Integrated decision making in healthcare

TL;DR: An integreated framework for healthcare planning and control is discussed in this thesis, which integrates all managerial areas involved in healthcare delivery operations and all hierarchical levels of control, to ensure completeness and coherence of responsibilities for every managerial area.

Abstract: Healthcare professionals face the challenging task to design and organize their processes more effectively and efficiently. Designing and organizing processes is known as planning and control. Healthcare planning and control lags behind manufacturing and control for various reasons. One of the main causes is the fragmented nature of healthcare planning and control. Healthcare organizations such as hospitals are typically formed as a cluster of autonomous departments, where planning and control is also often functionally dispersed. A more integrated approach to healthcare planning and control is likely to bring improvements, as healthcare planning and control in one department is frequently dependent on decision making in other departments in the patient’s care chain. Driven by the lack of frameworks for healthcare planning and control, an integreated framework for healthcare planning and control is discussed in this thesis. The developed framework integrates all managerial areas involved in healthcare delivery operations and all hierarchical levels of control, to ensure completeness and coherence of responsibilities for every managerial area. The framework is built upon the “classical” hierarchical decomposition often used in manufacturing planning and control, which discerns strategic, tactical, and operational levels of control. This decomposition is extended by discerning between offline and online on the operational level. This distinction reflects the difference between “in advance” decision making and “reactive” decision making. The integrated framework for planning and control in healthcare can be used to structure the various planning and control functions, and their interaction. It is applicable broadly, to an individual department, an entire healthcare organization, and to a complete supply chain of cure and care providers. The framework can be used to identify and position various types of managerial problems, to demarcate the scope of organization interventions, and to facilitate a dialogue between clinical staff and managers. (Chapter 2) To position the research in this thesis and to investigate integrated planning and control in the literature, Chapter 3 provides a comprehensive overview of the typical decisions to be made in resource capacity planning and control in healthcare, and a structured review of relevant articles within the field of Operations Research and Management Science (OR/MS) for each planning decision. First, to position the planning decisions, a taxonomy is presented. This taxonomy provides healthcare managers and OR/MS researchers with a method to identify, break down and classify planning and control decisions. It is based on the integrated planning and control framework for healthcare, presented in Chapter 2. Second, following the taxonomy, for six health-care services, an exhaustive specification of planning and control decisions in resource capacity planning and control is provided. For each planning and control decision, the key OR/MS articles and the OR/MS methods and techniques that are applied in the literature to support decision making are reviewed and discussed. Prior literature reviews conclude that there is a lack of models for complete healthcare processes. Although a body of literature focusing on two-departmental interactions was identified, very few contributions were found on complete hospital interactions, let alone on complete healthcare system interactions. The literature review in this thesis reconfirms these observations. (Chapter 3) The second part of this thesis describes methods and models for integrated planning and control in healthcare, developed with techniques from OR/MS. The integrated models are developed for the tactical level of planning and control. Tactical planning of resources in hospitals concerns elective patient admission planning and the intermediate term allocation of resource capacities. Its main objectives are to achieve equitable access for patients, to meet production targets/to serve the strategically agreed number of patients, and to use resources efficiently. A method is proposed to develop a tactical resource allocation and elective patient admission plan. These tactical plans allocate available resources to various care processes and determine the selection of patients to be served that are at a particular stage of their care process. The method is developed in a Mixed Integer Linear Programming (MILP) framework and copes with multiple resources, multiple time periods and multiple patient groups with various uncertain treatment paths through the hospital, thereby integrating decision making for a chain of hospital resources. Computational results indicate that the developed method leads to a more equitable distribution of resources and provides control of patient access times, the number of patients served and the fraction of allocated resource capacity. The developed approach is generic, as the base MILP and the solution approach allow for including various extensions to both the objective criteria and the constraints. Consequently, the proposed method is applicable in various settings of tactical hospital management. (Chapter 4) In Chapter 5 of this thesis, a method is proposed to develop tactical resource allocation and elective patient admission plans taking stochastic elements into consideration, thereby potentially providing more robust tactical plans. The stochastic formulation of the tactical planning problem is stated, and an exact Dynamic Programming (DP) solution approach is provided. As the exact DP approach is only tractable for extremely small instances, and it does not allow solving real-life sized instances of the tactical planning problem, a solution approach using an alternative technique within the framework of Approximate Dynamic Programming (ADP) is developed. The developed ADP approach copes with multiple resources, multiple time periods and multiple patient groups with various uncertain treatment paths through the hospital. It incorporates the stochastic processes for (emergency) patient arrivals and patient transitions between queues in developing tactical plans. Moreover, it integrates decision making for a chain of hospital resources while taking stochastic elements into consideration. Computational results show that the developed ADP approach is suitable for the tactical planning problem in healthcare and that it provides accurate results (close the exact results obtained with DP approach). The ADP approach performs significantly better than two alternative greedy planning approaches for large reallife-sized instances. (Chapter 5) Chapter 6 discusses a tactical planning problem in the outpatient clinic. Outpatient clinics traditionally organize processes such that the doctor remains in a consultation room while patients visit for consultation. This is called the Patient-to-Doctor policy in this thesis. An alternative approach is the Doctor-to-Patient policy, whereby the doctor travels between multiple consultation rooms, in which patients prepare for their consultation. In the latter approach, the doctor saves time by consulting fully prepared patients. Using a queueing theoretic and a discrete-event simulation approach, generic models are developed that enable performance evaluations of the two policies for different parameter settings. These models can be used by managers of outpatient clinics to compare the two policies and choose a particular policy when redesigning the patient process. In addition, methods are developed to calculate the required number of consultation rooms in the Doctor-to-Patient policy. In the discussed computational experiments, the developed methods are applied to a range of distributions and parameters, and to a case study in one of the general hospitals that inspired this research. (Chapter 6)