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Navigating Quality Assessment Hurdles in Clinical Laboratory Services: A Comprehensive Review in Resource-Limited Settings.

Negesse CherieTeshiwal Deress YazieDereje Mengesha BertaElias ChaneBisrat Birke TeketelewKasaw AdaneMesele Nigus
Published in: Risk management and healthcare policy (2024)
External quality assessment is the process of evaluating the performance of a laboratory and the competence of professionals. Participation in EQA and standardizing the quality of laboratory services are a mandatory requirements for accreditation. This review is aimed at identifying and discussing challenges that hinder the effective implementation of an EQA program in countries with resource limited setting. To obtain abundant information, articles were identified by searching the literature publishedin English using the National Library of Medicine, PubMed, Science Direct and AMC digital library databases. The articles identified in the references were manually searched and included. After the article was identified, it was imported to Endnote version 8.1 and exported to Microsoft Word for citation. Based on this review, the major identified challenges that hinder the implementation of an EQA program include the high cost of control materials, malfunction and lack of maintenance for equipment failure and breakdown, a knowledge gap among laboratory professionals, noncommutability of control samples, and difficulty in assigning target values. In addition, failing to participate in EQA and failing to take corrective action are the major challenges identified. As a result, applying to an EQA program in resource-limited counties was highly challenging. To attain high performance in the laboratory and to provide quality laboratory service for patient care, the EQA supplier and the user laboratory must pay attention to these issues and take appropriate corrective actions for ongoing quality improvement and accreditation.
Keyphrases
  • quality improvement
  • patient safety
  • healthcare
  • primary care
  • mental health
  • public health
  • machine learning
  • health insurance