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RTN1407_B5_Performance Measurement & High Reliability

*TS: Benchmark: Performance Measurement Leads the Way to High Reliability[®] [REF: GB, JCSC, PI, LDR] The Source, July 2014, Vol 12, #7, Pg 14  JCs1407_B5

Healthcare organizations (e.g., hospitals) are generally reported as performing at the level of 1-2 sigma.  This means something goes wrong (i.e., a failure) once in every 10 to 100 opportunities. On the other hand, airlines and nuclear plants are given as examples of High Reliability Organizations performing at the level of 6 sigma.  This means things go wrong (i.e., failures) not more than 3.4 times in every one million opportunities.  This article begins with a Key Def of ‘High Reliability Organization’ and a useful overview/review of the 3 critical changes an organization must undergo to achieve HRO status:

  1.  Leadership commitment to high reliability – to include investing in the resources, education, training and tools needed
  2.  Development of a culture of safety – to include the development of trust, respect and passion for preventing accidents, errors and injury
  3.  Utilization of Robust Process Improvement – to include the use of change management methodologies/tools, effective performance measurement and monitoring/evaluation of key data   

The bulk and balance of the article explicates that third factor of RPI and performance measurement.  It points out that “The purpose of data collection is to find the areas of care that can be improved and then to implement solutions to improve that care.”  It also encourages transparency in the sharing of performance data.  In the beginning this might just be with staff, but ultimately it should progress to patients and even external bodies.  A [Pearl] useful list of required data to collect as well as optional data suggestions is included on page 17.
Tip: Start a Leadership Reading Club and assign this as one of the readings. Then put it on the agenda of the appropriate leadership forum for a 5-10 minute discussion.
Tip: Ask PI to review the list of required and suggested data on page 17 to ensure standards compliance and identify opportunities for improvement.
See also: Related Key Definitions in the Comments section of this article

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One response to “RTN1407_B5_Performance Measurement & High Reliability”

  1. Is ‘Performance Measurement Leads the Way to High Reliability’ a valuable article? Take 10-seond RTN1407_B5 Poll and help us improve the quality of this feature.

    Key Definitions from Performance Measurement Leads the Way to High Reliability, The Source, July 2014, Vol 12, #7, Pg 14:

    Key Def: High Reliability Organization (HRO) –
    • High reliability organizations are those that demonstrate consistent performance at high levels of safety over long periods of time.

    Key Def: Accontability Measure –
    • The Joint Commission’s accountability measures are
    quality measures that meet the following four criteria designed to identify measures that produce the greatest positive impact on patient outcomes when hospitals demonstrate improvement6:
    1. Research: Strong scientific evidence demonstrates
    that performing the evidence-based care process
    improves health outcomes (either directly or by
    reducing risk of adverse outcomes).
    2. Proximity: Performing the care process is closely
    connected to the patient outcome; there are relatively
    few clinical processes that occur after the one that is
    measured and before the improved outcome occurs.
    3. Accuracy: The measure accurately assesses whether
    or not the care process has actually been provided.
    That is, the measure should be capable of indicating
    whether the process has been delivered with sufficient
    effectiveness to make improved outcomes likely.
    4. No Adverse Effects: Implementing the measure has
    little or no chance of inducing unintended adverse

    Key Def: DMAIC –
    • Acronym consisting of the following:
    – Define the problem.
    – Measure key aspects of the current process and
    collect relevant data.
    – Analyze data to investigate and verify cause-and-effect
    – Improve or optimize the current process based on
    data analysis.
    – Control the future state process to ensure that any
    deviations from the target are corrected before they
    result in defects.

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