عنوان

Data Analytics and Stochastic Optimization Models for Decision Support in Chronic Disease Operations Management

شماره

TL51527

زبان متن نوشتاري يا گفتاري و مانند آن

انگلیسی

عنوان اصلي

Data Analytics and Stochastic Optimization Models for Decision Support in Chronic Disease Operations Management

نام عام مواد

[Thesis]

نام نخستين پديدآور

Olya, Mohammad Hessam

نام ساير پديدآوران

Yang, Kai

نام ناشر، پخش کننده و غيره

Wayne State University

تاریخ نشرو بخش و غیره

2019

متن يادداشت

139 p.

جزئيات پايان نامه و نوع درجه آن

Ph.D.

کسي که مدرک را اعطا کرده

Wayne State University

امتياز متن

2019

متن يادداشت

Meeting the complex needs of patients with chronic illness is the single greatest challenge in medical practices. Chronic disease is a prevalent and high-cost issue in the United States healthcare systems. Efficient spending of healthcare funds and better management of healthcare operation costs lead to an enhanced access to high-quality healthcare services and reduces the overall healthcare cost. Thus, in this research, we have proposed a comprehensive framework for chronic disease operations management. Due to uncertainty in patient demand and workload, this framework consists of two predictive and prescriptive analysis phases. In the first phase, we have proposed a deep multi-task learning approach for predicting the required workload of patients. Then in the second phase, we have developed two stochastic optimization models for capacity planning and resource allocation for decision-making in strategic and tactical management levels where the scope of decision-making includes single and multiple facilities, respectively. One of the drawbacks of earlier studies in workload prediction is that the problem is not investigated for multiple facilities where the quality of provided services, equipment and resources used for provided services as well as diagnosis and treatment procedures may differ even for patients with similar conditions. Besides, the sparsity of chronic disease data is another challenge in workload prediction. To tackle the mentioned issues, we have considered patient-dependent and facility-dependent attributes as well as the relation between them into the proposed model and trained multiple related tasks simultaneously. In addition, we have transformed the data using multiple non-linear transformations through several hidden layers to capture data complexity and sparsity for providing a robust abstraction. The results of this study show that feature representation and training related instances jointly increase the performance of patient workload prediction. Moreover, we have addressed two critical issues in team-based healthcare strategic and tactical planning. The first issue is to determine the optimal number of providers for multiple facilities and eligible patients for pay-to-travel incentives where the demand and location of patients are unknown. The second issue is to minimize the number of different healthcare teams and balance their workload within every single facility. We have developed a stochastic workforce and workload optimization model under various scenarios to address this issue. The result of prescriptive analysis suggests considering the randomness rather than replacing the stochastic variables by their expected value significantly contributes in reducing the overall cost of healthcare and practically enhancing access to care.

اصطلاح موضوعی

Computer science

اصطلاح موضوعی

Epidemiology

اصطلاح موضوعی

Health care management

اصطلاح موضوعی

Health sciences

اصطلاح موضوعی

Industrial engineering

اصطلاح موضوعی

Management

اصطلاح موضوعی

Operations research

مستند نام اشخاص تاييد نشده

Olya, Mohammad Hessam

مستند نام اشخاص تاييد نشده

Yang, Kai

مستند نام تنالگان تاييد نشده

Wayne State University

نام الکترونيکي

فرمت انتشار

p

نوع ماده

[Thesis]

کد کاربرگه

276903

سطح دسترسي

a

تكميل شده

Y