System Dynamics models for long-term care

Kim and Goggi (2005) used system dynamics to model the system for long-term care for a state agency. The authors modelled both institutional care as well as home and community care, to show the impact of a major policy change in accessing care. Access to care is very fragmented: system dynamics was used to show what is likely to happen if a new single point of access were to be created. This single point would act as an information provider as well as a place to have an individual‘s needs accessed. This new system should reduce the number of people given institutional care unnecessarily, thus reducing costs. System dynamics proved to be a good way of modelling the whole system as well as assessing the implications of a policy change. The model was able to project costs until 2030 as well as the numbers requiring institutional and home and community care. The modelling period of the research presented in this thesis is until 2026 (see section 4.3.2, p.119). The model required population dynamics and thus included the ageing process through the use of an ageing chain model. A strength of the methodology is that the ageing process can be explored. A highly detailed stock and flow diagram was built to represent flows of the people through the system for long-term care.

There are some limitations noted by the authors. The modelling is incomplete due the lack of data. This in turn limited the validation process. The researchers were unable to include all the feedback identified due to the model structure which limits the usefulness of the application of the technique.

The consultancy group, the Whole System Partnership (Whole System Partnership, 2010) have shown that system dynamics can potentially be a useful way of modelling

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the long-term care at the regional level in the United Kingdom. The consultancy group modelled services for elderly people in the city of Leeds (Lacey, 2003) and region of Leicestershire (Lacey, 2007). One of the strengths of the modelling is the way key stakeholders were brought together in the modelling process. For example in Leeds, Lacey (2003) built two models, one for all older people and one for elderly people with mental health needs. The model looked at both the demand and supply until 2021. The model was potentially useful for the local authority to identify both short- and long-term pressures on the system in terms of commissioning services for elderly people. System dynamics is a useful technique for modelling a complex system over a long period of time. As the modellers worked closely with the clients throughout the modelling process, acceptance of the model was much more likely. The models presented in this thesis involved members of Hampshire County Council throughout the process (see Chapters 4 and 5).

Desai et al. (2008) constructed a system dynamics model for Hampshire County Council to project the demand for older people‘s service over the period 2006 through to 2011. The study was a result of concern relating to demographic change. Part of the study was to model the assessment process to calculate the number of people who make contact with the local authority and their outcomes.

Various modelling techniques were considered by the authors: system dynamics was finally chosen, for a number of reasons. The qualitative modelling was important; the concepts of feedback and causal loops proved to be useful. Even though system

dynamics does not model at the level of the individual, characteristics of the population could be included. A similar approach was taken with the research presented in this research (see section 4.1, p.115). The characteristics modelled were age, the source of referral and the initial level of need. The authors were able to provide the local authority with a set of potentially useful results through running various scenarios.

Wolstenholme (1993) also used system dynamics, although only the qualitative aspects, to investigate the impact of a policy change. The responsibility of community care for the elderly was passed onto the local authority during the time that the paper was

written. The study was brought about by health service managers who wanted to test the

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impact of the new policy on the interface between the two sources of public care

provision, the National Health Service and the local authority. Influence diagrams were able to aid in understanding the situation. Wolstenholme‘s objective was to try and represent the new system as simply as possible and to represent the movement of people across organisational boundaries. The diagrams grew in complexity as the project progressed and helped give a clear understanding on the possible consequences of the policy change. The concept of feedback illustrates the possible implications of a policy change in one sector upon other sectors. The study was useful at creating a thorough understanding of the impact of a policy change.

Gray et al. (2006) used two models in a study to investigate the interactions between the acute and aged care systems. The authors used both system dynamics and agent-based simulation. Carried out in Australia, the study built models at both the national and regional level. One of the reasons for the study is that 20% of beds utilised in hospitals were in fact for non-acute patients. It is noted that there are many stakeholders involved in both systems so system dynamics is a good way of representing all their views. Gray et al. note that simulation is advantageous where real life experiments cannot be carried out or when it would take too long to get any results or it could be too costly. The authors discuss strengths of simulation techniques when dealing with messy real-world systems.

Gray notes that system dynamics is good at understanding how a system behaves over time and agent-based simulation is good at understanding the interactions between individuals and their environment. This methodology could be used to answer many questions, such as, ―what is the average time spent waiting in acute care?‖ and ―how many people are waiting for a residential care place at any given time?‖ Whilst this paper attempts to answer a different set of questions compared to this thesis (see section 1.5, p.4), the advantages of the methods are well presented for their use in a social care setting.

Chen (2003) uses system dynamics to investigate non-acute services for elderly people in Norway. The author notes that existing studies do not separate acute and non-acute services. Non-acute services are treated as an addition to acute services. The study

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investigates patient flow through the non-acute system, with a ten-year time horizon.

The author was able to identify various types of feedback. Using the systems thinking methodology, different systems are no longer treated in isolation. Chen notes that system dynamics allows for the inclusion of lots of information and it can be tested with various stakeholders. It should be noted that no results were published in this paper but the use of system dynamics is well presented in the area of non-acute care.

The various applications of system dynamics have shown that this is a potentially a good way of modelling the system for long-term care. The technique was considered for selection for this thesis but as there was no data to support the inclusion of feedback it was not chosen (see section 4.1, p.115). Many benefits have been described such as: it can be used to model the impact of a policy; a whole system can be modelled; it can be used to project over a long period of time; it allows the inclusion of various

stakeholders, feedback is captured and it allows for what-if analysis. However,

quantitative system dynamics modelling is dependent on the data available: the model by Kim and Goggi (2005) was incomplete because of the lack of data.