1 / 10

Measuring Outcomes

Measuring Outcomes. Brian Eastwood TOP Implementation Manager 2 June 2009. Measuring Outcomes (in the drug field). Key performance indicators Numbers in effective treatment Waiting times 12 week retention.

Download Presentation

Measuring Outcomes

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. Measuring Outcomes Brian Eastwood TOP Implementation Manager 2 June 2009

  2. Measuring Outcomes(in the drug field) • Key performance indicators • Numbers in effective treatment • Waiting times • 12 week retention Clinical toolPerformance assessmentValue for moneyCapacity to predict, measure and reportAre US studies applicable to UK population?TOP will help answer questionsShift from proxy to real outcomes

  3. What we are planning to do with the data (1)

  4. Level 0 Baseline Report

  5. Level 1 Exit report

  6. A note on the reliable change index Formula for RCI = x2 - x1 Sdiff 28 What predicts membership in these categories? We first have to calculate the standard error of the difference term: Sdiff = √ 2 (SE)2 Where: SE = S1 √ 1 – rxx 0 RCI is conservative. There is a substantial boundary that must be crossed in order to be deemed different – be it improved or deteriorated Baseline Follow up

  7. A note on the reliable change index – is it reflected in other domains?

  8. A note on the case mix adjustment X Y • Two agencies delivering the same treatment modality and agency Y is twice as effective as agency X • BUT • Agency X has twice the proportion of service users who are associated with poor outcome than agency Y. • With case-mix adjustment agency X is shown to be relatively more effective. • Case-mix adjustment captures the relative progress made given the starting point.

  9. What we are planning to do with the data (2) • Research into • Opioid and crack treatment effects • RCI • Tier 4 • Cannabis • Employment, education and housing • Injecting • Health • Death • Integration into future key performance indicators

  10. Measuring Outcomes- Alcohol? Is it appropriate for alcohol clients? Could it be a useful clinical tool? Working group to investigate TOP as a possibility? Develop a new outcomes measure?

More Related