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Within North American institutions of higher education, the sociopolitical construct of whiteness comprises an often implicit set of lessons that are reflected not only in policy and curricula but also in the teaching practices of faculty. Such lessons perpetuate white centricity and supremacy, at enormous costs to those who have been negatively racialized. Therefore, it is critical for white faculty to engage meaningfully with ongoing processes of self-reflection, self-education, and skill development so that they can contribute positively to the interrogation and disruption of whiteness in higher education. This chapter discusses seven processual considerations for white educators who seek to interrogate and disrupt the problem of whiteness in teaching and learning.

Having provided a feasible framework for the use of big data and a learning health system (LHS) in addressing disparities in access to palliative care, this chapter seeks to substantiate the ethical underpinning of that framework, drawing from well-regarded existing sources. The author will also address issues which will likely arise from a successful transition to LHSs such as the nature of informed consent, the impact it will have on medical decision-making in general, and the transformative effect big data and implementation of LHSs will have on existing data sources.

All hypothesized relationships were significant. Specifically, H1 was supported as disconfirmation and surprising consumption were significantly correlated. Moreover, arousal (H2) and outrage (H4a) were functions of surprising consumption and negative affect (H3) and outrage (H4b) were functions of arousal. H4c was also supported as negative affect had a significant direct effect on consumer outrage. In addition, disconfirmation had negative direct effects on both negative affect (H5) and dissatisfaction (H6a) and dissatisfaction was a function of negative affect. Finally, both outrage (H7a) and dissatisfaction (H7b) had significant negative effects on behavioral intentions.

Respondents were recruited to participate in the data collection in a “college town” in the Southeastern United States. Respondents were provided a paper and pencil data collection instrument that include complete survey instructions and the balance of the research design. To adequately test all hypotheses, the researchers developed a unique scenario that described an extreme service failure that takes place during a hotel check-in. Each respondent was asked to read the scenario and then reflect upon it as they responded to items that assessed their feelings toward the hotel check-in experience.

The results provide additional evidence in support of the existence of the satisfaction-dissatisfaction continuum, as well as specifically identifying the affective nature of levels of satisfaction that fall surprisingly well-below the zone of tolerance. The authors feel that the present study is a necessary step to provide a more comprehensive view of the satisfaction-dissatisfaction continuum. Likewise, the authors posit initial evidence of the antecedents and consequences of consumer outrage. This research supports the prior assumptions of Westbrook (1987) about the vast detrimental effects of negative affective responses to service or product failures.

Future research needs to discover just how extremely deficient service has to be to elicit outrage. Is outrage a personal phenomenon with every consumer experiencing it to different degrees? As such, is outrage triggered at different points on the satisfaction-dissatisfaction continuum? The zone of tolerance seems to suggest this, but it would be interesting to discover if at some collective level of dissatisfaction consumers generally begin to show signs of outrage. Likewise, it would be interesting to understand how the level and pattern of outrage results in customers exiting the relationship but also results in loyal customers becoming enemies (e.g. Gregiore et al., 2009; Gregiore and Fisher, 2008).

The motivation for the current study is both pragmatic and theoretical. As alluded to above, it is evident that the level of service customers’ emotional responses to their service experiences are increasing in frequency and intensity. These negative emotions affect the efficacy of service workers and impede the financial performance of service providers. The popular mantra of “anti-woke” consumers, “Go Woke, Go Broke,” is indicative of the importance of negative emotion. Sometimes referred to as “brand activism” (Moorman, 2020; Sarkar and Kotlet, 2019), recent public stances on social and political issues have led to a boycott of Gillette razors, the burning of Nike shoes, and the canceling of Costco Memberships in what has been called “virtue signaling” (Vredenburg et al., 2020). While none of these actions are desirable, the importance of investigating the impact of strong negative emotions (i.e. outrage) is further demonstrated in reports that 65% of consumers expect companies to authentically support such issues (Barton et al., 2018; Edelman, 2018; Larcker and Tayan, 2018; Moorman, 2020).

A great concern regarding the use of data science in any field is privacy. Adequately protecting individuals from the negative effects of maliciously shared personal identifying information is essential. It is however, also important to understand the positive role that protected and shared information can play. This chapter provides a basic understanding of how the concept of privacy has developed in the United States (US) and suggests that continued development of that understanding and the protections provided will occur.

Synthetic patient data produced by Synthea was described in Chapter 6. That data is used to create a baseline for all patients, palliative patients, and deceased palliative patients. Distributions of comorbidities across the patient groups are examined and demographic characteristics. The factors used in palliative care groupings are presented with the synthesized data fields used. The size of the palliative population is again estimated to establish validity.

This chapter introduces Learning Health Systems (LHS) and the impact of data science on such systems. It also examines the necessary properties of data used in LHS and identifies patients who may benefit from a transition to palliative care. Finally, it examines the way LHS can be used to identify racial and social disparities in access to palliative care.

This chapter discusses the steps taken to access and use the ACS five-year data. The format of the data is discussed pointing out the fact that there is no requirement that an ACS five-year variable holds data for the same field year to year. The development of a cross-reference table is discussed allowing the data to be accessed by a common label.

This chapter provides an overview of the role of technology and policy in shaping care plans for patients. Historically, healthcare has lagged behind other industry sectors in adopting and deploying useful technologies, and policy surrounding use is an important component of establishing a long-term strategy. This chapter evaluates the current state of technology in the clinical setting and extends the widely adopted policy-based approaches into the palliative care context.

Data mapping from synthesized data to palliative care characteristics was the final step before the final analysis of survival. Background and foundation for Kaplan-Meier curves are provided before generating curves for the three Palliative Care Groups. Interpretations of the Kaplan-Meier curves are presented along with interpretation of the associated Hazard Curves. Three statistical hypothesis tests, completed on a pairwise basis, are used to verify that the survival curves differ by group. Patients mapped to specific groups may be further supported through advice, counseling, and other services to assist them in moving to a more advantageous care group.