COVID-19: A brief example exploring how to manage increased demand as a result of COVID-19

  • In this hypothetical scenario, a small third sector counselling service (prior to Covid19) was accepting, on average, 250 referrals per year (approx. 4.8 accepted referrals per week)
  • Sessions were delivered at a number of locations, face to face, which involved significant travel alongside a small proportion of therapy being delivered digitally
  • Treatment was on average 6.3 sessions per person - this includes the service DNA rate (more information on how to include your DNA rate in this figure is on the FAQ’s on the CReST website)
  • The service can currently deliver 35 appointments per week
  • They have been commissioned on the basis that they will see 85% of patients within 28 days

Baseline Senario

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Increased Demand Scenario

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  • The service lead then takes this information to discuss with the service director and together they consider the other impacts of Covid19
  • It soon becomes evident that there are some real benefits in delivering more of the therapy digitally
    • There is positive feedback about people receiving their counselling sessions remotely
    • Therapists report having more time for clinical notes and for professional liaison relating to their cases as they are not losing as much time traveling between appointments
    • Most notably, the DNA rate reduced significantly , which means treatment is now on average 6 sessions per person (previously 6.3)

Reduced DNA Scenario

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Adjusted waiting time breach target

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  • The commissioners and service leads used the modelling to discuss and agree the optimum number of appointments to commission
  • The modelling helped the all the stakeholders to see the impact of any change to the waiting time target and none wanted to alter it, as it was felt a key metric in ensuring those that need support can get access promptly
  • Given the increased percentage of sessions being delivered digitally, and the subsequent reduction in travel time, the service agreed that they can deliver 38 appointments per week (3 more than prior to Covid19) without having to increase staffing levels
  • This final position will mean:
    • An average waiting time of 11.7 days for 86% of cases
    • The appointments would be 94.1% utilised

Final Agreed Position

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  • As shown, to get started with CReST you only need 3 key figures (and these figures could be anticipated if you are modelling a new service)
    • The average number of accepted referrals for the service/unit in a period
    • The number of available appointments in the service in a period or the number of beds in the unit
    • The average number of appointments each accepted referral will receive during treatment, or the average length of stay for the unit
  • CReST is accessible to anyone in the UK working in a health or social care, and you can register and access the tool instantly on the website (http://dev.cypmh-model.nhs.uk/)
  • Today’s example and several other Covid19 specific modelling scenarios are explored on the website and there is a support team is on hand (This email address is being protected from spambots. You need JavaScript enabled to view it.) for any queries with registration / access and to support with modelling / analysis.