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.

[Educational film on the geriatric basic assessment in primary care-Methods of cinematic authenticity]. / Lehrfilm über das geriatrische Basisassessment in der hausärztlichen Versorgung ­ Methoden filmischer Authentizität.

BACKGROUND: The relevance of care of older persons in general practice requires the conveyance of procedural and conditional knowledge of the geriatric basic assessment (GBA) even during medical studies. There is a need for action with respect to student knowledge on specific problems of older patients. This paper describes how the primary care situation can be made tangible for students based on a film project at the Hannover Medical School (MHH). METHOD: During film production, strategies for creating authenticity were applied using cinematic means. The film is used in the teaching module of family medicine at MHH. Student evaluation investigated whether the educational film provided an emotional experience and whether the presentation mode of the GBA was perceived as authentic. RESULTS: The majority of students were emotionally touched by the film. The educational film was successful in conveying the complexity of care of older people and in presenting the special role of family physicians in geriatric care. DISCUSSION: The students recognized the necessity of the GBA and found the representation predominantly realistic; however, individual cinematic reception and previous experiences also influence the perception of the cinematic form with respect to the representation of reality. This method might improve the conveyance of authenticity in educational videos in medicine.

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.

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).

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).

Unintended Consequences of Wearable Sensor Use in Healthcare. Contribution of the IMIA Wearable Sensors in Healthcare WG.

OBJECTIVES: As wearable sensors take the consumer market by storm, and medical device manufacturers move to make their devices wireless and appropriate for ambulatory use, this revolution brings with it some unintended consequences, which we aim to discuss in this paper. METHODS: We discuss some important unintended consequences, both beneficial and unwanted, which relate to: modifications of behavior; creation and use of big data sets; new security vulnerabilities; and unforeseen challenges faced by regulatory authorities, struggling to keep pace with recent innovations. Where possible, we proposed potential solutions to unwanted consequences. RESULTS: Intelligent and inclusive design processes may mitigate unintended modifications in behavior. For big data, legislating access to and use of these data will be a legal and political challenge in the years ahead, as we trade the health benefits of wearable sensors against the risk to our privacy. The wireless and personal nature of wearable sensors also exposes them to a number of unique security vulnerabilities. Regulation plays an important role in managing these security risks, but also has the dual responsibility of ensuring that wearable devices are fit for purpose. However, the burden of validating the function and security of medical devices is becoming infeasible for regulators, given the many software apps and wearable sensors entering the market each year, which are only a subset of an even larger 'internet of things'. CONCLUSION: Wearable sensors may serve to improve wellbeing, but we must be vigilant against the occurrence of unintended consequences. With collaboration between device manufacturers, regulators, and end-users, we balance the risk of unintended consequences occurring against the incredible benefit that wearable sensors promise to bring to the world.

Health-Enabling and Ambient Assistive Technologies: Past, Present, Future.

BACKGROUND: During the last decades, health-enabling and ambient assistive technologies became of considerable relevance for new informatics-based forms of diagnosis, prevention, and therapy. OBJECTIVES: To describe the state of the art of health-enabling and ambient assistive technologies in 1992 and today, and its evolution over the last 25 years as well as to project where the field is expected to be in the next 25 years. In the context of this review, we define health-enabling and ambient assistive technologies as ambiently used sensor-based information and communication technologies, aiming at contributing to a person's health and health care as well as to her or his quality of life. METHODS: Systematic review of all original articles with research focus in all volumes of the IMIA Yearbook of Medical Informatics. Surveying authors independently on key projects and visions as well as on their lessons learned in the context of health-enabling and ambient assistive technologies and summarizing their answers. Surveying authors independently on their expectations for the future and summarizing their answers. RESULTS: IMIA Yearbook papers containing statements on health-enabling and ambient assistive technologies appear first in 2002. These papers form a minor part of published research articles in medical informatics. However, during recent years the number of articles published has increased significantly. Key projects were identified. There was a clear progress on the use of technologies. However proof of diagnostic relevance and therapeutic efficacy remains still limited. Reforming health care processes and focussing more on patient needs are required. CONCLUSIONS: Health-enabling and ambient assistive technologies remain an important field for future health care and for interdisciplinary research. More and more publications assume that a person's home and their interaction therein, are becoming important components in health care provision, assessment, and management.

Road Traffic Related Injury Research and Informatics. New Opportunities for Biomedical and Health Informatics as a Contribution to the United Nations' Sustainable Development Goals?

The United Nations has recently adopted 17 sustainable development goals for 2030, including ensuring healthy lives and promoting well-being for all at all ages, and making cities and human settlements inclusive, safe, resilient and sustainable. Road injuries remain among the ten leading causes of death in the world, and are projected to increase with rapidly increasing motorisation globally. Lack of comprehensive data on road injuries has been identified as one of the barriers for effective implementation of proven road safety interventions. Building, linking and analysing electronic patient records in conjunction with establishing injury event and care registries can substantially contribute to healthy lives and safe transportation. Appropriate use of new technological approaches and health informatics best practices could provide significant added value to WHO's global road safety work and assist Member States in identifying prevention targets, monitoring progress and improving quality of care to reduce injury-related deaths. This paper encourages the initiation of new multidisciplinary research at a global level.

Memorandum "Open Metadata". Open Access to Documentation Forms and Item Catalogs in Healthcare.

At present, most documentation forms and item catalogs in healthcare are not accessible to the public. This applies to assessment forms of routine patient care as well as case report forms (CRFs) of clinical and epidemiological studies. On behalf of the German chairs for Medical Informatics, Biometry and Epidemiology six recommendations to developers and users of documentation forms in healthcare were developed. Open access to medical documentation forms could substantially improve information systems in healthcare and medical research networks. Therefore these forms should be made available to the scientific community, their use should not be unduly restricted, they should be published in a sustainable way using international standards and sources of documentation forms should be referenced in scientific publications.

A novel approach for discovering human behavior patterns using unsupervised methods.

BACKGROUND: As is well known, elderly people gradually lose the ability of self-care. The decline can be reflected in changes in their daily life behavior. A solution to assess their health status is to design sensor-enhanced living environments to observe their behavior, in which unobtrusive sensors are usually used. With respect to information extraction from the dataset collected by means of these kinds of sensors, unsupervised methods have to be relied on for practical application. Under the assumption that human lifestyle is associated with health status, this study intends to propose a novel approach to discover behavior patterns using unsupervised methods. METHODS: To evaluate the feasibility of this approach it was applied to datasets collected in the GAL-NATARS study. The study is part of the Lower Saxony research network Design of Environments for Aging (GAL) and conducted in subjects' home environments. The subjects recruited in GAL-NATARS study are older people (age ≥ 70 years), who are discharged from hospital to live alone again at their homes after treatment of a femoral fracture. RESULTS: The change of lifestyle regularity is measured. By analyzing the correlation between the extracted information and medical assessment results of four subjects, two of them exhibited impressive association and the other two showed less association. CONCLUSIONS: The approach may provide complementary information for health assessment; however, the dominant relationship between the change of behavior patterns and the health status has to be shown and datasets from more subjects must be collected in future studies. LIMITATIONS: Merely environmental data were used and no wearable sensor for activity detection or vital parameter measurement is taken into account. Therefore, this cannot comprehensively reflect reality.

Multimodal sensor-based fall detection within the domestic environment of elderly people.

BACKGROUND: Falls represent a major threat to the health of the elderly and are a growing burden on the healthcare systems. With the growth of the elderly population within most societies efficient fall detection becomes increasingly important; however, existing fall detection systems still fail to produce reliable results. OBJECTIVES: A study was carried out on sensor-based fall detection, analysis of falls with the help of fall protocols and the analysis of user acceptance of fall detection sensor technology through questionnaires. MATERIAL AND METHODS: A total of 28 senior citizens were recruited from a German community-dwelling population. The primary goal was a sensor-based detection of falls with accelerometers, video cameras and microphones. Details of the falls were analyzed with the help of medical geriatric assessments and standardized fall protocols. The study duration was 8 weeks and required a maximum of nine visits per subject. RESULTS: The study participants were 28 subjects with a mean age of 74.3 and a standard deviation (SD) of ± 6.3 years of which 12 were male and 16 female. A total of 1225.7 measurement days were recorded from all participants and the algorithms detected 2.66 falls per day. During the study period 15 falls occurred and 12 of these falls were correctly recognized by the fall detection system. CONCLUSION: Current fall detection technologies work well under laboratory conditions but it is still problematic to produce reliable results when these technologies are applied to real life conditions. Acceptance towards the sensors decreased after study participation although the system was generally perceived as useful or very useful.

What Does Big Data Mean for Wearable Sensor Systems? Contribution of the IMIA Wearable Sensors in Healthcare WG.

OBJECTIVES: The aim of this paper is to discuss how recent developments in the field of big data may potentially impact the future use of wearable sensor systems in healthcare. METHODS: The article draws on the scientific literature to support the opinions presented by the IMIA Wearable Sensors in Healthcare Working Group. RESULTS: The following is discussed: the potential for wearable sensors to generate big data; how complementary technologies, such as a smartphone, will augment the concept of a wearable sensor and alter the nature of the monitoring data created; how standards would enable sharing of data and advance scientific progress. Importantly, attention is drawn to statistical inference problems for which big datasets provide little assistance, or may hinder the identification of a useful solution. Finally, a discussion is presented on risks to privacy and possible negative consequences arising from intensive wearable sensor monitoring. CONCLUSIONS: Wearable sensors systems have the potential to generate datasets which are currently beyond our capabilities to easily organize and interpret. In order to successfully utilize wearable sensor data to infer wellbeing, and enable proactive health management, standards and ontologies must be developed which allow for data to be shared between research groups and between commercial systems, promoting the integration of these data into health information systems. However, policy and regulation will be required to ensure that the detailed nature of wearable sensor data is not misused to invade privacies or prejudice against individuals.

Home-centered health-enabling technologies and regional health information systems. An integration approach based on international standards.

INTRODUCTION: This article is part of the Focus Theme of Methods of Information in Medicine on "Using Data from Ambient Assisted Living and Smart Homes in Electronic Health Records". OBJECTIVES: In this paper, we present a prototype of a Home-Centered Health-Enabling Technology (HET-HC), which is able to capture, store, merge and process data from various sensor systems at people's home. In addition, we present an architecture designed to integrate HET-HC into an exemplary regional Health Information System (rHIS). METHODS: rHIS are traditionally document-based to fit to the needs in a clinical context. However, HET-HC are producing continuous data streams for which documents might be an inappropriate representation. Therefore, the HET-HC could register placeholder-documents at rHIS. These placeholder-documents are assembled upon user-authenticated request by the HET-HC and are always up-to-date. Moreover, it is not trivial to find a clinical coding system for continuous sensor data and to make the data machine-readable in order to enhance the interoperability of such systems. Therefore, we propose the use of SNOCAP-HET, which is a nomenclature to describe the context of sensor-based measurements in health-enabling technologies. RESULTS: We present an architectural approach to integrate HET-HC into rHIS. Our solution is the centralized registration of placeholder-documents with rHIS and the decentralized data storage at people's home. CONCLUSIONS: We concluded that the presented architecture of integrating HET-HC into rHIS might fit well to the traditional approach of document-based data storage. Data security and privacy issues are also duly considered.

Development and pilot study of a bed-exit alarm based on a body-worn accelerometer.

BACKGROUND: Falls are a major problem in hospitals and nursing homes. The consequences of falls can be severe, both for the individual and for the caring institution. OBJECTIVE: The aim of the work presented here is to reduce the number of falls on a geriatric ward by monitoring patients more closely. To achieve this goal, a bed-exit alarm that reliably detects an attempt to get up has been constructed. MATERIALS AND METHODS: A requirements analysis revealed the nurses' and physicians' needs and preferences. Based on the gathered information, an incremental design process generated different prototypes. These were tested for the reliability of their ability to detect attempts to get up in both laboratory settings and with geriatric patients. Based on the result of these tests, a scalable technical solution has been developed and proven its reliability in a 1-year, randomized controlled pilot clinical trial on a geriatric ward. RESULTS: The developed system is unobtrusive and easy to deploy. It has been tested in laboratory settings, usability tests and a 1-year randomized clinical trial with 98 patients. This paper focuses on the technical development of the system. We present different prototypes, the experiments and the pilot study used to evaluate their performance. Last but not least, we discuss the lessons learned so far. CONCLUSION: The developed bed-exit alarm is able to reliably detect patients' attempts to get up. The results of the clinical trial show that the system is able to reduce the number of falls on a geriatric ward. Next steps are the design of a specialized sensor node that is easier to use and can be applied on an even larger scale due to its reduced cost. A multicenter trial with a larger number of patients is required to confirm the results of this pilot study.

Measurement of accelerometry-based gait parameters in people with and without dementia in the field: a technical feasibility study.

BACKGROUND: Gait analyses are an important tool to diagnose diseases or to measure the rehabilitation process of patients. In this context, sensor-based systems, and especially accelerometers, gain in importance. They are able to improve objectiveness of gait analyses. In clinical settings, there is usually a supervisor who gives instructions to the patients, but this can have an influence on patients' gait. It is expected that this effect will be smaller in field studies. OBJECTIVE: Aim of this study was to capture and evaluate gait parameters measured by a single waist-mounted accelerometer during everyday life of subjects. METHODS: Due to missing ground-truth in unsupervised conditions, another external criterion had to be chosen. Subjects of two different groups were considered: patients with dementia (DEM) and active older people (ACT). These groups were chosen, because of the expected difference in gait. The idea was to quantify the expected difference of accelerometric-based gait parameters. Gait parameters were e.g. velocity, step frequency, compensation movements, and variance of the accelerometric signal. RESULTS: Ten subjects were measured in each group. The number of walking episodes captured was 1,187 (DEM) vs. 1,809 (ACT). The compensation and variance parameters showed an AUC value (Area Under the Curve) between 0.88 and 0.92. In contrast, velocity and step frequency performed poorly (AUC values of 0.51 and 0.55). It was possible to classify both groups using these parameters with an accuracy of 89.2%. CONCLUSION: The results showed a much higher amount of walking episodes in field studies compared to supervised clinical trials. The classification showed a high accuracy in distinguishing between both groups.