Design of a Capacity Planning Model for Rural Healthcare.

Abstract

Proper healthcare accessibility is a result of a well capacity planning and resource allocations. Reducing waiting times is crucial for all businesses in general, and a mandatory for health services. Prolonged waiting periods can lead to lost sales, dissatisfied customers, and a tarnished reputation. Effective staff requirement planning plays a vital role in minimizing average queue times.

In Chapter 2of this study, we introduced a delay penalty that uses opportunity costs and the Value of a Statistical Life (VSL) to quantify the monetary value of patients waiting time. Then we present a comprehensive capacity planning model that takes into account the monetary value of patients’ waiting time. By considering this value, the model identifies the optimal capacity level aimed at enhancing overall effectiveness and outcomes within healthcare organizations, going beyond a sole focus on maximizing profits. The proposed model strikes a balance between capacity costs and customers’ waiting costs by integrating the delay penalty. We employed a MATLAB simulation code to estimate cycle times for transient conditions for most service systems, emphasizing its role in providing accurate estimations, while the closed-form formulas of Queuing Theory were exclusively used for steady-state conditions. This distinction ensures a comprehensive evaluation of waiting times and their associated costs. To evaluate the model’s effectiveness, we applied it to two inferred models from the healthcare industry and compared the current settings with the optimal ones derived from our approach. The results revealed that the optimal settings significantly reduced waiting times and increased profits. Consequently, our proposed model holds immense potential for aiding service organizations in achieving optimal staff planning and resource allocation.

In Chapter 3, we developed a Non-Linear Optimization (NLO) model that considers the spatial factor of healthcare accessibility to improve accessibility in rural areas. Although the U.S. spending on healthcare per capita is double that in other developed countries, the accessibility and quality of care are not as good as in most countries. The increased number of rural hospital closures in the United States has severely affected rural communities, which represent 20% of the U.S. population. Private companies, taking over many of the healthcare systems in the United States, decided to stop their operations in rural areas to improve their efficiency and cut costs. This decision made their costs better. However, it increases the overall costs for patients, not necessarily out-of-pocket costs, but in addition, the opportunity costs that include the lost opportunity for taking time out-of-work as people need to travel longer and, more importantly, health deterioration and lives lost as some people are dying just because they are not getting treatment as early as possible. To mitigate the impact of hospital closure, this research proposes a novel approach that incorporates a non-linear exponential distance decay function in a facility location-allocation optimization model to optimally locate telemedicine clinics that can duplicate most of the services offered in the traditional hospital by taking advantage of the enhanced technology. To demonstrate this, we used the model to propose optimal telemedicine urology clinic locations in Texas. This can allow a larger percentage of rural populations access to the service, compared to the current 42%. This model can be applied to the remaining states, which can eventually lead to mitigating the negative impact of hospital closure on people living in underserved areas.

Finally, in Chapter 4, we introduced an accessibility metric that integrates spatial and nonspatial factors. The floating catchment area (FCA) and the gravity models are often used to measure spatial accessibility to healthcare services. These models successfully considered the spatial factor to ensure proximity to services. However, proximity to the service without accounting for access time does not accurately describe accessibility. Although some improved versions of the FCA, such as the two-step floating catchment area (2SFCA) and the enhanced (2SFCA), have included the Huff Model to account for the size of the service, this does not provide a precise measure of accessibility. It is proven that waiting time can be a barrier to receiving healthcare services. In this work, we integrate waiting time instead of the capacity size, through a time decay function that utilizes the time to treatment initiation curve (TTI), to measure accessibility. A Non-linear Facility Location, Demand & Resource Allocations Model (NL-FLAM) with an embedded simulation model is then introduced to maximize healthcare accessibility based on the newly introduced definition. This macro-level capacity planning model simultaneously determines the optimal facility locations and the optimal number of providers to serve each proposed location. The distance-decay function estimates the potential demand that is then used by the embedded transient state simulation model to estimate the expected waiting time at each proposed location. Estimated waiting time is then used to estimate survival probability using the TTI curve. To demonstrate its applicability and usefulness, we used the proposed model to propose the optimal locations of urology’s clinics and the number of providers needed to increase the accessibility of Vermont rural patients to this service. This model can be used by public health authorities to help making decisions that utilize telemedicine to mitigate the impact of the rural hospital closure phenomena in the United States.