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Detection and Remediation of Misidentification Errors in Radiology Examination Ordering.
Sheehan, Scott E; Safdar, Nasia; Singh, Hardeep; Sittig, Dean F; Bruno, Michael A; Keller, Kelli; Kinnard, Samantha; Brunner, Michael C.
Afiliação
  • Sheehan SE; Department of Radiology, William S. Middleton Veterans Hospital, Madison, Wisconsin, United States.
  • Safdar N; Department of Medicine, William S. Middleton Memorial Veterans Hospital and University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin, United States.
  • Singh H; Center for Innovations in Quality, Effectiveness and Safety, Michael E. DeBakey VA Medical Center and Department of Medicine, Baylor College of Medicine, Houston, Texas, United States.
  • Sittig DF; School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, Texas, United States.
  • Bruno MA; Department of Radiology, Penn State Hershey, Hershey, Pennsylvania, United States.
  • Keller K; Department of Radiology, William S. Middleton Veterans Hospital, Madison, Wisconsin, United States.
  • Kinnard S; Department of Radiology, William S. Middleton Veterans Hospital, Madison, Wisconsin, United States.
  • Brunner MC; Department of Radiology, William S. Middleton Memorial Veterans Hospital and University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin, United States.
Appl Clin Inform ; 11(1): 79-87, 2020 01.
Article em En | MEDLINE | ID: mdl-31995835
ABSTRACT

BACKGROUND:

Despite progress in patient safety, misidentification errors in radiology such as ordering imaging on the wrong anatomic side persist. If undetected, these errors can cause patient harm for multiple reasons, in addition to producing erroneous electronic health records (EHR) data.

OBJECTIVES:

We describe the pilot testing of a quality improvement methodology using electronic trigger tools and preimaging checklists to detect "wrong-side" misidentification errors in radiology examination ordering, and to measure staff adherence to departmental policy in error remediation.

METHODS:

We retrospectively applied and compared two methods for the detection of "wrong-side" misidentification errors among a cohort of all imaging studies ordered during a 1-year period (June 1, 2015-May 31, 2016) at our tertiary care hospital. Our methods included (1) manual review of internal quality improvement spreadsheet records arising from the prospective performance of preimaging safety checklists, and (2) automated error detection via the development and validation of an electronic trigger tool which identified discrepant side indications within EHR imaging orders.

RESULTS:

Our combined methods detected misidentification errors in 6.5/1,000 of study cohort imaging orders. Our trigger tool retrospectively identified substantially more misidentification errors than were detected prospectively during preimaging checklist performance, with a high positive predictive value (PPV 88.4%, 95% confidence interval 85.4-91.4). However, two third of errors detected during checklist performance were not detected by the trigger tool, and checklist-detected errors were more often appropriately resolved (p < 0.00001, 95% confidence interval 2.0-6.9; odds ratio 3.6).

CONCLUSION:

Our trigger tool enabled the detection of substantially more imaging ordering misidentification errors than preimaging safety checklists alone, with a high PPV. Many errors were only detected by the preimaging checklist; however, suggesting that additional trigger tools may need to be developed and used in conjunction with checklist-based methods to ensure patient safety.
Assuntos

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Radiologia / Erros Médicos Tipo de estudo: Diagnostic_studies / Prognostic_studies Limite: Humans Idioma: En Ano de publicação: 2020 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Radiologia / Erros Médicos Tipo de estudo: Diagnostic_studies / Prognostic_studies Limite: Humans Idioma: En Ano de publicação: 2020 Tipo de documento: Article