Clinical Communication and Collaboration: Three Essays Examining the Impact of IT Interventions on At-Risk Populations Using Healthcare Analytic

This dissertation examines the impact of information technology intervention on clinical communication and collaboration between patients, physicians, specialized clinicians, care management organizations and researchers. Clinical communication and collaboration refers to the transfer and exchange of information; it is anticipated to reduce cost, improve outcomes, and increase the quality of healthcare. Within the highly fragmented healthcare sector partnerships facilitate operational and clinical efficiency. The study population for this dissertation includes Medicare, Medicaid, at-risk populations and disenfranchised individuals within the United States. The emphasis on individuals facing inequities is imperative as State and Federal statistics associate large percentages of these communities' with chronic diseases and high cost. This investigation presents an in-depth analysis by considering the impact of -- individual and organizational level attributes on a societally relevant quandary. The goals of the dissertation are accomplished through three essays.;Essay one examines the effect of message framing on the adoption of smart cards in high-risk, vulnerable patient populations. Smart card technology is used to exchange and transfer information from clinician to clinician and or from provider to healthcare agencies given appropriate patient consent. The study is theoretically grounded using the technology acceptance model; it assesses how communication (using framed messages as interventions) impacts the adoption of technology. A survey is a primary source of data, it enables the elicitation and explication of patient preferences. This work provides an understanding of the factors affecting adoption and how they vary across high and low-risk groups because an IT intervention cannot succeed without user buy-in.;An experimental survey design is utilized to collect patient data, and logistic regression is used to test the model hypothesis. The final sample of 673 participants included respondents 18 years or older, of which 54% are female. Twenty-seven percent of the participants reported 1 or 2 chronic conditions and 20% have 3 -- 8 chronic diagnosis. Results indicate that information accuracy, perceived ease of use, social influence, data security and location monitoring all impact a patient's likelihood to use the smart card. Within the high-risk group, gain-framed messaging changes the importance of two factors on likelihood to adopt; whereas, the loss message affects four. Amongst low-risk participants, the gain-frame and loss-framed messages vary the impact one distinct construct respectively when compared to the high-risk group.;This paper makes various theoretical contributions. First, adapting the technology acceptance model by adding concerns for location monitoring and information accuracy; also, by enhancing the perceived usefulness construct and narrowing the boundary of social influence to include healthcare actors. Second, we integrate the technology acceptance theory with the prospect theory, using message framing to assess the impact of gain and loss message framing on patient likelihood to use while considering the patient's disease based risk level.;Practical implications emanating from this study have the potential to foster consent management, clinical communications, and collaboration by maximizing the functionality of the smart card. Healthcare entities should work on a few main factors: before enrollment, patients should be informed of all the benefits of using smart cards. Specifically, the smart cards potential to improve information accuracy, provide financial incentives, improve the efficiency of care and decision making. Marketing material is potentially more influential on the likelihood to adopt when gain-framed messages are used. Additionally, though the loss-frame can be used to express financial and efficiency benefits of the smart card; providers should limit its use due to a negative impact on adoption.;The velocity at which health care data grows is well known. Statisticians estimate that US healthcare will accumulate a zettabyte of data -- the highest digital metric unit. Essay's two and three pay homage to this trend by utilizing secondary data to predict and assess the impact of IT intervention. Also in alignment, essay one conceptually models the factors impacting the implementation of an IT intervention.;To create better utilization of healthcare dollars, we need to decrease the waste in the system that is caused by inappropriate Hospital and Emergency Room utilization. Essay two develops a predictive model to assess emergency room utilization and provides prescriptive resource allocation measures for physician practices. Collaboration is supported by the implementation of clinical decision support systems and disease registries. (Abstract shortened by ProQuest.).