The role of simulation in Long Term Conditions
Jacquie White / Nov 20, 2015
The NHS program has used simulation from the outset to inform the understanding of how patients with multiple long term conditions use services as their conditions change, and the costs and resources likely to be required each year for a patient.
As early implementer sites have increased their knowledge of patient flows and processes, the simulation has been refreshed with new data and assumptions to allow other healthcare systems to test the impact of adopting a similar approach. As new research has been published which documents the impact of introducing proactive management of patients with multiple long term conditions who score highly on a risk stratification tool, “what if? scenarios” have been developed to allow health systems to test the impact of new models of care on patients in their health community.
The simulation models patients identified by a healthcare system as a result of risk stratification profiling and a count of the number of long term conditions. Users can segment patients into four groups representing different patient types. This varies by individual system. Some choose to group patients by numbers of long term conditions, others include further characteristics such as whether the patient lives in a residential care setting.
Each patient group has a different probability of accessing a service during the year, and the full range of services are included: primary and community, hospital and acute, mental health, social care. A frequency probability is applied so that each patient may access a service multiple times, and a cost and a resource linked to each service. The simulation transitions patients between the four groups as their conditions change.
The simulation runs forward over a number of years from the current state and results predict the annual cost of service by patient and category of service for each group of patients and the numbers of staff, beds or other resource required. Cost and capacity requirements are shown for each year to support planning decisions. Simulation users can also experiment with the new models of care they wish to implement and can test the impact of these on cost and capacity before deciding to move forward.
Healthcare systems across the world are all adopting new models of care to support the person, rather than focusing on their disease. This is a very complex area, and the simulation embodies the learning of the early implementer sites as they work through the issues on the ground. We expect it to change and evolve as it is refreshed with real world insight
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