Evaluation System of the Supply Chain Stability

Liu, Hanlin; Cao, Yue

doi:10.1007/978-3-642-32054-5_31

Cited by 3 publications

(1 citation statement)

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“…For example, in the phase of requirements and barriers, in addition to those identified, we also recommend to consider other resistances such as internal and external pressures, resistance to change from the staff, and patient satisfaction. Other concerns such as staff's willingness regarding the adoption of technology [18], IT sophistication, [3] and even the staff relationships are also recommended for future considerations of the integration…”

Section: E Pragmatic Interoperability Framework For Systems Integratmentioning

confidence: 99%

Pragmatic Oriented Data Interoperability for Smart Healthcare Information Systems

Liu

2014

2014 14th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing

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Smart healthcare is a complex domain for systems integration due to human and technical factors and heterogeneous data sources involved. As a part of smart city, it is such a complex area where clinical functions require smartness of multi-systems collaborations for effective communications among departments, and radiology is one of the areas highly relies on intelligent information integration and communication. Therefore, it faces many challenges regarding integration and its interoperability such as information collision, heterogeneous data sources, policy obstacles, and procedure mismanagement. The purpose of this study is to conduct an analysis of data, semantic, and pragmatic interoperability of systems integration in radiology department, and to develop a pragmatic interoperability framework for guiding the integration. We select an ongoing project at a local hospital for undertaking our case study. The project is to achieve data sharing and interoperability among Radiology Information Systems (RIS), Electronic Patient Record (EPR), and Picture Archiving and Communication Systems (PACS). Qualitative data collection and analysis methods are used. The data sources consisted of documentation including publications and internal working papers, one year of non-participant observations and 37 interviews with radiologists, clinicians, directors of IT services, referring clinicians, radiographers, receptionists and secretary. We identified four primary phases of data analysis process for the case study: requirements and barriers identification, integration approach, interoperability measurements, and knowledge foundations. Each phase is discussed and supported by qualitative data. Through the analysis we also develop a pragmatic interoperability framework that summaries the empirical findings and proposes recommendations for guiding the integration in the radiology context.

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Section: E Pragmatic Interoperability Framework For Systems Integratmentioning

confidence: 99%