Abstract
While healthcare technologies have revolutionized the effectiveness and efficiency of the healthcare services industry, the cost of implementing these innovations is exorbitant, driving the ever-increasing cost of healthcare services. Because the costs are so high, technology adoption decisions are pivotal.
The entire technology adoption process requires participation from stakeholders including frontline caregivers. Frontline healthcare employees charged with delivering care possess knowledge essential to successfully integrating technologies into healthcare facilities. Increased understanding of employee perspectives on the importance, effectiveness, and difficulty of implementing healthcare technologies enables facility leaders to make more informed choices.
This work explores how working within high-performing organizational structures, specifically Kaiser-Permanente’s (KP) Labor Management Partnership (LMP), and participation in Unit-Based Teams (UBTs), impact employee perceptions of technology and how facility-level characteristics affect the growth and development of those UBTs. The analyses presented are supported by two data sources. First, the January-February 2019 WayMark Analytics survey collected the KP employees’ perceptions of the ease and importance of various workplace characteristics, including the ease of implementing new technologies in their workplace and the importance of those technologies to their work. WayMark surveyed 3,357 front-line employees and management staff working in facilities including hospitals, medical offices, and call centers. Second, the Ben Hudnall Memorial Trust Project provided data tracking the creation and capability of 4,152 UBTs operating within the KP healthcare network between 2017 and 2024.
The dissertation has been divided into three chapters. The first chapter, The Kaiser Permanente Labor Management Partnership and the Implementation of Unit-Based Teams, analyzes the 2019 WayMark Analytics survey using fixed-effects linear regression models. These analyses focus upon front-line employees’ perspectives around KP’s implementation of UBTs and workplace technologies. Key findings include:
1. Increased employee UBT participation increases positive views of technology’s importance.2. LMP training significantly impacts perceptions of technology effectiveness, but not workplace technology’s importance and ease/difficulty of implementation.
3. KP union-represented frontline employees and KP managers possess similar perceptions of the importance, effectiveness, and ease/difficulty of implementing workplace technologies.
The second chapter, Categorization of Beneficial Environments for Unit-Based Teams, examines the impact of KP’s implementation of UBTs in conjunction with its LMP as a means to address department-level operational challenges. The aim is an environment where managers, doctors, nurses, and other union-represented employees collaborate on workplace problems. Through analysis of the Ben Hudnall Trust dataset, this paper demonstrates that while the rate at which facilities create new UBTs depends on facility-level factors, the rate at which those UBTs improve their operational effectiveness depends on higher-level, contextual factors such as operational region and time period. The number of UBTs operating within a facility during the previous year has only a negligible impact on current UBT performance or rate of UBT creation, which suggests that there is continuing capacity for growth.
The third chapter, Linkages Between Unit-Based Team Development and Views on Technology Inside Healthcare Facilities, analyzes UBT performance data from 2018, 2019, and 2020, using the Path to Performance (P2P) framework. Combining that dataset with the 2019 WayMark LMP employee survey indicates that employee perceptions of the importance, effectiveness, and ease/difficulty of implementing workplace technology are strongly associated with UBT performance. Quantitative analysis using Ordinary Least Squares (OLS) and Multinomial logistic regression models suggests a strong connection between UBT performance and employee perceptions of technology. Additionally, employees' lack of response to technology perception survey questions is a predictor of negative UBT development dynamics. Non-response patterns may be an important predictor of underlying divides with implications for the initiative.Note that this research effort used ChatGPT (OpenAI) for assistance with R programming syntax, table formatting, and presentation of statistical model equations. The author independently verified all code, results, and interpretations.