Integrating Lean Six Sigma and discrete-event simulation for shortening the appointment lead-time in gynecobstetrics departments: a case study

Long waiting time to appointment may be a worry for pregnant women, particularly those who need perinatology consultation since it could increase anxiety and, in a worst case scenario, lead to an increase in fetal, infant, and maternal mortality. Treatment costs may also increase since pregnant women with diverse pathologies can develop more severe complications. As a step towards improving this process, we propose a methodological approach to reduce the appointment lead-time in outpatient gynecobstetrics departments. This framework involves combining the Six Sigma method to identify defects in the appointment scheduling process with a discrete-event simulation (DES) to evaluate the potential success of removing such defects in simulation before we resort to changing the real-world healthcare system. To do these, we initially characterize the gynecobstetrics department using a SIPOC diagram. Then, six sigma performance metrics are calculated to evaluate how well the department meets the government target in relation to the appointment lead-time. Afterwards, a cause-and-effect analysis is undertaken to identify potential causes of appointment lead-time variation. These causes are later validated through ANOVA, regression analysis, and DES. Improvement scenarios are next designed and pretested through computer simulation models. Finally, control plans are deployed to maintain the results achieved through the implementation of the DES-Six sigma approach. The aforementioned framework was validated in a public gynecobstetrics outpatient department. The results revealed that mean waiting time decreased from 6.9 days to 4.1 days while variance passed from 2.46 days2 to 1.53 days2

Simulation analysis of the consequences of shifting the balance of health care: a system dynamics approach

Objectives: The shift in the balance of health care, bringing services 'closer to home', is a well-established trend. This study sought to provide insight into the consequences of this trend, in particular the stimulation of demand, by exploring the underlying feedback structure. Methods: We constructed a simulation model using the system dynamics method, which is specifically designed for the analysis of feedback structure. The model was calibrated to two cases of the shift in cardiac catheterization services in the UK. Data sources included archival data, observations and interviews with senior health care professionals. Key model outputs were the basic trends displayed by waiting lists, average waiting times, cumulative patient referrals, cumulative patient activity and cumulative overall costs. Results: Demand was stimulated in both cases via several different mechanisms. We revealed the roles for clinical guidelines and capacity changes, and the typical responses to imbalances between supply and demand. Our analysis also demonstrated the potential benefits of changing the goals that drive activity by seeking a waiting list goal rather than a waiting time goal. Conclusions: Appreciating the wider consequences of shifting the balance of care is essential if services are to be improved overall. The underlying feedback mechanisms of both intended and unintended effects need to be understood. Using a systemic approach, more effective policies may be designed through coordinated programmes rather than isolated initiatives, which may have only a limited impact

Modelling the feedback effects of reconfiguring health services

The shift in the balance of health care, bringing services ‘closer to home’, is a well-established trend, which has been motivated by the desire to improve the provision of services. However, these efforts may be undermined by the improvements in access stimulating demand. Existing analyses of this trend have been limited to isolated parts of the system with calls to control demand with stricter clinical guidelines or to meet demand with capacity increases. By failing to appreciate the underlying feedback mechanisms, these interventions may only have a limited effect. We demonstrate the contribution offered by system dynamics modelling by presenting a study of two cases of the shift in cardiac catheterization services in the UK. We hypothesize the effects of the shifts in services and produce model output that is not inconsistent with real world data. Our model encompasses several mechanisms by which demand is stimulated. We use the model to clarify the roles for stricter clinical guidelines and capacity increases, and to demonstrate the potential benefits of changing the goals that drive activity

A survey of health care models that encompass multiple departments

In this survey we review quantitative health care models to illustrate the extent to which they encompass multiple hospital departments. The paper provides general overviews of the relationships that exists between major hospital departments and describes how these relationships are accounted for by researchers. We find the atomistic view of hospitals often taken by researchers is partially due to the ambiguity of patient care trajectories. To this end clinical pathways literature is reviewed to illustrate its potential for clarifying patient flows and for providing a holistic hospital perspective

Analytical models to determine room requirements in outpatient clinics

Outpatient clinics traditionally organize processes such that the doctor remains in a consultation room while patients visit for consultation, we call this the Patient-to-Doctor policy (PtD-policy). A different approach is the Doctor-to-Patient policy (DtP-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. We use a queueing theoretic and a discrete-event simulation approach to provide generic models 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.We use the models to analytically show that the DtP-policy is superior to the PtD-policy under the condition that the doctor’s travel time between rooms is lower than the patient’s preparation time. In addition, to calculate the required number of consultation rooms in the DtP-policy, we provide an expression for the fraction of consultations that are in immediate succession; or, in other words, the fraction of time the next patient is prepared and ready, immediately after a doctor finishes a consultation. We apply our methods for a range of distributions and parameters and to a case study in a medium-sized general hospital that inspired this research

An analytical comparison of the patient-to-doctor policy and the doctor-to-patient policy in the outpatient clinic

Outpatient clinics traditionally organize processes such that the doctor remains in a consultation room, while patients visit for consultation, we call this the Patient-to-Doctor policy. A different 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. We compare the two policies via a queueing theoretic and a discrete-event simulation approach. We analytically show that the Doctor-to-Patient policy is superior to the Patient-to-Doctor policy under the condition that the doctor’s travel time between rooms is lower than the patient’s preparation time. Simulation results indicate that the same applies when the average travel time is lower than the average preparation time. In addition, to calculate the required number of consultation rooms in the Doctor-to-Patient policy, we provide an expression for the fraction of consultations that are in immediate succession; or, in other words, the fraction of time the next patient is prepared and ready, immediately after a doctor finishes a consultation.We apply our methods for a range of distributions and parameters and to a case study in a medium-sized general hospital that inspired this research

Taxonomic classification of planning decisions in health care: a review of the state of the art in OR/MS

We provide a structured overview of the typical decisions to be made in resource capacity planning and control in health care, and a review of relevant OR/MS articles for each planning decision. The contribution of this paper is twofold. First, to position the planning decisions, a taxonomy is presented. This taxonomy provides health care managers and OR/MS researchers with a method to identify, break down and classify planning and control decisions. Second, following the taxonomy, for six health care services, we provide an exhaustive specification of planning and control decisions in resource capacity planning and control. For each planning and control decision, we structurally review the key OR/MS articles and the OR/MS methods and techniques that are applied in the literature to support decision making

A decision support system for demand and capacity modelling of an accident and emergency department

© 2019 Operational Research Society.Accident and emergency (A&E) departments in England have been struggling against severe capacity constraints. In addition, A&E demands have been increasing year on year. In this study, our aim was to develop a decision support system combining discrete event simulation and comparative forecasting techniques for the better management of the Princess Alexandra Hospital in England. We used the national hospital episodes statistics data-set including period April, 2009 – January, 2013. Two demand conditions are considered: the expected demand condition is based on A&E demands estimated by comparing forecasting methods, and the unexpected demand is based on the closure of a nearby A&E department due to budgeting constraints. We developed a discrete event simulation model to measure a number of key performance metrics. This paper presents a crucial study which will enable service managers and directors of hospitals to foresee their activities in future and form a strategic plan well in advance.Peer reviewe

Waiting Times and Cost Sharing for a Public Health Care Service with a Private Alternative: A Multi-agent Approach

Cost sharing represent a well-established tool for the control of health care demand in many Oecd countries, even though it is used with caution, and in combination with other instruments, in order to avoid potential negative impacts on access to essential health care services. Waiting lists and waiting times represent an alternative (and implicit) way to control demand in public health care systems, even though rationing by waiting may be an inferior solution to cost-sharing in terms of welfare. This paper focuses on the use of waiting times, cost-sharing, and other tools (in particular, priority and appropriateness criteria) in order to control demand for a public outpatient health service in presence of a fully paid out-of-pocket private alternative. We develop an agent-based model where heterogeneous agents maximise their individual utility based on income and health status. On this basis, we develop some computational experiments based on micro-simulations that offer some useful insights for health care policy. In particular, we show that: i) the presence of a private alternative to public treatment can improve social welfare and health equity in a NHS, when public supply is constrained by a fixed budget and longer waiting times than the private one; ii) using prioritisation of waiting lists without any copayment to control the demand for public treatment may produce high performances in terms of social welfare, health equality and policy efficiency; iii) applying a moderate copayment rate as a tool to control public demand could determine the same policy efficiency of using only priority lists, if the copayment revenues are used to fund the public provision.health care demand; private provision; waiting times; cost-sharing; equity, agent-based model