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. 2018 Dec 13:2018:6945498.
doi: 10.1155/2018/6945498. eCollection 2018.

IT-Enabled Clinical Decision Support: An Empirical Study on Antecedents and Mechanisms

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IT-Enabled Clinical Decision Support: An Empirical Study on Antecedents and Mechanisms

Rogier van de Wetering. J Healthc Eng. .

Abstract

Modern hospitals increasingly make use of innovations and information technology (IT) to improve workflow and patient's clinical journey. Typical innovative solutions include patient records and clinical decision support systems to enhance the process of decision making by doctors and other healthcare practitioners. However, currently, it remains unclear how hospitals could facilitate and enable such a decision support capability in clinical practice. We ground our work on the resource-based view of the firm and put forth the notion of IT-enabled capabilities which emphasizes critical IT investment and capability development areas that hospitals could exploit in their quest to improve clinical decision support. We develop a research model that explains how "health information exchange" and enhanced "information capability" collectively drive a hospital's "clinical decision support capability." We used partial least squares path modeling on large-scale cross-sectional data from 720 European hospitals. Outcomes suggest that health information exchange positively impacts information capability. In turn, information capability complementary partially mediates the relationship between information exchange and clinical decision support. Hence, this research contributes to the literature on clinical decision support and provides valuable insights into how to support such innovative technologies and capabilities in clinical practice. We conclude with a discussion and conclusion. Also, we outline the inherent limitations of this study and outline directions for future research.

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Figures

Figure 1
Figure 1
Research model showing the hypothesized relationships.
Figure 2
Figure 2
Estimated causal relationships of the structural model. Note: p < 0.0001.

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