Big Data in Medizin und Gesundheitswesen

[Use of AI-based applications by hospital staff: task profiles and qualification requirements]

BACKGROUND

Artificial intelligence (AI) is becoming increasingly important for the future development of hospitals. To unlock the large potential of AI, job profiles of hospital staff members need to be further developed in the direction of AI and digitization skills through targeted qualification measures. This affects both medical and non-medical processes along the entire value chain in hospitals. The aim of this paper is to provide an overview of the skills required to deal with smart technologies in a clinical context and to present measures for training employees.

METHODS

As part of the "SmartHospital.NRW" project in 2022, we conducted a literature review as well as interviews and workshops with experts. AI technologies and fields of application were identified.

RESULTS

Key findings include adapted and new task profiles, synergies and dependencies between individual task profiles, and the need for a comprehensive interdisciplinary and interprofessional exchange when using AI-based applications in hospitals.

DISCUSSION

Our article shows that hospitals need to promote digital health literacy skills for hospital staff members at an early stage and at the same time recruit technology- and AI-savvy staff. Interprofessional exchange formats and accompanying change management are essential for the use of AI in hospitals.

Swapping data: A pragmatic approach for enabling academic-industrial partnerships

Objectives

Academic institutions have access to comprehensive sets of real-world data. However, their potential for secondary use-for example, in medical outcomes research or health care quality management-is often limited due to data privacy concerns. External partners could help achieve this potential, yet documented frameworks for such cooperation are lacking. Therefore, this work presents a pragmatic approach for enabling academic-industrial data partnerships in a health care environment.

Methods

We employ a value-swapping strategy to facilitate data sharing. Using tumor documentation and molecular pathology data, we define a data-altering process as well as rules for an organizational pipeline that includes the technical anonymization process.

Results

The resulting dataset was fully anonymized while still retaining the critical properties of the original data to allow for external development and the training of analytical algorithms.

Conclusion

Value swapping is a pragmatic, yet powerful method to balance data privacy and requirements for algorithm development; therefore, it is well suited to enable academic-industrial data partnerships.

Construction of a Legal System of Corporate Social Responsibility Based on Big Data Analysis Technology

The company is an essential organization in modern society, and the company has transformed from a purely economic organization to a corporate citizen that realizes economic responsibility and practices social responsibility at the same time. It is only by constructing a legal system of corporate social responsibility that companies can take social responsibility on the track of the legal system, realize the company's mission of the times, and achieve a win-win situation for both the company and society. This paper used the LDA and text clustering methods to analyze existing legal texts. It obtained the theme and text clustering results, thus proposing five aspects of the legal system construction framework to guide the corporate social responsibility legal system, which has pioneering significance.

Ethical Implications of e-Health Applications in Early Preventive Healthcare

As a means of preventive medicine early detection and prevention examinations can identify and treat possible health disorders or abnormalities from an early age onwards. However, pediatric examinations are often widely spaced, and thus only snapshots of the children’s and adolescents’ developments are obtained. With e-health applications parents and adolescents could record developmental parameters much more frequently and regularly and transmit data directly for ongoing evaluation. AI technologies could be used to search for new and previously unknown patterns. Although e-health applications could improve preventive healthcare, there are serious concerns about the unlimited use of big data in medicine. Such concerns range from general skepticism about big data in medicine to specific challenges and risks in certain medical areas. In this paper, we will focus on preventive health care in pediatrics and explore ethical implications of e-health applications. Specifically, we will address opportunities and risks of app-based data collection and AI-based data evaluation for complementing established early detection and prevention examinations. To this end, we will explore the principle of the best interest of the child. Furthermore, we shall argue that difficult trade-offs need to be made between group benefit on the one hand and individual autonomy and privacy on the other.

Interpretable Clinical Decision Support System for Audiology Based on Predicted Common Audiological Functional Parameters (CAFPAs)

Common Audiological Functional Parameters (CAFPAs) were previously introduced as abstract, measurement-independent representation of audiological knowledge, and expert-estimated CAFPAs were shown to be applicable as an interpretable intermediate layer in a clinical decision support system (CDSS). Prediction models for CAFPAs were built based on expert knowledge and one audiological database to allow for data-driven estimation of CAFPAs for new, individual patients for whom no expert-estimated CAFPAs are available. Based on the combination of these components, the current study explores the feasibility of constructing a CDSS which is as interpretable as expert knowledge-based classification and as data-driven as machine learning-based classification. To test this hypothesis, the current study investigated the equivalence in performance of predicted CAFPAs compared to expert-estimated CAFPAs in an audiological classification task, analyzed the importance of different CAFPAs for high and comparable performance, and derived explanations for differences in classified categories. Results show that the combination of predicted CAFPAs and statistical classification enables to build an interpretable but data-driven CDSS. The classification provides good accuracy, with most categories being correctly classified, while some confusions can be explained by the properties of the employed database. This could be improved by including additional databases in the CDSS, which is possible within the presented framework.

An African Relational Approach to Healthcare and Big Data Challenges

Big Data has amplified some challenges in the healthcare context. One significant challenge is how to use healthcare big data (HBD) in ways that honor individual rights to informed consent or privacy. Careful analysis from diverse backgrounds will be vital in contributing ethical guidelines that can adequately address healthcare Big Data's growing complexities globally. Especially, the study argues that an under-explored African philosophy of Ubuntu can usefully influence big data practices in ways that address this challenge without undermining its benefits. Ubuntu emphasizes harmonious relationships. Harmonious relations entail identifying with one another and exhibiting solidarity to each other. One can identify or exhibit solidarity with others through psychological attitudes such as thinking of oneself as part of a "we" and acting in ways that will more likely improve the quality of life of others. The African relational philosophy of Ubuntu deserves to be given an audience not only for epistemic justice but also because the continued absence of African perspective in the discourse on ethical use of HBD science represents a missed opportunity to enrich ethical thinking about HBD from diverse backgrounds. Research is, however, required to provide greater specificity on how Ubuntu values may be integrated into HBD analytic techniques.

Big data and artificial intelligence in future patient management. How is it all started? Where are we at now? Quo tendimus?

Background

This article is focused on the understanding of the key points and their importance and impact on the future of early disease predictive models, accurate and fast diagnosis, patient management, optimise treatment, precision medicine, and allocation of resources through the applications of Big Data (BD) and Artificial Intelligence (AI) in healthcare.

Content

BD and AI processes include learning which is the acquisition of information and rules for using the information, reasoning which is using rules to reach approximate or definite conclusions and self-correction. This can help improve the detection of diseases, rare diseases, toxicity, identifying health system barriers causing under-diagnosis. BD combined with AI, Machine Learning (ML), computing and predictive-modelling, and combinatorics are used to interrogate structured and unstructured data computationally to reveal patterns, trends, potential correlations and relationships between disparate data sources and associations.

Summary

Diagnosis-assisted systems and wearable devices will be part and parcel not only of patient management but also in the prevention and early detection of diseases. Also, Big Data will have an impact on payers, devise makers and pharmaceutical companies. BD and AI, which is the simulation of human intelligence processes, are more diverse and their application in monitoring and diagnosis will only grow bigger, wider and smarter.

Outlook

BD connectivity and AI of diagnosis-assisted systems, wearable devices and smartphones are poised to transform patient and to change the traditional methods for patient management, especially in an era where is an explosion in medical data.

Real-world evidence study for distribution of traditional Chinese medicine syndrome and its elements on chronic bronchitis in China

Objective: To investigate the distribution and characteristics of traditional Chinese medicine (TCM) syndromes and its elements on chronic bronchitis (CB) based on real-world data (RWD) so as to optimize the treatment strategies.

Methods: A real-world study based on 2207 medical records collected from five hospitals in China, to explore the relationship between TCM syndrome and CB using the big data methods. Factor analyses were used to reduce the dimensions of TCM syndrome elements and found common factors. Additionally, cluster analyses were performed to value combinations of TCM syndrome element. Finally, association rule analyses were employed to assess the structures of TCM syndromes elements and estimate the patterns of TCM syndrome.

Results: A total of 21 TCM syndromes were extracted from RWD in this work. There were four TCM syndromes consisting of Tan_Zhuo_Zu_Fei, Tan_Re_Yong_Fei, Feng_Han_Xi_Fei, and Feng_Re_Fan_Fei with [Formula: see text]% frequency based on the distribution frequency. The two top Xu TCM syndromes of Fei_Yin_Xu and Fei_Shen_Qi_Xu were identified. The top six pathogenesis TCM syndrome elements were Tan, Huo, Feng, Han, Qi_Xu, and Yin_Xu. Factor analyses, cluster analyses, and association rule analyses demonstrated that Tan, Huo, Feng, Han, Qi-Xu, Yin-Xu, Fei, and Shen were the core TCM syndrome elements.

Conclusion: The four common Shi TCM syndromes of Tan_Zhuo_Zu_Fei, Tan_Re_Yong_Fei, Feng_Han_Xi_Fei, and Feng_Re_Fan_Fei for CB were detected in the real world study, and the two Xu TCM syndromes of Fei_Yin_Xu and Fei_Shen_Qi_Xu were identified. The Mix TCM syndrome of Fei_Pi_Qi_Xu_Tan_Shi_Yun_Fei was the main syndrome. The core TCM syndrome elements of Tan, Huo, Feng, Han, Qi_Xu, and Yin_Xu, Fei, and Shen were determined in the entire sample.

Activity Theory as a Theoretical Framework for Health Self-Quantification: A Systematic Review of Empirical Studies

BACKGROUND

Self-quantification (SQ) is a way of working in which, by using tracking tools, people aim to collect, manage, and reflect on personal health data to gain a better understanding of their own body, health behavior, and interaction with the world around them. However, health SQ lacks a formal framework for describing the self-quantifiers' activities and their contextual components or constructs to pursue these health related goals. Establishing such framework is important because it is the first step to operationalize health SQ fully. This may in turn help to achieve the aims of health professionals and researchers who seek to make or study changes in the self-quantifiers' health systematically.

OBJECTIVE

The aim of this study was to review studies on health SQ in order to answer the following questions: What are the general features of the work and the particular activities that self-quantifiers perform to achieve their health objectives? What constructs of health SQ have been identified in the scientific literature? How have these studies described such constructs? How would it be possible to model these constructs theoretically to characterize the work of health SQ?

METHODS

A systematic review of peer-reviewed literature was conducted. A total of 26 empirical studies were included. The content of these studies was thematically analyzed using Activity Theory as an organizing framework.

RESULTS

The literature provided varying descriptions of health SQ as data-driven and objective-oriented work mediated by SQ tools. From the literature, we identified two types of SQ work: work on data (ie, data management activities) and work with data (ie, health management activities). Using Activity Theory, these activities could be characterized into 6 constructs: users, tracking tools, health objectives, division of work, community or group setting, and SQ plan and rules. We could not find a reference to any single study that accounted for all these activities and constructs of health SQ activity.

CONCLUSIONS

A Health Self-Quantification Activity Framework is presented, which shows SQ tool use in context, in relation to the goals, plans, and competence of the user. This makes it easier to analyze issues affecting SQ activity, and thereby makes it more feasible to address them. This review makes two significant contributions to research in this field: it explores health SQ work and its constructs thoroughly and it adapts Activity Theory to describe health SQ activity systematically.