An umbrella review of systematic reviews on contributory factors to medication errors in health-care settings

ABSTRACT

Introduction

Medication errors are common events that compromise patient safety and are prevalent in all health-care settings. This umbrella review aims to systematically evaluate the evidence on contributory factors to medication errors in health-care settings in terms of the nature of these factors, methodologies and theories used to identify and classify them, and the terminologies and definitions used to describe them.

Areas covered

Medline, Cumulative Index to Nursing and Allied Health Literature, Embase, and Google Scholar were searched from inception to March 2022. The data extraction form was derived from the Joanna Briggs Institute (JBI) Reviewers’ Manual, and critical appraisal was conducted using the JBI quality assessment tool. A narrative approach to data synthesis was adopted.

Expert opinion

Twenty-seven systematic reviews were included, most of which focused on a specific health-care setting or clinical area. Decision-making mistakes such as non-consideration of patient risk factors most commonly led to error, followed by organizational and environmental factors (e.g. understaffing and distractions). Only 10 studies had a pre-specified methodology to classify contributory factors, among which the use of theory, specifically Reason’s theory was commonly used. None of the reviews evaluated the effectiveness of interventions in preventing errors. The collated contributory factors identified in this umbrella review can inform holistic theory-based intervention development.

KEYWORDS:

• Contributory factors
• medication errors
• methodology
• umbrella review

Article highlights

• The dominant contributory factors were decision-making mistakes, which include failure to consider risk factors (e.g. chronic kidney disease and pediatrics), and system failures, such as inadequate opportunities for training, work overload, inadequate staffing levels, and suboptimal work environment.

• Among studies that had a prespecified methodology to identify and classify contributory factors, the use of theory, specifically Reason’s Accident Causation Model, was predominant.

• Methodological limitations were mainly related to search strategy, quality assessment, and data extraction processes. The lack of a predetermined methodology to classify contributory factors was also noted.

• ”Contributory factors” and ”causes” were the most frequently used terms to refer to contributory factors.

• Multiple definitions for contributory factors have emerged in the included reviews; however, the summary presented in our review does not reflect all proposed definitions in the literature.

• The findings of this review will inform the development of holistic theory-based interventions that target different levels of the healthcare system. Such theory-based interventions have the potential to reduce the occurrence of medication errors and promote patient safety.

• Our findings emphasize the need for consistent use of terminology, definitions, and methodology used in research aiming to identify and quantify contributory factors to medication errors.

This box summarizes key points contained in the article.

Declaration of interest statement

The authors have no relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript. This includes employment, consultancies, honoraria, stock ownership or options, expert testimony, grants or patents received or pending, or royalties.

Reviewer disclosures

Peer reviewers on this manuscript have no relevant financial or other relationships to disclose.

Author contribution

L Naseralallah contributed to: study design, screening, developing data extraction tool, piloting the data extraction tool, data extraction, quality assessment, data synthesis, writing original draft, writing (review and editing). D Stewart contributed to: study design, developing data extraction tool, writing (review and editing), supervising. RA Ali contributed to: screening, piloting the data extraction tool, writing (review and editing). V Paudyal contributed to: study design, developing data extraction tool, verifying data extraction and quality assessment results, writing (review and editing), supervising.

Data availability

All relevant data are within the manuscript and its supplementary material.

Supplementary material

Supplemental data for this article can be accessed online at https://doi.org/10.1080/14740338.2022.2147921

Additional information

Funding

This study received no external funding.