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Background:
Contact tracing apps are potentially useful tools for supporting national COVID-19 containment strategies. Various national apps with different technical design features have been commissioned and issued by governments worldwide.
Objective:
Our goal was to develop and propose an item set that was suitable for describing and monitoring nationally issued COVID-19 contact tracing apps. This item set could provide a framework for describing the key technical features of such apps and monitoring their use based on widely available information.
Methods:
We used an open-source intelligence approach (OSINT) to access a multitude of publicly available sources and collect data and information regarding the development and use of contact tracing apps in different countries over several months (from June 2020 to January 2021). The collected documents were then iteratively analyzed via content analysis methods. During this process, an initial set of subject areas were refined into categories for evaluation (ie, coherent topics), which were then examined for individual features. These features were paraphrased as items in the form of questions and applied to information materials from a sample of countries (ie, Brazil, China, Finland, France, Germany, Italy, Singapore, South Korea, Spain, and the United Kingdom [England and Wales]). This sample was purposefully selected; our intention was to include the apps of different countries from around the world and to propose a valid item set that can be relatively easily applied by using an OSINT approach.
Results:
Our OSINT approach and subsequent analysis of the collected documents resulted in the definition of the following five main categories and associated subcategories: (1) background information (open-source code, public information, and collaborators); (2) purpose and workflow (secondary data use and warning process design); (3) technical information (protocol, tracing technology, exposure notification system, and interoperability); (4) privacy protection (the entity of trust and anonymity); and (5) availability and use (release date and the number of downloads). Based on this structure, a set of items that constituted the evaluation framework were specified. The application of these items to the 10 selected countries revealed differences, especially with regard to the centralization of the entity of trust and the overall transparency of the apps’ technical makeup.
Conclusions:
We provide a set of criteria for monitoring and evaluating COVID-19 tracing apps that can be easily applied to publicly issued information. The application of these criteria might help governments to identify design features that promote the successful, widespread adoption of COVID-19 tracing apps among target populations and across national boundaries.
Background:
Large health organizations often struggle to build complex health information technology (HIT) solutions and are faced with ever-growing pressure to continuously innovate their information systems. Limited research has been conducted that explores the relationship between organizations’ innovative capabilities and HIT quality in the sense of achieving high-quality support for patient care processes.
Objective:
The aim of this study is to explain how core constructs of organizational innovation capabilities are linked to HIT quality based on a conceptual sociotechnical model on innovation and quality of HIT, called the IQHIT model, to help determine how better information provision in health organizations can be achieved.
Methods:
We designed a survey to assess various domains of HIT quality, innovation capabilities of health organizations, and context variables and administered it to hospital chief information officers across Austria, Germany, and Switzerland. Data from 232 hospitals were used to empirically fit the model using partial least squares structural equation modeling to reveal associations and mediating and moderating effects.
Results:
The resulting empirical IQHIT model reveals several associations between the analyzed constructs, which can be summarized in 2 main insights. First, it illustrates the linkage between the constructs measuring HIT quality by showing that the professionalism of information management explains the degree of HIT workflow support (R²=0.56), which in turn explains the perceived HIT quality (R²=0.53). Second, the model shows that HIT quality was positively influenced by innovation capabilities related to the top management team, the information technology department, and the organization at large. The assessment of the model’s statistical quality criteria indicated valid model specifications, including sufficient convergent and discriminant validity for measuring the latent constructs that underlie the measures of HIT quality and innovation capabilities.
Conclusions:
The proposed sociotechnical IQHIT model points to the key role of professional information management for HIT workflow support in patient care and perceived HIT quality from the viewpoint of hospital chief information officers. Furthermore, it highlights that organizational innovation capabilities, particularly with respect to the top management team, facilitate HIT quality and suggests that health organizations establish this link by applying professional information management practices. The model may serve to stimulate further scientific work in the field of HIT adoption and diffusion and to provide practical guidance to managers, policy makers, and educators on how to achieve better patient care using HIT.
Background
Diabetes mellitus is a major global health issue with a growing prevalence. In this context, the number of diabetic complications is also on the rise, such as diabetic foot ulcers (DFU), which are closely linked to the risk of lower extremity amputation (LEA). Statistical prediction tools may support clinicians to initiate early tertiary LEA prevention for DFU patients. Thus, we designed Bayesian prediction models, as they produce transparent decision rules, quantify uncertainty intuitively and acknowledge prior available scientific knowledge.
Method
A logistic regression using observational collected according to the standardised PEDIS classification was utilised to compute the six-month amputation risk of DFU patients for two types of LEA: 1.) any-amputation and 2.) major-amputation. Being able to incorporate information which is available before the analysis, the Bayesian models were fitted following a twofold strategy. First, the designed prediction models waive the available information and, second, we incorporated the a priori available scientific knowledge into our models. Then, we evaluated each model with respect to the effect of the predictors and validity of the models. Next, we compared the performance of both models with respect to the incorporation of prior knowledge.
Results
This study included 237 patients. The mean age was 65.9 (SD 12.3), and 83.5% were male. Concerning the outcome, 31.6% underwent any- and 12.2% underwent a major-amputation procedure. The risk factors of perfusion, ulcer extent and depth revealed an impact on the outcomes, whereas the infection status and sensation did not. The major-amputation model using prior information outperformed the uninformed counterpart (AUC 0.765 vs AUC 0.790, Cohen’s d 2.21). In contrast, the models predicting any-amputation performed similarly (0.793 vs 0.790, Cohen’s d 0.22).
Conclusions
Both of the Bayesian amputation risk models showed acceptable prognostic values, and the major-amputation model benefitted from incorporating a priori information from a previous study. Thus, PEDIS serves as a valid foundation for a clinical decision support tool for the prediction of the amputation risk in DFU patients. Furthermore, we demonstrated the use of the available prior scientific information within a Bayesian framework to establish chains of knowledge.
Wirtschaftsinformatik und Medizinische Informatik gehören zu den sogenannten Bindestrich-Informatik-Fächern, die sich mit der Anwendung der Methoden und Erkenntnisse der Informatik, aber auch mit der Weiterentwicklung solcher Methoden und Erkenntnisse für gewisse Anwendungsgebiete befassen. Auf einer Podiumsdiskussion der Jahrestagung 2018 der Deutschen Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie (GMDS) wurde für Wirtschaftsinformatik, Medizinische Informatik und Informatik analysiert wie sie zueinander stehen. Die Analyse erfolgte anhand von fünf Fragen:
1. Welche grundlegenden Ziele bestimmen die jeweilige wissenschaftliche Arbeit?
2. Wie ist der Praxisbezug ausgeprägt?
3. Inwieweit sind Besonderheiten von Medizin bzw. Ökonomie prägend für die jeweilige wissenschaftliche Arbeit?
4. Welche Rolle spielen Theoriefundierung und Evidenz?
5. Was können Wirtschaftsinformatik und Informatik von Medizinischer Informatik und Medizin lernen – und umgekehrt?
Die Analyse zeigt, dass die drei Disziplinen von einem systematischen wechselseitigen Austausch profitieren können. Das „Lernende Gesundheitssystem“ bietet Ansätze für einen entsprechenden Rahmen.
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.
Elektronisch unterstützte transsektorale Kommunikation im Gesundheitswesen ist eine der essentiellen Säulen von eHealth. Sie ist eine menschliche Handlung, die eine Verbesserung der Versorgung Einzelner und ganzer Bevölkerungsgruppen bewirken soll. Ethik bewertet menschliches Handeln in Bezug auf dessen Auswirkungen und die ihm zugrunde liegenden Werte und Normen. Dabei werden die Auswirkungen auf Individuen und Allgemeinheiten betrachtet. Im Gesundheits- und Sozialwesen gelten die Prinzipien der Autonomie, der Schadensverhütung, der Fürsorge und der Gerechtigkeit als Maßstäbe. Es gilt also die Fragen herauszuarbeiten, die an elektronische transsektorale Kommunikation aus ethischer Sicht gestellt werden müssen, um zu untersuchen, ob sie innerhalb der genannten Prinzipien ethischen Anforderungen genügt.
Aus den Ergebnissen einer systematischen Literaturrecherche wurden zunächst allgemein Aussagen zum Thema Information und Technologie im Zusammenhang mit Ethik extrahiert, und daraufhin geprüft, auf welche Fragen sie Antworten anbieten. Diese wurden innerhalb der genannten fünf Prinzipien als Fragen an elektronische transsektorale Kommunikation formuliert.
Aus den Aussagen der Literatur ließen sich sieben Fragen ableiten und den ethischen Prinzipien zuordnen, um mit ihnen elektronische transsektorale Kommunikation zu untersuchen. Auf diese Weise kann geprüft werden ob diese in der Lage sind, das Wohl Einzelner wie auch von Gemeinschaften im Gesundheitswesen zu fördern, wovon Betroffene, Professionelle und das Gesundheitssystem insgesamt profitieren könnten.
Für die Versorgungsforschung ist wichtig, dass verteilte und heterogene Daten so integriert werden, dass sie offen für neue Analyse-Anforderungen und leicht um neue Datenquellen erweiterbar sind. Für die Integration von Versorgungsdaten werden bislang hauptsächlich Data-Warehouses eingesetzt, die Daten dimensional oder als Entity-Attribute-Value-Struktur (EAV) modellieren. Diese Datenmodelle sind jedoch entweder unflexibel oder weisen ein zu geringes Maß an Datenorganisation auf, was longitudinale Analysen erschwert. Wir haben den EAV-Ansatz um die Data-Vault-Modellierung ergänzt und damit die Datenstrukturen der Krankenhaus-Qualitätsberichte des Gemeinsamen Bundesausschusses (G-BA) modelliert sowie die Daten der Jahre 2011 bis 2015 integriert. Dies ermöglicht eine Historisierung der Metadaten für Merkmale, insbesondere der Qualitätsindikatoren, sowie ein hohes Maß an Erweiterbarkeit gegenüber neuen heterogenen Datenquellen. Der vorgeschlagene Ansatz erlaubt es, den Abstraktionsgrad für die zu modellierenden Entitäten frei zu wählen, so dass auch ein vollständig generisches EAV-Modell mit historisierten Metadaten erstellt werden kann.
Background: For more than 30 years, there has been close cooperation between Japanese and German scientists with regard to information systems in health care. Collaboration has been formalized by an agreement between the respective scientific associations. Following this agreement, two joint workshops took place to explore the similarities and differences of electronic health record systems (EHRS) against the background of the two national healthcare systems that share many commonalities.
Objectives: To establish a framework and requirements for the quality of EHRS that may also serve as a basis for comparing different EHRS.
Methods: Donabedian's three dimensions of quality of medical care were adapted to the outcome, process, and structural quality of EHRS and their management. These quality dimensions were proposed before the first workshop of EHRS experts and enriched during the discussions.
Results: The Quality Requirements Framework of EHRS (QRF-EHRS) was defined and complemented by requirements for high quality EHRS. The framework integrates three quality dimensions (outcome, process, and structural quality), three layers of information systems (processes and data, applications, and physical tools) and three dimensions of information management (strategic, tactical, and operational information management).
Conclusions: Describing and comparing the quality of EHRS is in fact a multidimensional problem as given by the QRF-EHRS framework. This framework will be utilized to compare Japanese and German EHRS, notably those that were presented at the second workshop.
Multinational health IT benchmarks foster cross-country learning and have been employed at various levels, e.g. OECD and Nordic countries. A bi-national benchmark study conducted in 2007 revealed a significantly higher adoption of health IT in Austria compared to Germany, two countries with comparable healthcare systems. We now investigated whether these differences still persisted. We further studied whether these differences were associated with hospital intrinsic factors, i.e. the innovative power of the organisation and hospital demographics. We thus performed a survey to measure the “perceived IT availability” and the “innovative power of the hospital” of 464 German and 70 Austrian hospitals. The survey was based on a questionnaire with 52 items and was given to the directors of nursing in 2013/2014. Our findings confirmed a significantly greater IT availability in Austria than in Germany. This was visible in the aggregated IT adoption composite score “IT function” as well as in the IT adoption for the individual functions “nursing documentation” (OR = 5.98), “intensive care unit (ICU) documentation” (OR = 2.49), “medication administration documentation” (OR = 2.48), “electronic archive” (OR = 2.27) and “medication” (OR = 2.16). “Innovative power” was the strongest factor to explain the variance of the composite score “IT function”. It was effective in hospitals of both countries but significantly more effective in Austria than in Germany. “Hospital size” and “hospital system affiliation” were also significantly associated with the composite score “IT function”, but they did not differ between the countries. These findings can be partly associated with the national characteristics. Indicators point to a more favourable financial situation in Austrian hospitals; we thus argue that Austrian hospitals may possess a larger degree of financial freedom to be innovative and to act accordingly. This study is the first to empirically demonstrate the effect of “innovative power” in hospitals on health IT adoption in a bi-national health IT benchmark. We recommend directly including the financial situation into future regression models. On a political level, measures to stimulate the “innovative power” of hospitals should be considered to increase the digitalisation of healthcare.
Objective: To pilot benchmark measures of health information and communication technology (ICT) availability and use to facilitate cross-country learning.
Materials and Methods: A prior Organization for Economic Cooperation and Development–led effort involving 30 countries selected and defined functionality-based measures for availability and use of electronic health records, health information exchange, personal health records, and telehealth. In this pilot, an Organization for Economic Cooperation and Development Working Group compiled results for 38 countries for a subset of measures with broad coverage using new and/or adapted country-specific or multinational surveys and other sources from 2012 to 2015. We also synthesized country learnings to inform future benchmarking.
Results: While electronic records are widely used to store and manage patient information at the point of care—all but 2 pilot countries reported use by at least half of primary care physicians; many had rates above 75%—patient information exchange across organizations/settings is less common. Large variations in the availability and use of telehealth and personal health records also exist.
Discussion: Pilot participation demonstrated interest in cross-national benchmarking. Using the most comparable measures available to date, it showed substantial diversity in health ICT availability and use in all domains. The project also identified methodological considerations (e.g., structural and health systems issues that can affect measurement) important for future comparisons.
Conclusion: While health policies and priorities differ, many nations aim to increase access, quality, and/or efficiency of care through effective ICT use. By identifying variations and describing key contextual factors, benchmarking offers the potential to facilitate cross-national learning and accelerate the progress of individual countries.