Large-scale assessment of physical activity in a population using high-resolution hip-worn accelerometry: the German National Cohort (NAKO)

Large population-based cohort studies utilizing device-based measures of physical activity are crucial to close important research gaps regarding the potential protective effects of physical activity on chronic diseases. The present study details the quality control processes and the derivation of physical activity metrics from 100 Hz accelerometer data collected in the German National Cohort (NAKO). During the 2014 to 2019 baseline assessment, a subsample of NAKO participants wore a triaxial ActiGraph accelerometer on their right hip for seven consecutive days. Auto-calibration, signal feature calculations including Euclidean Norm Minus One (ENMO) and Mean Amplitude Deviation (MAD), identification of non-wear time, and imputation, were conducted using the R package GGIR version 2.10-3. A total of 73,334 participants contributed data for accelerometry analysis, of whom 63,236 provided valid data. The average ENMO was 11.7 ± 3.7 mg (milli gravitational acceleration) and the average MAD was 19.9 ± 6.1 mg. Notably, acceleration summary metrics were higher in men than women and diminished with increasing age. Work generated in the present study will facilitate harmonized analysis, reproducibility, and utilization of NAKO accelerometry data. The NAKO accelerometry dataset represents a valuable asset for physical activity research and will be accessible through a specified application process.

Landmark-based registration of a cochlear model to a human cochlea using conventional CT scans

Cochlear implants can provide an advanced treatment option to restore hearing. In standard pre-implant procedures, many factors are already considered, but it seems that not all underlying factors have been identified yet. One reason is the low quality of the conventional computed tomography images taken before implantation, making it difficult to assess these parameters. A novel method is presented that uses the Pietsch Model, a well-established model of the human cochlea, as well as landmark-based registration to address these challenges. Different landmark numbers and placements are investigated by visually comparing the mean error per landmark and the registrations' results. The landmarks on the first cochlear turn and the apex are difficult to discern on a low-resolution CT scan. It was possible to achieve a mean error markedly smaller than the image resolution while achieving a good visual fit on a cochlear segment and directly in the conventional computed tomography image. The employed cochlear model adjusts image resolution problems, while the effort of setting landmarks is markedly less than the segmentation of the whole cochlea. As a next step, the specific parameters of the patient could be extracted from the adapted model, which enables a more personalized implantation with a presumably better outcome.

Improving mental well-being in psychocardiology-a feasibility trial for a non-blended web application as a brief metacognitive-based intervention in cardiovascular disease patients

Background

Many patients with cardiovascular disease also show a high comorbidity of mental disorders, especially such as anxiety and depression. This is, in turn, associated with a decrease in the quality of life. Psychocardiological treatment options are currently limited. Hence, there is a need for novel and accessible psychological help. Recently, we demonstrated that a brief face-to-face metacognitive therapy (MCT) based intervention is promising in treating anxiety and depression. Here, we aim to translate the face-to-face approach into digital application and explore the feasibility of this approach.

Methods

We translated a validated brief psychocardiological intervention into a novel non-blended web app. The data of 18 patients suffering from various cardiac conditions but without diagnosed mental illness were analyzed after using the web app over a two-week period in a feasibility trial. The aim was whether a non-blended web app based MCT approach is feasible in the group of cardiovascular patients with cardiovascular disease.

Results

Overall, patients were able to use the web app and rated it as satisfactory and beneficial. In addition, there was first indication that using the app improved the cardiac patients' subjectively perceived health and reduced their anxiety. Therefore, the approach seems feasible for a future randomized controlled trial.

Conclusion

Applying a metacognitive-based brief intervention via a non-blended web app seems to show good acceptance and feasibility in a small target group of patients with CVD. Future studies should further develop, improve and validate digital psychotherapy approaches, especially in patient groups with a lack of access to standard psychotherapeutic care.

Eos and OMOCL: Towards a seamless integration of openEHR records into the OMOP Common Data Model

BACKGROUND

The reuse of data from electronic health records (EHRs) for research purposes promises to improve the data foundation for clinical trials and may even support to enable them. Nevertheless, EHRs are characterized by both, heterogeneous structure and semantics. To standardize this data for research, the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) standard has recently seen an increase in use. However, the conversion of these EHRs into the OMOP CDM requires complex and resource intensive Extract Transform and Load (ETL) processes. This hampers the reuse of clinical data for research. To solve the issues of heterogeneity of EHRs and the lack of semantic precision on the care site, the openEHR standard has recently seen wider adoption. A standardized process to integrate openEHR records into the CDM potentially lowers the barriers of making EHRs accessible for research. Yet, a comprehensive approach about the integration of openEHR records into the OMOP CDM has not yet been made.

METHODS

We analyzed both standards and compared their models to identify possible mappings. Based on this, we defined the necessary processes to transform openEHR records into CDM tables. We also discuss the limitation of openEHR with its unspecific demographics model and propose two possible solutions.

RESULTS

We developed the OMOP Conversion Language (OMOCL) which enabled us to define a declarative openEHR archetype-to-CDM mapping language. Using OMOCL, it was possible to define a set of mappings. As a proof-of-concept, we implemented the Eos tool, which uses the OMOCL-files to successfully automatize the ETL from real-world and sample EHRs into the OMOP CDM.

DISCUSSION

Both Eos and OMOCL provide a way to define generic mappings for an integration of openEHR records into OMOP. Thus, it represents a significant step towards achieving interoperability between the clinical and the research data domains. However, the transformation of openEHR data into the less expressive OMOP CDM leads to a loss of semantics.

Programming techniques for improving rule readability for rule-based information extraction natural language processing pipelines of unstructured and semi-structured medical texts

BACKGROUND

Extraction of medical terms and their corresponding values from semi-structured and unstructured texts of medical reports can be a time-consuming and error-prone process. Methods of natural language processing (NLP) can help define an extraction pipeline for accomplishing a structured format transformation strategy.

OBJECTIVES

In this paper, we build an NLP pipeline to extract values of the classification of malignant tumors (TNM) from unstructured and semi-structured pathology reports and import them further to a structured data source for a clinical study. Our research interest is not focused on standard performance metrics like precision, recall, and F-measure on the test and validation data. We discuss how with the help of software programming techniques the readability of rule-based (RB) information extraction (IE) pipelines can be improved, and therefore minimize the time to correct or update the rules, and efficiently import them to another programming language.

METHODS

The extract rules were manually programmed with training data of TNM classification and tested in two separate pipelines based on design specifications from domain experts and data curators. Firstly we implemented each rule directly in one line for each extraction item. Secondly, we reprogrammed them in a readable fashion through decomposition and intention-revealing names for the variable declaration. To measure the impact of both methods we measure the time for the fine-tuning and programming of the extractions through test data of semi-structured and unstructured texts.

RESULTS

We analyze the benefits of improving through readability of the writing of rules, through parallel programming with regular expressions (REGEX), and the Apache Uima Ruta language (AURL). The time for correcting the readable rules in AURL and REGEX was significantly reduced. Complicated rules in REGEX are decomposed and intention-revealing declarations were reprogrammed in AURL in 5 min.

CONCLUSION

We discuss the importance of factor readability and how can it be improved when programming RB text IE pipelines. Independent of the features of the programming language and the tools applied, a readable coding strategy can be proven beneficial for future maintenance and offer an interpretable solution for understanding the extraction and for transferring the rules to other domains and NLP pipelines.

Recommendations of the International Medical Informatics Association (IMIA) on Education in Biomedical and Health Informatics: Second Revision

BACKGROUND

The purpose of educational recommendations is to assist in establishing courses and programs in a discipline, to further develop existing educational activities in the various nations, and to support international initiatives for collaboration and sharing of courseware. The International Medical Informatics Association (IMIA) has published two versions of its international recommendations in biomedical and health informatics (BMHI) education, initially in 2000 and revised in 2010. Given the recent changes to the science, technology, the needs of the healthcare systems, and the workforce of BMHI, a revision of the recommendations is necessary.

OBJECTIVE

The aim of these updated recommendations is to support educators in developing BMHI curricula at different education levels, to identify essential skills and competencies for certification of healthcare professionals and those working in the field of BMHI, to provide a tool for evaluators of academic BMHI programs to compare and accredit the quality of delivered programs, and to motivate universities, organizations, and health authorities to recognize the need for establishing and further developing BMHI educational programs.

METHOD

An IMIA taskforce, established in 2017, updated the recommendations. The taskforce included representatives from all IMIA regions, with several having been involved in the development of the previous version. Workshops were held at different IMIA conferences, and an international Delphi study was performed to collect expert input on new and revised competencies.

RESULTS

Recommendations are provided for courses/course tracks in BMHI as part of educational programs in biomedical and health sciences, health information management, and informatics/computer science, as well as for dedicated programs in BMHI (leading to bachelor's, master's, or doctoral degree). The educational needs are described for the roles of BMHI user, BMHI generalist, and BMHI specialist across six domain areas - BMHI core principles; health sciences and services; computer, data and information sciences; social and behavioral sciences; management science; and BMHI specialization. Furthermore, recommendations are provided for dedicated educational programs in BMHI at the level of bachelor's, master's, and doctoral degrees. These are the mainstream academic programs in BMHI. In addition, recommendations for continuing education, certification, and accreditation procedures are provided.

CONCLUSION

The IMIA recommendations reflect societal changes related to globalization, digitalization, and digital transformation in general and in healthcare specifically, and center on educational needs for the healthcare workforce, computer scientists, and decision makers to acquire BMHI knowledge and skills at various levels. To support education in BMHI, IMIA offers accreditation of quality BMHI education programs. It supports information exchange on programs and courses in BMHI through its Working Group on Health and Medical Informatics Education.

Tracing the Progression of Sepsis in Critically Ill Children: Clinical Decision Support for Detection of Hematologic Dysfunction

Background  One of the major challenges in pediatric intensive care is the detection of life-threatening health conditions under acute time constraints and performance pressure. This includes the assessment of pediatric organ dysfunction (OD) that demands extraordinary clinical expertise and the clinician's ability to derive a decision based on multiple information and data sources. Clinical decision support systems (CDSS) offer a solution to support medical staff in stressful routine work. Simultaneously, detection of OD by using computerized decision support approaches has been scarcely investigated, especially not in pediatrics.

Objectives  The aim of the study is to enhance an existing, interoperable, and rule-based CDSS prototype for tracing the progression of sepsis in critically ill children by augmenting it with the capability to detect SIRS/sepsis-associated hematologic OD, and to determine its diagnostic accuracy.

Methods  We reproduced an interoperable CDSS approach previously introduced by our working group: (1) a knowledge model was designed by following the commonKADS methodology, (2) routine care data was semantically standardized and harmonized using openEHR as clinical information standard, (3) rules were formulated and implemented in a business rule management system. Data from a prospective diagnostic study, including 168 patients, was used to estimate the diagnostic accuracy of the rule-based CDSS using the clinicians' diagnoses as reference.

Results  We successfully enhanced an existing interoperable CDSS concept with the new task of detecting SIRS/sepsis-associated hematologic OD. We modeled openEHR templates, integrated and standardized routine data, developed a rule-based, interoperable model, and demonstrated its accuracy. The CDSS detected hematologic OD with a sensitivity of 0.821 (95% CI: 0.708–0.904) and a specificity of 0.970 (95% CI: 0.942–0.987).

Conclusion  We could confirm our approach for designing an interoperable CDSS as reproducible and transferable to other critical diseases. Our findings are of direct practical relevance, as they present one of the first interoperable CDSS modules that detect pediatric SIRS/sepsis-associated hematologic OD.

Forecasting admissions in psychiatric hospitals before and during Covid-19: a retrospective study with routine data

The COVID-19 pandemic has strong effects on most health care systems. Forecasting of admissions can help for the efficient organisation of hospital care. We aimed to forecast the number of admissions to psychiatric hospitals before and during the COVID-19 pandemic and we compared the performance of machine learning models and time series models. This would eventually allow to support timely resource allocation for optimal treatment of patients. We used admission data from 9 psychiatric hospitals in Germany between 2017 and 2020. We compared machine learning models with time series models in weekly, monthly and yearly forecasting before and during the COVID-19 pandemic. A total of 90,686 admissions were analysed. The models explained up to 90% of variance in hospital admissions in 2019 and 75% in 2020 with the effects of the COVID-19 pandemic. The best models substantially outperformed a one-step seasonal naïve forecast (seasonal mean absolute scaled error (sMASE) 2019: 0.59, 2020: 0.76). The best model in 2019 was a machine learning model (elastic net, mean absolute error (MAE): 7.25). The best model in 2020 was a time series model (exponential smoothing state space model with Box-Cox transformation, ARMA errors and trend and seasonal components, MAE: 10.44). Models forecasting admissions one week in advance did not perform better than monthly and yearly models in 2019 but they did in 2020. The most important features for the machine learning models were calendrical variables. Model performance did not vary much between different modelling approaches before the COVID-19 pandemic and established forecasts were substantially better than one-step seasonal naïve forecasts. However, weekly time series models adjusted quicker to the COVID-19 related shock effects. In practice, multiple individual forecast horizons could be used simultaneously, such as a yearly model to achieve early forecasts for a long planning period and weekly models to adjust quicker to sudden changes.

Early Career Support for Biomedical Exchange Students with an International Mentor-to-Mentor Concept - The Biomedical Education Program (BMEP)

In medicine, many international exchange opportunities exist, yet often only towards the end of the course of study. Opportunities for students to gain high-level international research experience early during the studies are rare. A good student-mentor relationship during a research stay abroad is a key factor for scientific success. The aims of this paper are to report on an international exchange and education program that has funded more than 700 students and has been carefully developed and advanced over more than 40 years, its mentor-to-mentor concept and potential success factors for building and maintain such programs. A summary of the history, the concept and the experiences of students is provided, along with a discussion of evaluation results and success factors. The Biomedical Education Program (BMEP) team has - within the last seven years of leadership by the authors - selected and funded 83 German students from different biomedical studies who went abroad for research projects. Preliminary evaluation results show a high degree of satisfaction with the program and its mentor-to-mentor concept, which we deem to be the key to success. Further factors include continued funding, determination, self-organization and assertiveness, an excellent alumni network and a meticulous selection process for both, students and hosts. Further, more detailed evaluation of survey results has to follow. Our results may support the build-up of similar exchange programs.

It's All in the Mix: A New Interprofessional, Blended-Learning Masters' Program for Biomedical Data Science Addressing Physicians and Students from Life Sciences - Didactic Concept and First Experiences

Progress in methods for biomedical research, such as multi-omics analyses and in data-driven healthcare, such as new procedures in diagnostic imaging lead, along with the rising availability of additional data sources, to a growing demand for experts in biomedical data analysis. Addressing this need in academic education and the challenge of interdisciplinary teamwork in the biomedical domain, the authors have designed and implemented a new Master's program for biomedical data science that accepts students with different educational backgrounds, medical doctors, veterinarians and students with a Bachelor's degree in life sciences, and incorporates blended learning. This paper aims to present the didactic concept of the program, report on feedback from the students and first evaluation results, and discuss the benefits and drawbacks of this approach. Our results show that the program is well-accepted by the students, who stress the benefits of working in interprofessional teams, the option for part-time study along with their jobs with flexible learning opportunities, and of good and intensive interaction offers with their peers and teachers. Readjustments are necessary to improve tutoring support and alignment of content among distinct modules and to decrease workload peaks. While our evaluation results are still preliminary, we are convinced that our approach of mostly online offers, yet with a strong focus on teamwork, practical exercises guided by experts and communication skills, may serve to educate students to be well-prepared for their future tasks and operations in biomedical data science, in research, clinical care and industry.

Data Literacy in Medical Education - An Expedition into the World of Medical Data

With the advancing digitization in medicine, digital medical data is playing an increasingly important role in health care and research, which is why data literacy must already be taught in medical education. To this end, a 28-hour online elective for medical students - following a constructivist approach - has been implemented. It teaches learners different aspects of data literacy for a critical collection and use of sensitive medical data. The assessment of the learners' reflections on the course topics shows, on the one hand, the importance of data literacy from learners' perspective and, on the other hand, the importance of taking an overarching and coherent view of medical data. In further curricular courses, such as medical ethics, and statistics, special themes are to be deepened in an application-oriented manner.

Implementation of a Mobile Application in Acute Stroke Care Documentation

Acute stroke care is a time-critical process. Improving communication and documentation process may support a positive effect on medical outcome. To achieve this goal, a new system using a mobile application has been integrated into existing infrastructure at Hannover Medical School (MHH). Within a pilot project, this system has been brought into clinical daily routine in February 2022. Insights generated may support further applications in clinical use-cases.

Tracing COVID-19 Infection Chains Within Healthcare Institutions - Another Brick in the Wall Against SARS-CoV-2

Early anticipation of COVID-19 infection chains within hospitals is of high importance for initiating suitable measures at the right time. Infection control specialists can be supported by application systems able of consolidating and analyzing heterogeneous, up-to-now non-standardized and distributed data needed for tracking COVID-19 infections and infected patients' hospital contacts. We developed a system, Co-Surv-SmICS, assisting in infection chain detection, in an open and standards-based way to ensure reusability of the system across institutions. Data is modelled in alignment to various national modelling initiatives and consensus data definitions, queried in a standardized way by the use of OpenEHR as information modelling standard and its associated model-based query language, analyzed and interactively visualized in the application. A first version has been published and will be enhanced with further features and evaluated in detail with regard to its potentials to support specialists during their work against SARS-CoV-2.

The COVID-19 Data Exchange Platform of the German University Medicine

COVID-19 has challenged the healthcare systems worldwide. To quickly identify successful diagnostic and therapeutic approaches large data sharing approaches are inevitable. Though organizational clinical data are abundant, many of them are available only in isolated silos and largely inaccessible to external researchers. To overcome and tackle this challenge the university medicine network (comprising all 36 German university hospitals) has been founded in April 2020 to coordinate COVID-19 action plans, diagnostic and therapeutic strategies and collaborative research activities. 13 projects were initiated from which the CODEX project, aiming at the development of a Germany-wide Covid-19 Data Exchange Platform, is presented in this publication. We illustrate the conceptual design, the stepwise development and deployment, first results and the current status.

Fallzahlen, Re-Identifikation und der technische Datenschutz

Die SARS-CoV-2-Pandemie hat viele sehr spezielle Fragen des Datenschutzes und der Datensicherheit aufgeworfen. Der vorliegende Beitrag widmet sich den mit der Veröffentlichung von Infiziertenzahlen verbundenen Re-Identifikationsrisiken. Er zeigt einen Weg auf, diese Risiken mit Mitteln des technischen Datenschutzes zu reduzieren, um sowohl das öffentliche Informationsbedürfnis zu befriedigen als auch das informelle Selbstbestimmungsrecht der Betroffenen zu wahren.

Developing a Data Driven Approach for Early Detection of SIRS in Pediatric Intensive Care Using Automatically Labeled Training Data

Critical care can benefit from analyzing data by machine learning approaches for supporting clinical routine and guiding clinical decision-making. Developing data-driven approaches for an early detection of systemic inflammatory response syndrome (SIRS) in patients of pediatric intensive care and exploring the possibility of an approach using training data sets labeled automatically beforehand by knowledge-based approaches rather than clinical experts. Using naïve Bayes classifier and an artificial neuronal network (ANN), trained with real data labeled by (1) domain experts ad (2) a knowledge-based decision support system (CDSS). Accuracies were evaluated by the data set labeled by domain experts using a 10-fold cross validation. The ANN approach trained with data labeled by domain experts yielded a specificity of 0.9139 and sensitivity of 0.8979, whereas the approach trained with a data set labeled by a knowledge-based CDSS achieves a specificity of 0.9220 and a sensitivity of 0.8887. ANN yielded promising results for data-driven detection of pediatric SIRS with real data. Our comparison shows the feasibility of using training data labeled automatically by knowledge-based approaches rather than manually allocated by experts.

Factors for Individualization of Therapeutic Exercises for the Design of Health-Enabling Technologies

Designing health-enabling technologies (HETs) to support individualized physiotherapeutic exercises requires comprehensive knowledge of bio-psycho-social factors to be considered. Therefore, this review identified factors for individualization of therapeutic exercises in patients with musculoskeletal shoulder disorders in peer-reviewed articles searched in MEDLINE. The final full-text analysis included 16 of 335 search results and extracted nineteen main categories of individualization factors. The most frequently identified main categories include progression of exercises, exercise framework, and assessment. An iterative approach with constant reassessments represents the key principle for the process of individualization. Categories that are difficult to standardize were rarely mentioned, but should also be considered. The identified factors can improve the design-process of HETs by sensitizing developers, enable further formal modelling, and support communication between developers, physiotherapists, and patients.

openEHR-to-FHIR: Converting openEHR Compositions to Fast Healthcare Interoperability Resources (FHIR) for the German Corona Consensus Dataset (GECCO)

The German Corona Consensus (GECCO) established a uniform dataset in FHIR format for exchanging and sharing interoperable COVID-19 patient specific data between health information systems (HIS) for universities. For sharing the COVID-19 information with other locations that use openEHR, the data are to be converted in FHIR format. In this paper, we introduce our solution through a web-tool named "openEHR-to-FHIR" that converts compositions from an openEHR repository and stores in their respective GECCO FHIR profiles. The tool provides a REST web service for ad hoc conversion of openEHR compositions to FHIR profiles.

Does an Audiovisual Dissection Manual Improve Medical Students' Learning in the Gross Anatomy Dissection Course?

The gross anatomy dissection course is considered to be one of the most important subjects in medical school. Advancing technology facilitates the production of e-learning material that can improve the learning of topographic anatomy during the course. The purpose of this study was to examine a locally produced audiovisual dissection manual's effects on performance in dissection, formal knowledge gained, motivation, emotions, learning behavior, and learning efficiency of the medical students. The results, combined with the total effort put into the production of the manual, should support decisions on further implementation of this kind of audiovisual e-learning resource into the university's curriculum. First-year medical students (n = 279) were randomly divided into three groups for two weeks within the regular dissection course hours during the dissection of the anterior and posterior triangles of the neck. Two groups received an audiovisual dissection manual (n = 96) or an improved written manual (n = 94) as an intervention, the control group (n = 89) received the standard dissection manual. After dissection, each student filled out tests and surveys and their dissections were evaluated. The audiovisual dissection manual did not have any significant positive effects on the examined parameters. The effects of the audiovisual dissection manual on the medical students' learning experience, as observed in this study, did not support further curriculum implementation of this kind of e-learning resource. This study can serve as an orientation for further evaluation and design of e-learning resources for the gross anatomy dissection course.

Pharmacotherapy, drug-drug interactions and potentially inappropriate medication in depressive disorders

Introduction

The aim of this study was to describe the number and type of drugs used to treat depressive disorders in inpatient psychiatry and to analyse the determinants of potential drug-drug interactions (pDDI) and potentially inappropriate medication (PIM).

Methods

Our study was part of a larger pharmacovigilance project funded by the German Innovation Funds. It included all inpatients with a main diagnosis in the group of depressive episodes (F32, ICD-10) or recurrent depressive disorders (F33) discharged from eight psychiatric hospitals in Germany between 1 October 2017 and 30 September 2018 or between 1 January and 31 December 2019.

Results

The study included 14,418 inpatient cases. The mean number of drugs per day was 3.7 (psychotropic drugs = 1.7; others = 2.0). Thirty-one percent of cases received at least five drugs simultaneously (polypharmacy). Almost one half of all cases received a combination of multiple antidepressant drugs (24.8%, 95% CI 24.1%–25.5%) or a treatment with antidepressant drugs augmented by antipsychotic drugs (21.9%, 95% CI 21.3%–22.6%). The most frequently used antidepressants were selective serotonin reuptake inhibitors, followed by serotonin and norepinephrine reuptake inhibitors and tetracyclic antidepressants. In multivariate analyses, cases with recurrent depressive disorders and cases with severe depression were more likely to receive a combination of multiple antidepressant drugs (Odds ratio recurrent depressive disorder: 1.56, 95% CI 1.41–1.70, severe depression 1.33, 95% CI 1.18–1.48). The risk of any pDDI and PIM in elderly patients increased substantially with each additional drug (Odds Ratio: pDDI 1.32, 95% CI: 1.27–1.38, PIM 1.18, 95% CI: 1.14–1.22) and severity of disease (Odds Ratio per point on CGI-Scale: pDDI 1.29, 95% CI: 1.11–1.46, PIM 1.27, 95% CI: 1.11–1.44), respectively.

Conclusion

This study identified potential sources and determinants of safety risks in pharmacotherapy of depressive disorders and provided additional data which were previously unavailable. Most inpatients with depressive disorders receive multiple psychotropic and non-psychotropic drugs and pDDI and PIM are relatively frequent. Patients with a high number of different drugs must be intensively monitored in the management of their individual drug-related risk-benefit profiles.

Polypharmacy and the risk of drug-drug interactions and potentially inappropriate medications in hospital psychiatry

PURPOSE

The aim of this study was to analyze the epidemiology of polypharmacy in hospital psychiatry. Another aim was to investigate predictors of the number of drugs taken and the associated risks of drug-drug interactions and potentially inappropriate medications in the elderly.

METHODS

Daily prescription data were obtained from a pharmacovigilance project sponsored by the Innovations Funds of the German Federal Joint Committee.

RESULTS

The study included 47 071 inpatient hospital cases from eight different study centers. The mean number of different drugs during the entire stay was 6.1 (psychotropic drugs = 2.7; others = 3.4). The mean number of drugs per day was 3.8 (psychotropic drugs = 1.6; others = 2.2). One third of cases received at least five different drugs per day on average during their hospital stay (polypharmacy). Fifty-one percent of patients received more than one psychotropic drug simultaneously. Hospital cases with polypharmacy were 18 years older (p < 0.001), more likely to be female (52% vs. 40%, p < 0.001) and had more comorbidities (5 vs. 2, p < 0.001) than hospital cases without polypharmacy. The risks of drug-drug interactions (OR = 3.7; 95% CI = 3.5-3.9) and potentially inappropriate medication use in the elderly (OR = 2.2; CI = 1.9-2.5) substantially increased in patients that received polypharmacy.

CONCLUSION

Polypharmacy is frequent in clinical care. The number of used drugs is a proven risk factor of adverse drug reactions due to drug-drug interactions and potentially inappropriate medication use in the elderly. The potential interactions and the specific pharmacokinetics and -dynamics of older patients should always be considered when multiple drugs are used.

Transformation of microbiology data into a standardised data representation using OpenEHR

The spread of multidrug resistant organisms (MDRO) is a global healthcare challenge. Nosocomial outbreaks caused by MDRO are an important contributor to this threat. Computer-based applications facilitating outbreak detection can be essential to address this issue. To allow application reusability across institutions, the various heterogeneous microbiology data representations needs to be transformed into standardised, unambiguous data models. In this work, we present a multi-centric standardisation approach by using openEHR as modelling standard. Data models have been consented in a multicentre and international approach. Participating sites integrated microbiology reports from primary source systems into an openEHR-based data platform. For evaluation, we implemented a prototypical application, compared the transformed data with original reports and conducted automated data quality checks. We were able to develop standardised and interoperable microbiology data models. The publicly available data models can be used across institutions to transform real-life microbiology reports into standardised representations. The implementation of a proof-of-principle and quality control application demonstrated that the new formats as well as the integration processes are feasible. Holistic transformation of microbiological data into standardised openEHR based formats is feasible in a real-life multicentre setting and lays the foundation for developing cross-institutional, automated outbreak detection systems.

Predicting the risk of drug-drug interactions in psychiatric hospitals: a retrospective longitudinal pharmacovigilance study

OBJECTIVES

The aim was to use routine data available at a patient's admission to the hospital to predict polypharmacy and drug-drug interactions (DDI) and to evaluate the prediction performance with regard to its usefulness to support the efficient management of benefits and risks of drug prescriptions.

DESIGN

Retrospective, longitudinal study.

SETTING

We used data from a large multicentred pharmacovigilance project carried out in eight psychiatric hospitals in Hesse, Germany.

PARTICIPANTS

Inpatient episodes consecutively discharged between 1 October 2017 and 30 September 2018 (year 1) or 1 January 2019 and 31 December 2019 (year 2).

OUTCOME MEASURES

The proportion of rightly classified hospital episodes.

METHODS

We used gradient boosting to predict respective outcomes. We tested the performance of our final models in unseen patients from another calendar year and separated the study sites used for training from the study sites used for performance testing.

RESULTS

A total of 53 909 episodes were included in the study. The models' performance, as measured by the area under the receiver operating characteristic, was 'excellent' (0.83) and 'acceptable' (0.72) compared with common benchmarks for the prediction of polypharmacy and DDI, respectively. Both models were substantially better than a naive prediction based solely on basic diagnostic grouping.

CONCLUSION

This study has shown that polypharmacy and DDI can be predicted from routine data at patient admission. These predictions could support an efficient management of benefits and risks of hospital prescriptions, for instance by including pharmaceutical supervision early after admission for patients at risk before pharmacological treatment is established.

Predicting the risk of drug-drug interactions in psychiatric hospitals

Introduction

The most common medical decision is the prescription of medicines. More than 130 different drugs with proven efficacy are currently available for the treatment of patients with mental disorders.

Objectives

The aim was to use routine data available at a patient’s admission to the hospital to predict polypharmacy and drug-drug interactions (DDI).

Methods

The study used data obtained from a large clinical pharmacovigilance study sponsored by the Innovations Funds of the German Federal Joint Committee. It included all inpatient episodes admitted to eight psychiatric hospitals in Hesse, Germany, over two years. We used gradient boosting to predict respective outcomes. We tested the performance of our final models in unseen patients from another calendar year and separated the study sites used for training from the study sites used for performance testing.

Results

A total of 53,909 episodes were included in the study. The models’ performance, as measured by the area under the ROC, was “excellent” (0.83) and “acceptable” (0.72) compared to common benchmarks for the prediction of polypharmacy and DDI, respectively. Both models were substantially better than a naive prediction based solely on basic diagnostic grouping.

Conclusions

This study has shown that polypharmacy and DDI at a psychiatric hospital can be predicted from routine data at patient admission. These predictions could support an efficient management of benefits and risks of hospital prescriptions, for instance by including pharmaceutical supervision early after admission for patients at risk before pharmacological treatment is established

Disclosure

This work was supported by the Innovations Funds of the German Federal Joint Committee (grant number: 01VSF16009). The funding body played no role in the design of the study and collection, analysis, and interpretation of data and in writing the manuscrip

A method for interoperable knowledge-based data quality assessment

BACKGROUND

Assessing the quality of healthcare data is a complex task including the selection of suitable measurement methods (MM) and adequately assessing their results.

OBJECTIVES

To present an interoperable data quality (DQ) assessment method that formalizes MMs based on standardized data definitions and intends to support collaborative governance of DQ-assessment knowledge, e.g. which MMs to apply and how to assess their results in different situations.

METHODS

We describe and explain central concepts of our method using the example of its first real world application in a study on predictive biomarkers for rejection and other injuries of kidney transplants. We applied our open source tool-openCQA-that implements our method utilizing the openEHR specifications. Means to support collaborative governance of DQ-assessment knowledge are the version-control system git and openEHR clinical information models.

RESULTS

Applying the method on the study's dataset showed satisfactory practicability of the described concepts and produced useful results for DQ-assessment.

CONCLUSIONS

The main contribution of our work is to provide applicable concepts and a tested exemplary open source implementation for interoperable and knowledge-based DQ-assessment in healthcare that considers the need for flexible task and domain specific requirements.

Clinical evaluation of an interoperable clinical decision-support system for the detection of systemic inflammatory response syndrome in critically ill children

Background

Systemic inflammatory response syndrome (SIRS) is defined as a non-specific inflammatory process in the absence of infection. SIRS increases susceptibility for organ dysfunction, and frequently affects the clinical outcome of affected patients. We evaluated a knowledge-based, interoperable clinical decision-support system (CDSS) for SIRS detection on a pediatric intensive care unit (PICU).

Methods

The CDSS developed retrieves routine data, previously transformed into an interoperable format, by using model-based queries and guideline- and knowledge-based rules. We evaluated the CDSS in a prospective diagnostic study from 08/2018–03/2019. 168 patients from a pediatric intensive care unit of a tertiary university hospital, aged 0 to 18 years, were assessed for SIRS by the CDSS and by physicians during clinical routine. Sensitivity and specificity (when compared to the reference standard) with 95% Wald confidence intervals (CI) were estimated on the level of patients and patient-days.

Results

Sensitivity and specificity was 91.7% (95% CI 85.5–95.4%) and 54.1% (95% CI 45.4–62.5%) on patient level, and 97.5% (95% CI 95.1–98.7%) and 91.5% (95% CI 89.3–93.3%) on the level of patient-days. Physicians’ SIRS recognition during clinical routine was considerably less accurate (sensitivity of 62.0% (95% CI 56.8–66.9%)/specificity of 83.3% (95% CI 80.4–85.9%)) when measurd on the level of patient-days. Evaluation revealed valuable insights for the general design of the CDSS as well as specific rule modifications. Despite a lower than expected specificity, diagnostic accuracy was higher than the one in daily routine ratings, thus, demonstrating high potentials of using our CDSS to help to detect SIRS in clinical routine.

Conclusions

We successfully evaluated an interoperable CDSS for SIRS detection in PICU. Our study demonstrated the general feasibility and potentials of the implemented algorithms but also some limitations. In the next step, the CDSS will be optimized to overcome these limitations and will be evaluated in a multi-center study.

Trial registration: NCT03661450 (ClinicalTrials.gov); registered September 7, 2018.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12911-021-01428-7.

Health-Enabling Technologies to Assist Patients With Musculoskeletal Shoulder Disorders When Exercising at Home: Scoping Review

Background

Health-enabling technologies (HETs) are information and communication technologies that promote individual health and well-being. An important application of HETs is telerehabilitation for patients with musculoskeletal shoulder disorders. Currently, there is no overview of HETs that assist patients with musculoskeletal shoulder disorders when exercising at home.

Objective

This scoping review provides a broad overview of HETs that assist patients with musculoskeletal shoulder disorders when exercising at home. It focuses on concepts and components of HETs, exercise program strategies, development phases, and reported outcomes.

Methods

The search strategy used Medical Subject Headings and text words related to the terms upper extremity, exercises, and information and communication technologies. The MEDLINE, Embase, IEEE Xplore, CINAHL, PEDro, and Scopus databases were searched. Two reviewers independently screened titles and abstracts and then full texts against predefined inclusion and exclusion criteria. A systematic narrative synthesis was performed. Overall, 8988 records published between 1997 and 2019 were screened. Finally, 70 articles introducing 56 HETs were included.

Results

Identified HETs range from simple videoconferencing systems to mobile apps with video instructions to complex sensor-based technologies. Various software, sensor hardware, and hardware for output are in use. The most common hardware for output are PC displays (in 34 HETs). Microsoft Kinect cameras in connection with related software are frequently used as sensor hardware (in 27 HETs). The identified HETs provide direct or indirect instruction, monitoring, correction, assessment, information, or a reminder to exercise. Common parameters for exercise instructions are a patient’s range of motion (in 43 HETs), starting and final position (in 32 HETs), and exercise intensity (in 20 HETs). In total, 48 HETs provide visual instructions for the exercises; 29 HETs report on telerehabilitation aspects; 34 HETs only report on prototypes; and 15 HETs are evaluated for technical feasibility, acceptance, or usability, using different assessment instruments. Efficacy or effectiveness is demonstrated for only 8 HETs. In total, 18 articles report on patients’ evaluations. An interdisciplinary contribution to the development of technologies is found in 17 HETs.

Conclusions

There are various HETs, ranging from simple videoconferencing systems to complex sensor-based technologies for telerehabilitation, that assist patients with musculoskeletal shoulder disorders when exercising at home. Most HETs are not ready for practical use. Comparability is complicated by varying prototype status, different measurement instruments, missing telerehabilitation aspects, and few efficacy studies. Consequently, choosing an HET for daily use is difficult for health care professionals and decision makers. Prototype testing, usability, and acceptance tests with the later target group under real-life conditions as well as efficacy or effectiveness studies with patient-relevant core outcomes for every promising HET are required. Furthermore, health care professionals and patients should be more involved in the product design cycle to consider relevant practical aspects.

In Search of Patient Zero: Visual Analytics of Pathogen Transmission Pathways in Hospitals

Pathogen outbreaks (i.e., outbreaks of bacteria and viruses) in hospitals can cause high mortality rates and increase costs for hospitals significantly. An outbreak is generally noticed when the number of infected patients rises above an endemic level or the usual prevalence of a pathogen in a defined population. Reconstructing transmission pathways back to the source of an outbreak - the patient zero or index patient - requires the analysis of microbiological data and patient contacts. This is often manually completed by infection control experts. We present a novel visual analytics approach to support the analysis of transmission pathways, patient contacts, the progression of the outbreak, and patient timelines during hospitalization. Infection control experts applied our solution to a real outbreak of Klebsiella pneumoniae in a large German hospital. Using our system, our experts were able to scale the analysis of transmission pathways to longer time intervals (i.e., several years of data instead of days) and across a larger number of wards. Also, the system is able to reduce the analysis time from days to hours. In our final study, feedback from twenty-five experts from seven German hospitals provides evidence that our solution brings significant benefits for analyzing outbreaks.

Risk factors for Covid-19 severity and fatality: a structured literature review

PURPOSE

Covid-19 is a global threat that pushes health care to its limits. Since there is neither a vaccine nor a drug for Covid-19, people with an increased risk for severe and fatal courses of disease particularly need protection. Furthermore, factors increasing these risks are of interest in the search of potential treatments. A systematic literature review on the risk factors of severe and fatal Covid-19 courses is presented.

METHODS

The review is carried out on PubMed and a publicly available preprint dataset. For analysis, risk factors are categorized and information regarding the study such as study size and location are extracted. The results are compared to risk factors listed by four public authorities from different countries.

RESULTS

The 28 records included, eleven of which are preprints, indicate that conditions and comorbidities connected to a poor state of health such as high age, obesity, diabetes and hypertension are risk factors for severe and fatal disease courses. Furthermore, severe and fatal courses are associated with organ damages mainly affecting the heart, liver and kidneys. Coagulation dysfunctions could play a critical role in the organ damaging. Time to hospital admission, tuberculosis, inflammation disorders and coagulation dysfunctions are identified as risk factors found in the review but not mentioned by the public authorities.

CONCLUSION

Factors associated with increased risk of severe or fatal disease courses were identified, which include conditions connected with a poor state of health as well as organ damages and coagulation dysfunctions. The results may facilitate upcoming Covid-19 research.

Teamwork, communication and exchange despite Covid-19 - experiences from a digital elective in human medicine studies as part of the HiGHmed project

Introduction: In order to promote training and further education on topics related to the digitization of medicine, the HiGHmeducation consortium is developing online learning modules. These modules could also be offered across locations. For students of human medicine, an elective for the acquisition of data literacy has been implemented. Originally designed as a blended learning offer, the elective was then carried out completely online due to the Covid-19 pandemic. Despite the lack of classroom teaching, the aim was to achieve intensive cooperation between the students. Project description: In the elective, the students worked on a total of 14 learning tasks, so-called e-tivities, which stimulate collaborative work and thus promote the examination of the learning content. These asynchronous learning activities were supplemented by video conferences, in which the students also took on the role of presenters. The teachers accompanied this learning process as e-moderators. Results: In April/May 2020, the elective course was carried out with 12 students entirely online. Despite a workload that was experienced as high, the elective was rated very well by the students. Discussion: The didactic concept of the elective enabled an active engagement with the learning material and the social interaction between the learners. With the digital learning offers, the learners were able to gain new experiences which are also of professional relevance. Conclusion: The didactic concept of the elective can be transferred to other courses. Future studies must show which long-term learning effects can be generated by digital teaching based on teamwork, communication and exchange.

Designing an openEHR-Based Pipeline for Extracting and Standardizing Unstructured Clinical Data Using Natural Language Processing

Background  Merging disparate and heterogeneous datasets from clinical routine in a standardized and semantically enriched format to enable a multiple use of data also means incorporating unstructured data such as medical free texts. Although the extraction of structured data from texts, known as natural language processing (NLP), has been researched at least for the English language extensively, it is not enough to get a structured output in any format. NLP techniques need to be used together with clinical information standards such as openEHR to be able to reuse and exchange still unstructured data sensibly.

Objectives  The aim of the study is to automatically extract crucial information from medical free texts and to transform this unstructured clinical data into a standardized and structured representation by designing and implementing an exemplary pipeline for the processing of pediatric medical histories.

Methods  We constructed a pipeline that allows reusing medical free texts such as pediatric medical histories in a structured and standardized way by (1) selecting and modeling appropriate openEHR archetypes as standard clinical information models, (2) defining a German dictionary with crucial text markers serving as expert knowledge base for a NLP pipeline, and (3) creating mapping rules between the NLP output and the archetypes. The approach was evaluated in a first pilot study by using 50 manually annotated medical histories from the pediatric intensive care unit of the Hannover Medical School.

Results  We successfully reused 24 existing international archetypes to represent the most crucial elements of unstructured pediatric medical histories in a standardized form. The self-developed NLP pipeline was constructed by defining 3.055 text marker entries, 132 text events, 66 regular expressions, and a text corpus consisting of 776 entries for automatic correction of spelling mistakes. A total of 123 mapping rules were implemented to transform the extracted snippets to an openEHR-based representation to be able to store them together with other structured data in an existing openEHR-based data repository. In the first evaluation, the NLP pipeline yielded 97% precision and 94% recall.

Conclusion  The use of NLP and openEHR archetypes was demonstrated as a viable approach for extracting and representing important information from pediatric medical histories in a structured and semantically enriched format. We designed a promising approach with potential to be generalized, and implemented a prototype that is extensible and reusable for other use cases concerning German medical free texts. In a long term, this will harness unstructured clinical data for further research purposes such as the design of clinical decision support systems. Together with structured data already integrated in openEHR-based representations, we aim at developing an interoperable openEHR-based application that is capable of automatically assessing a patient's risk status based on the patient's medical history at time of admission.

A Structured Measurement of Highly Synchronous Real-Time Ballistocardiography Signal Data of Heart Failure Patients

Ballistocardiography (BCG) has gained more attention due to the fundamental goal of medical intervention in diagnostics and follow-up. BCG is particularly suitable for the study of heart failure, which a recent study has shown. The results of this working group shall be validated and reproduced with another study trial. Therefore, acceleration sensor prototypes will be placed on various parts of the patient's body and be connected to a computer unit, which allows a high data quality and high signal resolution. A temporal shift of only 20 ns ensures real-time measurement of BCG parameters. The reference measurement will be done with a 12-channel ECG. The study will include patients with heart failure. All conducted tests take place as part of the diagnostic-therapeutic routine. The only change in the procedure concerns the additional equipment with the measuring sensors. The results will be the validation of the data from the other working group, as well as the information about the choice of sensors and clock frequency, the measuring points and the needed features for early detection of heart failure in BCG signals.

Trodden Lanes or New Paths: Ballisto- and Seismocardiography Till Now

Ballisto- and seismocardiography (BCG/SCG) are methods of studying blood circulation and cardiac function by using the vibration measurements at the body surface, e.g. via accelerometers. The aim of this work is to show the current relevance of BCG/SCG for the target medical diagnostics. To reach this goal and to examine the relevance, an overall search for all BCG and SCG articles in the databases PubMed and IEEEXplore was first carried out ("ballistocardiography OR seismocardiography") for the years till 2019. The results of this literature study show, overall 425 papers for the years from 2003 till 2019, with BCG (317) as significantly stronger represented than SCG (120). The distribution of the included subjects shows that a smaller group (n<=10) of mostly healthy people is more common. Last but not least, we examined which sensors have been included in the articles since 2003, with the result that accelerometers, whether as self-developed prototypes or installed in smartphones, were used in slightly less than 50% of the articles found. The differences in the numbers of publications between BCG and SCG may also be due to the distinction's complexity between BCG, which is more intuitive, and SCG. Looking at the number and distribution of included subjects, it is noticeable that this is rather low and primarily healthy subjects are used. However, the publication increase indicates that we are at a threshold in this topic and actual benefit to medicine.

Using openEHR Archetypes for Automated Extraction of Numerical Information from Clinical Narratives

Up to 80% of medical information is documented by unstructured data such as clinical reports written in natural language. Such data is called unstructured because the information it contains cannot be retrieved automatically as straightforward as from structured data. However, we assume that the use of this flexible kind of documentation will remain a substantial part of a patient's medical record, so that clinical information systems have to deal appropriately with this type of information description. On the other hand, there are efforts to achieve semantic interoperability between clinical application systems through information modelling concepts like HL7 FHIR or openEHR. Considering this, we propose an approach to transform unstructured documented information into openEHR archetypes. Furthermore, we aim to support the field of clinical text mining by recognizing and publishing the connections between openEHR archetypes and heterogeneous phrasings. We have evaluated our method by extracting the values to three openEHR archetypes from unstructured documents in English and German language.

Clinical Decision-Support Systems for Detection of Systemic Inflammatory Response Syndrome, Sepsis, and Septic Shock in Critically Ill Patients: A Systematic Review

BACKGROUND

The design of computerized systems able to support automated detection of threatening conditions in critically ill patients such as systemic inflammatory response syndrome (SIRS) and sepsis has been fostered recently. The increase of research work in this area is due to both the growing digitalization in health care and the increased appreciation of the importance of early sepsis detection and intervention. To be able to understand the variety of systems and their characteristics as well as performances, a systematic literature review is required. Existing reviews on this topic follow a rather restrictive searching methodology or they are outdated. As much progress has been made during the last 5 years, an updated review is needed to be able to keep track of current developments in this area of research.

OBJECTIVES

To provide an overview about current approaches for the design of clinical decision-support systems (CDSS) in the context of SIRS, sepsis, and septic shock, and to categorize and compare existing approaches.

METHODS

A systematic literature review was performed in accordance with the preferred reporting items for systematic reviews and meta-analyses (PRISMA) statement. Searches for eligible articles were conducted on five electronic bibliographic databases, including PubMed/MEDLINE, IEEE Xplore, Embase, Scopus, and ScienceDirect. Initial results were screened independently by two reviewers based on clearly defined eligibility criteria. A backward as well as an updated search enriched the initial results. Data were extracted from included articles and presented in a standardized way. Articles were classified into predefined categories according to characteristics extracted previously. The classification was performed according to the following categories: clinical setting including patient population and mono- or multicentric study, support type of the system such as prediction or detection, systems characteristics such as knowledge- or data-driven algorithms used, evaluation of methodology, and results including ground truth definition, sensitivity, and specificity. All results were assessed qualitatively by two reviewers.

RESULTS

The search resulted in 2,373 articles out of which 55 results were identified as eligible. Over 80% of the articles describe monocentric studies. More than 50% include adult patients, and only four articles explicitly report the inclusion of pediatric patients. Patient recruitment often is very selective, which can be observed from highly varying inclusion and exclusion criteria. The task of disease detection is covered in 62% of the articles; prediction of upcoming conditions in 33%. Sepsis is covered in 67% of the articles, SIRS as sole entity in only 4%, whereas 27% focus on severe sepsis and/or septic shock. The most common combinations of categories "algorithm used" and "support type" are knowledge-based detection of sepsis and data-driven prediction of sepsis. In evaluations, manual chart review (38%) and diagnosis coding (29%) represent the most frequently used ground truth definitions; most studies present a sample size between 10,001 and 100,000 cases (31%) and performances highly differ with only five articles presenting sensitivities and specificities above 90%; four of them using knowledge-based rather than machine learning algorithms. The presentations of holistic CDSS approaches, including technical implementation details, system interfaces, and data and interoperability aspects enabling the use of CDSS in routine settings are missing in nearly all articles.

CONCLUSIONS

The review demonstrated the high variety of research in this context successfully. A clear trend is observable toward the use of data-driven algorithms, and a lack of research could be identified in covering the pediatric population as well as acknowledging SIRS as an independent and threatening condition. The quality as well as the significance of the presented evaluations for assessing the performances of the algorithms in clinical routine settings are often not meeting the current standard of scientific work. Our future interest will be concentrated on these realistic settings by implementing and evaluating SIRS detection approaches as well as considering factors to make the CDSS useable in clinical routine from both technical and medical perspectives.

Design of a joint research data platform: A use case for severity assessment

Severity assessment in animal models is a data-driven process. We therefore present a use case for building a repository for interlaboratory collaboration with the potential of uploading specific content, making group announcements and internal prepublication discussions. We clearly show that it is possible to offer such a structure with minimal effort and a basic understanding of web-based services, also taking into account the human factor in individual data collection. The FOR2591 Online Repository serves as a blueprint for other groups, so that one day not only will data sharing among consortium members be improved but the transition from the private to the persistent domain will also be easier.

Extending a Knowledge-Based System with Learning Capacity

Informal caregivers often complain about missing knowledge. A knowledge-based personalized educational system is developed, which provides caregiving relatives with the information needed. Yet, evaluation against domain experts indicated, that parts of the knowledge-base are incorrect. To overcome these problems the system can be extended by a learning capacity and then be trained further utilizing feedback from real informal caregivers. To extend the existing system an artificial neural network was trained to represent a large part of the knowledge-based approach. This paper describes the found artificial neural network's structure and the training process. The found neural network structure is not deep but very wide. The training terminated after 374.700 epochs with a mean squared error of 7.731 ∗ 10-8 for the end validation set. The neural network represents the parts of the knowledge-based approach and can now be retrained with user feedback, which will be collected during a system test in April and May 2019.

The nephrology eHealth-system of the metropolitan region of Hannover for digitalization of care, establishment of decision support systems and analysis of health care quality

Background

Even though a high demand for sector spanning communication exists, so far no eHealth platform for nephrology is established within Germany. This leads to insufficient communication between medical providers and therefore suboptimal nephrologic care. In addition, Clinical Decision Support Systems have not been used in Nephrology until now.

Methods

The aim of NEPHRO-DIGITAL is to create a eHealth platform in the Hannover region that facilitates integrated, cross-sectoral data exchange and includes teleconsultation between outpatient nephrology, primary care, pediatricians and nephrology clinics to reduce communication deficits and prevent data loss, and to enable the creation and implementation of an interoperable clinical decision support system. This system will be based on input data from multiple sources for early identification of patients with cardiovascular comorbidity and progression of renal insufficiency. Especially patients will be able to enter and access their own data. A transfer to a second nephrology center (metropolitan region of Erlangen-Nuremburg) is included in the study to prove feasibility and scalability of the approach.

Discussion

A decision support system should lead to earlier therapeutic interventions and thereby improve the prognosis of patients as well as their treatment satisfaction and quality of life. The system will be integrated in the data integration centres of two large German university medicine consortia (HiGHmed (highmed.org) and MIRACUM (miracum.org)).

Trial registration

ISRCTN16755335 (09.07.2019).

Evaluating the Validity of a Knowledge-Based System for Proactive Knowledge Transfer for Caregiving Relatives

The evaluation of the validity of a knowledge-based system is of great importance during its development. It determines whether the system represents the experts' knowledge correctly. This is highly important, but also particularly difficult, if the expert knowledge is not explicit, but only implicit and tacit. In the following the validity's evaluation of a system for education of caregiving relatives is presented. To evaluate the system's knowledge delivery strategy against the experts' opinion, several fictious characters were created. The evaluation revealed inconsistencies in the knowledge base. After resolving these, the experts' opinion is represented to a large extent by the system. Nevertheless, the used evaluation approach is not capable of detecting all inconsistencies. Therefore, a strong need of a system's learning capacity to integrate feedback from a larger group of real caregiving relatives exists. In addition, a rule-based component, representing disease specific knowledge, should be implemented.

Usage of Accelerometers in the Medical Field of Application and Their Clinical Integration

We conducted a literature review on the use of accelerometers in medical context, on Pubmed and IEEEXplore. This includes 440 relevant articles. The subsequently identified publications were classified with regard to the medical context (prevention, diagnostics, therapy) as well as according to medical-informatics field of application, e.g. activity-tracking, fall prevention/detection and gait analysis. Furthermore, we analyzed their clinical integration or potential for the clinical usage, including both the technical integration into the clinical structures and respective claims and requirements, e.g. privacy or hygiene. This analysis shows five categories ("without indication" to "concrete implementation"). In 90% no statement was made on clinical integration. Only two articles could be found with concrete implementations, but these descriptions are limited to a more conceptual technical side. This poor situation in final clinical integration has to change in the future, because only by the premise "from workbench to bedside" the medical benefit is given.

CADDIE2-evaluation of a clinical decision-support system for early detection of systemic inflammatory response syndrome in paediatric intensive care: study protocol for a diagnostic study

INTRODUCTION

Systemic inflammatory response syndrome (SIRS) is one of the most critical indicators determining the clinical outcome of paediatric intensive care patients. Clinical decision support systems (CDSS) can be designed to support clinicians in detection and treatment. However, the use of such systems is highly discussed as they are often associated with accuracy problems and 'alert fatigue'. We designed a CDSS for detection of paediatric SIRS and hypothesise that a high diagnostic accuracy together with an adequate alerting will accelerate the use. Our study will (1) determine the diagnostic accuracy of the CDSS compared with gold standard decisions created by two blinded, experienced paediatricians, and (2) compare the system's diagnostic accuracy with that of routine clinical care decisions compared with the same gold standard.

METHODS AND ANALYSIS

CADDIE2 is a prospective diagnostic accuracy study taking place at the Department of Pediatric Cardiology and Intensive Care Medicine at the Hannover Medical School; it represents the second step towards our vision of cross-institutional and data-driven decision-support for intensive care environments (CADDIE). The study comprises (1) recruitment of up to 300 patients (start date 1 August 2018), (2) creation of gold standard decisions (start date 1 May 2019), (3) routine SIRS assessments by physicians (starts with recruitment), (4) SIRS assessments by a CDSS (start date 1 May 2019), and (5) statistical analysis with a modified approach for determining sensitivity and specificity and comparing the accuracy results of the different diagnostic approaches (planned start date 1 July 2019).

ETHICS AND DISSEMINATION

Ethics approval was obtained at the study centre (Ethics Committee of Hannover Medical School). Results of the main study will be communicated via publication in a peer-reviewed journal.

TRIAL REGISTRATION NUMBER

ClinicalTrials.gov NCT03661450; Pre-results.

INBED: A Highly Specialized System for Bed-Exit-Detection and Fall Prevention on a Geriatric Ward

OBJECTIVE

In geriatric institutions, the risk of falling of patients is very high and frequently leads to fractures of the femoral neck, which can result in serious consequences and medical costs. With regard to the current numbers of elderly people, the need for smart solutions for the prevention of falls in clinical environments as well as in everyday life has been evolving.

METHODS

Hence, in this paper, we present the Inexpensive Node for bed-exit Detection (INBED), a comprehensive, favourable signaling system for bed-exit detection and fall prevention, to support the clinical efforts in terms of fall reduction. The tough requirements for such a system in clinical environments were gathered in close cooperation with geriatricians.

RESULTS

The conceptional efforts led to a multi-component system with a core wearable device, attached to the patients, to detect several types of movements such as rising, restlessness and-in the worst case-falling. Occurring events are forwarded to the nursing staff immediately by using a modular, self-organizing and dependable wireless infrastructure. Both, the hardware and software of the entire INBED system as well as the particular design process are discussed in detail. Moreover, a trail test of the system is presented.

CONCLUSIONS

The INBED system can help to relieve the nursing staff significantly while the personal freedom of movement and the privacy of patients is increased compared to similar systems.

Machine Learning and Data Analytics in Pervasive Health

INTRODUCTION

This accompanying editorial provides a brief introduction to this focus theme, focused on "Machine Learning and Data Analytics in Pervasive Health".

OBJECTIVE

The innovative use of machine learning technologies combining small and big data analytics will support a better provisioning of healthcare to citizens. This focus theme aims to present contributions at the crossroads of pervasive health technologies and data analytics as key enablers for achieving personalised medicine for diagnosis and treatment purposes.

METHODS

A call for paper was announced to all participants of the "11th International Conference on Pervasive Computing Technologies for Healthcare", to different working groups of the International Medical Informatics Association (IMIA) and European Federation of Medical Informatics (EFMI) and was published in June 2017 on the website of Methods of Information in Medicine. A peer review process was conducted to select the papers for this focus theme.

RESULTS

Four papers were selected to be included in this focus theme. The paper topics cover a broad range of machine learning and data analytics applications in healthcare including detection of injurious subtypes of patient-ventilator asynchrony, early detection of cognitive impairment, effective use of small data sets for estimating the performance of radiotherapy in bladder cancer treatment, and the use negation detection in and information extraction from unstructured medical texts.

CONCLUSIONS

The use of machine learning and data analytics technologies in healthcare is facing a renewed impulse due to the availability of large amounts and new sources of human behavioral and physiological data, such as that captured by mobile and pervasive devices traditionally considered as nonmainstream for healthcare provision and management.

Results of a Literature Review to Prepare Data Modelling in the Context of Kidney Transplant Rejection Diagnosis

Due to demographic change the number of serious kidney diseases and thus required transplantations will increase. The increased demand for donor organs and a decreasing supply of these organs underline the necessity for effective early rejection diagnostic measures to improve the lifetime of transplants. Expert systems might improve rejection diagnostics but for the development of such systems data models are needed that encompass the relevant information to enable optimal data aggregation and evaluation. Results of a literature review concerning published data models and information systems concerned with kidney transplant rejection diagnostic lead to a set of data elements even if no papers could be identified that publish data models explicitly.

Wearable Sensors for Nurses: Which Requirements Have to Be Considered?

To measure and compare the workload of nurses in a clinical setting raises up some questions. On the one hand we worked out which criteria can represent workload and how it can be measured. On the other hand we compile different requirements for wearable sensors. These requirements can be categorized in four groups: data, robustness, hygiene and usability. These results can support the selection of wearable sensors for a survey of the workload of nurses in a clinical setting.

Early Detection of Infection Chains & Outbreaks: Use Case Infection Control

Within the HiGHmed Project there are three medical use cases. The use cases include the scopes cardiology, oncology and infection. They serve to specify the requirements for the development and implementation of a local and federated platform, with the result that data from medical care and research should be retrievable, reusable and interchangeable. The Use Case Infection Control aims to establish an early detection of transmission events as well as clusters and outbreaks of various pathogens. Therefore the use case wants to establish the smart infection control system (SmICS).

Clinical Information Model Based Data Quality Checks: Theory and Example

INTRODUCTION

We describe principles of leveraging clinical information models (CIMs) for data quality (DQ) checks and present the exemplary application of these principles.

METHODS

openEHR compliant CIMs are used to express DQ-checks as constraints. Test setting is the process of extracting, transforming and loading (ETL) assisted ventilation data from two patient data management systems (PDMS) of a pediatric intensive care unit into a local openEHR-based data repository.

RESULTS

A generic component logs aggregated DQ-check results for ~28 million entries. DQ-issue types in the presented results are range-, format- and value set violations.

DISCUSSION

CIMs are suitable means to define DQ-checks for range-, format-, value set and cardinality constraints. However, they cannot constitute a complete solution for standardized DQ-assessment.