Mental Health PbR

Prototyping and Simulation

Implementing payment by results in mental health services remains a policy priority for the foreseeable future. With this comes a range of organisational and system wide challenges that to date have not been fully tested. We still don’t fully understand the impact of MH PbR on the overall health of the system, whether it is financially sustainable, and whether it enables or restrains the ambitions of other policies such as personalisation and whether it drives up quality for all concerned.

To enable this we have developed an archetype (simplified below) of the core patient and financial flows which allows commissioners and their providers to stress-test the impact of PbR implementation in a sophisticated system dynamic simulation environment.

This approach allows colleagues to work together agreeing and setting all the parameters that will be present in a fully functioning PbR system. For example, test your clustering assumptions, your patient flow assumptions, your cluster to cluster transfer protocols, your capacity assumptions and your tariff assumptions. See how these play out over time and test for sensitivity before implementation.



PbR Cluster Allocations

In addition to this we developed the first cluster allocation tool which allows clinicians to organise and cluster all their current and new patients in a simple and effective environment.





Developing Packages of Care

Developing consensus regarding the care offer or currency remains a fine balance between the evidence, local clinical opinion, market availability and consumer preference. For the first time we have used 'Crowd Sourcing' to develop a simple online tool that allows all interested parties to contribute to a cycle of development that allows the structured evolution of care packages. The key benefits of this approach are that it;

  • Allows managers to engage 1000s of stakeholders
  • Shows how opinion from a multi-stakeholder environment can shape care packages
  • Allows a more considered analysis of the areas of contention
  • Builds into a meaningful tariff, based on quantifiable data