Innovative Applications of O.R.
A system dynamics view of the acute bed blockage problem in the Irish healthcare system

https://doi.org/10.1016/j.ejor.2015.05.043Get rights and content

Highlights

  • An application of system dynamics in examining bed blockage at Irish hospitals.

  • Implications of future demands including changes in demography are considered.

  • Interventions using stock (post-acute beds) and flow (elderly patients) policies.

  • Increasing capacity in post-acute beds is a counter-intuitive policy.

  • Integrated solution is required to mitigate the bed blockage problem at large.

Abstract

Global population ageing is creating immense pressures on hospitals and other healthcare services, compromising their abilities to meet the growing demand from elderly patients. Current demand–supply gaps result in prolonged waiting times in emergency departments (EDs), and several studies have focused on improving ED performance. However, the overcrowding in EDs generally stems from delayed patient flows to inpatient wards – which are congested with inpatients waiting for beds in post-acute facilities. This problem of bed blocking in acute hospitals causes substantial cost burdens on hospitals. This study presents a system dynamics methodology to model the dynamic flow of elderly patients in the Irish healthcare system aimed at gaining a better understanding of the dynamic complexity caused by the system's various parameters. The model evaluates the stock and flow interventions that Irish healthcare executives have proposed to address the problem of delayed discharges, and ultimately reduce costs. The anticipated growth in the nation's demography is also incorporated in the model. Policy makers can also use the model to identify the potential strategic risks that might arise from the unintended consequences of new policies designed to overcome the problem of the delayed discharge of elderly patients.

Introduction

The fact that there are more elderly people than ever before is an indicator of advances in global health (McDermid & Bagshaw, 2011). Worldwide, there are around 600 million elderly people – commonly defined as those aged 65 years and over (Paul & Hariharan, 2007) – a total that is set to double by 2025, and to reach virtually two billion by 2050 (WHO, 2011). There are currently 108 million elderly people in Europe: they constitute 15 percent of the continent's population, a proportion that is expected to reach 26 percent by 2050 (Piers et al., 2013). In Ireland, the elderly population is projected to grow from 0.5 to 1.3 million over the next 30 years (Connell & Pringle, 2004). As people across the globe age – causing the cost of providing health and social care to rise – finding innovative approaches to delivering such services is becoming increasingly important. Discrete-Event Simulation (DES) has been proven to be an excellent and flexible tool for modelling processes in such complex stochastic environments (Eldabi, Paul and Young, 2006, Duguay and Chetouane, 2007). Healthcare managers apply DES to assess current performance, to predict the impact of operational changes, and to examine the trade-offs between system variables (Abo-Hamad, & Arisha, 2013 Abo-Hamad and Arisha, 2014, Litvak et al., 2008, Thorwarth, Arisha and Harper, 2009). DES seeks to reduce a system down to its basic elements in order to study them in detail and understand the types of interactions that exist between them (Ng, Sy, & Li, 2011).

This paper describes a nation-wide project carried out for the Irish Health Service Executive (HSE), considering all the country's public acute hospitals using data from 2010 as the base year. The project's goal is to find solutions to help overcome the problem of the delayed discharge of elderly patients, and plan to meet growing demand over the next five years. The project's first phase began in 2012 when a DES model was developed to model the flow of elderly patients through Irish hospitals (Ragab et al., 2013). The main focus of this model is to investigate the impact of transitional beds to mitigate the delayed discharge problem in short term (i.e., one year). Although the DES method was found to be a very powerful tool for understanding such systems, several problems arose in this phase whose sources were difficult to identify. Data problems included issues of irrelevance, insufficiency and inaccuracy. Attempting to overcome these challenges, the study recommended using a System Dynamics (SD) methodology, which offers a wider system view than DES. SD is more useful for modelling large and complex systems that takes the holistic view (Brailsford & Hilton, 2001), as well as for modelling dynamic changes over time explicitly.

Healthcare systems often have many different stakeholders- e.g., health providers, medical/professional interests, funders, and patients’ groups – and actions and activities undertaken in one part of the healthcare system designed to meet the needs of one set of stakeholders can often result in unexpected and unwanted consequences elsewhere, which can work against the interests of another set. SD offers a methodology that can help businesses and government institutions to develop strategy and analyse policy interventions by modelling causal relationships and feedback systems (Sweetser, 1999). The method has been applied to model such strategic aspects of policy and national issues in care systems as patient's pathways (Desai et al., 2008, Monefeldt, Lane and Rosenhead, 2000) and planning care for the elderly (Wolstenholme, 1999, Walker and Haslett, 2001) .

The primary objective of this study is to deliver a holistic and strategic national level capacity-planning model which can support policy makers in making decisions that are well assessed and carry fewer risks for elderly patients. It is also envisaged that this effort will have a positive impact on the delayed discharge issue.

The remainder of the paper is organized as follows. Section 2 presents a background and defines the challenges and problems facing the Irish healthcare system. Section 3 reviews the literature, focusing on studies that have used SD methodology to model patient pathways so as to address the problem of delayed patient discharges. Section 4 presents the case study of delayed discharges in the Irish healthcare system, which led to the development of the SD model. This section describes the model development and conceptualization, and presents our data calibration and model validation approaches. Section 5 proposes policies designed to alleviate the delayed discharge problem and to reduce bed blocking, comparing them under two different scenarios. Section 6 presents the results of these interventions, and section 7 concludes with some suggestions for future research work.

Section snippets

Background

In the years prior to 2008 Ireland enjoyed one of the highest economic growth rates in Europe, and public expenditure rose rapidly – by nearly 40 percent – between 2005 and 2008 (HSE, 2012b). However, public debt, unemployment and outward migration have subsequently increased sharply. The worsening economic outlook, and the conditions of the financial assistance received from the European Union and the International Monetary Fund have meant substantial cuts in public spending on health have had

Problem statement

Delayed discharge is a term used to describe the situation where, although medically well enough for discharge, patients are unable to leave acute care beds because arrangements for 'step-down' care services have not been completed (Bryan, Gage & Gilbert, 2006), thus causing them to stay for unnecessarily long periods in acute hospitals (Majeed et al., 2012). Such delayed transfers, which particularly involve older people with complex needs, lead inevitably to a phenomenon known as ‘bed

Literature review

At the end of the 1950s, Professor Jay Forrester introduced System Dynamics (SD) at the Massachusetts Institute of Technology's Sloan School of Management. He brought engineering feedback control principles and methods to management and social science situations, and then applied this approach to any complex system that exhibited dynamic behaviours over time. SD methodology attempts to simulate the system's behaviour over time by representing the causal relationships between its key variables,

Model conceptualisation and formulation

The most challenging elderly patients are those referred to as ‘frail’ patients, who are suffering from an array of medical conditions that individually may be treatable, but which, collectively, create complex and potentially overwhelming medical burdens (McDermid & Bagshaw, 2011). They account for 18–20 percent of elderly admissions, and generally need longer treatment in healthcare facilities followed by extended rehabilitation or community care. Adhering to the LOS-based cut-off point set

Policy analysis

Ireland is experiencing significant population growth, especially in elderly people, who are expected to reach 15.4 percent of the population by 2021 (Wren et al., 2012). The demand for health care for elderly people is expected to increase dramatically in the coming years, and this growth will be associated with a rapid increase in the proportion of frail elderly patients, who are more likely to need long-term care. It is imperative that healthcare policymakers consider the projected future

Discussion

Results from the base model simulation revealed that there are, on average, about 600 delayed discharges daily, and more than 75 percent of these are due to delays in making long-term care arrangements. Given the costs of running an acute bed are €850 per night, the cost of caring for 600 delayed patients exceeds €0.5m per day. Hence, decreasing the number of delayed discharges could lead to significant financial savings that could be re-directed into improving community-based care services.

Conclusion

The mounting demand for elderly healthcare services due to population aging is confronting Irish healthcare executives with critical capacity planning issues. Addressing these challenges requires advanced planning tools that can handle the complex interlinked service constraints on proposed interventions and operational strategies. This study has used conceptual modelling to illustrate different elderly patients’ care pathways, and this qualitative model provided a better understanding of the

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