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Poster Numbers 185 – 241 – People & places: Poster No: 234
Audit on NHS fast tract applications: prognostic prediction & preferred place of care/death
  1. Thiru Thirukkumaran,
  2. Siva Subramaniam,
  3. Sinead de Nogla,
  4. Declan Cawley and
  5. Simon Fisher
  1. Pilgrims Hospice, Ashford, UK


Background ‘NHS fast tract’ enables urgent care allocation to a patient whose primary health need is classified as ‘rapidly deteriorating condition with an increasing level of dependency and approaching end of life care’. The guidance states clearly that life expectancy does not form a part of the eligibility criteria under fast tract route. This approach aims to ensure that the individual in question with get the care they require and this be their preferred place of care and be in place within 48 h.

Aim To explore the possibility that the fast track process can act as a useful aide memoire when professionals are trying to predict prognosis in individuals with progressive, life limiting conditions.

Methodology A retrospective case note review as well as fast track documentation within a hospice setting over 3 months.

Results Fourty-two patients identified who applied for the ‘NHS fast tract’ process with 41 applications approved. In 39/42 (93%) applications a decision was reached within 48 h with the remaining applications getting a decision in 72 h. In terms of accuracy of prediction: 36/42 (86%) died with 12 weeks of approval.28/42 (67%) achieved their preferred place of care (PPoC) and death (PPoD) with 6/42 (14%) not able to have the conversation regarding PPoC/D.

Conclusion The possibility that the Fast Track process may help clinicians in trying to build an approach in aiding prognostication and facilitating individuals to be cared for and die in their preferred setting is promising. The use of an existing process that can timely facilitate end of life care in individuals with the helpfulness of timelines can only help manage resources that are currently so limited. We aim to use this approach with additional data methods to try and gain further knowledge in best planning end of life care.

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