Technology investment in the healthcare industry has targeted both transaction support systems, such as Electronic MedicalRecords (EMR), and decision support technologies, such as clinical data warehouses and data mining software. While EMRtechnology adoption has received considerable attention in research studies, decision support technology adoptiondeterminants have received less attention. This study aims to investigate the determinants of adoption of decision analyticssystems in hospitals and the resulting impact on hospital performance. Using the Heckman selection model (to correct fordiscrete strategic decision-making endogeneity) on a cross-sectional and representative set of U.S. hospitals integrated fromvarious data sources, we examine the determinants of choice and resulting quality performance impacts of adopting clinicalanalytics systems. We find that EMR systems implementations are good predictors of clinical analytics systems adoption. Wealso find that the performance impacts of process enabled EMR systems are partially influenced by adoption of analyticssoftware.
Baird, Aaron; Furukawa, Michael; and Raghu, T.S., "Hospital Analytics Adoption: Determinants of Choice and Performance Impacts" (2011). AMCIS 2011 Proceedings - All Submissions. 75.