Hasil untuk "Management information systems"

M. Moore, G. Kearsley

R. Handfield, Ernest L. Nichols

R. Gray, D. Owen, C. Adams

G. DeSanctis, M. S. Poole

Tim Berners-Lee, R. Fielding, H. Nielsen

M. Lv ] ~ ~ A, Fred P Sloan, M. Lynne et al.

K. Aberer, Zoran Despotovic

M. Whitman, H. Mattord

T. Bodenheimer, E. Wagner, K. Grumbach

David Tse, P. Viswanath

Johannes Blömer

L. Diamond

Dave Yates, Scott Paquette

Lawrence A. Gordon, V. Narayanan

K. Henricksen, J. Indulska, A. Rakotonirainy

Qi Yang, Harilaos N. Koutsopoulos

Chao Ma, Haiyu Zhao, Kaiqi Zhang et al.

With the rapid development of supply chain finance, effectively managing risks while safeguarding participant data privacy has become a critical area of research. However, existing traditional risk control models predominantly rely on centralized data processing, which leads to the phenomenon of ”data silos,” hindering the flow and sharing of information. Furthermore, the significant privacy risks associated with centralized processing restrict collaboration among financial institutions, exacerbating the challenges of risk management. In this context, this study proposes a federated risk control method for supply chain finance based on personalized differential privacy optimization. This approach introduces a personalized differential privacy mechanism, enabling different institutions to collaboratively optimize model parameters without directly exchanging sensitive data. This methodology not only effectively safeguards data privacy but also enhances the overall performance of risk control, facilitating multi-party collaboration. Experimental results indicate that, compared to traditional centralized risk control models and other privacy protection methods, the proposed solution demonstrates favorable outcomes in terms of predictive accuracy and model performance while adhering to data privacy protection requirements. This research lays a theoretical foundation for the future development of safer and more efficient cross-institutional risk control systems and provides new insights and technical support for innovative risk management in the field of supply chain finance.

Anthony M. Provenzano, Faiyaz Syed, Jodyn E. Platt et al.

Abstract Background With new payment systems to prompt more sophisticated data activities, primary care practices are developing technological capabilities to manage patient care and information. One burgeoning capability is the collection of social determinants of health (SDOH) data and using that information to provide social care. This study describes the information infrastructure and technological capabilities developed by community health centers (CHCs) and examines the factors influencing SDOH data integration and management in primary care practice. It offers health care leaders insights and strategies to build capacity for managing social care and quality. Methods An observational design was used to examine the technological capabilities of CHCs in Michigan via a practice survey, and factors related to developing information infrastructure were qualitatively explored. The practice survey, semi-structured interviews, and national health center data were analyzed. Sociotechnical systems and organizational theories were used to develop the survey and interview guide. A sample of Michigan CHCs (n = 15) was recruited for the study. The practice survey was administered to CHC leaders, clinicians, and staff (n = 27). Semi-structured interviews (n = 25) were then conducted to explore infrastructural, organizational, and technological factors associated with managing social care and information. Results Michigan CHCs developed capabilities to exchange patient information with state and local partners. Data were typically shared with maternal and infant health (n = 5, 33.3%), mental health (n = 5, 33.3%), substance use (n = 6, 40%), domestic violence (n = 6, 40%), and food assistance (n = 6, 40%) providers, but CHCs did not develop the same capabilities with all social services examined. The interviews revealed that CHCs leveraged health care and government investments in information technology (IT) as a strategy to share data and address quality. The survey results revealed that CHCs developed the ability to use SDOH data to manage population health and provide value-based care. Conclusions IT used to manage social care and address quality is necessary but insufficient in primary care settings. The technological capabilities developed to integrate SDOH data into practice and exchange health information support critical infrastructure and learning opportunities to improve care, quality, and outcomes.

Anand D. Sarwate, Flavio P. Calmon, Oliver Kosut et al.

Since being proposed in 2006, differential privacy has become a standard method for quantifying certain risks in publishing or sharing analyses of sensitive data. At its heart, differential privacy measures risk in terms of the differences between probability distributions, which is a central topic in information theory. A differentially private algorithm is a channel between the underlying data and the output of the analysis. Seen in this way, the guarantees made by differential privacy can be understood in terms of properties of this channel. In this article we examine a few of the key connections between information theory and the formulation/application of differential privacy, giving an ``operational significance'' for relevant information measures.

Raúl Gutiérrez, Víctor Rampérez, Horacio Paggi et al.

The information fusion field has recently been attracting a lot of interest within the scientific community, as it provides, through the combination of different sources of heterogeneous information, a fuller and/or more precise understanding of the real world than can be gained considering the above sources separately. One of the fundamental aims of computer systems, and especially decision support systems, is to assure that the quality of the information they process is high. There are many different approaches for this purpose, including information fusion. Information fusion is currently one of the most promising methods. It is particularly useful under circumstances where quality might be compromised, for example, either intrinsically due to imperfect information (vagueness, uncertainty) or because of limited resources (energy, time). In response to this goal, a wide range of research has been undertaken over recent years. To date, the literature reviews in this field have focused on problem-specific issues and have been circumscribed to certain system types. Therefore, there is no holistic and systematic knowledge of the state of the art to help establish the steps to be taken in the future. In particular, aspects like what impact different information fusion methods have on information quality, how information quality is characterised, measured and evaluated in different application domains depending on the problem data type or whether fusion is designed as a flexible process capable of adapting to changing system circumstances and their intrinsically limited resources have not been addressed. This paper aims precisely to review the literature on research into the use of information fusion techniques specifically to improve information quality, analysing the above issues in order to identify a series of challenges and research directions, which are presented in this paper.