Theses and Dissertations at Montana State University (MSU)
Permanent URI for this collectionhttps://scholarworks.montana.edu/handle/1/733
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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 Predictive models for 30-day patient readmissions in a small community hospital(Montana State University - Bozeman, College of Engineering, 2013) Lovejoy, Matthew Walter; Chairperson, Graduate Committee: David ClaudioPresently, national healthcare initiatives have a strong emphasis on improving patient quality of care through a reduction in patient readmissions. Current federal regulations created through the Patient Protection and Affordable Care Act (PPACA); focus on the reduction in readmissions to improve patient quality of care (Stone & Hoffman, 2010). This legislation mandates decreased reimbursement for services if a facility has high 30-day patient readmissions related to the core measures Congestive Heart Failure (CHF), Acute Myocardial Infarction (AMI) and Pneumonia (PNM). This research focuses on building predictive models to aid Bozeman Deaconess Health Services (BDHS), a small community hospital, reduce their readmission rates. Assistance was performed through identification of patient characteristics influencing patient readmission risk, along with advanced statistical regression techniques used to develop readmission risk prediction models. Potential predictor variables and prediction models were obtained through retrospective analysis of patient readmission data from BDHS during January 2009 through December 2010. For increased prediction accuracy seven separate readmission dataset types were developed: General population, and ICD-9 code related populations for AMI, CHF, PNM, Alcoholic Psychoses (291), Cardiac Dysrhythmias (427) and Disorders of the Function of the Stomach (536). For the greatest benefit from readmission reduction, analysis focused on readmissions categorized as Potentially Preventable Readmissions (PPR); defined as unplanned, medically related readmissions within 30-days of a patient's previous inpatient visit. General exploratory analysis was performed on the PPR patient data to discover patterns which may indicate certain variables as good predictors of patient readmission risk. The prediction model methods compared were binary logistic regression, and multivariate adaptive regression splines (MARS). Usable binary logistic regression models for 536 (Nagelkerke R 2=0.676) and CHF (Nagelkerke R 2=0.974) were achieved. MARS developed usable models for 427 (Naïve Adj. R 2=0.63288), 536 (Naïve Adj. R 2=0.77395), AMI (Naïve Adj. R 2=0.76705), CHF (Naïve Adj. R 2=0.99385) and PNM (Naïve Adj. R 2=0.82615). Comparison of the modeling methods suggest MARS is more accurate at developing usable prediction models, however a tradeoff between model complexity and predictability is present. The usable readmission risk prediction models developed for BDHS will aid BDHS in reducing their readmissions rates, consequently improving patient quality of care.