Date

5-23-2025

Department

School of Communication and the Arts

Degree

Doctor of Philosophy in Strategic Media (PhD)

Chair

Crystal Sears

Keywords

call center, contact center, artificial intelligence, strategic communication, patient satisfaction, healthcare contact center, quantitative research

Disciplines

Communication

Abstract

With the advancement of telecommunication and digital technologies over the past few decades, there has been a concomitant fundamental shift in the contact center industry and the role it plays. What was once an internally focused center with a goal of cost-reduction in providing a necessary service has now evolved to become a proactive frontline for anticipating customer needs, promoting quality customer service, as well as playing a larger role in corporate decision-making (Gans et al., 2002). In order to fulfill these complex customer interactions, modern day contact centers are increasingly turning to artificial intelligence and large language models to help meet the demands of consumers. Traditional contact centers are being forced to re-evaluate their processes, particularly in the context of implementing new digital strategies such as chatbots and automated triaging systems. This becomes exponentially more complex when applied to healthcare contact centers that must be able to triage patient calls according to urgency, schedule appointments, and input patient information (Rohleder et al., 2013). What remains unexplored, however, is the overall effect on patient satisfaction when using these digital technologies implemented by contact center agencies which could potentially lead to unintended downstream population health effects. Guided by the media richness theory, this research evaluates the richness of certain communication channels that are provided by a healthcare-related contact center. Overall organizational success hinges on its ability to process information at different levels of appropriate richness to clarify ambiguity and deliver appropriate answers or results (Daft & Lengel, 1983).

This retrospective quantitative study sought to evaluate how the current use of artificial intelligence within a single contact center called PatientSync has affected patient satisfaction in relation to internal efficiency metrics. Previously gathered data for both patient satisfaction survey scores as well as internal call center efficiency metrics were acquired and analyzed during a 12-month period prior to the implementation of an AI-drive chatbot as well as during a 12-month period post implementation. Relevant statistical methods were used as described in Chapter 3. The research is divided into four primary research questions which guided the results and discussion chapters. RQ1 evaluated the relationship between the use of AI-based digital tools in call centers and customer satisfaction. Results showed that there was a significant increase in patient satisfaction post-implementation, however, there was no clear answer as to why there was an increased due to the nebulous reasons provided by the chatbot and no AI-centered survey metrics. RQ2 evaluated the relationship between the use of AI-based digital tools in call centers and the internal efficiency metrics. Results showed that although the total number of calls increased post-impl

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