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Research has examined how new ventures strengthen local economic outcomes; however, limited research examines health-oriented ventures and their impact on social outcomes, including health outcomes. Increased VC investment in healthcare service start-ups signals more activity toward this end, and the need for further academic inquiry. We examine the relationship between these start-ups and county-level health outcomes, health factors, and hospital utilization.

Data on start-ups funded via institutional venture capital from PitchBook were merged with US county-level outcomes from the County Health Rankings and Area Health Resources Files for 2010 to 2019. We investigated how the number of VC-funded healthcare service start-ups, as well as a subset defined as innovative, were associated with county-level health measures. We used panel models with two-way fixed effects and Propensity Score Matched (PSM), controlling for demographics and socioeconomic factors.

Each additional VC-funded healthcare service start-up was related to a significant 0.01 percentage point decrease in diabetes prevalence (p < 0.01), a decrease of 1.54 HIV cases per 100,000 population (p < 0.1), a 0.02 percentage point decrease in obesity rates (p < 0.01), and a 0.03 percentage point decrease in binge drinking (p < 0.01). VC-funded healthcare service start-ups were not related to hospital utilization.

This work expands our understanding of how industry-specific start-ups, in this case healthcare start-ups, relate to positive social outcomes. The results underscore the importance of evidence-based evaluation, the need for expanded outcome measures for VC investment, and the possibilities for integration of healthcare services and entrepreneurship ecosystems.

This papers aims to study lattice structures in terms of geometric variables, manufacturing variables and material-based variants and their correlation with compressive behaviour for their application in a methodology for the design and development of personalized elastic therapeutic products.

Lattice samples were designed and manufactured using extrusion-based additive manufacturing technologies. Mechanical tests were carried out on lattice samples for elasticity characterization purposes. The relationships between sample stiffness and key geometric and manufacturing variables were subsequently used in the case study on the design of a pressure cushion model for validation purposes. Differentiated areas were established according to patient’s pressure map to subsequently make a correlation between the patient’s pressure needs and lattice samples stiffness.

A substantial and wide variation in lattice compressive behaviour was found depending on the key study variables. The proposed methodology made it possible to efficiently identify and adjust the pressure of the different areas of the product to adapt them to the elastic needs of the patient. In this sense, the characterization lattice samples turned out to provide an effective and flexible response to the pressure requirements.

This study provides a generalized foundation of lattice structural design and adjustable stiffness in application of pressure cushions, which can be equally applied to other designs with similar purposes. The relevance and contribution of this work lie in the proposed methodology for the design of personalized therapeutic products based on the use of individual lattice structures that function as independent customizable cells.

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.

The Covid-19 pandemic generated significant changes in the operating methods of hospital logistics departments. The objective of this research is to understand how these changes took place, what collaboration mechanisms were developed with clinical authorities and, to what extent, logistics and clinical care activities should be decoupled to maximize each area's contribution?

The case study is selected to investigate practices implemented during the COVID-19 pandemic in hospitals in Canada. The pandemic presented an opportunity to contrast practices implemented in response to this crisis with those historically used in this environment.

The strategy of decoupling logistical tasks of an operational nature from clinical activities is well-founded and helps free clinical staff from tasks for which they are not trained. However, the decoupling of operational tasks should be combined with an integration of the clinical information flow to the logistics hub players. With this clinical information, the logistics hub can generate its full potential enabling better inventory management decisions to be made.

The concept of decoupling is studied to identify configurations that offer the best benefits for clinical staff.

Due to the rapid development of artificial intelligence (AI) technology, increasing the use of AI in healthcare is critical, but few studies have explored the extent to which physicians cooperate with AI in their work to achieve productive and innovative performance, which is a key issue in operations management (OM). We conducted empirical research to answer this question.

We developed a conceptual model based on the ambidextrous perspective. To test our model, we collected data from 200 Chinese hospitals. One senior and one junior physician from each hospital participated in this research so that we could get a more comprehensive view. Based on the sample of 400 participants and the conceptual model, we examined whether different types of AI use have distinct impacts on physicians’ productivity and innovation by conducting hierarchical regression and post hoc tests. We also introduced team psychological safety climate (TPSC) and AI technology uncertainty (AITU) as moderators to investigate this topic in further detail.

We found that augmentation AI use is positively related to overall productivity and innovative job performance, while automation AI use is negatively related to these two outcomes. Furthermore, we focused on the impacts of the ambidextrous use of AI on these two outcomes. The results highlight the positive impacts of complementary use on both outcomes and the negative impact of balance on innovative job performance. TPSC enhances the positive impacts of complementary use on productivity, whereas AITU inhibits the negative impacts of automation and balanced use on innovative job performance.

In the age of AI, organizations face greater trade-offs between performance and technology management. This study contributes to the OM literature from the perspectives of operational performance and technology management in three ways. First, it distinguishes among different AI implementations and their diverse impacts on productivity and innovative performance. Second, it identifies the different conditions under which automation AI use and augmentation are superior. Third, it extends the ambidextrous perspective by becoming an early adopter of this approach to explore the implications of different types of AI use in light of contingency factors.

The anticipation of organizational change and the transition process often creates uncertainty for employees and can lead to stress and anxiety. It is therefore essential for all organizations, especially those that operate in high-demand working environments, to support the well-being of staff throughout the change process.

Research on how employees respond to the organizational change of relocating to a new work space is limited. To fill this gap in the research, we present a case study examining the well-being of clinical and health care employees before and after a disruptive change: relocation in workplace facilities. In addition, factors that enabled successful change in this high-stress, high-demand working environment were investigated. Interviews were conducted with 20 participants before the relocation and 11 participants after relocation. Following an inductive approach, data were analyzed using thematic analysis to identify key themes.

Our findings suggest that a supportive team, inclusive leadership and a psychologically safe environment, may buffer negative employee well-being outcomes during disruptive organizational change.

This research contributes to the literature on successful organizational change in health care by highlighting the resources which support well-being throughout the change process and enabling the successful transition to a new facility.

Adoption of Clinical Decision Support Systems (CDSS) is a crucial step towards the digital transition of the healthcare sector. This review aims to determine and synthesise the influential factors in CDSS adoption in inpatient healthcare settings in order to grasp an understanding of the phenomenon and identify future research gaps.

A systematic literature search of five databases (Medline, EMBASE, PsycINFO, Web of Science and Scopus) was conducted between January 2010 and June 2023. The search strategy was a combination of the following keywords and their synonyms: clinical decision support, hospital or secondary care and influential factors. The quality of studies was evaluated against a 40-point rating scale.

Thirteen papers were systematically reviewed and synthesised and deductively classified into three main constructs of the Technology–Organisation–Environment theory. Scarcity of papers investigating CDSS adoption and its challenges, especially in developing countries, was evident.

This study offers a summative account of challenges in the CDSS procurement process. Strategies to help adopters proactively address the challenges are: (1) Hospital leaders need a clear digital strategy aligned with stakeholders' consensus; (2) Developing modular IT solutions and conducting situational analysis to achieve IT goals; and (3) Government policies, accreditation standards and procurement guidelines play a crucial role in navigating the complex CDSS market.

To the best of the authors’ knowledge, this is the first review to address the adoption and procurement of CDSS. Previous literature only addressed challenges and facilitators within the implementation and post-implementation stages. This study focuses on the firm-level adoption phase of CDSS technology with a theory refining lens.

The purpose of this paper is to design a new trajectory error compensation method to improve the trajectory tracking performance and compliance of the knee exoskeleton in human–exoskeleton interaction motion.

A trajectory error compensation method based on admittance-extended Kalman filter (AEKF) error fusion for human–exoskeleton interaction control. The admittance controller is used to calculate the trajectory error adjustment through the feedback human–exoskeleton interaction force, and the actual trajectory error is obtained through the encoder feedback of exoskeleton and the designed trajectory. By using the fusion and prediction characteristics of EKF, the calculated trajectory error adjustment and the actual error are fused to obtain a new trajectory error compensation, which is feedback to the knee exoskeleton controller. This method is designed to be capable of improving the trajectory tracking performance of the knee exoskeleton and enhancing the compliance of knee exoskeleton interaction.

Six volunteers conducted comparative experiments on four different motion frequencies. The experimental results show that this method can effectively improve the trajectory tracking performance and compliance of the knee exoskeleton in human–exoskeleton interaction.

The AEKF method first uses the data fusion idea to fuse the estimated error with measurement errors, obtaining more accurate trajectory error compensation for the knee exoskeleton motion control. This work provides great benefits for the trajectory tracking performance and compliance of lower limb exoskeletons in human–exoskeleton interaction movements.

The institutional conditions of primary care provision remain understudied in low- and middle-income countries. This study analyzes how primary care doctors cope with medical uncertainty in municipal clinics in urban India. As street-level bureaucrats, the municipal doctors occupy two roles simultaneously: medical professional and state agent. They operate under conditions that characterize health systems in low-resource contexts globally: inadequate state investment, weak regulation and low societal trust. The study investigates how, in these conditions, the doctors respond to clinical risk, specifically related to noncommunicable diseases (NCDs).

The analysis draws on year-long ethnographic fieldwork in Pune (2013–14), a city of three million, including 30 semi-structured interviews with municipal doctors.

Interpreting their municipal mandate to exclude NCDs and reasoning their medical expertise as insufficient to treat NCDs, the doctors routinely referred NCD cases. They expressed concerns about violence from patients, negative media attention and unsupportive municipal authorities should anything go wrong clinically.

The study contextualizes street-level service-delivery in weak institutional conditions. Whereas street-level workers may commonly standardize practices to reduce workload, here the doctors routinized NCD care to avoid the sociopolitical consequences of clinical uncertainty. Modalities of the welfare state and medical care in India – manifest in weak municipal capacity and healthcare regulation – appear to compel restraint in service-delivery. The analysis highlights how norms and social relations may shape primary care provision and quality.