Studies in health technology and informatics最新文献

Traumatic brain injuries (TBI) significantly impact global health, often resulting in death or long-term disability. We developed a quality dashboard to monitor adherence to severe TBI guidelines, leveraging data from Rennes University Hospital's clinical data warehouse collected between January 2020 and December 2023. We included 193 patients from the surgical ICU who were over 18 years old and excluded those without adequate intracranial pressure (ICP) monitoring data. The study utilized the French Anesthesiology and Intensive Care Society guidelines and the Brain Trauma Foundation's 4th Guidelines Edition to assess guideline adherence over the first seven days of hospitalization. Our dashboard, built using the flexdashboard and Plotly R libraries, presents patient demographics, clinical assessments, and treatment adherence. Despite limitations, such as reduced interoperability and the absence of clinician usability testing, our tool represents a pioneering effort in TBI guideline compliance, with plans for future enhancements including expanded guideline evaluation and improved dashboard sharing capabilities.

The AKTIN Emergency Department Registry, a German health data network, faces operational challenges due to rapid growth. Manual data request processes have become inefficient, hindering timely research and straining personnel. To address these challenges, we undertook a user-centered analysis utilizing Design Thinking principles to identify pain points and functional requirements in current data request creation and management processes. Future work will prioritize iterative implementation of the created concepts with continuous user engagement and rigorous software validation.

Technology in the medical field is continuously advancing due to its numerous subdomains and the ever-growing medical needs of people. Information systems have become integral to doctors' daily routines in patient care, offering flexibility and support in repetitive tasks, thereby allowing more time for critical activities. This paper presents the implementation of a machine learning algorithm, leveraging natural language processing (NLP) and labeling techniques, to analyze medical leaflets from Romania. The aim is to assist pediatricians in determining appropriate treatment doses for children based on various parameters such as age, weight, and other significant factors.

The paper describes a cohort of patients with post-acute COVID-19 syndrome, evaluated for the first time between week 3 and week 12 from the onset of symptoms following the acute COVID-19 infection. The patient's baseline clinical features were used as predictors. The analysis showed that older patients with comorbidities are at higher risk of developing more long-lasting post COVID-19 symptoms. Further integration with a personal monitoring device and combination with the Fast Healthcare Interoperability Resources extends the standardization, interoperability and possibility of integration and harmonization with other hospital systems. By employing advanced machine learning techniques, insights can be derived and further examined to improve the outcome and early treatment options for patients.

The PROSurvival project aims to improve the prediction of recurrence-free survival in prostate cancer by applying federated machine learning to whole slide images combined with selected clinical data. Both the image and clinical data will be aggregated into an anonymized dataset compliant with the General Data Protection Regulation and published under the principles of findable, accessible, interoperable, and reusable data. The DICOM standard will be used for the image data. For the accompanying clinical data, a human-readable, compact and flexible standard is yet to be defined. From the set of existing standards, mostly extendable with varying degrees of modifications, we chose oBDS as a starting point and modified it to include missing data points and to remove mandatory items not applicable to our dataset. Clinical and survival data from clinic-specific spreadsheets were converted into this modified standard, ensuring on-site data privacy during processing. For publication of the dataset, both image and clinical data are anonymized using established methods. The key challenges arose during the clinical data anonymization and in identifying research repositories meeting all of our requirements. Each clinic had to coordinate the publication with their responsible data protection officers, requiring different approval processes due to the individual states' differing interpretations of the legal regulations. The newly established German Health Data Utilization Act is expected to simplify future data sharing in a responsible and powerful way.

The present study explored alternative methods for photographing skin lesions in the absence of specialized instruments like dermatoscopes, aiming to enhance remote diagnostic capabilities, particularly in light of the increasing incidence of melanoma cases annually. Using two lenses attached to a smartphone camera, one macroscopic and the other microscopic, study images of nevus formations from one individual were captured, and, in the absence of a collaboration with a dermatologist, subsequently labeled as melanoma or non-melanoma using a Convolutional Neural Network (CNN) which was trained, with dermoscopic images of melanoma and non-melanoma formations, to see on which image set better performances would be attained. The CNN demonstrated better performance on microscopic images, with 75% of the dataset being labeled correctly, compared to the macroscopic one, with 63% of the dataset being labeled correctly. These findings highlight the potential of smartphone-based imaging with specialized micro lenses to improve diagnostic accuracy for melanoma and other dermatological conditions in remote healthcare settings.

Pediatric growth hormone deficiency (PGHD) is a chronic condition where the pituitary gland fails to produce sufficient growth hormone, leading to delayed growth and developmental challenges. Patient journey maps can provide insight into pain points and potential opportunities for new or improved interventions to enhance care. However, a patient journey map does not yet exist for PGHD. Secondary data analysis was performed on interviews and focus groups from five cohorts in Sweden, the United Kingdom, Luxembourg, France, and The Netherlands. Participants included 62 patients and caregivers who used a prototype digital health solution, which was used to guide discussions. Grounded theory was used to analyze the data, resulting in a patient journey map comprising six stages: awareness, diagnosis, treatment planning, treatment initiation, treatment maintenance and transition. This provides the first detailed PGHD patient journey map, revealing emotional sensitivities and challenges at each stage, and suggesting areas for targeted interventions to improve adherence and long-term outcomes.

Structured data are the capital of empirical health research. The value of these data relates to their quality and to their fit for use. A German guideline for the management of data quality in registries and cohort studies lists 51 quality indicators organized into the categories organization, integrity, and trueness. An update of the guideline will take into account the current view on dimensions of data, the appropriate structure for the definition of an indicator, and the collection of quality indicators itself. In the next version, the collection will explicitly address measures of metadata quality. The first step of a literature review revealed a high number of potential sources of evidence. These will be categorized into the topics dimensions, structure, and indicators respectively. Special attention will be paid to new challenges of data quality control arising from big data and artificial intelligence.

Multiple sclerosis (MS) is a complex neurodegenerative disease with a variable prognosis that complicates effective management and treatment. This study leverages machine learning (ML) to enhance the understanding of disease progression and uncover gender-based differences in MS by analyzing clinical data integrated with patient-reported outcomes (PROMs). We conducted a prospective cohort study involving 250 MS patients at a secondary care hospital in Spain over an 18-month period. Using REDCap for data management, we collected comprehensive demographic, clinical, and PROMs data. Our analysis utilized Decision Trees, Random Forest, and Support Vector Machine algorithms to classify patients based on disease evolution and infer Expanded Disability Status Scale (EDSS) levels. Additionally, we employed propensity score matching to analyze gender differences, focusing on clinical outcomes and quality of life measures. The results could indicate that integrating diverse data sets through ML would significantly improve the diagnostic accuracy and serve as a support for clinician's decision making. Our models achieved high accuracy in classifying MS types and predicting disability levels, demonstrating the potential of ML in personalized treatment planning. Furthermore, our findings suggest notable gender differences in disease progression and response to treatment. These insights advocate for a gender-specific approach in MS management and highlight the importance of personalized medicine. This study underscores the transformative potential of ML in enhancing the understanding and management of MS through integrated data analysis.

Sesterterpenoids, a subset of the terpene family, exhibit notable biological activities. These natural compounds are present in a variety of organisms such as plants, fungi, bacteria, insects and marine life. The therapeutic potential and structural diversity of sesterterpenoids have attracted considerable interest in pharmacological and chemical research. This study illustrates the development of a database to structure and manage data on these compounds. The design process involves the collection of user requirements, creation of a conceptual model with and Entity-Relationship Diagram (ERD), development of a logical model, and implementation in Microsoft SQL Server 2022. Data collection began with an extensive literature review and organization in an Excel spreadsheet. The resulting database improves data acquisition, organization, and accessibility. Future work will include building a website to facilitate data entry, editing, reading and extraction, and automation of data updates via external web services.