Literature DB >> 25543064

Anatomy and history of an external quality assessment program for interpretative comments in clinical biochemistry.

Samuel D Vasikaran1.   

Abstract

The provision of clinical interpretation of results, either verbally or in the printed report, may be considered an integral part of clinical biochemistry diagnostic service. Proficiency testing or external quality assessment (EQA) of such activity may be useful in education, training, continuing professional development and ensuring the quality of such service. Details of the Patient Report Comments Program (RPCProgram) developed by the Royal College of Pathologists of Australasia (RCPA) Chemical Pathology Quality Assurance Programs Pty Ltd (QAP) is described in this review. The program is aimed at pathologists, clinical scientists and trainees. Registered participants are provided a report with case details and a set of clinical biochemistry results at monthly intervals and submit an interpretative comment for the report. Comments received are broken up into components that are translated into common key phrases. An expert panel evaluates the key phrases, classifies them according to appropriateness and drafts a suggested comment, a case summary and a rationale, which are included in a summary report returned to participants. There is considerable diversity in the quality of interpretative comments received from participants of the PRCProgram. The primary purpose of EQA of interpretative commenting is educational self-assessment, and they are recognized as a continuing professional development activity. Whilst there is some evidence for the utility of interpretative comments in improving patient outcomes, evidence for the utility of EQA in improving quality of comments is awaited.
Copyright © 2014 The Canadian Society of Clinical Chemists. Published by Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Clinical biochemistry; Clinical chemistry; Continuing professional development; Interpretative commenting; Quality assessment

Mesh:

Year:  2014        PMID: 25543064     DOI: 10.1016/j.clinbiochem.2014.12.014

Source DB:  PubMed          Journal:  Clin Biochem        ISSN: 0009-9120            Impact factor:   3.281


  1 in total

1.  Predicting the Reasons of Customer Complaints: A First Step Toward Anticipating Quality Issues of In Vitro Diagnostics Assays with Machine Learning.

Authors:  Stephane Aris-Brosou; James Kim; Li Li; Hui Liu
Journal:  JMIR Med Inform       Date:  2018-05-15
  1 in total

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