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Talent is a key resource for urban development, and building and disseminating urban brands have an important impact on attracting talent. This paper explores what kind of urban brand ecology (UBE) can effectively enhance urban talent attraction (UTA). We explore this question using a novel grey quantitative configuration analysis (GQCA) model.

To develop the GQCA model, grey clustering is combined with qualitative configuration analysis (QCA). We conducted comparative configuration analysis of UTA using fuzzy set QCA (fsQCA) and the proposed GQCA.

We find that the empirical results of fsQCA may contradict the facts, and that the proposed GQCA effectively solves this problem.

Based on the theory of UBE, we identify bottleneck factors for improving UTA at different stages. Seven configuration paths are described for cities to enhance UTA. Theoretically, this study expands the application boundaries of UBE.

The proposed GQCA effectively solves the problem of inconsistent analysis and facts caused by the use of a binary threshold by the fsQCA. In practical case studies, the GQCA significantly improves the reliability of configuration comparisons and the sensitivity of QCA to cases, demonstrating excellent research performance.

The primary objective of this paper is to show a systematic and methodological approach for the digitalization of critical clinical pathways (CPs) within the healthcare domain.

The methodology entails the integration of service design (SD) and action research (AR) methodologies, characterized by iterative phases that systematically alternate between action and reflective processes, fostering cycles of change and learning. Within this framework, stakeholders are engaged through semi-structured interviews, while the existing and envisioned processes are delineated and represented using BPMN 2.0. These methodological steps emphasize the development of an autonomous, patient-centric web application alongside the implementation of an adaptable and patient-oriented scheduling system. Also, business processes simulation is employed to measure key performance indicators of processes and test for potential improvements. This method is implemented in the context of the CP addressing transient loss of consciousness (TLOC), within a publicly funded hospital setting.

The methodology integrating SD and AR enables the detection of pivotal bottlenecks within diagnostic CPs and proposes optimal corrective measures to ensure uninterrupted patient care, all the while advancing the digitalization of diagnostic CP management. This study contributes to theoretical discussions by emphasizing the criticality of process optimization, the transformative potential of digitalization in healthcare and the paramount importance of user-centric design principles, and offers valuable insights into healthcare management implications.

The study’s relevance lies in its ability to enhance healthcare practices without necessitating disruptive and resource-intensive process overhauls. This pragmatic approach aligns with the imperative for healthcare organizations to improve their operations efficiently and cost-effectively, making the study’s findings relevant.