Browsing by Author "Yang, Ning"
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Item Effect of Patient-Physician Relationship on Withholding Information Behavior: Analysis of Health Information National Trends Survey (2011-2018) Data(JMIR Publications, 2020) Yang, Xin; Parton, Jason; Lewis, Dwight; Yang, Ning; Hudnall, Matthew; University of Alabama TuscaloosaBackground: Patients' withholding information from doctors can undermine medical treatment, create barriers for appropriate diagnoses, and increase systemic cost in health care systems. To date, there is limited literature detailing the association between trends of patients withholding information behavior (WIB) and the patient-physician relationship (PPR). Objective: The aim of this study was to explore the prevalence trend of WIB after 2011 and examine the effects of PPR on WIB and its time trend. Methods: A total of 5 iterations of data from the Health Information National Trends Survey (years: 2011-2018; n=11,954) were used to explore curvilinear trends of WIB among the US population. Multiple logistic regression models were used to examine curvilinear time trends of WIB, effects of PPR on WIB, and moderation effects of PPR on the WIB time trend. Results: The WIB prevalence has an increasing trend before 2014, which has the highest rate of 13.57%, and then it decreases after 2014 to 8.65%. The trend of WIB is curvilinear as the quadratic term in logistic regression model was statistically significant (P=.04; beta=-.022; SE=0.011; odds ratio [OR] 0.978, 95% CI 0.957-0.999). PPR is reversely associated with WIB (P<.001; beta=-.462; SE=0.097; OR 0.630, 95% CI 0.518-0.766) and has a significant moderation effect on time trends (P=.02; beta=-.06; SE=0.025; OR 0.941, 95% CI 0.896-0.989). In general, poor quality of PPR not only significantly increased the WIB probability but also postponed the change of point for WIB curvilinear trend. Conclusions: Findings suggest that the time trend of WIB between 2011 and 2018 is curvilinear and moderated by the quality of the PPR. Given these results, providers may reduce WIB by improving PPR. More research is needed to confirm these findings.Item Patterns and Influencing Factors of eHealth Tools Adoption Among Medicaid and Non-Medicaid Populations From the Health Information National Trends Survey (HINTS) 2017-2019: Questionnaire Study(JMIR Publications, 2021) Yang, Xin; Yang, Ning; Lewis, Dwight; Parton, Jason; Hudnall, Matthew; University of Alabama TuscaloosaBackground: Evidence suggests that eHealth tools adoption is associated with better health outcomes among various populations. The patterns and factors influencing eHealth adoption among the US Medicaid population remain obscure. Objective: The objective of this study is to explore patterns of eHealth tools adoption among the Medicaid population and examine factors associated with eHealth adoption. Methods: Data from the Health Information National Trends Survey from 2017 to 2019 were used to estimate the patterns of eHealth tools adoption among Medicaid and non-Medicaid populations. The effects of Medicaid insurance status and other influencing factors were assessed with logistic regression models. Results: Compared with the non-Medicaid population, the Medicaid beneficiaries had significantly lower eHealth tools adoption rates for health information management (11.2% to 17.5% less) and mobile health for self-regulation (0.8% to 9.7% less). Conversely, the Medicaid population had significantly higher adoption rates for using social media for health information than their counterpart (8% higher in 2018, P=.01; 10.1% higher in 2019, P=.01). Internet access diversity, education, and cardiovascular diseases were positively associated with health information management and mobile health for self-regulation among the Medicaid population. Internet access diversity is the only factor significantly associated with social media adoption for acquisition of health information (OR 1.98, 95% CI 1.26-3.11). Conclusions: Our results suggest digital disparities in eHealth tools adoption between the Medicaid and non-Medicaid populations. Future research should investigate behavioral correlates and develop interventions to improve eHealth adoption and use among underserved communities.Item Technology That Rocks the Cradle: Introducing Artificial Intelligence Awareness(University of Alabama Libraries, 2022) Yang, Ning; Carter, Michelle; Bott, Gregory; University of Alabama TuscaloosaThere is increasing attention and interest being paid to artificial intelligence (AI). We can see AI applications in our everyday lives, including web search engines, recommendation systems, devices understanding human speech or input, self-driving cars, and automated decision-making systems (“Artificial intelligence” 2022). The extensive uses of AI cover all aspects of our lives. As we enter the era of cloud computing, the power of AI has been accelerated. We enjoy more customized information feeds AI provides, and it allows organizations to make smarter and faster decisions on a larger scale (Accenture 2022). There has been a variety of research investigating AI, its goals, and its uses as a tool in the field of IS. However, to our knowledge, no prior research has addressed people’s perception of the existence of AI and AI’s influences on our decisions. Although we choose to use AI to help us make better decisions, we need to reflect on how we perceive AI, its purpose, its practice, and its impacts on our decisions and our behaviors associated with its use. In this two-essay dissertation, we accomplish two primary goals. First, in Essay One, we propose the new concept of Artificial Intelligence Awareness (AIA), demonstrate how it can be applied to many subareas of IS, and develop several opportunities for future research related to AI uses. Second, in Essay Two, we develop the measure to capture AIA and test the model to show how it impacts thoughts, emotions, and even behaviors related to AI-powered smart technology uses in the context of the virtual assistant.