ABSTRACT
Workforce burnout is an increasing problem across many industries and professions, with significant impacts on both manufacturing and service sectors. For example, burnout is a major problem for more than 80% of healthcare systems, with the costs of replacing doctors who leave their job and reduced clinical hours related to burnout are estimated at $4.6 billion annually. Productivity is reduced, mental health is affected, family relations are weakened, and a solution to all of this is not clear. More than 10 years ago, the global cost of burnout was estimated to exceed $300 billion annually. Major societal events, such as the recent ongoing COVID-19 pandemic or political unrest, can further exacerbate individual burnout and its impacts. Advancements in burnout research over the past two decades have mostly been to develop methods for measuring and classifying burnout and in lessons learned empirically from various intervention implementations, but with little-to-no analytic modeling research to help inform effective policies. In a recent report in fact, the National Academy of Medicine emphasized the need to develop analytic models that better quantify the extent of the problem in a way that translates into actionable results in addition to approaches for understanding the impact of interventions. The overall aim of our proposed research accordingly is to develop and apply analytic disease progression models to help understand burnout dynamics and evaluate the long-term benefits of interventions prior to wide-scale implementation testing. The proposed dissertation includes three fundamental contributions. First, we develop and introduce two disease progression models of individual and organizational burnout based on Markov chains, parameterized and linked from limited data via optimization and simulation models. We also illustrate the use of the developed models to estimate and compare the relative effectiveness of various strategies and interventions to reduce burnout, with a focus on estimating long-term impacts from limited early testing data, contributing to pre-randomized trial methods. Second, we leverage the models to estimate the effect of COVID-19 on two healthcare professional populations in two case studies. Finally, we propose several potential methodological extensions to disease progression modeling including investigating the effect of higher order nesting, bootstrapping and time non homogeneity. Results indicate that the disease progression models of the proposed type can accurately model individual and institutional burnout progression to help better understand the dynamics of burnout and analyze the effectiveness of potential interventions to make more informed decisions. Sensitivity analysis investigates the impact of data limitations on model accuracy, while sampling provides limits for model results. Model extensions provide empirical approach to the time non-homogenous problem which if approached mathematically requires extensive longitudinal data. (PsycInfo Database Record (c) 2023 APA, all rights reserved)
ABSTRACT
OBJECTIVES: Opioid misuse has resulted in significant morbidity and mortality in the United States, and safer opioid use represents an important challenge in the primary care setting. This article describes a research collaborative of health service researchers, systems engineers, and clinicians seeking to improve processes for safer chronic opioid therapy management in an academic primary care center. We present implementation results and lessons learned along with an intervention toolkit that others may consider using within their organization. METHODS: Using iterative improvement lifecycles and systems engineering principles, we developed a risk-based workflow model for patients on chronic opioids. Two key safe opioid use process metrics-percent of patients with recent opioid treatment agreements and urine drug tests-were identified, and processes to improve these measures were designed, tested, and implemented. Focus groups were conducted after the conclusion of implementation, with barriers and lessons learned identified via thematic analysis. RESULTS: Initial surveys revealed a lack of knowledge regarding resources available to patients and prescribers in the primary care clinic. In addition, 18 clinicians (69%) reported largely "inheriting" (rather than initiating) their chronic opioid therapy patients. We tracked 68 patients over a 4-year period. Although process measures improved, full adherence was not achieved for the entire population. Barriers included team structure, the evolving opioid environment, and surveillance challenges, along with disruptions resulting from the 2019 novel coronavirus. CONCLUSIONS: Safe primary care opioid prescribing requires ongoing monitoring and management in a complex environment. The application of a risk-based approach is possible but requires adaptability and redundancies to be reliable.