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رویکردی مبتنی بر مهندسی صنایع برای طراحی و ارزیابی سیستم های مراقبت های بهداشتی برای بهبود دسترسی جانبازان به مراقبت
An Industrial Engineering-Based Approach to Designing and Evaluating Healthcare Systems to Improve Veteran Access to Care
Access to healthcare is a critical public health issue in the United States, especially for veterans. Veterans are older on average than the general U.S. population and are thus at higher risk for chronic disease. Further, veterans report more delays when seeking healthcare. The Veterans Affairs (VA) Healthcare System continuously works to develop policies and technologies that aim to improve veteran access to care. Industrial engineering methods can be effective in analyzing the impact of such policies, as well as designing or modifying systems to better align veteran patients’ needs with providers and resources. This dissertation demonstrates how industrial engineering tools can guide policy decisions to improve healthcare access by connecting veterans with the most appropriate healthcare resources, while highlighting the trade-offs inherent in such decisions.
This work comprises four stages: (1) using optimization methods to design a healthcare network when introducing new provider options for chronic disease screening, (2) developing simulation tools to model how access to care is impacted when scheduling policies accommodate patient preferences, and (3) simulating triage strategies for non-emergency care during COVID-19, and (4) evaluating how treatment decisions impact patient access when guided by risk-based prediction models compared to current practice.
In the first stage, we consider veteran access to chronic eye disease screening. Ophthalmologists in the VA have developed a platform in which ophthalmic technicians screen patients for major chronic eye diseases during primary care visits. We use mixed-integer programming-based facility location models to understand how the VA can determine which clinics should offer eye screenings, which provider type(s) should staff those clinics, and how to distribute patients among clinics. The results of this work show how the VA can achieve various objectives including minimizing the cost or maximizing the number of patients receiving care.
In the second stage, we simulate patients seeking care for gastroesophageal reflux disease with primary care and gastrointestinal providers. This simulation incorporates policies about how to schedule patients for visits in various modalities, including face-to-face and telehealth, and also considers uncertainty in key factors like patient arrivals and demographics. Results of these models can help us understand how scheduling based on these preferences impacts access, including time to first appointment and number of patients seen. Such metrics can guide healthcare administrators as new technologies are introduced that offer options for how patients interact with their providers.
In the third stage, we simulate patients seeking non-emergency outpatient care under reduced appointment capacity due to the COVID-19 pandemic. We demonstrate this using endoscopy visits as a central example. We use our simulation model to understand how various strategies for adjusting patient triage and/or clinic operations can mitigate patient backlog and reduce patient waiting times.
In the fourth stage, we integrate multiple industrial engineering methods to examine how access is impacted among chronic liver disease patients when predictive modeling is introduced into treatment planning. We developed a simulation model to help clinical decision-makers better understand how using a predictive model may change the care pathway for a specific patient and also impact system decisions, such as required staffing levels and clinical data acquired at specific patient visits. The model also helps clinicians understand the value of specific clinical data (lab values, vitals, etc.) by demonstrating how better or worse inputs to the predictive models have larger system impacts to patient access.
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A Feasibility Study of Additive Manufacturing for Rapid Prototyping at an Air Force Depot
This study was conducted to explore qualitative factors impacting the likelihood of adoption of additive manufacturing (AM) tools, techniques, and principles at the United States Air Force (USAF) intercontinental ballistic missile (ICBM) depot. The USAF ICBM depot is the only organization in the United States that repairs missile components and support equipment that cannot be maintained in field units. Other depot organizations have directly or indirectly adopted AM for one or more production or prototyping purposes and benefitted from several of AM’s advantages, yet the USAF ICBM depot has yet to do so. Adoption at the ICBM depot may alleviate parts shortages and prototyping development time that negatively impact weapon readiness for maintaining a credible nuclear deterrence. This study used semi-structured interviews with professional engineers directly supporting the USAF ICBM depot to illicit thoughts about the overall feasibility of adoption AM, merits of AM, and leadership attitudes about the adoption of AM. Responses were collected and analyzed for themes using ATLAS.ti qualitative analysis software and manual methods. Participants generally responded positively about technical feasibility and merits of adopting AM in the USAF ICBM depot, but indicated non-technical barriers may have slowed adoption efforts. Participants also responded positively about the general technical merits of AM, but their responses were often provided in the context of consumer-grade equipment rather than industrial equipment that may better benefit their organization. Responses about leadership attitudes toward and commitment to AM adoption fell mostly in the neutral to negative spectrum. As a result of the contextual knowledge of the participants and suboptimal leadership attitude and commitment, this study concluded that it is not yet feasible to adopt AM at the USAF ICBM depot. This study recommends additional research directly in the USAF ICBM depot using quantitative data such as cost analysis to corroborate or refute this conclusion.