Reengineering care transition in public healthcare system - Sheng, Lingling (2013)
The rapidly aging population is putting enormous strains on healthcare systems around the world, as elderly people tend to be more vulnerable to chronic diseases. The special medical and social concerns of chronically ill elders are pushing the cash-strapped governments to shift the focus from specialized acute care to integrated continuing care. In Singapore, the concern for seamless integration among acute care hospitals and step-down care facilities is already on the government’s agenda. Smooth care transition is critical to achieving the seamless integration of healthcare system. However, on-going research of care transition is still scarce and lack of in-depth analysis. A large portion of existing research focuses on conceptual discussion with qualitative analyses. Most of these studies are piecemeal projects that only address some aspects of care transition at the operational level. Meanwhile, a considerable amount of other research has exposed the huge complexity in the healthcare system. The complex nature of healthcare system makes it difficult to formulate the messy problems of care transition directly with traditional operations research (OR) methods. Making use of Singapore’s healthcare system as a study case, this research explores ways in which to achieve more effective and robust solutions. It leverages on a range of relevant systems theories and methodologies to systemically examine the care transition situation from a systems perspective. The soft systems methodology (SSM) is adopted as an overarching framework and applied to gain appreciation on the problem situation of care transition. Concurrently, a combination of multiple soft and hard operational research methods are integrated into the framework at different stages of the research to investigate the relevant research problems. Soft systems methodology-multi-method framework is proposed. This research firstly enquires into the care transition situation with the intent to gain a comprehensive picture of the current situation. Multiple face to face interviews with various medical staff are conducted to identify the problems in the care transition. These identified problems are then grouped and structured based on their causal relationships. From the structured problem expression, the research problem is scoped to be care transition plan and process improvement. Need for changes is analyzed using viable system model. An ‘aggregative’ care transition model is proposed to achieve these changes. Discrete event simulation is adopted to compare the ‘AS-IS’ process and the ‘TO-BE’ process. The simulation result shows that the proposed ‘aggregative’ care transition model is superior to the current ‘third party’ care transition model in terms of healthcare expenditure and system accessibility. More rigorous methods for further improvement of the care transition process are developed to achieve optimal solutions by answering how, when and where to discharge patients. For how to discharge patients, a two-stage optimization approach is proposed by converting the care transition process into a network optimization problem. The objective function of the first stage of the optimization is to minimize the duration of a process. Participating activities are selected from various candidate activities and their topological structure is determined to make the process a feasible solution. The second stage of the optimization decides who should perform each of these selected activities to achieve the best service quality of the care transition with resource constraints. The optimal solutions are obtained using the IBM ILOG CPLEX Optimization Studio. The optimal care transition process design from the two-stage optimization model is generally consistent with the previously proposed ‘aggregative’ care transition model, which is based on the qualitative analysis. With the objective of further smoothing the care transition process and improving healthcare system performance, this research provides decision support on when and where to discharge patients. A simulation model of patient flow at system level is developed to reflect the correlations among: disease severity, length of stay (LOS), health status, discharge policy, care transition decision and readmission rate. A finite state discrete Markov chain is adopted to capture the transition of patients’ health status during their stay in the hospital and predict their length of stay. The objective is to minimize the cost subject to several system performance requirements. The tradeoff between length of stay (LOS) and readmission rate is balanced through the simulation based optimization. Different optimization scopes are considered and compared. The optimal solutions are searched based on Genetic Algorithm (GA). Optimal computing budget allocation for constrained optimization (OCBA-CO) is incorporated into the search algorithm to reduce the computational time. The optimal solution set indicates that different stakeholders would prefer different solutions with their own objectives. To reduce the healthcare system cost and cycle cost per patient, community hospitals should take on more responsibilities. The results of this research would make the care planners to transfer needy elders within the healthcare system safer and faster, which would further enable the current system to care for more patients with better care quality at a lower cost.