Theses and Dissertations at Montana State University (MSU)
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Item Risk mitigation focused on surgical care using process improvement methodologies in rural health systems(Montana State University - Bozeman, College of Engineering, 2023) Sitar, Nejc; Chairperson, Graduate Committee: Bernadette J. McCrory; This is a manuscript style paper that includes co-authored chapters.Rural healthcare is represented by approximately one-third of community hospitals in the United States primarily in the Midwest and Western United States. Due to the lack of resources and the demographic characteristics of rural populations, rural community hospitals are under constant pressure to meet Center for Medicare & Medicaid Services (CMS) quality requirements. Meeting CMS quality requirements is particularly challenging in surgical care, due to the lower volumes and research opportunities, in addition to a shortage of qualified surgical specialists. The perioperative surgical home (PSH) model was established as a health management concept in a rural community hospital located in the Northwest of the United States to improve the quality of care by providing a longitudinal approach to patient treatment. The main opportunities for PSH improvement were identified in the "decision for surgery," "preoperative," and "postoperative" stages of the PSH model. To improve PSH clinic performance this thesis proposes an improved National Surgical Quality Improvement Program (NSQIP) calculator User Interface (UI), as well as a new prediction model for predicting total joint arthroplasty (TJA) Length of Stay (LOS). The improved layout of the NSQIP calculator was developed based on two approved surveys by card sorting and Borda count methodology, while the new prediction model for predicting TJA patients' LOS was based on the Decision Tree (DT) machine learning model. A usability study of the NSQIP calculator UI identified opportunities for future improvements, such as the reorganized layout of postoperative complications and the addition of a supporting tool that would clearly define postoperative complications. The new DT prediction model outperformed a currently used NSQIP calculator in the prediction accuracy of TJA LOS, as it resulted in lower Root-mean-Square-Error values. Furthermore, the structure of the DT model allowed better interpretability of the decision-making process compared to the NSQIP calculator, which increased the trust and reliability of the calculated prediction. Despite some limitations such as a small sample size, this study provided valuable information for future improvements in rural healthcare, that would enable Rural Community Hospitals to better predict future outcomes and meet the strict CMS quality standard.Item Healthcare analytics at a perioperative surgical home implemented community hospital(Montana State University - Bozeman, College of Engineering, 2022) Sridhar, Srinivasan; Chairperson, Graduate Committee: Bernadette J. McCrory; This is a manuscript style paper that includes co-authored chapters.The Perioperative Surgical Home (PSH) is a novel patient-centric surgical system developed by American Society of Anesthesiologists (ASA) to improve surgical outcomes and patient satisfaction. Compared to a traditional surgical system, the PSH is a coordinated interdisciplinary team encompassing all surgical care provided to patients from the perioperative phase to recovery phase. However, limited research has been performed in augmenting the PSH surgical care using healthcare analytics. In addition, the spread of the PSH is limited in rural hospitals. Compared to urban hospitals, rural hospitals have higher surgical care inequality due to limited availability of clinicians, resources, resulting in poor access to surgical care. With an increase in the rate of Total Joint Replacement (TJR) procedures in the United States (US), rural hospitals are often under-resourced with coordinating perioperative services resulting in inadequate communication, poor care continuity, and preventable complications. This study focused on developing a novel analytical framework to predict, evaluate, and improve TJR outcomes at a PSH implemented rural community hospital. The study was segmented into three parts where the first part explored the effectiveness of the digital engagement platform to longitudinally engage with TJR patients located in rural areas. The second part evaluated the impact of PSH system in the rural setting by analyzing and comparing the TJR surgical outcomes. Finally, the third part explained the importance of machine learning in the rural PSH system to identify critical patient factors, enhance decision-making, and plan for preventive interventions for better surgical outcomes. Results from this research demonstrated the importance of healthcare analytics in PSH system and how it can help to enhance TJR surgical outcomes and experience for both clinicians and patients.Item Optimizing operating room scheduling considering instrument sterilization processing(Montana State University - Bozeman, College of Engineering, 2019) Harris, Sean Paul; Chairperson, Graduate Committee: David ClaudioThe United States healthcare system represents approximately 18% of the nation's GDP and its numerous challenges continue to receive significant attention from researchers. Within healthcare, operating rooms (ORs) often represent the largest source of revenue and costs in a hospital. Consequently, OR surgical scheduling strategies have been thoroughly examined from a wide variety of performance measures such as overtime, patient waiting time, and utilization rates. ORs are a complex system, and researchers have begun to consider the upstream and downstream resources involved in the surgical process such as the Post Anesthesia Care Unit, Intensive Care Unit, and bed availability. However, two factors that have only begun to be examined are the sterilization process of OR instrumentation and the assignment of instruments into trays and preference cards, either by surgical procedure or individual surgeon preference. Using both collected and historical data, this research 1) examined and improved how the block schedule of an OR suite affected the Sterilization Processing Department (SPD) and 2) examined and improved preference cards for surgical cases. A series of mathematical models optimized surgical block schedules while considering the impact on the SPD and a goal programming model was developed for the tray optimization problem. A comprehensive simulation model of the OR suite and SPD tested the output of the mathematical models. The simulation results confirmed block scheduling does affect SPD performance. A linear goal programming formulation that smoothed SPD workload across block times was the most effective type of model to optimize block scheduling. A goal programming tray optimization model improved expected instrument utilization rates. For practical applications, this research suggests reducing SPD staff turnover is a more effective method for improving SPD performance than rearranging the OR block schedule. This research is among the first of its kind to consider SPD workload as an objective in OR block scheduling models, to consider expected instrument non-usage rates in the tray optimization problem, and to develop a comprehensive simulation model of an OR suite and its SPD to test the results of mathematical models.