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P-118 Two steps in to embedding outcome measures within hospice services: the outcomes adoption and completion challenge
  1. Oliver-Jon Tidball and
  2. Christina Eldridge
  1. Heart of Kent Hospice, Maidstone, UK


Background Outcome measures play a pivotal role in enhancing the quality, efficiency and availability of palliative care services. The Outcome Assessment and Complexity Collaborative (OACC) provides a suite of validated measures that is designed to measure, demonstrate, and improve care for patients and their loved ones. Outcomes data can be used to inform and guide clinical care/interventions at the bedside, MDT decision making, future strategic service planning and benchmarking.

Aim While some OACC measures had already been adopted and influenced a service restructure, it lacked coordination and clarity of purpose across the organisation. Poor data quality and volume therefore affected clinical leadership’s ability to demonstrate service efficacy. Aim to improve OACC measures utilisation and processes across community and inpatient settings.

Method Working party convened to understand current practices alongside a review of OACC ECHO resources and creation of an organisational relaunch programme. Delivery of a suite of face-to-face and online education events. Production of resources for colleagues. Hospice referral process adapted to include outcome measures. Feedback mechanisms between clinical delivery and clinical leadership created.

Results One year since the re-launch, 667 patients had at least one IPOS assessment. 595 of those had the minimum of two assessments. Scoring for majority of symptoms or concerns decreased, although some worsened. Overall, 11% decrease in symptom burden for patients on the Inpatient Unit. Phase of Illness and AKPS are now a unified language across services and within clinical meetings. Views on Care demonstrates high level (97% of inpatients) of improvement in quality-of-life scores.

Conclusion Organisationally we’ve made a great start and have areas of excellent quality data. Some processes aren’t working as well as originally intended and further development and training is required. First time dealing with large outcome data requires additional time to understand, interpret and now decide how, and what to report on.

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