gms | German Medical Science

G-I-N Conference 2012

Guidelines International Network

22.08 - 25.08.2012, Berlin

Performance measures: mapping process to outcomes

Meeting Abstract

  • D. Sutcliffe - National Institute for Health and Clinical Excellence, Manchester, UK
  • B. Bennett - National Institute for Health and Clinical Excellence, Manchester, UK
  • T. Lacey - National Institute for Health and Clinical Excellence, Manchester, UK
  • E. Shaw - National Institute for Health and Clinical Excellence, Manchester, UK
  • T. Stokes - National Institute for Health and Clinical Excellence, Manchester, UK

Guidelines International Network. G-I-N Conference 2012. Berlin, 22.-25.08.2012. Düsseldorf: German Medical Science GMS Publishing House; 2012. DocP158

DOI: 10.3205/12gin270, URN: urn:nbn:de:0183-12gin2707

Published: July 10, 2012

© 2012 Sutcliffe et al.
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Outline

Text

Background: Performance measures (PMs) should measure healthcare processes that, when performed correctly, lead to improved health outcomes. PMs should also address processes with few intervening care processes between measured actions and intended outcomes; intended effect of processes far upstream from those measured is minimised if important processes closer to the outcome are not performed effectively. PMs can be categorized as follows: 1) Registers (lists of included ‘cases’) 2) Processes indirectly linked to outcomes (e.g., blood pressure measurement) 3) Processes directly linked to outcomes (e.g., proportion of people with hypertension on BP lowering therapy) 4) Intermediate outcomes (e.g, proportion of people with hypertension whose BP within target range) 5) Outcomes (e.g., subsequent cardiovascular event)

Objectives: To develop graphical representations of how indicators link to outcomes; to assess relative likelihood of indicators achieving outcomes.

Methods: A national PM subset was used to pilot graphical representations of intended influence on outcomes.

Results: Graphical representation shows (not included for submission) that, while outcomes are influenced by numbers of interventions some, as measured by PMs may be closer to intended outcomes than others. This approach also highlights where PMs are potentially conflicting or overlap. Results on how this was used in prioritising PMs will be presented.

Discussion: PMs can be categorised and some considered as being ‘nearer’ to intended outcomes, shown through simple graphics. Implications for guideline developers/users Using a novel approach can help PM developers understand influences of measured actions on outcomes, and prioritise use of PMs considered ‘nearer’ to outcomes.