Workload measurement for molecular genetics laboratory: A survey study

PLoS One. 2018 Nov 27;13(11):e0206855. doi: 10.1371/journal.pone.0206855. eCollection 2018.

Abstract

Genetic testing availability in the health care system is rapidly increasing, along with the diffusion of next-generation sequencing (NGS) into diagnostics. These issues make imperative the knowledge-drive optimization of testing in the clinical setting. Time estimations of wet laboratory procedure in Italian molecular laboratories offering genetic diagnosis were evaluated to provide data suitable to adjust efficiency and optimize health policies and costs. A survey was undertaken by the Italian Society of Human Genetics (SIGU). Forty-two laboratories participated. For most molecular techniques, the most time-consuming steps are those requiring an intensive manual intervention or in which the human bias can affect the global process time-performances. For NGS, for which the study surveyed also the interpretation time, the latter represented the step that requiring longer times. We report the first survey describing the hands-on times requested for different molecular diagnostics procedures, including NGS. The analysis of this survey suggests the need of some improvements to optimize some analytical processes, such as the implementation of laboratory information management systems to minimize manual procedures in pre-analytical steps which may affect accuracy that represents the major challenge to be faced in the future setting of molecular genetics laboratory.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Genetic Testing / economics
  • Genetic Testing / statistics & numerical data*
  • Genetic Testing / trends
  • High-Throughput Nucleotide Sequencing / economics
  • High-Throughput Nucleotide Sequencing / statistics & numerical data
  • Italy
  • Laboratories / economics
  • Laboratories / statistics & numerical data*
  • Laboratories / trends
  • Management Information Systems
  • Surveys and Questionnaires / statistics & numerical data*
  • Time Factors
  • Workload / economics
  • Workload / statistics & numerical data*

Grants and funding

Funding was provided by Programma di ricerca Regione-Università "Next-generation sequencing and gene therapy to diagnose and cure rare diseases in Regione Emilia Romagna (RARER)" - Area1, Strategic Programmes (grant number E35E09000880002) to AF. Synlab Italy provided support in the form of salaries for author CL, but did not have any additional role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. The specific role of this author is articulated in the ‘author contributions’ section.