Developing the model

To reflect the emphasis on targets, the interactive three boundary model (Rasmussen, 1997) is developed by adding an additional boundary of ‘target failure’ (Williams, 2008). The development builds on the work of Rasmussen (1997), Cook and Rasmussen (2005) and Miller and Xiao (2007). Rasmussen’s (1997) boundary of economic failure

is split into the two boundaries of finance and target failure. The four boundaries of the developed SWE (version 3) are: financial failure, target failure, unacceptable workload, and safety failure (Figure 5.2).

Figure 5.2: The four boundary SWE for NHS hospitals

Rasmussen’s (1997) SWE has wide applicability. The developed version (v3) is limited to the specific context of the NHS in England. The developed and contextualised model can be described as a ‘mid-range’ model (Merton, 1968, Meredith, 1993) in that it is context specific.

The SWE model uses the idea of gradients influencing the position of the OP in relation to the boundaries (Rasmussen, 1997). The gradient depicts the pressure that is exerted

Financial failure boundary

Unacceptable workload failure boundary Operating Point

Safety failure boundary

Target failure boundary Marginal zone

(Buffer capacity)

on the OP to keep it away from the boundary. If all the gradient pressures are equal then the OP is held in the mid point of the envelope. The limitation of moving to a four boundary model is that it appears to place the gradients as directly opposing each other (Figure 5.2). However, it is important to note that each gradient can interact with any other. So for example, an increased pressure from the gradient towards an unacceptable workload can move the OP away from the workload failure boundary towards any of the other three boundaries. The direction that the OP moves in will depend on the dynamic of the competing pressures exerted.

Figure 5.3: Four boundary SWE with gradients

Within the context in which NHS hospitals work there are a number of external influences that contribute to the gradients. The model seeks to show the constantly changing pressures that apply on the OP of a SWE for NHS hospitals. The SWE (v3) is

Financial failure boundary

Unacceptable workload failure boundary

Target failure boundary Safety

failure boundary

Gradient toward increased efficiency

Gradient toward increased production Gradient

toward acceptable workload Gradient toward increased safety

not exhaustive in depicting the external pressures. Figure 5.4 illustrates that there a number of stakeholders who place sometimes conflicting goals on NHS hospitals (Ham, 2009). For example, there is a strong political and managerial requirement to achieve financial balance or better, whilst at the same time experiencing additional pressure to meet waiting time targets (Department of Health, 2009b). The independent regulator of NHS Foundation Trusts, ‘Monitor’ sets out clear requirements for meeting both

nationally set targets / standards and achieving financial surplus (Monitor, 2008).

Similarly, the Care Quality Commission (CQC) sets out the indicators which it takes into account when making assessments of NHS hospitals. These include the national targets / standards (Care Quality Commission, 2010) and the management of financial resources, which is undertaken by the Audit Commission (Audit Commission, 2009).

There are nationally negotiated staff contracts that specify the working arrangements for staff, which limits the working hours of key groups, such as junior doctors (Department of Health, 2009a). There is a broader social context where the public has expectations both in terms of access to services alongside assumptions about the quality and safety of the services (Salter, 2004). It is argued that these external influences in a public

healthcare system create some of the latent conditions and competing dynamics within which the hospital operates.

Figure 5.4: SWE set within the wider context of stakeholder influences

It is suggested that these external pressures influence decision making agents within the hospital in both the setting and monitoring of the boundaries. For example, research within the NHS indicates that meeting performance and financial targets is a

‘precondition to permit organisations to focus on quality and safety, since the pressures to meet targets compete for senior leadership time.’(Burnett et al., 2010) Equally, there are internal dynamics, which combine with the external influences to create conditions that impact upon the stability and location of the OP in relation to the failure

boundaries.

The SWE (v3) model includes four boundaries set within a wider context of influences.

In Section 5.1 the original Rasmussen (1997) model is extended by the use of SD and Patient safety

campaigns

Trade Unions

European Working

Time

Royal Colleges

League tables Political Competition

Regulator Department of

Health

Political Media &

public expectations

Care Quality Commission

Media & public expectations

Care Quality Commission

social theory to take account of the combination of internal and external dynamics within the envelope (Figure 5.5). The construct that is depicted through the use of the SFDs and CLDs from SD is the system ‘structure’. In this context ‘structure’ consists of the feedback loops, stocks and flows, and nonlinearities created by the interaction of the physical and institutional structure of the system with the decision-making processes of the agents acting within it’ (Sterman, 2001). In a hospital a ‘stock’ depicts the place where patients accumulate, such as a ward or department. The ‘flow’ depicts the direction of movement between stocks.

Figure 5.5: Four boundary SWE with SD diagrams to depict system ‘structure’ within the envelope

SD can be applied to the context of hospital operations conducted within the boundaries of the SWE. Set out below are a contextualised SFD and CLD for hospital systems. (In Appendix 2.1 examples are given of the basic conventions used in SFDs and CLDs.)

Financial failure boundary

Unacceptable workload failure boundary

Target failure boundary Safety

failure boundary

Stock Flow Diagrams depicting the ‘stocks’ &

‘flows’ in hospitals

Casual Loop Diagrams depicting the ‘feedback’

These are high level diagrams which are developed further in Chapter 8 to display data from the case studies. Figure 5.6 illustrates the basic design of a hospital system for the flow of emergency medical admissions. There are two routes into the hospital. The first is patients attending the Emergency Department (ED) where, once they have been assessed, they are either treated and discharged, or admitted to the Medical Assessment Unit (MAU). The second route in is via a General Practitioner who decides the patient needs direct admission to the MAU. From the MAU the patient is subsequently transferred to a medical ward for ongoing treatment.

Emergency Department

Medical Assessment

Unit Wards

attendance rate

admission rate

transf er rate

discharges

GP Ref errals Dishcharges

Figure 5.6: Basic stock flow diagram of the emergency medical patient pathway into and through a NHS hospital

SFDs can be used to illustrate changes in flows that can occur when the stocks reach capacity and patients have to be diverted. For example, Figure 5.7 shows an additional flow of diverted patients from MAU to ED. This occurs in hospitals when the MAU has no empty beds to accommodate GP referred patients. Modelling the stocks, flows and feedback loops provide an additional insight into the dynamics within the SWE. The implication of this type of event on the OP is explored in Chapter 7.

Emergency Department

Medical Assessment

Unit Wards

attendance rate

admission rate

transf er rate

discharges

GP Ref errals

Div ersions Dishcharges

Figure 5.7: Basic stock flow diagram of the emergency medical patient pathway into and through a NHS hospital with diversion flow from MAU to ED

A further insight into the feedback loops that are generated within the ‘structure’ can be illustrated by using CLDs. Figure 5.8 builds on the SFD in Figure 5.7 where there is a flow of diverted patients from MAU to ED. The CLD shows the relationship between the rate of inputs into the hospital system (attendance and GP referral rate) and the occupancy of ED and MAU. The relationship is in the same direction – if the referral rate increases the occupancy increases and vice versa. A reinforcing feedback loop is created between the ED and MAU when patients are diverted. This means that when there are diversions from MAU to ED the occupancy in ED will continue to rise until some balancing feedback loop is initiated. Such a balancing effect can occur by

increasing the rate of discharges. Speeding up discharges may have safety implications as research indicates a high rate of adverse events due to poor communication and handover of patients leaving hospitals (Forester et al., 2003, Forester et al., 2004, Kripalani et al., 2007).

ED Occupancy

Figure 5.8: Causal Loop Diagram showing the relationship of the emergency medical patients rate of arrival to a hospital system

A CLD can depict the consequences for other parts of the system, including the

relationship to policy achievement. In this example, the link is to meeting the 4 hour ED waiting time target. When the ED occupancy increases, then the risk of breaching the target increases and vice versa.

CLDs are used in this research to illustrate the changes that occur in the type of feedback loops that are found within a hospital when it faces continuous stress or perturbation. Such diagrams also help to show how decision makers seek to balance the system, whilst experiencing competing pressures from the gradients, by taking

compensating actions to avoid the OP breaching a failure boundary.