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Statistical Process Control (SPC) is a method used to monitor and control quality within processes, particularly in manufacturing and service delivery, by employing statistical methods. SPC aims to distinguish between natural (common cause) variation and variation due to specific changes or events (special cause), allowing for timely improvements and sustained quality. The control chart, a pivotal tool in SPC, visually displays data over time alongside a central line of upper and lower control...
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Implementing glucose control in intensive care: a multicenter trial using statistical process control.

Saeid Eslami1, Ameen Abu-Hanna, Nicolette F de Keizer

  • 1Department of Medical Informatics, Academic Medical Center, University of Amsterdam, Meibergdreef 15, 1105 AZ, Amsterdam, The Netherlands. s.eslami@amc.uva.nl

Intensive Care Medicine
|June 10, 2010
PubMed
Summary
This summary is machine-generated.

Implementing glucose control (GC) in intensive care units (ICUs) improves patient outcomes but increases hypoglycemia events. Statistical process control aids in monitoring GC effectiveness and managing these events over time.

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Area of Science:

  • Critical Care Medicine
  • Endocrinology
  • Health Services Research

Background:

  • Glucose control (GC) using insulin is vital for reducing morbidity and mortality in critically ill patients.
  • Implementing GC strategies in intensive care units (ICUs) requires careful evaluation of performance over time.

Purpose of the Study:

  • To investigate the performance of GC strategies over time during their implementation in routine clinical practice within three ICUs.
  • To assess the impact of GC guideline implementation and revisions on various indicators of effectiveness, efficiency, safety, and protocol adherence.

Main Methods:

  • A retrospective analysis of 17,111 adult critically ill patients admitted between 1999 and 2007.
  • Evaluation of 714,141 blood glucose level (BGL) measurements.
  • Measurement of effectiveness/efficiency, safety, and protocol-related indicators before and after GC guideline implementation and revisions.

Main Results:

  • Introduction of GC protocols significantly altered mean BGL, time to target, hyperglycemia index, sampling frequency, hyperglycemia events, and in-range measurements.
  • Initial GC protocol introduction had the most substantial impact; subsequent revisions had diminishing effects.
  • Hypoglycemia events increased upon protocol introduction and did not return to pre-implementation levels despite revisions; however, GC was not discontinued.
  • Intensive implementation strategies, including decision support systems, improved process control.

Conclusions:

  • Various GC strategies exist for routine clinical practice with variable success.
  • Increased hypoglycemia is a consistent outcome of GC implementation, accepted and managed within clinical practice.
  • Statistical process control is valuable for monitoring GC phenomena and capturing institutional changes over time.