Methoden und Werkzeuge für eine datengetriebene Entwicklung digitaler Gesundheitsanwendungen

Dem Paradigma der Präzisionsmedizin folgend schaffen digitale Gesundheitsanwendungen die Grundlage für eine personalisierte Versorgung, um damit die Effizienz und Effektivität von Gesundheitssystemen zu erhöhen. Im Kontext weltweit entstehender digitaler Gesundheitsökosysteme stehen dabei Daten als treibender Faktor im Mittelpunkt des Entwicklungsprozesses. Welche Methoden und Werkzeuge benötigt werden, um das dadurch mögliche Zusammenspiel zwischen einer datengetriebenen und einer wissensbasierten Entwicklung von digitalen Gesundheitsanwendungen zu unterstützen, wird in dieser Arbeit untersucht und anhand eines Rahmenwerks beschrieben. Durch Anwendung der Design Science Research Methode werden diesbezügliche Artefakte einem probleminitiierten Ansatz folgend entworfen, implementiert und durch quantitative sowie qualitative Methoden evaluiert. Dafür wird zunächst ein Vorgehensmodell abgeleitet, welches die zu beantwortenden Fragen in den Phasen der Digitalisierung, Automatisierung und Optimierung bis hin zur Translation in die medizinische Versorgung adressiert. Unter Beachtung entsprechender Normen findet eine Verknüpfung von interdisziplinären Methoden, Anforderungen sowie technologischen Ansätzen zu einer Wissensbasis statt, womit die Grundlage für zu entwickelnde Werkzeuge gelegt wird. Diese werden im Anwendungskontext dementieller Syndrome eruiert und pro Artefakt demonstriert sowie im Detail mit $n$ Probanden multiperspektivisch validiert. In Kooperation mit einer gerontopsychiatrischen Klinik werden diesbezüglich domänenspezifische Anforderungen an digitale Gesundheitsanwendungen bestimmt. Hierfür findet exemplarisch die explorative Entwicklung eines ambulanten Systems zur Messung kognitiver Leistungsparameter statt. Eine im Kontext dieser Zusammenarbeit durchgeführte Feldstudie ($n=55$) mit kognitiv eingeschränkten Personen zeigt Potentiale und Herausforderungen, welche durch die digitale Erfassung, Vernetzung und Auswertung von neuropsychologischen Daten entstehen. Dabei werden ebenfalls Anforderungen bezüglich der zielgruppenspezifischen Gestaltung einer gebrauchstauglichen Nutzerschnittstelle ($n=91$) gesammelt, welche in einem Leitfaden zusammenfließen und in einer grafischen Benutzeroberfläche iterativ implementiert werden. Aus der Perspektive von Datensubjekten ($n=238$) wird zusätzlich untersucht, welchen Stellenwert ein selbstbestimmter Umgang mit dieser Art von personenbezogenen Daten hat und für welche Zwecke diese aus deren Sicht eingesetzt werden sollten. Im Zuge dieses Entwicklungsprozesses sind ebenfalls Ansätze zur Automatisierung und Optimierung der Datenauswertung für die Ableitung des Gesundheitszustandes notwendig. Diese Schritte liefern als Artefakte, neben den Ergebnissen zum Vergleich verschiedener Algorithmen aus dem Bereich des maschinellen Lernens, die Identifikation von dafür geeigneten Leistungs- und Optimierungsmaßen sowie Merkmalsselektionsverfahren. Im Vergleich mit schwellwertbasierten Verfahren zur Operationalisierung von Bewertungsmetriken (maximaler Cohen\u27s Kappa $\kappa = 0,67$) erreicht die durch maschinelles Lernen gestützte Softwareanwendung eine höhere durchschnittliche Sensitivität von 83% bei einer 93%igen Spezifität (maximaler Cohen\u27s Kappa $\kappa = 0,79$) für die Erkennung von kognitiven Einschränkungen. Die automatisierte Erfassung hierfür notwendiger Merkmale erfolgt durch neu entwickelte Ansätze und zeigt zukünftige Forschungsaktivitäten auf, welche die damit verbundenen Herausforderungen adressieren. Dabei werden Indikatoren identifiziert, wodurch sich die Potentiale in computergestützten Modellen aufzeigen. Diese liefern zusätzliche Erkenntnisse über das Spannungsfeld zwischen einer zuverlässigen Erfüllung klinischer Leitlinien sowie regulatorischer Implikationen insbesondere hinsichtlich der Erklärbarkeit datengetriebener Optimierungs- und Automatisierungsansätze. Eine Untersuchung der Transferpotentiale in die deutsche Regelversorgung aus der Perspektive unterschiedlicher Interessenvertreter unterstreicht diese Punkte. Hierfür konzipierte Werkzeuge und Methoden ermöglichen einerseits die empirische Untersuchung der Adhärenz solcher digitaler Lösungen bezüglich der Nutzungsbereitschaft ($n=29$) sowie deren zeitliche Entwicklung ($n=18$). Andererseits werden damit die Akzeptanzkriterien der kassenärztlich organisierten Leistungserbringer im deutschen Gesundheitswesen ($n=301$) erhoben und dargestellt, welchen Einfluss diese auf Markteintrittsstrategien haben. Darauf aufbauend werden Wege definiert, um einen Beitrag zur Entlastung des Gesundheitssystems zu leisten. Die gesammelten Erkenntnisse werden hierfür in einem ganzheitlichen Plattformkonzept zur Entwicklung personalisierter Präventions- und Behandlungsprogramme gebündelt

Distributed Ledger Technology for the systematic Investigation and Reduction of Information Asymmetry in Collaborative Networks

Costs, risks and inefficiencies in Collaborative Networks (CNs) resulting from information asymmetries have been discussed in the scientific community for years. In this work, supply chain networks, as common representative of CNs, are used as object of investigation. Therein, problems and requirements of interorganizational information exchange are elaborated as well as the potential role Distributed Ledger Technology (DLT) could play to address them. As major challenge, convincing all relevant network partners to resolve asymmetric information by sharing sensitive data is identified. To face this issue, the value of shared information is prioritized as a motivational aspect. Finally, we propose a search process to systematically assess the benefits of information sharing in collaborative networks. To coordinate and implement this process regarding the derived requirements of CNs we propose system components based on DLT design patterns

Data Sovereignty in Data Donation Cycles - Requirements and Enabling Technologies for the Data-driven Development of Health Applications

Personalized healthcare is expected to increase the efficiency and the effectiveness of health services using different kinds of algorithms on existing data. This approach is currently confronted with the lack of digital data and the desire for self-determined personal data handling. However, the issue of health data donation is on the political agenda of some governments. Within this work, a knowledge base will be created by reviewing existing approaches and technologies regarding this topic with the focus on chronic diseases. A list of requirements will be derived from which we conceptualize a data donation cycle to demonstrate the challenges and opportunities of health data sovereignty and its future possibilities concerning data-driven health application development. By linking the requirements to technological approaches, the baseline for future open ecosystems will be presented

Decision model to design a blockchain-based system for storing sensitive health data

The storage and sharing of sensitive health data in Blockchain-based systems implicates data protection issues that must be addressed when designing such systems. Those issues can be traced back to the properties of decentralized systems. A blessing but also a curse in the context of health data is the transparency of the Blockchain, because it allows the stored data to be viewed by all participants of the network. In addition, the property of immutability is in contrast to the possibility to delete the personal data upon request according to the European General Data Protection Regulation (GDPR). Accordingly, approaches to tackle these issues have recently been discussed in research and industry, e.g. by storing sensitive data encrypted On-Chain or Off-Chain on own servers connected to a Blockchain. These approaches deal with how the confidentiality and integrity of stored data can be guaranteed and how data can be deleted. By reviewing the proposed approaches, we develop a taxonomy to summarize their specific technical characteristics and create a decision model that will allow the selection of a suitable approach for the design of future Blockchain-based systems for the storage of sensitive health data. Afterwards, we demonstrate the utility of the decision model based on a use case for storing test results from a digital dementia screening application. The paper concludes with a discussion of the results and suggestions for future research

Model-Driven Dementia Prevention and Intervention Platform

Most types of dementia, including Alzheimer’s disease, are not curable. However, there are risk factors, such as obesity or hypertension, that can promote the development of dementia. Holistic treatment of these risk factors can prevent the onset of dementia or delay it in its early stages. To support individualized treatment of risk factors in dementia, this paper presents a model-driven digital platform. It enables monitoring of biomarkers using smart devices from the internet of medical things (IoMT) for the target group. The collected data from such devices can be used to optimize and adjust treatment in a patient in the loop manner. To this end, providers such as Google Fit and Withings have been connected to the platform as example data sources. To achieve treatment and monitoring data interoperability with existing medical systems, internationally accepted standards such as FHIR are used. The configuration and control of the personalized treatment processes are achieved using a self-developed domain-specific language. For this language, an associated diagram editor was implemented, which allows the management of the treatment processes through graphical models. This graphical representation should help treatment providers to understand and manage these processes more easily. To investigate this hypothesis, a usability study was conducted with twelve participants. We were able to show that such graphical representations provide advantages in clarity in reviewing the system, but lack in easy set-up (compared to wizard-style systems)

AI, Robotics, and Clinical Research for Innovative Dementia Interventions: A Japanese-German Collaboration

After a successful international workshop in Karlsruhe, Germany in June 2023, transformative initiative is underway involving major institutions: the RIKEN Cognitive Behavioral Assistive Technology (CB-AT) Team in Japan, the Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE) Rostock/Greifswald, Rostock, the Forschungszentrum Informatik (FZI) and the Karlsruhe Institute of Technology, Institute for Information Processing Technology as well as the Institute for Entrepreneurship, Technology Management and Innovation. The unique strengths of these institutions unite in an interdisciplinary collaboration focusing on novel dementia interventions. This consortium envisions the future of dementia care and the prevention of its progress – a model that brings together the strengths of AI, robotics, digital platforms, and clinical research, not just targeting patients but considering dyadic interventions that support both patients and caregivers. The KIT and FZI from Karlsruhe bring to the table expertise in software and AI engineering, and experience in research transfer. Particularly crucial is the role of the METIS platform, which supports multi-stage treatment processes for neurodegenerative diseases in an outpatient setting, integrating modern wearables and AI personalization of treatment strategies. RIKEN CB-AT complements this with robotics and system integration capabilities, including access to robots ready for integration into care regimens. The institute is renowned for its speech intervention strategies in dementia prevention, fostering the idea of using robots to aid caregivers and patients alike. Ultimately, the robots could serve as a base station, actively engaging with caregivers, assessing their stress levels, and providing mitigation strategies while simultaneously collecting crucial data. DZNE Rostock/Greifswald rounds out the partnership with a robust clinical background and access to well-defined clinical cohorts. Their research provides valuable insights into patient needs. Furthermore, their proficiency in qualitative research and dyadic interventions adds an essential layer of complexity to the project. In this alliance, a shared ethos of participatory approach, modern digital and wearable technology adoption, and individualized intervention strategies enable a unified research vision. The potential outcomes are manifold: they include technologies for outpatient measurements of intervention, prevention and care, robots aiding caregivers and patients, digitalization of care pathways, stress mitigation, and more. All partners strive to establish bi-lateral connections between existing technology and new integrations, enabling data insights from a variety of sources, including smartwatches, smartphones, robots, novel technology, and caregiver-patient interactions. These insights can be used for the personalization of intervention and care, medication, early detection of emergency situations, and strategies to empower patients and enhance the resilience of caregivers. Once addressed, the opportunity for transformative early prevention of dementia progression are immense. The expected outcomes span joint research projects, scientific publications, societal impact, and entrepreneurial initiatives. In conclusion, this collaborative venture aspires to make strides in dementia care and intervention through the integrative use of platform-based AI, robotics, and clinical research, fostering an enhanced care ecosystem that values patients and caregivers

Digital Health Apps in the Context of Dementia: Questionnaire Study to Assess the Likelihood of Use Among Physicians

Background: Age-related diseases such as dementia are playing an increasingly important role in global population development. Thus, prevention, diagnostics, and interventions require more accessibility, which can be realized through digital health apps. With the app on prescription, Germany made history by being the first country worldwide to offer physicians the possibility to prescribe and reimburse digital health apps as of the end of the year 2020. Objective: Considering the lack of knowledge about correlations with the likelihood of use among physicians, this study aimed to address the question of what makes the use of a digital health app by physicians more likely. Methods: We developed and validated a novel measurement tool—the Digital Health Compliance Questionnaire (DHCQ)—in an interdisciplinary collaboration of experts to assess the role of proposed factors in the likelihood of using a health app. Therefore, a web-based survey was conducted to evaluate the likelihood of using a digital app called DemPredict to screen for Alzheimer dementia. Within this survey, 5 latent dimensions (acceptance, attitude toward technology, technology experience, payment for time of use, and effort of collection), the dependent variable likelihood of use, and answers to exploratory questions were recorded and tested within directed correlations. Following a non–probability-sampling strategy, the study was completed by 331 physicians from Germany in the German language, of whom 301 (90.9%) fulfilled the study criteria (eg, being in regular contact with patients with dementia). These data were analyzed using a range of statistical methods to validate the dimensions of the DHCQ. Results: The DHCQ revealed good test theoretical measures—it showed excellent fit indexes (Tucker-Lewis index=0.98; comparative fit index=0.982; standardized root mean square residual=0.073; root mean square error of approximation=0.037), good internal consistency (Cronbach α=.83), and signs of moderate to large correlations between the DHCQ dimensions and the dependent variable. The correlations between the variables acceptance, attitude toward technology, technology experience, and payment for the time of use and the dependent variable likelihood of use ranged from 0.29 to 0.79, and the correlation between effort of the collection and likelihood of use was −0.80. In addition, we found high levels of skepticism regarding data protection, and the age of the participants was found to be negatively related to their technical experience and attitude toward technology. Conclusions: In the context of the results, increased communication between the medical and technology sectors and significantly more awareness raising are recommended to make the use of digital health apps more attractive to physicians as they can be adjusted to their everyday needs. Further research could explore the connection between areas such as adherence on the patient side and its impact on the likelihood of use by physicians