Burnout among primary care providers, which include physicians, nurse practitioners, and physician assistants, can negatively impact patients, providers, and organizations. Researchers have reported that up to 37% of primary care physicians experience burnout, yet the prevalence, predictors, and outcomes associated with primary care nurse practitioner burnout remains unknown. Since 69% of nurse practitioners provide primary care to patients, this dissertation investigates the predictors and outcomes associated with primary care nurse practitioner burnout. A history of burnout as well as the importance of investigating burnout among primary care nurse practitioners are discussed in the first chapter. A systematic review of the predictors and outcomes of primary care provider burnout is discussed in the second chapter. The third chapter describes a cross-sectional study conducted among 396 primary care nurse practitioners from New Jersey and Pennsylvania, which investigated whether the practice environment is associated with nurse practitioner burnout. The fourth chapter describes a cross-sectional study investigating whether the use of multifunctional electronic health records is associated with primary care nurse practitioner burnout. The fifth chapter includes another cross-sectional study examining the relationship between primary care nurse practitioner burnout and quality of care, and if the practice environment moderates the relationship between burnout and quality of care. Finally, the sixth concluding chapter summarizes the findings from chapters two to five and provides recommendations for future research, practice, and policy.

This dissertation describes the patient experience. In Chapter One, we review the currentstate of patient-centered care with a specific focus on women’s experiences in infertility treatments, highlighting the gaps in our understanding of their experiences. The four independent but complementary aims of the dissertation studies are then introduced, and we identify how they address current gaps to advance our understanding of women’s experiences using reproductive technology. In Chapter One, we also introduce an innovative service design tool, journey mapping, as well as the Burden of Treatment Framework which guided the dissertation. Chapter Two, An Integrative Review of Journey Mapping to Document the Patient Experience, presents an integrative review examining the use of journey mapping in health care services research. In the analysis of these twenty-two studies, we demonstrate how journey mapping has been adapted to describe the patient experience. While the qualitative rigor of the included studies is of good quality, the inconsistent application of design standards in the accompanying visualizations, when present, suggests that further work and guidance is needed in the adaptation of this service design tool for the health research field. In Chapter Three, Journey Mapping the Patient Experience of IVF: A Social Media Analysis, we used posts from the largest infertility subreddit and patient-facing online resources to describe and visually depict the patient experience of IVF in two journey maps representing the experiences of women in their first cycle of infertility treatment and women in repeat cycles. Findings highlighted problems and unmet needs in the infertility treatment experience, including information needs, communication needs, and support needs. Chapter Four, The Burden of Treatment in IVF: An Analysis of Social Media Using a Framework for Chronic Complex Conditions, used qualitative descriptive methodology to guide inductive and deductive content analysis of posts from the same infertility subreddit. Findings suggest that Eton’s framework is applicable to infertility, but can be expanded by the inclusion of three new constructs that we identified. Finally, Chapter Five synthesizes the key findings across these four aims, outlining their strengths and limitations, and discussing implications for future research, policy, and clinical practice.

Federally Qualified Community Health Centers - aka FQHCs, Community Health Centers (CHCs), Neighborhood Health Centers, or simply Health Centers - are public and private non-profit healthcare organizations funded under Section 330 of the Public Health Service Act, directed by a consumer board of directors, and complying with Federal requirements to serve medically underserved populations. In 2017 FQHCs saw more than 27 million individual patients in the United States, of whom approximately two million were seen by health centers in New York State (Bureau of Primary Health Care 2017). Despite these staggering figures, relatively little academic work has investigated how these health centers operate at an administrative and bureaucratic level. To study the bureaucratic practice of FQHCs, this research utilizes an ethnographic approach, conducted over a period of three-plus years at a FQHC in New York State (pseudonymously called Care Center). It incorporates structured interviews, informal interviews, the collection of fieldnotes, and participant observation, as well as qualitative data analysis. Collectively this research approach produces a complex portrait of how bureaucratic activity at the specific FQHC field site was organized, conducted, and structured within the context of substantial growth in the FQHC program. The setting of the study offers a unique opportunity to explore the implications of this bureaucratic activity on FQHCs and, by extension, other safety-net healthcare institutions in the United States. This research also delivers a substantial historical account of the emergence of the FQHC program in order to connect that account to the broader arc of healthcare history in the United States during the 20th and 21st centuries. This connection demonstrates the linkages between specific aspects of FQHC bureaucratic practice and larger trends in health care more generally. The emphasis on “need” as a discursive object that is frequently referenced and utilized as an organizing mechanism by FQHC bureaucracy allows us to better understand and problematize the use of need as a criterion for organizational growth.

As the delivery and management of healthcare continues to become increasingly complex, confronted by numerous economic, political and public health challenges, it is crucial for healthcare organizations to effectively develop and support their clinical and administrative leaders. A national staffing shortage in healthcare coupled with a global pandemic has increased burnout for healthcare providers. Retaining, engaging, and developing talent has taken on an urgency not seen before in healthcare organizations. The academic healthcare system has additional layers of intricacy with its tripartite clinical, educational, and research missions. Collaboration – and the behaviors and attitudes that enable it – is key for all leaders to negotiate across disciplinary boundaries within the academic health enterprise. Healthcare organizations must carefully consider how to develop and empower leaders capable of both leading teams and leading collaboratively, with peer leaders across the system. This study used a mixed-method case study approach to evaluate an interprofessional, team-based leadership development program in an academic health system to identify the program elements and learning experiences that influenced the participants’ ability to demonstrate the tenets of collaborative leadership and the factors that fostered or inhibited their abilities. Mid-level level leadership teams were nominated to the 10-month program, which utilized a blended learning approach of in-person days, live virtual sessions and online content and interaction. Teams were also required to complete a capstone project to allow the opportunity for integrating program learning through action while contributing to organizational change and innovation. Historical data from focus groups, evaluations and pre and post self-efficacy assessments from six cohorts were analyzed for this study. Ten semi-structured interviews comprised the new data collected. Focus group and evaluation data was derived from all program participants from six cohorts, consisting of 145 participants, in which 97/145 (67%) were females and 48/145 (33%) were males. Physicians comprised 28% of the group, nurses/other clinical specialists 31%, and 40% were administrators. The pre and post self-efficacy assessment data was limited to 65 participants. It is evident from the findings that leadership development as an interprofessional team, and as part of a larger cohort, is a powerful way to improve team cohesion, build relationships across the healthcare system, and enable boundary crossing – all vital capabilities to collaborative leadership. Learning as a team revised participants’ mental models by enabling an appreciation of diverse perspectives and self-awareness. The design of the program, particularly the program’s length and the action learning experience, were key factors in allowing the leadership ability of teams to mature. The program gave participants the dedicated time and space to build trust, solve problems, and reflect as individuals and as team. During the program and after, the most prevalent challenge for leaders was finding the time to establish and maintain relationships, which is critical to collaborative leadership. Other limiting factors were leadership turnover, lack of senior leadership support, and barriers to physician engagement. The organization in this study, like other academic health systems, employs a matrixed structure. While the matrixed structure necessitates collaboration, inherent stressors such as ambiguity around roles, decision-making abilities, power, and competing priorities emerged as a challenge to collaborating with peer leaders. The major recommendations for leadership development in an academic health system derived from this study are to: 1) design leadership development learning experiences within the context of an interprofessional team, 2) consider how participants may be enabled and inhibited in their leadership practice by organizational structures and cultures and, 3) ensure that the program length allows for the adequate time and space to learn, integrate and apply new ways of leading.

Markov Decision Processes (MDP) is a common framework for modeling sequential decision-making problems, with applications ranging from inventory and supply chains to healthcare applications, autonomous driving and solving repeated games. Despite its modeling power, two fundamental challenges arise when using the MDP framework in real-worldapplications. First, the optimal decision rule may be highly dependent on the MDP parameters (e.g., transition rates across states and rewards for each state-action pair). When the parameters are miss-estimated, the resulting decision rule may be suboptimal when deployed in practice. Additionally, the optimal policies computed by state-of-the-art algorithms may not be interpretable and can be seen as a black-box. Among other reasons, this is problematic as the policy may not be understandable for the people that are supposed to operationalize it. In this thesis, we aim to broaden the applicability of the MDP framework by addressing the challenges of robustness and interpretability. In the first part of the thesis, we focus on robustness. We introduce a novel model for parameter uncertainty in Chapter 2, that is significantly less pessimistic than prior models of uncertainty while enabling the efficient computation of a robust policy. In Chapter 3, we consider a healthcare application, where we focus on proactively transferring patients of a hospital to the Intensive Care Unit, to ameliorate the overall survival rates and patients’ flow. In the second part of this thesis, we focus on interpretable algorithms, with an emphasis on the application to find novel triage protocols for ventilator allocations for COVID-19 patients. In Chapter 4, we introduce a simulation model to estimate the performance of the official New York State (NYS) triage protocol at various levels of shortages of ventilators, using a real data set of patients intubated during Spring 2020 because of COVID-19 complications. In Chapter 5, we introduce our algorithmic framework for computing interpretable (tree) policies and apply our methods to learn novel triage protocols.

Background: In late 2019, the SARS-CoV-2 virus was identified in Wuhan, China. Within a matter of weeks, the virus spread to the United States and many other countries around the world. By March 5, 2020, the first case of COVID-19 (the disease caused by the SARS-CoV-2 virus) was identified in the state of Tennessee, and the first known COVID-19 associated death in the state followed on March 20, 2020. In 2020 alone, there were 514,922 confirmed cases and 6,760 COVID-19 attributed deaths in the state of Tennessee. It is important to understand, however, that confirmed COVID-19 deaths do not accurately capture the overall impact of the COVID-19 pandemic. Many additional deaths resulted either directly from COVID-19 disease, or indirectly from the COVID-19 pandemic. This report seeks to briefly summarize the early stages of the COVID-19 pandemic and its immediate impact on the state of Tennessee and outline a novel tool for rapidly identifying excess mortality using publicly available data. Methods: Calculating excess mortality is likely to accurately capture the total deaths that occurred because of the COVID-19 pandemic, including deaths that may not have been directly attributed to COVID-19 disease. To calculate excess mortality, four years (2017-2020) of data were collected from three sources, online funeral home listings, newspaper obituaries, and the state health department. Using simple linear regression, number of deaths by month for 2017, 2018 and 2019 were used to predict expected deaths, by data source, for each month of 2020 as the baseline for comparison. The percent difference of actual deaths from the expected deaths was then calculated and compared by data source. Results: By quarter, 2020 actual funeral home listings differed from the expected by Q1:-9.29%, Q2:11.50%, Q3:7.36% and Q4:55.90%. Newspaper obituaries differed from the expected by Q1:8.05%, Q2:20.00%, Q3:9.93% and Q4:44.55%. State reported Washington County data differed from the expected by Q1:7.68%, Q2:18.85%, Q3:12.21%, Q4:48.91%. When comparing online funeral listings and newspaper obituaries to state reported data for Washington County, it is clear that either of the public data sources could have been used to identify spikes in excess mortality throughout 2020. Conclusion: These findings reveal that publicly available online funeral home death listings and local newspaper obituaries can be used as a tool to help identify spikes in excess mortality in ‘real-time’. The most notable impact being that the newspaper obituary and funeral home data was available in near real time, whereas the state reported Washington County data was not made available until October of 2021.

This thesis describes an exploratory research project initiated to facilitate curriculum planning for and evaluation of a social work training program in the field of community mental health. The literature concerning community mental health, the historical relationship of social work to this field, and current issues in social work manpower and education was reviewed as part of the thesis project. On the basis of the review, a study was undertaken to determine appropriate priorities for training Master ' s level social worker’s specializing in this field of practice. The researcher's decided that these priorities would be determined in terms of the future professional practice and educational needs of social workers in this field, as reflected in the five variable s of the roles, functions, and tasks they may be performing in ten years and the skills and knowledge they ma y be utilizing in this practice.

This research was undertaken to determine whether the growth of computer-supported information systems in community mental health centers can be characterized by distinct stages of development. Data collection and analysis were designed to answer the following two questions: (1) Can distinct stages of information system growth be characterized by common profiles of computer-supported applications? (2) Are there characteristic groups of enabling factors (i.e., organization of data processing activities, management planning and control techniques, and user involvement) consistent among community mental health centers at any given stage of growth? This study draws upon earlier work by Nolan who identified distinct stages which characterize the pattern of information system growth in business organizations. A model reflecting the unique characteristics of community mental health centers was formulated to describe the aspects of information system growth addressed by this study. The components of the model were used to develop three scenarios describing the hypothesized characteristics of mental health information systems at three different stages of growth. Data for this study were obtained through a two-phase survey. The preliminary survey identified which community mental health centers are using computer-supported applications. The second survey collected detailed data about each model component using a stratified random sample of centers using computer-supported information systems. Responses to the preliminary survey showed that seventy-nine percent of the centers are using computer-supported information systems. In addition, the majority of centers with manual systems have plans to automate within one year. By contrast, a 1974 survey reported that only one-fourth of the centers were using computerized information systems. The number of centers using computers has therefore increased dramatically during the last five years. The analysis of computer-supported applications showed that a refinement of the hypothesized applications profile for each stage would be more representative of the state of the art of computerized applications in community mental health centers. The original model depicting three stages of applications development was extended to four stages. The predominant types of applications being developed are those supporting administrative and clinical recordkeeping functions. These findings indicate that the development of computer-supported applications in centers parallels applications development in other mental health programs. The analysis of characteristics of enabling factors revealed distinct differences among centers in each stage of development. The study results clearly showed that centers which are developing the most comprehensive sets of computerized applications are implementing formal planning and control techniques and user involvement strategies. These centers also reported the most favorable staff attitudes toward the usefulness of the information system and the most interest in developing new applications. While distinct characteristics of data processing organization variables were identified, these characteristics did not reflect a progression toward increased formalization of the data processing function.

No description available.

Interventions that promote alternative income generation (AIG) to address social determinants of health are associated with increased household income and improved health outcomes. Organizations have used beekeeping to address poverty and other development goals with mixed outcomes. Beekeeping presents a viable option to promote AIG. Organizations increase their risk of failing to achieve positive outcomes when beekeeping interventions are inadequate. This project aims to develop an organizational assessment tool that measures organizational position in relation to evidence-based factors for income generation and improved quality of life among global beekeepers. The assessment is organized into seven domains that correspond with PRECEDE-PROCEED and may be a useful iterative diagnosis, evaluation, and monitoring tool. The instrument was developed through a literature review and adapted to PRECEDE-PROCEED to include organizational, ecological, and evaluation factors. The instrument was pretested among content experts and revised before being administered to BEECause Gambia (BCG). BEECause’s mission is to reduce poverty and promote pollinator and honeybee populations. The assessment results were analyzed to propose recommendations to BCG to consider in future strategic planning processes. The pretest included a survey and cognitive interviews to identify and revise problematic statements. Pretest survey statements were evaluated using a five-point Likert scale. Scores £ 3 directly informed the cognitive interview protocol, which allowed respondents to explicate their feedback and concerns. Interview results were analyzed for trends between respondents and corroborated against evidence-based factors. Statements were revised to consider optimal domain alignment, content validity, and meaningfulness. The revised survey included seven domains, 49 statements and one open-ended response. BEECause’s overall assessment score was 4.1. By domain their scores were 4.1 in design, 4.1 in implementation, 4.2 in predisposing factors, 3.1 in enabling factors, 3.8 in reinforcing factors, 4.2 in impact, and 5.0 in outcomes. Domain and statement scores underwent SWOT analysis. Scores ³ 4 were considered strengths. Scores < 4 were considered weaknesses, opportunities, and threats. The assessment resulted in three recommendations to BCG including 1) build financial security, 2) build stakeholder confidence, and 3) address enabling and reinforcing factors. This project may serve as a model for public health efforts in AIG.