Why use simulation in healthcare?

Simulation in Healthcare



Co-author: Dr. James Sheffield

Healthcare demand is rising as our populations get older and live with more complex medical conditions and somehow healthcare systems have to manage the rise in costs.  More effective management includes preventative medicine to keep us healthy for longer, proactive support for those most likely to have a crisis which needs emergency care to prevent hospitalization and discharge to appropriate services without delay once hospitalized.

Healthcare systems aim to intervene early, manage risk, and provide the most appropriate care to maintain an individual’s health status and slow progression to a more acute phase. New models of care are being developed and implemented all over the Western world but the clinical evidence that they will achieve their aims is behind the curve. Clinical and operational evidence is needed to support adoption. Even if the model is successful in one locality, how can another healthcare system ensure that they have similar conditions to enable successful implementation?

We have been using simulation to develop models that mimic a system and the change they wish to enact. The changes include anything from:

  • Providing 24/7 services in the community for older people living with multiple conditions
  • Night nursing services for short term care to support people during a crisis or at the end of their lives
  • Community support for people who have had a fall in partnership with ambulance services
  • Healthcare support for care homes when residents may need to transition to a healthcare-led environment

Understanding the system

Simulation allows healthcare systems to understand how the current system works from the healthcare system and the patient perspective. Patients often use services more than once and will frequently cross organizational boundaries. Visibility of patient journeys, their cost and capacity impacts are important in helping healthcare systems to understand what happens to patients and, combined with risk stratification tools, can help to identify which groups of patients will need the most help.

People can receive care from the GP or family practitioner; hospital and community based services in a seemingly complex and confusing pattern.  Care provision often stumbles as it crosses those boundaries and there can be a lack of integration and shared knowledge along integrated care pathways which detracts from effective treatment. It can be hard to visualise workable solutions.

Simulation of integrated care pathways can provide a common knowledge base for care professionals, planners and service managers, helping to bridge organisational boundaries and ensure that critical areas of care delivery are identified and managed.  Simulation can help develop better care scenarios, highlight potential care provision risks and calculate costs and resource requirements.

Managing real world variability

Once stakeholders in a healthcare system have understood the way patients are using services, they can better define alternative interventions, how they could be implemented and the likely impact on resource and cost. These “what if?” scenarios can take into account the differing views and concerns of professional groups and organizations in the system.

  • who will need to increase their capacity to cope with demand and how can this be funded?
  • who might lose revenue as a result?
  • How long will a preventative approach take to show an impact on health service utilization?

For example, a new community service is expected to reduce emergency department arrivals and subsequent admissions. The hospital wants to know how that will affect bed capacity. Operationally, bed occupancy is affected by variability of patient types, times of arrival, lengths of stay and discharge times. Knowing that there will be five less emergency admissions on a Monday is helpful, but knowing the time of arrival of those admissions would allow the hospital to test the real impact on bed occupancy, patient outcomes and length of stay. Simulation can manage real world variability and combines with predictive analytics to forecast immediate problems so hospital managers can plan bed capacity in the long term and the short-term and resolve a crisis before it happens.

The results from simulation scenarios provide the evidence which in turn informs the discussions between the different parties to help to make a collective decision which drives overall improvement.

Sharing the experience

Finally, once a simulation is built and the decisions made, the same model can be used to disseminate the understanding to different healthcare systems who can test what the same solution would look like if applied locally. A great example of this is the NHS UK Long Term Conditions Year of Care Commissioning Simulation which has “bottled” the three year experience of the early implementers of a capitated budget for people living with multiple chronic conditions to test out how the same concept would work in their locality. This disseminates the “nuts and bolts” of how it actually works in practice, the data required to understand the way in which patients are using services and allows users to test “what if?” scenarios of those who have already implemented. This, alongside the stories of those healthcare systems who have implemented the change, is helping to share the experience and support adoption of new models of care.

Once the system is understood, and evidence for decision-making provided, live data feeds can be used to monitor performance and predict problems enabling better communication across stakeholders within a system, and determining actions to be taken to manage patient flow on a routine basis.

There’s no risk to using simulation to help rethink and redesign patient pathways, and the alternative is a costly real world experiment. Give it a try.

 

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