A Review of Medical Error Reporting System Design Considerations and a Proposed Cross-Level Systems Research Framework
Abstract
Objective: To review the literature on medical error reporting systems, identify gaps in the literature, and present an integrative cross-level systems model of reporting to address the gaps and to serve as a framework for understanding and guiding reporting system design and research. Background: Medical errors are thought to be a leading cause of death among adults in the United States. However, no review exists summarizing what is known about the barriers and facilitators for successful reporting systems, and no integrated model exists to guide further research into and development of medical error reporting systems. Method: Relevant literature was identified using online databases; references in relevant articles were searched for additional relevant articles. Results: The literature review identified components of medical error reporting systems, error reporting system design choices, barriers and incentives for reporting, and suggestions for successful reporting system design. Little theory was found to guide the published research. An integrative cross-level model of medical error reporting system design was developed and is proposed as a framework for understanding the medical error reporting literature, addressing existing limitations, and guiding future design and research. Conclusion: The medical error reporting research provides some guidance for designing and implementing successful reporting systems. The proposed cross-level systems model provides a way to understand this existing research. However, additional research is needed on reporting and related safety actions. The proposed model provides a framework for such future research. Application: This work can be used to guide the design, implementation, and study of medical error reporting systems.
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Studies using self-reported data, including this one, are limited by the possibility of reporters' recall bias or knowledge.
“While studies using self-reported data, including this one, are limited by the possibility of reporters’ recall bias or knowledge,56 our methods may allow for a more complete representation of an incident and the underlying safety concern.”
From An analysis of electronic health record-related patient safety concerns (2014) · cites this paper as [56]
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Self-citationAn analysis of electronic health record-related patient safety concerns
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Self-citation
- Authors
- Holden, R. J., & Karsh, B.-T.
- Affiliations
- University of Wisconsin–Madison (US)
- Corresponding author
- Karsh, Ben-Tzion
- ORCID
- Holden, Richard J.
- Venue
- Human Factors: The Journal of the Human Factors and Ergonomics Society
- Publisher
- SAGE Publications
- Published
- 2007-04
- Volume
- 49
- Issue
- 2
- Pages
- 257-276
- ISSN
- 0018-7208 (ISSN-L)
- 1547-8181
- OpenAlex
- W2153449837
- Cited by
- 101
- References
- 120
- Citations by year
- 2026: 3 · 2025: 5 · 2024: 1 · 2023: 4 · 2022: 5 · 2021: 3 · 2020: 4 · 2019: 6 · 2018: 3 · 2017: 9 · 2016: 4 · 2015: 8 · 2014: 8 · 2013: 4 · 2012: 7
- FWCI
- 1.44
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Human Factors The Journal of the Human Factors and Ergonomics Society (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Health Professions › Emergency Medical Services › Patient Safety and Medication Errors
- Health Sciences › Medicine › Family Practice › Clinical Reasoning and Diagnostic Skills
- Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Toxicology › Pharmacovigilance and Adverse Drug Reactions
- Keywords
- medical errors, reporting system design, patient safety, research framework
- MeSH terms
- Humans, Information Systems, Risk Management, Safety, Systems Analysis, Medical Errors
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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