee arthroplasty (TKA) and moon phase on the day of surgery, as well as operations performed on a Friday 13th. PARTICIPANTS: The data of all patients that received TKA between 2003 and 2019 were extracted from the Tyrol arthroplasty registry. Patients that had undergone previous total or partial knee arthroplasty as well as patients that had missing pre- or post-operative WOMAC were excluded. Patients were allocated to one of the following four groups according to moon phase on the day of surgery: new, waxing, full and waning. Patients operated on a Friday 13th were also identified and compared to patients operated on any other days/dates. A total of 5923 patients met the inclusion criteria, with mean age of 69 ± 9 years, and comprising 62% women. RESULTS: There were no significant differences in revision-free survival among the four moon phase groups (p = 0.479), and no significant differences in preoperative and postoperative total WOMAC (p = 0.260, p = 0.122), There were no significant differences in revision-free survival patients operated on Friday 13th vs. other days/dates (p = 0.440). The preoperative total WOMAC was significantly worse for patients operated on a Friday 13th (p = 0.013), which was observed in the pain (p = 0.032) and function (p = 0.010) subscales. There were no significant differences in postoperative total WOMAC at 1 year follow-up (p = 0.122). CONCLUSIONS: Neither moon phase on the day of surgery nor Friday 13th were associated with revision-free survival or clinical scores of TKA. Patients operated on a Friday 13th had significantly worse preoperative total WOMAC but similar postoperative total WOMAC at 1-year follow-up. These findings could help reassure patients that TKA renders consistent outcomes regardless of the preoperative pain or function, and in spite of bad omens or moon phases.

Classification of Clinical Notes from a Heart Failure Telehealth Network.

Heart failure is a common chronic disease which is associated with high re-hospitalization and mortality rates. Within the telemedicine-assisted transitional care disease management program HerzMobil, monitoring data such as daily measured vital parameters and various other heart failure related data are collected in a structured way. Additionally, involved healthcare professionals communicate with one another via the system using free-text clinical notes. Since manual annotation of such notes is too time-consuming for routine care applications, an automated analysis process is needed. In the present study, we established a ground truth classification of 636 randomly selected clinical notes from HerzMobil based on annotations of 9 experts with different professional background (2 physicians, 4 nurses, and 3 engineers). We analyzed the influence of the professional background on the inter annotator reliability and compared the results with the accuracy of an automated classification algorithm. We found significant differences depending on the profession and on the category. These results indicate that different professional backgrounds should be considered when selecting annotators in such scenarios.

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.

Towards a Verification Approach of a Smart Registry for Chronic Heart Failure Patients.

BACKGROUND: The daily increasing amount of health data from different sources like electronic medical records and telehealth systems go hand in hand with the ongoing development of novel digital and data-driven analytics. Unifying this in a privacy-preserving data aggregation infrastructure can enable services for clinical decision support in personalized patient therapy. OBJECTIVES: The goal of this work was to consider such an infrastructure, implemented in a smart registry for heart failure, as a comparative method for the analysis of health data. METHODS: We analyzed to what extent the dataset of a study on the telehealth program HerzMobil Tirol (HMT) can be reproduced with the data from the smart registry. RESULTS: A table with 96 variables for 251 patients of the HMT publication could theoretically be replicated from the smart registry for 248 patients with 80 variables. The smart registry contained the tables to reproduce a large part of the information, especially the core statements of the HMT publication. CONCLUSION: Our results show how such an infrastructure can enable efficient analysis of health data, and thus take a further step towards personalized health care.