Accelerating the Front End of Medicine: Three Digital Use Cases and HCI Implications.

Digital applications in health care are a concurrent research and management question, where implementation experiences are a core field of information systems research. It also contributes to fighting pandemic crises like COVID-19 because contactless information flow and speed of diagnostics are improved. This paper presents three digital application case studies from emergency medicine, administration management, and cancer diagnosis with AI support from the University Medical Centers of Münster and Göttingen in Germany. All cases highlight the potential of digitalization to increase speed and efficiency within the front end of medicine as the crucial phase before patient treatment starts. General challenges for health care project implementations and human-computer interaction (HCI) concepts in health care are derived and discussed, including the importance of specific processes together with user analysis and adaption. A derived concept for HCI includes the criteria speed, accuracy, modularity, and individuality to achieve sustainable improvements within the front end of medicine.

Public opinion towards global distribution of COVID-19 vaccines - Data from Germany and the United States.

This study gathered evidence from Germany and the United States on public opinion towards fair distribution of COVID-19 vaccines across the world. Analytical Hierarchy Process and discrete choice experiments were used for this purpose. The sample is nationally representative of adults (aged 18 and above) for both countries using quotas on age, gender, education, state, and COVID-19 vaccination rates at the time of the fieldwork (25 May 2021 to 26 June 2021). Overall 1,003 responses in Germany and 1,000 in the United States were collected.

Public opinion on global distribution of COVID-19 vaccines: Evidence from two nationally representative surveys in Germany and the United States.

Despite ongoing calls for a more even global distribution of COVID-19 vaccines, there remains a great disparity between high- and low-income countries. We conducted representative surveys among the adult populations in the United States (N = 1,000) and Germany (N = 1,003) in June 2021 to assess public opinion in these countries on the distributive justice of COVID-19 vaccines. We conducted two instances of analytic hierarchy processes (AHP) to elicit how the public weighs different principles and criteria for vaccine allocation. In further discrete choice experiments, respondents were asked to split a limited supply of vaccine doses between a hypothetical high-income and a hypothetical low-income country. AHP weights in the United States and Germany were 37.4% (37.2-37.5) and 49.4% (49.2-49.5) for "medical urgency", 32.7% (32.6-32.8) and 25.4% (25.2-25.5) for "equal access for all", 13.7% (13.6-13.8) and 13.3% (13.2-13.4) for "production contribution", and 16.3% (16.2-16.4) and 12.0% (11.9-12.1) for "free market rules", respectively, with 95% CI shown in parentheses. In the discrete choice experiment, respondents in the United States and Germany split available vaccine doses such that the low-income country, which was three times more populous than the high-income country, on average received 53.9% (95% CI: 52.6-55.1) and 57.5% (95% CI: 56.3-58.7) of available doses, respectively. When faced with a dilemma where a vulnerable family member was waiting for a vaccine, 20.7% (95% CI: 18.2-23.3) of respondents in the United States and 18.2% (95% CI: 15.8-20.6) in Germany reduced the amount they allocated to the low-income country sufficiently to secure a vaccine for their family member. Our results indicate that the public in the United States and Germany favours utilitarian and egalitarian distribution principles of vaccines for COVID-19 over libertarian or meritocratic principles. This implies that political decisions favouring higher levels of redistribution would be supported by public opinion in these two countries.

COVID-19 health policy evaluation: integrating health and economic perspectives with a data envelopment analysis approach.

The COVID-19 pandemic is a global challenge to humankind. To improve the knowledge regarding relevant, efficient and effective COVID-19 measures in health policy, this paper applies a multi-criteria evaluation approach with population, health care, and economic datasets from 19 countries within the OECD. The comparative investigation was based on a Data Envelopment Analysis approach as an efficiency measurement method. Results indicate that on the one hand, factors like population size, population density, and country development stage, did not play a major role in successful pandemic management. On the other hand, pre-pandemic healthcare system policies were decisive. Healthcare systems with a primary care orientation and a high proportion of primary care doctors compared to specialists were found to be more efficient than systems with a medium level of resources that were partly financed through public funding and characterized by a high level of access regulation. Roughly two weeks after the introduction of ad hoc measures, e.g., lockdowns and quarantine policies, we did not observe a direct impact on country-level healthcare efficiency, while delayed lockdowns led to significantly lower efficiency levels during the first COVID-19 wave in 2020. From an economic perspective, strategies without general lockdowns were identified as a more efficient strategy than the full lockdown strategy. Additionally, governmental support of short-term work is promising. Improving the efficiency of COVID-19 countermeasures is crucial in saving as many lives as possible with limited resources.

The Buy-Online-Pick-Up-in-Store Retailing Model: Optimization Strategies for In-Store Picking and Packing

Online shopping is growing fast due to the increasingly widespread use of digital services. During the COVID-19 pandemic, the desire for contactless shopping has further changed consumer behavior and accelerated the acceptance of online grocery purchases. Consequently, traditional brick-and-mortar retailers are developing omnichannel solutions such as click-and-collect services to fulfill the increasing demand. In this work, we consider the Buy-Online-Pick-up-in-Store concept, in which online orders are collected by employees of the conventional stores. As labor is a major cost driver, we apply and discuss different optimizing strategies in the picking and packing process based on real-world data from a German retailer. With comparison of different methods, we estimate the improvements in efficiency in terms of time spent during the picking process. Additionally, the time spent on the packing process can be further decreased by applying a mathematical model that guides the employees on how to organize the articles in different shopping bags during the picking process. In general, we put forward effective strategies for the Buy-Online-Pick-up-in-Store paradigm that can be easily implemented by stores with different topologies.

Digital University Teaching and Learning in Management—The Gini from the COVID-19 Bottle and Its Empirical Representations in Germany

Digitalization of teaching, learning, and assessment in higher education has gained increasing attention in research in the recent years. While previous research investigated issues of effectiveness, course attendance, and course evaluation from a long-term perspective, the current COVID-19 pandemic forced higher education institutions to digitalize teaching, learning, and assessment in a very short time. In this context, we investigate the effects of the digitalization of three courses from operations research and management science in the summer term 2020, namely two large lectures and tutorials for undergraduate, and a seminar for graduate students. To that end, student performance, course and exam attendance rates, and course evaluations are compared to the setting of the same courses in the previous year 2019 with a traditional, non-digitalized setting. Next to the quantitative data, qualitative statements from the course evaluations and students’ expectations expressed during the term are investigated. Findings indicate that the lecturers’ understanding of learning behavior has to develop further as interaction is required in any format, on-site or digital. Absenteeism and procrastination are important risk areas especially in digital management education. Instruments would have to be adapted to digital settings, but with care and relating to course specifics (including digital evaluation). Digital education does not make learning per se easier or harder, but we observed that the students’ understanding and performance gap increased in digital teaching times. As an outlook, we propose the longitudinal investigation of the ongoing digitalization during the COVID-19 pandemic, and going beyond, investigate opportunities of the current crisis situation for implementing the long-term transition to digital education in higher institution institutions.

Artificial Intelligence for Hospital Health Care: Application Cases and Answers to Challenges in European Hospitals.

The development and implementation of artificial intelligence (AI) applications in health care contexts is a concurrent research and management question. Especially for hospitals, the expectations regarding improved efficiency and effectiveness by the introduction of novel AI applications are huge. However, experiences with real-life AI use cases are still scarce. As a first step towards structuring and comparing such experiences, this paper is presenting a comparative approach from nine European hospitals and eleven different use cases with possible application areas and benefits of hospital AI technologies. This is structured as a current review and opinion article from a diverse range of researchers and health care professionals. This contributes to important improvement options also for pandemic crises challenges, e.g., the current COVID-19 situation. The expected advantages as well as challenges regarding data protection, privacy, or human acceptance are reported. Altogether, the diversity of application cases is a core characteristic of AI applications in hospitals, and this requires a specific approach for successful implementation in the health care sector. This can include specialized solutions for hospitals regarding human-computer interaction, data management, and communication in AI implementation projects.