Background: Interorganizational networks in healthcare do not always attain their goals. Existing models outline the factors that could explain poor network performance: governance; structure; and the alignment of professional, organizational and network levels. However, these models are very generic and assume a functional approach. We investigate available empirical knowledge on how network structure and governance relate to each other and to network performance in a multilevel context, to get deeper insight, supported with empirics, of why networks (fail to) achieve their goals.
Method: A systematic literature review based on a search of Web of Science, Business Source Complete and PubMed was executed in May 2021 and repeated in January 2022. Full papers were included if they were written in English and reported empirical data in a healthcare interorganizational setting. Included papers were coded for the topics of governance, structure, performance and multilevel networks. Papers from the scientific fields of management, administration and healthcare were compared. Document citation and bibliographic coupling networks were visualized using Vosviewer, and network measures were calculated with UCINET.
Results: Overall, 184 papers were included in the review, most of which were from healthcare journals. Research in healthcare journals is primarily interested in the quality of care, while research in management and administration journals tend to focus on efficiency and financial aspects. Cross-citation is limited across different fields. Networks with a brokered form of governance are the most prevalent. Network performance is mostly measured at the community level. Only a few studies employed a multilevel perspective, and interaction effects were not usually measured between levels.
Conclusions: Research on healthcare networks is fragmented across different scientific fields. The current review revealed a range of positive, negative and mixed effects and points to the need for more empirical research to identify the underlying reasons for these outcomes. Hardly any empirical research is available on the effects of different network structures and governance modes on healthcare network performance at different levels. We find a need for more empirical research to study healthcare networks at multiple levels while acknowledging hybrid governance models that may apply across different levels.
Download full-text PDF |
Source |
---|---|
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9289349 | PMC |
http://dx.doi.org/10.1186/s12913-022-08314-6 | DOI Listing |
J Assist Reprod Genet
January 2025
Medical Genetics & Genomics Unit, AULSS8 Berica, Vicenza, Italy.
This document aims to provide good practice recommendations in order to support maternal-foetal medicine specialists, clinical geneticists and clinical laboratory geneticists in the management of pregnancies obtained after the transfer of an embryo tested with preimplantation genetic testing (PGT). It was drafted by geneticists expert in preimplantation genetics and prenatal genetic diagnosis belonging to the "Working Group in Cytogenomics, Prenatal and Reproductive Genetics" of the "Italian Society of Human Genetics" (SIGU). In particular, the paper addresses the diagnostic algorithm to be applied in prenatal follow-up depending on the type of PGT performed, the results obtained and the related diagnostic value based on the most recent literature data and Italian and international recommendations.
View Article and Find Full Text PDFArch Sex Behav
January 2025
Instituto Nacional de Infectologia Evandro Chagas, Fundação Oswaldo Cruz (INI-Fiocruz), Rio de Janeiro, Brazil.
Perceived risk for HIV acquisition among gay, bisexual, and other men who have sex with men (GBMSM) may not align with their actual sexual HIV exposure. Factors associated with low/moderate perceived risk among GBMSM eligible for pre-exposure prophylaxis (PrEP) (based on their high estimated HIV exposure) have been poorly described in Latin America. This is a secondary analysis of a 2018 web-based cross-sectional survey in Brazil, Mexico, and Peru.
View Article and Find Full Text PDFJ Imaging Inform Med
January 2025
Department of Ophthalmology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, National Clinical Research Center for Eye Disease, Shanghai, 200080, China.
The objectives of this study are to construct a deep convolutional neural network (DCNN) model to diagnose and classify meibomian gland dysfunction (MGD) based on the in vivo confocal microscope (IVCM) images and to evaluate the performance of the DCNN model and its auxiliary significance for clinical diagnosis and treatment. We extracted 6643 IVCM images from the three hospitals' IVCM database as the training set for the DCNN model and 1661 IVCM images from the other two hospitals' IVCM database as the test set to examine the performance of the model. Construction of the DCNN model was performed using DenseNet-169.
View Article and Find Full Text PDFJ Imaging Inform Med
January 2025
School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, USA.
Vision transformer (ViT)and convolutional neural networks (CNNs) each possess distinct strengths in medical imaging: ViT excels in capturing long-range dependencies through self-attention, while CNNs are adept at extracting local features via spatial convolution filters. While ViT may struggle with capturing detailed local spatial information, critical for tasks like anomaly detection in medical imaging, shallow CNNs often fail to effectively abstract global context. This study aims to explore and evaluate hybrid architectures that integrate ViT and CNN to leverage their complementary strengths for enhanced performance in medical vision tasks, such as segmentation, classification, reconstruction, and prediction.
View Article and Find Full Text PDFJ Imaging Inform Med
January 2025
Leiden University Medical Center (LUMC), Leiden, the Netherlands.
Rising computed tomography (CT) workloads require more efficient image interpretation methods. Digitally reconstructed radiographs (DRRs), generated from CT data, may enhance workflow efficiency by enabling faster radiological assessments. Various techniques exist for generating DRRs.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!