Your browser doesn't support javascript.
loading
Impact of an Automated Closed-Loop Communication and Tracking Tool on the Rate of Recommendations for Additional Imaging in Thoracic Radiology Reports.
DeSimone, Ariadne K; Kapoor, Neena; Lacson, Ronilda; Budiawan, Elvira; Hammer, Mark M; Desai, Sonali P; Eappen, Sunil; Khorasani, Ramin.
Afiliación
  • DeSimone AK; Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts. Electronic address: adesimone@bwh.harvard.edu.
  • Kapoor N; Director of Diversity, Inclusion, and Equity and Quality and Patient Safety Officer, Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts.
  • Lacson R; Director of Education, Center for Evidence-Based Imaging, Brigham and Women's Hospital, and Director of Clinical Informatics, Harvard Medical School Library of Evidence, Boston, Massachusetts.
  • Budiawan E; Center for Evidence-Based Imaging, Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts.
  • Hammer MM; Cardiothoracic Fellowship Program Director, Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts.
  • Desai SP; Senior Vice President and Chief Quality Officer, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts.
  • Eappen S; Senior Vice President, Medical Affairs, and Chief Medical Officer, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts.
  • Khorasani R; Vice Chair of Radiology Quality and Safety, Mass General Brigham; Director of the Center for Evidence-Based Imaging and Vice Chair of Quality/Safety, Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts.
J Am Coll Radiol ; 20(8): 781-788, 2023 08.
Article en En | MEDLINE | ID: mdl-37307897
ABSTRACT

OBJECTIVE:

Assess the effects of feedback reports and implementing a closed-loop communication system on rates of recommendations for additional imaging (RAIs) in thoracic radiology reports.

METHODS:

In this retrospective, institutional review board-approved study at an academic quaternary care hospital, we analyzed 176,498 thoracic radiology reports during a pre-intervention (baseline) period from April 1, 2018, to November 30, 2018; a feedback report only period from December 1, 2018, to September 30, 2019; and a closed-loop communication system plus feedback report (IT intervention) period from October 1, 2019, to December 31, 2020, promoting explicit documentation of rationale, time frame, and imaging modality for RAI, defined as complete RAI. A previously validated natural language processing tool was used to classify reports with an RAI. Primary outcome of rate of RAI was compared using a control chart. Multivariable logistic regression determined factors associated with likelihood of RAI. We also estimated the completeness of RAI in reports comparing IT intervention to baseline using χ2 statistic.

RESULTS:

The natural language processing tool classified 3.2% (5,682 of 176,498) reports as having an RAI; 3.5% (1,783 of 51,323) during the pre-intervention period, 3.8% (2,147 of 56,722) during the feedback report only period (odds ratio 1.1, P = .03), and 2.6% (1,752 of 68,453) during the IT intervention period (odds ratio 0.60, P < .001). In subanalysis, the proportion of incomplete RAI decreased from 84.0% (79 of 94) during the pre-intervention period to 48.5% (47 of 97) during the IT intervention period (P < .001).

DISCUSSION:

Feedback reports alone increased RAI rates, and an IT intervention promoting documentation of complete RAI in addition to feedback reports led to significant reductions in RAI rate, incomplete RAI, and improved overall completeness of the radiology recommendations.
Asunto(s)
Palabras clave

Texto completo: 1 Banco de datos: MEDLINE Asunto principal: Radiología / Método Teach-Back Tipo de estudio: Diagnostic_studies / Observational_studies / Risk_factors_studies Idioma: En Revista: J Am Coll Radiol Asunto de la revista: RADIOLOGIA Año: 2023 Tipo del documento: Article

Texto completo: 1 Banco de datos: MEDLINE Asunto principal: Radiología / Método Teach-Back Tipo de estudio: Diagnostic_studies / Observational_studies / Risk_factors_studies Idioma: En Revista: J Am Coll Radiol Asunto de la revista: RADIOLOGIA Año: 2023 Tipo del documento: Article