Literature DB >> 35788428

Automated Notification of Relevant Expected or Incidental Findings in Imaging Exams in a Verticalized Healthcare System.

Edivaldo Nery de Oliveira Filho1, Fabrício Próspero Machado2, Maria Fernanda Arruda Almeida1, Paula Nicole Vieira Pinto Barbosa3.   

Abstract

To describe the implementation of a standardized code system for notification of relevant expected or incidental findings in imaging exams and use of an automated textual mining tool of radiological report narratives, created to facilitate directing patients to specific lines of care, reducing the waiting time for interventions, consultations, and minimizing delays to treatment. We report our 12-month initial experience with the process. A standardized code was attached to every radiology report when a relevant finding was observed. On a daily basis, the notifications was sent to a dedicated medical team to review the notified abnormality and decide a proper action. Between October 1, 2020, and September 30, 2021, 40,296 sectional examinations (CT and MR scans) were evaluated in 35,944 patients. The main findings reported were calcified plaques on the trunk of the left coronary artery or trunk like, pulmonary nodule/mass and suspected liver disease. Data of follow-up was available in 10,019 patients. The age ranged from 24 to 101 years (mean of 71.3 years) and 6,626 were female (66.1%). In 2,548 patients a complementary study or procedure was indicated, and 3,300 patients were referred to a specialist. Customized database searches looking for critical or relevant findings may facilitate patient referral to specific care lines, reduce the waiting time for interventions or consultations, and minimize delays to treatment.
© 2022. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.

Entities:  

Keywords:  Communication of findings; Practice of radiology; Radiology reports; Semi automated coding

Mesh:

Year:  2022        PMID: 35788428     DOI: 10.1007/s10916-022-01842-y

Source DB:  PubMed          Journal:  J Med Syst        ISSN: 0148-5598            Impact factor:   4.460


  4 in total

1.  A vision for the systematic monitoring and improvement of the quality of electronic health data.

Authors:  Brian E Dixon; Marc Rosenman; Yuni Xia; Shaun J Grannis
Journal:  Stud Health Technol Inform       Date:  2013

Review 2.  Current Applications and Future Impact of Machine Learning in Radiology.

Authors:  Garry Choy; Omid Khalilzadeh; Mark Michalski; Synho Do; Anthony E Samir; Oleg S Pianykh; J Raymond Geis; Pari V Pandharipande; James A Brink; Keith J Dreyer
Journal:  Radiology       Date:  2018-06-26       Impact factor: 11.105

Review 3.  Mining electronic health records: towards better research applications and clinical care.

Authors:  Peter B Jensen; Lars J Jensen; Søren Brunak
Journal:  Nat Rev Genet       Date:  2012-05-02       Impact factor: 53.242

4.  Mortality due to cancer treatment delay: systematic review and meta-analysis.

Authors:  Timothy P Hanna; Will D King; Stephane Thibodeau; Matthew Jalink; Gregory A Paulin; Elizabeth Harvey-Jones; Dylan E O'Sullivan; Christopher M Booth; Richard Sullivan; Ajay Aggarwal
Journal:  BMJ       Date:  2020-11-04
  4 in total

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