Ebook: dHealth 2019 – From eHealth to dHealth
We have all become familiar with the term ‘eHealth’, used to refer to health informatics and the digital aspects of healthcare; but what is dHealth?
This book presents the proceedings of the 13th annual conference on Health Informatics Meets Digital Health (dHealth 2019), held in Vienna, Austria, on 28 – 29 May 2019. In keeping with its interdisciplinary mission, the conference series provides a platform for researchers, practitioners, decision makers and vendors to discuss innovative health informatics and eHealth solutions to improve the quality and efficiency of healthcare using digital technologies.
The subtitle and special focus of dHealth 2019 is ‘from eHealth to dHealth’, which stresses that healthcare will in future become ever more data-driven. While eHealth in general concerns healthcare IT solutions and professional healthcare providers, dHealth addresses broader fields of application in many areas of life, including sensors and sensor informatics, networks, genomics and bioinformatics, data-centered solutions, machine learning, and many more.
The 32 papers included here provide an insight into the state-of-the-art of different aspects of dHealth, including the design and evaluation of user interfaces, patient-centered solutions, electronic health/medical/patient records, machine learning in healthcare and biomedical data analytics, and the book offers the reader an interdisciplinary approach to digital health. It will be of interest to researchers, developers, and healthcare professionals alike.
Since its beginning in 2007, the dHealth conference series is organized by the Austrian Working Group of Health Informatics and eHealth. Each year, this event attracts around 300 participants from academia, industry, government and health care organizations.
In keeping with its interdisciplinary mission, the dHealth conference series provides a platform for researchers, practitioners, decision makers and vendors to discuss innovative health informatics and eHealth solutions to improve the quality and efficiency of healthcare by digital technologies.
A special topic for the dHealth 2019 was “from eHealth to dHealth”, stressing that healthcare will be more and more data-driven in the future. While eHealth in general concerns healthcare IT solutions at professional healthcare providers, dHealth addresses broader fields of application in all areas of life, including sensors, networks, genomics and bioinformatics, data centered solutions, machine learning, etc.
The present proceedings give insights into the state of the art of different aspects of dHealth, including the design and evaluation of user interfaces, patient centered solutions, electronic health/medical/patient records, machine learning in healthcare and biomedical data analytics. These topics address the data path “from sensors to decisions”, providing an interdisciplinary approach to digital health, including aspects of biomedical and sensor informatics.
Diabetes mellitus (DM) is a chronic disease that affects many people in Switzerland and around the world. Once diagnosed, a patient has to continuously monitor blood glucose, manage medications or inject insulin. Technical skills and competencies as well as knowledge on disease management have to be acquired right after being diagnosed. Diabetes consultants support patients in this process and provide educational material. While the process of generating patient-tailored material is currently complex and time consuming, in future, the eDiabetes platform can help. The platform developed in cooperation with the consulting section of the Swiss Diabetes Society offers the opportunity to create individual patient information and instructions to teach technical skills and knowledge on diabetes. Further, an integrated forum allows exchanging information and discussing issues regarding diabetes counselling on a secure platform. Usability tests showed that eDiabetes is easy to use and provides benefits for diabetes consultants and patients.
Standard toilets in Western countries often do not meet the needs of elderly and disabled people with physical limitations. While the existing concept of barrier-free toilets and the emerging “changing places” concept offer more space and support, the fixed height of the toilet seat still imposes a major problem during all phases of toilet use and can limit the users' autonomy by requiring personal assistance. Thus, in the EU project iToilet an innovative ICT-based modular height adjustable toilet system was designed to support the autonomy, dignity and safety of older people living at home by digital technology enhancements adapting the toilet to their needs and preferences. The main requirements were: double foldable handrails, height and tilt adjustment, emergency detection and call, and ease of use. The ICT component in this approach serves a double purpose of enhancing usability of the base assistive technology while at the same time providing safety for independent use. A field test of a prototype system in real environments of a day care center and a rehabilitation clinic has been successfully finished. The application of the iToilet concept also in semi-public settings is currently studied in the Toilet4me project.
Children are rarely affected by medical emergencies. The experience of doctors or paramedics with child emergencies is correspondingly poor. The anatomical features and individual calculations make such an emergency much more error-prone than a comparable adult emergency. Particularly in dose calculations, critical errors occur time and again. Since these calculations are based on the child's weight, which is preclinically often derived from the size of the child, the number of errors can be minimized with an assistance service that performs all calculations based on the size. Technically, it is possible to detect the size with a depth camera, which is occasionally installed in smartphones or head-mounted displays. In order to investigate to what extent these cameras provide precise results, a study with 33 children was carried out. The children were measured with both an emergency ruler and an augmented reality app with associated smartphone with depth camera. The result is that the depth camera does not provide significantly different results than an emergency ruler. This allows further research, e.g. the automatic recognition of patients with the help of machine learning or usability studies, to be tackled.
Identifying data elements of electronic medical record systems (EMRs) is one of the essential steps for the comprehensive and proper health data collection. The aim of this study was to determine the data elements required for EMRs in the field of mental disorders. We conducted a literature review and also we randomly selected 50 medical records of patients with mental disorders to identify a preliminary list of essential data elements for EMRs for mental disorders. Then, 33 mental health specialists were surveyed to validate the list of data elements through a questionnaire. We identified that health data elements of EMRs for patients with mental disorders can be categorized into seven classes (demographic data of patients, administrative data of physicians, administrative data of patients, history, clinical data, treatment, and financial data) and 10 subclasses. After the validation process, 140 essential data elements for EMRs for patients with mental disorders were introduced.
The introduction of national electronic patient records such as the electronic patient dossier EPD in Switzerland provides a new basis for digitizing healthcare processes at a national level. One process however, that is currently neglected within the Swiss EPD, is the scheduling process in healthcare. The objective of this work is to analyze the appointment scheduling process and the involved IT systems in order to develop an appointment data structure and a concept for cross-institutional exchange of appointment data. The analysis showed that various outpatient and inpatient information systems support appointment booking through proprietary solutions. A true standard for appointment data exchange is missing. We suggest an appointment data structure and a corresponding data exchange process based on the FHIR standard. In its current implementation, the Swiss EPD does not support this proposed appointment scheduling process. We discuss how potential additions such as the IHE Care Services Discovery (CSD) profile can provide better compatibility.
As there is no consensus about how to store the results of echocardiography examinations, information extraction from them is a non-trivial task. Successful named entity recognition (NER) is key to getting access to the stored information and the process of identification has been recognized as a bottleneck in text mining. Our goal was to develop and compare such NER methods that are capable of achieving this task. Our practical results show that the text mining-based NER method is able to perform at a similar level in finding and identifying terms as the regular expression-based NER method. The paper highlights the advantages and disadvantages of both methods.
There is a great number of complex data concerning the Austrian Health Care System. The goal was to process this data and present it to the general public on an easily accessible information platform. The platform focuses on data about the burden of disease of the Austrian Population, the available medical care and the services provided by the physicians. Due to the vast differences in the underlying source data, the methods used for the data acquisition range from statistical linkage over web scraping to aggregating data on the reimbursed services. The results are published on a website and are mainly displayed with interactive graphics. Overall, these dynamic and interactive websites provide a good overview of the situation of the Austrian Health Care System and presents the information in an intuitive and comprehensible manner. Furthermore, the information given in the atlases can contribute to the health care planning in order to identify distinctive service provision in Austria.
Background: Crowding in emergency departments (ED) has a negative impact on quality of care and can be averted by allocating additional resources based on predictive crowding models. However, there is a lack in effective external overall predictors, particularly those representing public activity.
Objectives: This study, therefore, examines public activity measured by regional road traffic flow as an external predictor of ED crowding in an urban hospital.
Methods: Seasonal autoregressive cross-validated models (SARIMA) were compared with respect to their forecasting error on ED crowding data.
Results: It could be shown that inclusion of inflowing road traffic into a SARIMA model effectively improved prediction errors.
Conclusion: The results provide evidence that circadian patterns of medical emergencies are connected to human activity levels in the region and could be captured by public monitoring of traffic flow. In order to corroborate this model, data from further years and additional regions need to be considered. It would also be interesting to study public activity by additional variables.
Background: In a database of electronic health records, the amount of available information varies widely between patients. In a real-time prediction scenario, a machine learning model may receive limited information for some patients.
Objectives: Our aim was to evaluate the influence of missing data on real-time prediction of delirium, and detect changes in prediction performance when training separate models for patients with missing data.
Methods: We compared a model trained specifically on data with missing values to the currently implemented model predicting delirium. Also, we simulated five test data sets with different amount of missing data and compared the prediction results to the prediction on complete data set when using the same model.
Results: For patients with missing laboratory and nursing assessment data, a model trained especially for this scenario performed significantly better than the implemented model. The combination of procedure data and demographic data achieved the closest results to a prediction with a complete data set.
Conclusion: An ongoing evaluation of real-time prediction is indispensable. Additional models adapted to the information available might improve prediction performance.
In medical education Virtual Patients (VP) are often applied to train students in different scenarios such as recording the patient's medical history or deciding a treatment option. Usually, such interactions are predefined by software logic and databases following strict rules. At this point, Natural Language Processing/Machine Learning (NLP/ML) algorithms could help to increase the overall flexibility, since most of the rules can derive directly from training data. This would allow a more sophisticated and individual conversation between student and VP. One type of technology that is heavily based on such algorithmic advances are chatbots or conversational agents. Therefore, a literature review is carried out to give insight into existing educational ideas with such agents. Besides, different prototypes are implemented for the scenario of taking the patient's medical history, responding with the classified intent of a generic anamnestic question. Although the small number of questions (n=109) leads to a high SD during evaluation, all scores (recall, precision, f1) reach already a level above 80% (micro-averaged). This shows a first promising step to use these prototypes for taking the medical history of a VP.
Speaker attribution and labeling of single channel, multi speaker audio files is an area of active research, since the underlying problems have not been solved satisfactorily yet. This especially holds true for non-standard voices and speech, such as children and impaired speakers. Being able to perform speaker labelling of pathological speech would potentially enable the development of computer assisted diagnosis and treatment systems and is thus a desirable research goal. In this manuscript we investigate on the applicability of embeddings of audio signals, in the form of time and frequency-band based segments, into arbitrary vector spaces on diarization of pathological speech. We focus on modifying an existing embedding estimator such that it can be used for diarization. This is mainly done via clustering the time and frequency band dependant vectors and subsequently performing a majority vote procedure on all frequency dependent vectors of the same time segment to assign a speaker label. The result is evaluated on recordings of interviews of aphasia patients and language therapists. We demonstrate general applicability, with error rates that are close to what has been previously achieved in diarizing children's speech. Additionally, we propose to enhance the processing pipelines with smoothing and a more sophisticated, energy based, voting scheme.
Background: Machine learning is one important application in the area of health informatics, however classification methods for longitudinal data are still rare.
Objectives: The aim of this work is to analyze and classify differences in metabolite time series data between groups of individuals regarding their athletic activity.
Methods: We propose a new ensemble-based 2-tier approach to classify metabolite time series data. The first tier uses polynomial fitting to generate a class prediction for each metabolite. An induced classifier (k-nearest-neighbor or naïve bayes) combines the results to produce a final prediction. Metabolite levels of 47 individuals undergoing a cycle ergometry test were measured using mass spectrometry.
Results: In accordance with our previous work the statistical results indicate strong changes over time. We found only small but systematic differences between the groups. However, our proposed stacking approach obtained a mean accuracy of 78% using 10-fold cross-validation.
Conclusion: Our proposed classification approach allows a considerable classification performance for time series data with small differences between the groups.
Modern healthcare faces multiple challenges: diagnosis and treatment happens multidisciplinary and distributed. The key principle to accomplish this is interoperability. Some disciplines like radiology are well experienced in interoperable workflows and cross institution data exchange; other disciplines just realize the growing importance. In this paper we analyze the situation in neurology and give an overview of attempts made in the past to establish an interchangeable, interoperable data format for biomedical signal data, which would be suitable for neurology, too. Focusing on EEG data we will discuss how DICOM Waveforms could be used to cover many of the requirements. As a result necessary adaptions and remaining issues are identified. With DICOM Waveforms a specification is available that covers most of the interoperability requirements. With only little adjustments DICOM Waveforms could establish data interoperability in neurology.
Background: ChIP-seq is a method to identify genome-wide transcription factor (TF) binding sites. The TF FXR is a nuclear receptor that controls gene regulation of different metabolic pathways in the liver.
Objectives: To re-analyze, standardize and combine all publicly available FXR ChIP-seq data sets to create a global FXR signaling atlas.
Methods: All data sets were (re-)analyzed in a standardized manner and compared on every relevant level from raw reads to affected functional pathways.
Results: Public FXR data sets were available for mouse, rat and primary human hepatocytes in different treatment conditions. Standardized re-analysis shows that the data sets are surprisingly heterogeneous concerning baseline quality criteria. Combining different data sets increased the depth of analysis and allowed to recover more peaks and functional pathways.
Conclusion: Published single FXR ChIP-seq data sets do not cover the full spectrum of FXR signaling. Combining different data sets and creating a “FXR super-signaling atlas” enhances understanding of FXR signaling capacities.
In this manuscript we propose a novel method to compare simultaneously recorded electroencephalography (EEG) signals from different devices. Although standard methods like correlation and spectral analysis give quantitative answers to this question, these methods often penalize certain artifacts such as eye blinking too strongly. In our analysis we instead utilize an unsupervised labeling technique to evaluate the matching of two signals by comparing their label sequences. The proposed method was successfully tested on artificial data, where it showed a reduced deviation from the ground truth compared to the correlation coefficient. Furthermore, the method was applied on a real use-case to assess the quality of a low-cost EEG device compared to a clinical one. Here it showed more consistent results than the correlation coefficient, while it also did not rely on outlier removal prior to the analysis. However, the proposed method still suffers from accidental matches of labels, so that unrelated data sets may be assigned an unexpectedly high matching score. This paper suggests extensions to the proposed method, which could improve this issue.
Background: In July 2015, Iran Food and Drug Administration convened a multi-stakeholder workgroup (workgroup) to help develop recommendations for electronic prescribing implementation in Iran.
Objectives: In general, the consensus of the workgroup was to focus on solutions that incrementally reduce the burden on patients, providers, and payers, and require minimal rework by using national standards that have already been used for Health Information Interchange. We used a road mapping method which includes a number of systematic steps and is adapted from the standard scientific method. Medical Informatics Experts Developed protocols for Scoping Reviews, Systematic reviews and Health Technology Assessment study and then collected evidence from peer-reviewed scholarly journal publications and gray literature. Health Insurance companies representatives and Electronic Prescribing pilot studies executives were asked to report their experiences in the case of e-prescribing.
Results: After five meetings, by comparing and contrasting the national and international evidence, the recommendations were finalized in expert panels. In this paper, we report recommendations from this roadmap.
Background: electronic prescription is shown to have many benefits in terms of reducing medication errors, improving patient safety, productivity, and resource management, but it may cause new errors and physician frustration if not designed and implemented properly. Improving usability and user-centered design is essential for physicians' adoption.
Objectives: To enhance the efficiency of the e-prescribing system by reducing the risk of inappropriate selection of the medication and also to reduce the prescribing time and effort to reach the desired drug.
Methods: Important data fields for predicting medications were determined through interviews with pharmacists. Among those, fields which were available in a claims dataset of 16 million prescriptions were extracted and were used to develop a neural network model to be used by a recommender system that displays the most probable medications on top of the drop-down list in the e-prescription application.
Results: Offline and field evaluations both showed that this model could improve performance.
Conclusion: smart recommenders systems can improve e-prescription usability, safety, and enhanced physicians' adoption.
Background: Classifications of primary care must be as interoperable as possible with current international health terminology and classifications.
Objectives: The aim of the work was to point out the strengths and weaknesses of the ICPC-2 coding and to work out recommendations for further dissemination from the user's point of view.
Methods: Selected studies on the experience with the use of ICPC-2 in several countries were analyzed, a quantitative study on the prevalence in Austria was carried out. On this basis, a qualitative study was then initiated, which analyzes the strengths and weaknesses from the perspective of practice.
Results: Although there are recommendations and agreements from a political point of view, the scope of application in Austria is limited.
Conclusion: Due to the reorganization of primary health care and other health economics requirements, unified documentation, which is already common in the intramural field, will be essential.
Background: This article is based on an ongoing long-term study, in which customary motion trackers measure steps during rehabilitation of geriatric trauma patients (Med=86 years).
Objectives: Exploring steps after 28 days of measurement. Finding similarities in the data by running cluster analysis and formulating linear regressions models to predict steps through time.
Methods: Two types of motion trackers (FitBitAlta HR and Garmin vívofit 3) have been used to measure patients' (N=24) steps after hip fracture in two study groups. Cluster analysis detected three clusters for progress in number of steps that were tested for group differences with ANOVA. Regression analysis tested models for individual patients.
Results: Three-cluster solutions showed significant differences for the average amount of steps after 5, 14, 21 and 28 days. Regression models could predict 71 % of the individual patients' progress in study group 2.
Conclusion: The long-term study will provide more data in the future to examine the three-cluster solution and to find out in what stage of rehabilitation the measurement of the steps could be used to predict individual rehabilitation.
E-health, especially telemedicine, has undergone a remarkably dynamic development over the last decade. Most experience is currently in the field of telemedical care for heart failure (HF) patients. However, HF patients with an implanted left-ventricular assist device (LVAD) have been more or less excluded from consistent telemonitoring until now. The majority of complications associated with LVAD therapy occur during the post-implantation phase. Effective outpatient management is therefore the key to improving long-term outcome of LVAD patients. Thereby, implementation of a telemedicine center for close monitoring could play an important role, e.g. through early detection of complications. This study provides insights into structural, staff and spatial requirements for a telemedicine center to monitor the special group of LVAD patients, based on comprehensive literature research and expert interviews.
The number of people with diabetes is increasing in every European country and like all chronic diseases it cannot be cured. However, patient empowerment is an acknowledged strategy for improving the patients' health situation. This paper describes the Action Plan Engine developed as a tool for diabetes patients in the POWER2DM project. The Action Plan Engine offers a guided workflow based on treatment goals and activities. A periodic review evaluates how successful a patient has fulfilled these goals and activities. Part of the evaluation is detailed feedback, in particular about 170 interventions based on Behaviour Change Techniques in order to change a patient's lifestyle behaviour towards a healthier, diabetes-appropriate lifestyle. Additionally, the Action Plan Engine offers decision trees for coping with barriers regarding glucose monitoring, exercise, carbohydrate, insulin and stress.
Background: Nurses are increasingly confronted with IT-based systems as part of their daily work. However, they often lack basic competencies in managing these complex systems.
Objectives: To analyze the need for continuous education in health informatics among Austrian nurses.
Methods: Survey within five of the largest healthcare organizations in Austria. Overall, 280 nursing practitioners with IT responsibilities and nursing managers from middle and top management participated.
Results: Participants assessed five topics (IT project management, IT in nursing, eHealth, nursing terminologies, and computer science basics) as important for continuous education in health informatics. Top management rated the importance of most topics higher than middle management did. Nursing practitioners gave ratings in between middle and top management.
Conclusion: Austrian nursing practitioners with IT responsibilities and nursing managers see a need for continuous education in health informatics. This supports findings of international recommendations of nursing informatics continuous education. There is, however, a lack of suitable opportunities for continuous education in Austria.
Background: Pre-navigational tools can assist visually impaired people when navigating unfamiliar environments. Assistive technology products (eg tactile maps or auditory simulations) can stimulate cognitive mapping processes to provide navigational assistance in these people.
Objectives: We compared how well blind and visually impaired people could learn a map presented via a tablet computer auditory tactile map (ATM) in contrast to a conventional tactile map accompanied by a text description objectives.
Methods: Performance was assessed with a multiple choice test that quizzed participants on orientation and spatial awareness. Semi-structured interviews explored participant experiences and preferences.
Results: A statistically significant difference was found between the conditions with participants using the ATM performing much better than those who used a conventional tactile map and text description. Participants preferred the flexibility of learning of the ATM.
Conclusion: This computer-based ATM provided an effective, easy to use and cost-effective way of enabling blind and partially sighted people learn a cognitive map and enhance their wellbeing.
In-patient care of the elderly is currently being put to the test in all developed industrial nations. The aim is to make the resident-centered and nursing-related care more professional. In addition to the organizational and interdisciplinary orientation, the use of socially assistive robot technologies and artificial intelligence is increasingly coming to the fore. By means of literature research, expert interviews and an online survey of Upper Austrian nursing home directors, current and future challenges and challenges for the use of socially assistive robots (SAR) in in-patient care for the elderly were identified and prioritized. It becomes clear that the technological and application-oriented maturity of SAR as well as the modular adaptation of the hybrid SAR services to the existing structures and processes from the point of view of the nursing home management are in the foreground. In the future, it will be increasingly important to bring the process-related and technological support of human-machine interaction through SAR to a value-adding level.