Using Fault Trees to Advance Understanding of Diagnostic Errors.
Jt Comm J Qual Patient Saf
; 43(11): 598-605, 2017 11.
Article
em En
| MEDLINE
| ID: mdl-29056180
ABSTRACT
PROBLEM DEFINITION Diagnostic errors annually affect at least 5% of adults in the outpatient setting in the United States. Formal analytic techniques are only infrequently used to understand them, in part because of the complexity of diagnostic processes and clinical work flows involved. In this article, diagnostic errors were modeled using fault tree analysis (FTA), a form of root cause analysis that has been successfully used in other high-complexity, high-risk contexts. How factors contributing to diagnostic errors can be systematically modeled by FTA to inform error understanding and error prevention is demonstrated. INITIAL APPROACH:
A team of three experts reviewed 10 published cases of diagnostic error and constructed fault trees. The fault trees were modeled according to currently available conceptual frameworks characterizing diagnostic error. The 10 trees were then synthesized into a single fault tree to identify common contributing factors and pathways leading to diagnostic error. KEY INSIGHTS FTA is a visual, structured, deductive approach that depicts the temporal sequence of events and their interactions in a formal logical hierarchy. The visual FTA enables easier understanding of causative processes and cognitive and system factors, as well as rapid identification of common pathways and interactions in a unified fashion. In addition, it enables calculation of empirical estimates for causative pathways. Thus, fault trees might provide a useful framework for both quantitative and qualitative analysis of diagnostic errors. NEXTSTEPS:
Future directions include establishing validity and reliability by modeling a wider range of error cases, conducting quantitative evaluations, and undertaking deeper exploration of other FTA capabilities.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Erros de Diagnóstico
/
Melhoria de Qualidade
/
Análise de Causa Fundamental
Tipo de estudo:
Clinical_trials
/
Diagnostic_studies
/
Prognostic_studies
/
Qualitative_research
Limite:
Humans
País/Região como assunto:
America do norte
Idioma:
En
Revista:
Jt Comm J Qual Patient Saf
Assunto da revista:
SERVICOS DE SAUDE
Ano de publicação:
2017
Tipo de documento:
Article