COVID-19 pandemic has questioned the way healthcare operations take place globally as the healthcare professionals face an unprecedented task of controlling and treating the COVID-19 infected patients with a highly straining and draining facility due to the erratic admissions of infected patients. However, COVID-19 is considered as a white swan event. Yet, the impact of the COVID-19 pandemic on healthcare operations is highly uncertain and disruptive making it as a black swan event. Therefore, the study explores the impact of the COVID-19 outbreak on healthcare operations and develops machine learning-based forecasting models using time series data to foresee the progression of COVID-19 and further using predictive analytics to better manage healthcare operations. The prediction error of the proposed model is found to be 0.039 for new cases and 0.006 for active COVID-19 cases with respect to mean absolute percentage error. The proposed simulated model further could generate predictive analytics and yielded future recovery rate, resource management ratios, and average cycle time of a patient tested COVID-19 positive. Further, the study will help healthcare professionals to devise better resilience and decision-making for managing uncertainty and disruption in healthcare operations.

Hospitals and other healthcare settings use various simulation methods to improve their operations, management, and training. The COVID-19 pandemic, with the resulting necessity for rapid and remote assessment, has highlighted the critical role of modeling and simulation in healthcare, particularly distributed simulation (DS). DS enables integration of heterogeneous simulations to further increase the usability and effectiveness of individual simulations. This article presents a DS system that integrates two different simulations developed for a hospital intensive care unit (ICU) ward dedicated to COVID-19 patients. AnyLogic has been used to develop a simulation model of the ICU ward using agent-based and discrete event modeling methods. This simulation depicts and measures physical contacts between healthcare providers and patients. The Unity platform has been utilized to develop a virtual reality simulation of the ICU environment and operations. The high-level architecture, an IEEE standard for DS, has been used to build a cloud-based DS system by integrating and synchronizing the two simulation platforms. While enhancing the capabilities of both simulations, the DS system can be used for training purposes and assessment of different managerial and operational decisions to minimize contacts and disease transmission in the ICU ward by enabling data exchange between the two simulations.

This research employs design ethnography to study the design process of a design science research (DSR) project conducted over eight years. The DSR project focuses on chronic wounds and how Information Technology (IT) might support the management of those wounds. Since this is a new and complex problem not previously addressed by IT, it requires an exploration and discovery process. As such, we found that traditional DSR methodologies were not well-suited to guiding the design process. Instead we discovered that focusing on search, and in particular, the co-evolution of the problem and solution spaces, provides a much better focus for managing the DSR design process. The presentation of our findings from the ethnographic study includes a new representation for capturing the co-evolving problem/solution spaces, an illustration of the search process and co-evolving problem/solution spaces using the DSR project we studied, the need for changes in the purpose of DSR evaluation activities when using a search-focused design process, and how our proposed process extends and augments current DSR methodologies. Studying the DSR design process generates the knowledge that research project managers need for managing and guiding a DSR project, and contributes to our knowledge of the design process for research-oriented projects.

A pandemic calls for large-scale action across national and international innovation systems in order to mobilize resources for developing and manufacturing crisis-critical products efficiently and in the huge quantities needed. Nowadays, these products also include a wide range of digital innovations. Given that many responses to the pandemic are technology driven, stakeholders involved in the development and manufacturing of crisis-critical products are likely to face intellectual property (IP)-related challenges. To (governmental) decision makers, IP challenges might not appear to be of paramount urgency compared to the many undoubtedly huge operational challenges to deploy critical resources. However, if IP challenges are considered too late, they may cause delays to urgently mobilize resources effectively. Innovation stakeholders could then be reluctant to fully engage in the development and manufacturing of crisis-critical products. This article adopts an IP and innovation perspective to learn from the currently unfolding COVID-19 pandemic using secondary data, including patent data, synthesized with an IP roadmap. We focus on technical aspects related to research, development, and upscaling of capacity to manufacture crisis-critical products in the huge volumes suddenly in demand. In this article, we offer a set of contributions. We provide a structure, framework, and language for those concerned with steering clear of IP challenges to avoid delays in fighting a pandemic. We provide a reasoning why IP needs to be considered earlier rather than too late in a global health crisis. Major stakeholders we identify include 1) governments; 2) manufacturing firms owning existing crisis-critical IP (incumbents in crisis-critical sectors); 3) manufacturing firms normally not producing crisis-critical products suddenly rushing into crisis-critical sectors to support the manufacturing of crisis-critical products in the quantities that far exceed incumbents' production capacities; and 4) voluntary grassroot initiatives that form during a pandemic, often by highly skilled engineers and scientists in order to contribute to the development and dissemination of crisis-critical products. For these major stakeholders, we draw up three scenarios, from which we identify associated IP challenges they face related to the development and manufacturing of technologies and products for 1) prevention (of spread); 2) diagnosis of infected patients; and 3) the development of treatments. This article provides a terminology to help policy and other decision makers to discuss IP considerations during pandemics. We propose a framework that visualizes changing industrial organizations and IP-associated challenges during a pandemic and derive initial principles to guide innovation and IP policy making during a pandemic. Obviously, our findings result only from observations of one ongoing pandemic and thus need to be verified further and interpreted with care.

The dynamics of knowledge transfer is an important topic for engineering managers. In this paper, we study knowledge boundaries - barriers to knowledge transfer - in groups of experts, using topic modeling, a natural language processing technique, applied to transcript data from the U.S. Food and Drug Administration's Circulatory Systems Advisory Panel. As predicted by prior theory, we find that knowledge boundaries emerge as the group faces increasingly challenging problems. Beyond this theory, we find that knowledge boundaries cease to structure communications between communities of practice when the group's expert ability is insufficient to solve its task, such as in the presence of high novelty. We conjecture that the amount of expert knowledge that the group can collectively bring to bear is a determining factor in boundary formation. This implies that some of the factors underlying knowledge boundary formation may aid - rather than hinder - knowledge aggregation. We briefly explore this conjecture using qualitative exploration of several relevant meetings. Finally, we discuss implications of these results for organizations attempting to leverage their expertise given the state of their collective knowledge.

In this article, we extend the innovation literature by examining how firms respond to crisis, specifically exogenous crises. At their early onset, crises may represent a window of opportunity for innovation, but it is not equally allocated across firms. We created a unique database of 636 biopharmaceutical firms, from 24 countries and territories, developing innovative treatments during the early outbreak of the COVID-19 crisis to study this phenomenon. We found that firms acted strategically to the shifting external environment and attempted to capitalize on the opportunity by pursuing different but complementary innovation strategies (i.e., radical versus repurposed). The successful outcome of a chosen strategy was highly dependent upon a firm's accumulated knowledge resources, which varied in degree of diversity (i.e., homogeneous versus heterogeneous). We found that firms with more focused R&D (i.e., homogeneous knowledge) developed more radical innovations, whereas firms with more diverse R&D (i.e., heterogeneous knowledge) repurposed innovations. We controlled for firm size (small versus large), firm age (startup versus mature), and country classification (developing versus emerging). We also controlled for a firm's prior knowledge and expertise in coronavirus research and found that it did not influence innovation. Our results suggest that this unique period of environmental uncertainty and crisis created a window of opportunity and a level playing field for innovation.