Application of a human factors classification framework for patient safety to identify precursor and contributing factors to adverse clinical incidents in hospital

Rebecca J Mitchell; Ann Williamson; Brett Molesworth

doi:10.1016/j.apergo.2015.07.018

Application of a human factors classification framework for patient safety to identify precursor and contributing factors to adverse clinical incidents in hospital

Appl Ergon. 2016 Jan:52:185-95. doi: 10.1016/j.apergo.2015.07.018. Epub 2015 Aug 3.

Authors

Rebecca J Mitchell¹, Ann Williamson², Brett Molesworth³

Affiliations

¹ Australian Institute of Health Innovation, Macquarie University, Australia. Electronic address: r.mitchell@mq.edu.au.
² Transport and Road Safety (TARS) Research, University of New South Wales, Australia; School of Aviation, University of New South Wales, Australia.
³ School of Aviation, University of New South Wales, Australia.

PMID: 26360210
DOI: 10.1016/j.apergo.2015.07.018

Abstract

This study aimed to identify temporal precursor and associated contributing factors for adverse clinical incidents in a hospital setting using the Human Factors Classification Framework (HFCF) for patient safety. A random sample of 498 clinical incidents were reviewed. The framework identified key precursor events (PE), contributing factors (CF) and the prime causes of incidents. Descriptive statistics and correspondence analysis were used to examine incident characteristics. Staff action was the most common type of PE identified. Correspondence analysis for all PEs that involved staff action by error type showed that rule-based errors were strongly related to performing medical or monitoring tasks or the administration of medication. Skill-based errors were strongly related to misdiagnoses. Factors relating to the organisation (66.9%) or the patient (53.2%) were the most commonly identified CFs. The HFCF for patient safety was able to identify patterns of causation for the clinical incidents, highlighting the need for targeted preventive approaches, based on an understanding of how and why incidents occur.

Keywords: Classification framework; Clinical incident; Error; Patient safety.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Diagnostic Errors / classification
Diagnostic Errors / statistics & numerical data
Female
Hospitals / standards
Hospitals / statistics & numerical data
Humans
Male
Medical Errors / classification*
Medical Errors / statistics & numerical data
Medication Errors / classification
Medication Errors / statistics & numerical data
Middle Aged
Patient Safety / standards*