Chairperson, Graduate Committee: Bernadette J. McCrorySitar, NejcThis is a manuscript style paper that includes co-authored chapters.2024-06-252024-06-252023https://scholarworks.montana.edu/handle/1/18303Rural 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.enRural healthSurgeryRiskMachine learningThesisCopyright 2023 by Nejc Sitar