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A method to identify pediatric high-risk diagnoses missed in the emergency department.
Sundberg, Melissa; Perron, Catherine O; Kimia, Amir; Landschaft, Assaf; Nigrovic, Lise E; Nelson, Kyle A; Fine, Andrew M; Eisenberg, Matthew; Baskin, Marc N; Neuman, Mark I; Stack, Anne M.
  • Sundberg M; Boston Children's Hospital, Division of Emergency Medicine, 300 Longwood Ave, Boston, MA 02115, USA.
  • Perron CO; Boston Children's Hospital, Division of Emergency Medicine, Boston, MA, USA.
  • Kimia A; Boston Children's Hospital, Division of Emergency Medicine, Boston, MA, USA.
  • Landschaft A; Harvard University Extension School, Cambridge, MA, USA.
  • Nigrovic LE; Boston Children's Hospital, Division of Emergency Medicine, Boston, MA, USA.
  • Nelson KA; Boston Children's Hospital, Division of Emergency Medicine, Boston, MA, USA.
  • Fine AM; Boston Children's Hospital, Division of Emergency Medicine, Boston, MA, USA.
  • Eisenberg M; Boston Children's Hospital, Division of Emergency Medicine, Boston, MA, USA.
  • Baskin MN; Boston Children's Hospital, Division of Emergency Medicine, Boston, MA, USA.
  • Neuman MI; Boston Children's Hospital, Division of Emergency Medicine, Boston, MA, USA.
  • Stack AM; Boston Children's Hospital, Division of Emergency Medicine, Boston, MA, USA.
Diagnosis (Berl) ; 5(2): 63-69, 2018 Jun 27.
Article en En | MEDLINE | ID: mdl-29858901
ABSTRACT

BACKGROUND:

Diagnostic error can lead to increased morbidity, mortality, healthcare utilization and cost. The 2015 National Academy of Medicine report "Improving Diagnosis in Healthcare" called for improving diagnostic accuracy by developing innovative electronic approaches to reduce medical errors, including missed or delayed diagnosis. The objective of this article was to develop a process to detect potential diagnostic discrepancy between pediatric emergency and inpatient discharge diagnosis using a computer-based tool facilitating expert review.

METHODS:

Using a literature search and expert opinion, we identified 10 pediatric diagnoses with potential for serious consequences if missed or delayed. We then developed and applied a computerized tool to identify linked emergency department (ED) encounters and hospitalizations with these discharge diagnoses. The tool identified discordance between ED and hospital discharge diagnoses. Cases identified as discordant were manually reviewed by pediatric emergency medicine experts to confirm discordance.

RESULTS:

Our computerized tool identified 55,233 ED encounters for hospitalized children over a 5-year period, of which 2161 (3.9%) had one of the 10 selected high-risk diagnoses. After expert record review, we identified 67 (3.1%) cases with discordance between ED and hospital discharge diagnoses. The most common discordant diagnoses were Kawasaki disease and pancreatitis.

CONCLUSIONS:

We successfully developed and applied a semi-automated process to screen a large volume of hospital encounters to identify discordant diagnoses for selected pediatric medical conditions. This process may be valuable for informing and improving ED diagnostic accuracy.
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Texto completo: 1 Banco de datos: MEDLINE Asunto principal: Garantía de la Calidad de Atención de Salud / Errores Diagnósticos / Servicio de Urgencia en Hospital / Registros Electrónicos de Salud Tipo de estudio: Diagnostic_studies / Etiology_studies / Observational_studies / Prognostic_studies / Risk_factors_studies Límite: Child / Humans Idioma: En Año: 2018 Tipo del documento: Article

Texto completo: 1 Banco de datos: MEDLINE Asunto principal: Garantía de la Calidad de Atención de Salud / Errores Diagnósticos / Servicio de Urgencia en Hospital / Registros Electrónicos de Salud Tipo de estudio: Diagnostic_studies / Etiology_studies / Observational_studies / Prognostic_studies / Risk_factors_studies Límite: Child / Humans Idioma: En Año: 2018 Tipo del documento: Article