Date of Award

Summer 7-26-2013

Degree Type


Degree Name

Doctor of Philosophy (PhD)


Computer Information Systems

First Advisor

Dr. Mark Keil

Second Advisor

Dr. Arun Rai

Third Advisor

Dr. Lars Mathiassen

Fourth Advisor

Dr. Edward Rigdon

Fifth Advisor

Dr. Rajiv Kohli - William and Mary



The 2009 American Recovery and Reinvestment Act has earmarked 27 billion dollars to promote the adoption of Health Information Technologies (HIT) in the US, and to gain access to these funds, providers must document “Meaningful Use” during the care process. While individual HIT use according to lean measures, including meaningful use, is prevalent in the IS literature, few studies have incorporated rich measures to account for the task, the technology, and the user in a team context. This dissertation conceptualizes Team Deep Structure Use of Computerized Provider Order Entry (CPOE) as an IT- enabled coordination mechanism, and Relational Coordination as the inherent ability of clinical teams to coordinate care spontaneously using informal, relationship based mechanisms. IT-enabled and Relational Coordination mechanisms are each evaluated across five maximally different patient conditions to simultaneously examine their impact on our outcome measure, Patient Satisfaction with the clinical care team.

The extant literature has established a deep understanding of IT adoption shortly after implementation, yet the literature is silent on the antecedents of IT use according to rich measures well after the shake down phase, a period in which the majority of organizations operate. We incorporate the Adaptive Structuration Theory (AST) constructs of Faithfulness of Appropriation, and Consensus on Appropriation as the focal antecedents of Deep Structure Use of the clinical system by team members. To our knowledge, no prior research has linked these two AST constructs to clinical outcomes through the incorporation of a rich use mediator such as Deep Structure Use of a Health IT.

To test our model, we relied on survey responses from 555 physicians, nurses and mid-levels which had cared for 261 patients across five patient conditions, ranging from vaginal birth, to organ transplant, as well as pneumonia, knee/hip replacement and cardiovascular surgery. Our results confirm that the Adaptive Structuration constructs of Faithfulness of Appropriation and Consensus on Appropriation, generate positive and statistically significant path coefficients predicting Team Deep Structure Use of CPOE. We also report differential effects on Patient Satisfaction with the care team resulting from technology use. Results range from a significant positive path coefficient (.285) associated with higher Team Deep Structure Use on combined Pneumonia and Organ Transplant teams, to a significant negative path coefficient (-.174) on cardiovascular surgery teams. As expected, Pneumonia, Organ Transplant and Cardiovascular Surgery teams all reported positive effects on Patient Satisfaction with the care team as a result of higher Relational Coordination scores. For teams caring for patient conditions consistently associated with a shorter length of stay, including vaginal birth and knee/hip replacement, higher reported use of IT- enabled, or Relational Coordination mechanisms, did not result in a significant increase in Patient Satisfaction.

This dissertation contributes to the growing Health IT literature, and has practical implications for clinicians, hospital administrators and Health IT professionals. This dissertation is the first to operationalize a rich measure of use of an HIT by clinical teams, and to simultaneously measure the impact of IT enabled and Relational Coordination mechanisms on Patient Satisfaction. Secondly, through the introduction of Adaptive Structuration constructs, our model establishes a methodology for predicting rich, nuanced use in teams well after the initial shake down phase associated with recent HIT implementation. Through the juxtaposition of the impact of IT-enabled and Relational Coordination mechanisms across patient conditions, practitioners can design interventions and adjust the level of resources applied to process improvement accordingly.