Studies in Health Technology and Informatics最新文献

Introduction: The diagnosis and treatment of Parkinson's disease depend on the assessment of motor symptoms. Wearables and machine learning algorithms have emerged to collect large amounts of data and potentially support clinicians in clinical and ambulant settings.

State of the art: However, a systematical and reusable data architecture for storage, processing, and analysis of inertial sensor data is not available. Consequently, datasets vary significantly between studies and prevent comparability.

Concept: To simplify research on the neurodegenerative disorder, we propose an efficient and real-time-optimized architecture compatible with HL7 FHIR backed by a relational database schema.

Lessons learned: We can verify the adequate performance of the system on an experimental benchmark and in a clinical experiment. However, existing standards need to be further optimized to be fully sufficient for data with high temporal resolution.

The System Usability Scale (SUS) is a reliable tool for usability measurement and evaluation. Since its original language is English, a translation is required before a target group can answer it in their native language. The challenge of translating questionnaires lies in the preservation of its original properties. Different versions of a German SUS have been proposed and are currently in use. Objective of this work is to find and compare available German translations. Four versions were found and compared in terms of the translation process and the exact wording of the translation. Only the version of Gao et al. has been systematically validated, but has an unnatural wording. Although not validated yet, the proposed version of Rummel et al. is a good compromise between wording and methodically clean development. The version of Lohmann and Schäffer is the close runner up, as it may improve the wording at the expense of methodological accuracy. Since the version of Rauer gives no information about its translation process, it is considered least preferred of the four compared translations.

The German Medical Informatics Initiative has agreed on a HL7 FHIR-based core data set as the common data model that all 37 university hospitals use for their patient's data. These data are stored locally at the site but are centrally queryable for researchers and accessible upon request. This infrastructure is currently under construction, and its functionality is being tested by so-called Projectathons. In the 6th Projectathon, a clinical hypothesis was formulated, executed in a multicenter scenario, and its results were analyzed. A number of oddities emerged in the analysis of data from different sites. Biometricians, who had previously performed analyses in prospective data collection settings such as clinical trials or cohorts, were not consistently aware of these idiosyncrasies. This field report describes data quality problems that have occurred, although not all are genuine errors. The aim is to point out such circumstances of data generation that may affect statistical analysis.

Introduction: This paper proposes an eye blink detection system that automatically detects eye blinks, which can be an indicator of fatigue or cognitive load, among others. As a key feature, the real-time capability of the system is being required to use it, for example, as a monitoring system for people in potentially critical situations (e.g., drivers or operators of heavy machinery).

Methods: The system uses the Viola-Jones algorithm for face detection and the median flow tracker to track the face in video sequences. Eye detection is implemented using face proportions, and template matching is used for blink detection.

Results: The resulting system processes 40-47 frames per second on default consumer hardware and achieves an accuracy of 80.33% and a precision of 85.22% in the evaluation.

Discussion: The proposed system shows promising results under ideal viewing conditions but has difficulty maintaining high precision during head movements. The proposed system could be integrated with various health-related assistance systems to monitor the individual's well-being in real time, as long as their head is observed from the front if possible.

Appropriate data models are essential for the systematic collection, aggregation, and integration of health data and for subsequent analysis. However, recommendations for modeling health data are often not publicly available within specific projects. Therefore, the project Zukunftslabor Gesundheit investigates recommendations for modeling. Expert interviews with five experts were conducted and analyzed using qualitative content analysis. Based on the condensed categories "governance", "modeling" and "standards", the project team generated eight hypotheses for recommendations on health data modeling. In addition, relevant framework conditions such as different roles, international cooperation, education/training and political influence were identified. Although emerging from interviewing a small convenience sample of experts, the results help to plan more extensive data collections and to create recommendations for health data modeling.

The detection and prevention of medication-related health risks, such as medication-associated adverse events (AEs), is a major challenge in patient care. A systematic review on the incidence and nature of in-hospital AEs found that 9.2% of hospitalised patients suffer an AE, and approximately 43% of these AEs are considered to be preventable. Adverse events can be identified using algorithms that operate on electronic medical records (EMRs) and research databases. Such algorithms normally consist of structured filter criteria and rules to identify individuals with certain phenotypic traits, thus are referred to as phenotype algorithms. Many attempts have been made to create tools that support the development of algorithms and their application to EMRs. However, there are still gaps in terms of functionalities of such tools, such as standardised representation of algorithms and complex Boolean and temporal logic. In this work, we focus on the AE delirium, an acute brain disorder affecting mental status and attention, thus not trivial to operationalise in EMR data. We use this AE as an example to demonstrate the modelling process in our ontology-based framework (TOP Framework) for modelling and executing phenotype algorithms. The resulting semantically modelled delirium phenotype algorithm is independent of data structure, query languages and other technical aspects, and can be run on a variety of source systems in different institutions.

Introduction: Contradiction is a relevant data quality indicator to evaluate the plausibility of interdependent health data items. However, while contradiction assessment is achieved using domain-established contradictory dependencies, recent studies have shown the necessity for additional requirements to reach conclusive contradiction findings. For example, the oral or rectal methods used in measuring the body temperature will influence the thresholds of fever definition. The availability of this required information as explicit data items must be guaranteed during study design. In this work, we investigate the impact of activities related to study database implementation on contradiction assessment from two perspectives including: 1) additionally required metadata and 2) implementation of checks within electronic case report forms to prevent contradictory data entries.

Methods: Relevant information (timestamps, measurement methods, units, and interdependency rules) required for contradiction checks are identified. Scores are assigned to these parameters and two different studies are evaluated based on the fulfillment of the requirements by two selected interdependent data item sets.

Results: None of the studies have fulfilled all requirements. While timestamps and measurement units are found, missing information about measurement methods may impede conclusive contradiction assessment. Implemented checks are only found if data are directly entered.

Discussion: Conclusive contradiction assessment typically requires metadata in the context of captured data items. Consideration during study design and implementation of data capture systems may support better data quality in studies and could be further adopted in primary health information systems to enhance clinical anamnestic documentation.

Introduction: Prospective data collection in clinical trials is considered the gold standard of clinical research. Validating data entered in input fields in case report forms is unavoidable to maintain good data quality. Data quality checks include both the conformance of individual inputs to the specification of the data element, the detection of missing values, and the plausibility of the values entered.

State-of-the-art: Besides Libre-/OpenClinica there are many applications for capturing clinical data. While most of them have a commercial approach, free and open-source solutions lack intuitive operation.

Concept: Our ocRuleTool is made for the specific use case to write validation rules for Open-/LibreClinica, a clinical study management software for designing case report forms and managing medical data in clinical trials. It addresses parts of all three categories of data quality checks mentioned above.

Implementation: The required rules and error messages are entered in the normative Excel specification and then converted to an XML document which can be uploaded to Open-/LibreClinica. The advantage of this intermediate step is a better readability as the complex XML elements are broken down into easy to fill out columns in Excel. The tool then generates the ready to use XML file by itself.

Lessons learned: This approach saves time, is less error-prone and allows collaboration with clinicians on improving data quality.

Conclusion: Our ocRuleTool has proven useful in over a dozen studies. We hope to increase the user base by releasing it to open source on GitHub.

Introduction: While there is growing evidence of the benefits of assistive technologies little is known about their adoption under real circumstances and prevalence for everyday use.

Objective: The aim of this analysis therefore was (i) to investigate the adoption rates in the real world and (ii) to identify potential determinants of their adoption by care-dependant persons and family caregivers.

Methods: The present study is a secondary analysis based on the data set of the VdK study on home care arrangements (n=53,678). The analysis of the adoption rates included 22,666 care-dependant persons and caregivers, the identification of potential determinants via binary logistic regressions included 5,275 persons.

Results: Emergency call systems and technical (smart) aids reached an adoption rate of 40.4 % (care-dependant persons) and 55.3 % (family caregivers). Fall detectors, orientations aids, nursing apps and monitoring systems were used in less than 5 % of the cases. Care degree and the use of an ambulatory nursing service increased the likelihood of using technical aids.

Conclusion: It can be concluded that innovative and sophisticated types of assistive technologies are still rather scarcely used for home care arrangements in the real world despite large research efforts in the last twenty years.

Gamification has many positive effects, such as increased motivation, engagement, and well-being of users. For this purpose, a wide field of game mechanics is already available that can be used in teaching. For the development of gamified teaching methods, it's important to adapt the mechanics used to the students. There are different models that divide target groups of games and gamification into player types to understand what motivates the respective users. This paper describes a study of player types among students of health-related disciplines and analyses the data by a K-Means clustering procedure. The player types Socializer, Player and Achiever are found, and game elements for this groups are suggested. Thus, in the field of health education, game mechanics can be used, which are suitable for students of this domain.