Search

The COVID-19 pandemic has caused huge and disruptive technological changes in the healthcare sector, transforming the way businesses and societies function. To respond to the global health crisis, there have been numerous innovation projects in the healthcare sector, including the fast design and manufacturing of personal protective equipment (PPE) and medical devices, and testing, treatment, and vaccine technologies. Many of these innovative activities happen beyond organizational boundaries with collaboration and open innovation. In this paper, we review the current literature on open innovation strategy during the pandemic and adopt the co-evolution view of business ecosystems to address the context of change. Based on a detailed exploration of the COVID-19-related technologies in the UK and global healthcare sectors, we identify the key emerging themes of open innovation in crisis. Further discussions are conducted in relation to each theme. Our results and analysis can help provide policy recommendations for the healthcare sector, businesses, and society to recover from the crisis. [ABSTRACT FROM AUTHOR]

Clinical trials are typically conducted over a population within a defined time period in order to illuminate certain characteristics of a health issue or disease process. Cross-sectional studies provide a snapshot of these disease processes over a large number of people but do not allow us to model the temporal nature of disease, which is essential for modelling detailed prognostic predictions. Longitudinal studies, on the other hand, are used to explore how these processes develop over time in a number of people but can be expensive and time-consuming, and many studies only cover a relatively small window within the disease process. This paper explores the application of intelligent data analysis techniques for building reliable models of disease progression from both cross-sectional and longitudinal studies. The aim is to learn disease 'trajectories' from cross-sectional data by building realistic trajectories from healthy patients to those with advanced disease. We focus on exploring whether we can 'calibrate' models learnt from these trajectories with real longitudinal data using Baum-Welch re-estimation so that the dynamic parameters reflect the true underlying processes more closely. We use Kullback-Leibler distance and Wilcoxon rank metrics to assess how calibration improves the models to better reflect the underlying dynamics. [ABSTRACT FROM AUTHOR]

Regulatory frameworks associated to building design are usually complex, representing extensive sets of requirements. For healthcare projects in the UK, this includes statutory and guidance documents. Existing research indicates that they contain subjective requirements, which challenge the practical adoption of automated compliance checking, leading to limited outcomes. This paper aims to propose recommendations for the adoption of automated compliance checking in the design of healthcare buildings. Design Science Research was used to gain a detailed understanding of how information from existing regulatory requirements affects automation, through an empirical study in the design of a primary healthcare facility. In this study, a previously proposed taxonomy was implemented and refined, resulting in the identification of different types of subjective requirements. Based on empirical data emerging from the research, a set of recommendations was proposed focusing on the revision of regulatory documents, as well as to aid designers implementing automated compliance in practice. • Automated compliance checking has been approached mostly replacing human operations. • Automation can be easily achieved for quantitative and objective requirements. • Subjectivity is still a fundamental constraint in the automated compliance process. • Requirements subjectivity has been identified as natural or artificial. • Need of multiple automated compliance approaches due to diversity of requirements. [ABSTRACT FROM AUTHOR]