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Jt Comm J Qual Patient Saf. 2010 Aug;36(8):359-64.

Reducing errors during patient-controlled analgesia therapy through failure mode and effects analysis.

Author information

1
Advocate Christ Medical Center, Oak Lawn, Illinois, USA. janet.moss@advocatehealth.com

Abstract

BACKGROUND:

Despite the technologic advances in design, resulting in the development of "smart" pumps to help deliver analgesia more safely, patient-controlled analgesia (PCA) is still involved in a significant proportion of the medication errors ascribed to intravenous (IV) drug administration, many of which have harmed patients. In 2003, Failure Mode and Effects Analysis (FMEA) was used to assess the PCA process at a 695-bed teaching and research tertiary hospital. IDENTIFYING AND ADDRESSING FAILURE MODES: For the three processes with hazard scores > 8--patient selection, prescribing, and medication administration-the potential cause(s) were identified, allowing the process to be redesigned to eliminate the potential cause(s).

RESULTS:

In January 2003 to May 2003, before the FMEA process began, there were 11 PCA errors (extrapolated to 26 for the entire 2003 calendar year). In 2004, when most of the corrective actions were taken, there were 22 reported PCA errors. In October 2007, a new online occurrence-reporting program was implemented, making reporting much easier. From October 2007 through September 2008, there were only 8 reported PCA errors, representing a 69% reduction from baseline. No serious adverse events were associated with any of these PCA errors.

DISCUSSION:

Despite the reduction in PCA errors since the FMEA was conducted, misprogramming of drug concentration remains a common PCA error. Solutions include safety software for IV infusion pumps, an integral bar-code reader for detecting concentration errors, and interoperability of the software with other hospital information systems. One lesson learned was that an FMEA can lead to resolution of problems beyond the scope of original intent-in this case, the development of a new system for identifying all broken equipment.

PMID:
20860242
[Indexed for MEDLINE]

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