Operational Decision Making Across Patient Care Cycle PDF Download

Author: Hyo Kyung Lee
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Languages : en
Pages : 0

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Book Description
In recent decades, healthcare has become increasingly expensive, creating pressure on care providers to deliver quality care while reducing costs. Consuming almost 20% of the gross domestic products (GDP), healthcare is one of the largest industry sectors in the United States (US). However, such high expenses may not always result in an adequate healthcare service quality where long waits, limited access and resource overloads are commonly observed. Now more than ever, efforts to deliver care more efficiently and effectively are being pursued throughout the US. Moreover, the US healthcare system is facing incredible challenges as healthcare is shifting from the traditional, volume-driven, fee-for-service model towards value-based payment and care delivery model. Such value initiatives are becoming increasingly prevalent and important as patient costs continue to rise and access to affordable care is threatened. Given such widespread movements toward value-based care models, all provider segments have a more growing role to play in quality improvement and enhancing their care delivery at all points along the patient care continuum. That said, the healthcare industry can no longer focus on acute care providers and hospitals only; post-acute, sub-acute, and non-acute providers play an increasingly important role as patient outcomes are being tied to readmissions and value-based payments, increasing the importance of all provider roles across the care delivery cycle. This dissertation is dedicated to improving the efficiency and quality of the healthcare system across the care delivery cycle: from prevention to diagnosis to treatment and to home care. Specifically, analytical tools and models to support systematic and evidence-based decision making are introduced for each stage within the cycle. Starting from the prevention stage, we introduce a Markov chain based modeling framework to assess the impact of implementing a new service model in primary care clinics. An application study at Dean East Clinic of SSM Health is presented and managerial insights from the model regarding the impact of various workload allocation policies are discussed as well. In the following chapter, focusing on the diagnosis stage, a system-theoretic method is introduced to analyze the diagnosis-to-treatment process for lung cancer patients. As the process commonly involves frequent and potentially harmful delays, speeding up the timeliness without sacrificing the care quality is critical to improve patient outcome as well as satisfaction. To do so, we decompose the complex care delivery process to evaluate the system performance and derive indicator measures that can be used to identify the bottleneck waiting steps. Moreover, the complete distribution of the total waiting time is formulated to estimate the probability to receive the surgery within a desired or given time period. Finally, the applicability of the proposed method is illustrated via a case study at Baptist Memorial Hospital. For the treatment stage, the next chapter investigates the delays or blockings that occur during the intra hospital patient flow between different departments. Specifically, a finite capacity queueing network model based iterative procedure is formulated to evaluate the transition delay times, average bed occupancy rates, and probabilities of full occupancy. Finally, we investigate system properties to provide managerial guidance to improve patient transitions and reduce delays. To complete the patient care cycle analysis, the subsequent chapter focuses on the last stage of the care cycle: postdischarge or home care phase. As there are considerable variations in the postdischarge care process for total joint replacement (TJR) patients, we formulate the TJR postdischarge intervention process as a finite-horizon discrete-time Markov decision process. Specifically, we dynamically model the post-TJR intervention process by directly incorporating the readmission risk and penalty, and considering the varying effectiveness of interventions depending on where the patient is located at. The applicability of the model is illustrated through a case study at St. Mary's Hospital where the derived optimal policy provides guidance to healthcare professionals in determining the optimal timing and target group of interventions. In summary, the work developed in this dissertation provides quantitative tools to support operational decision making across the patient care cycle, and ultimately contributes to delivering safe and patient-centered care in a coordinated and seamless system.