Detecting professional interpreter use among patients with limited English proficiency: Derivation and validation study

Author:

Soleimani Jalal1ORCID,Marquez Alberto2,Fathma Sawsan1,Weister Timothy J2,Barwise Amelia K3ORCID

Affiliation:

1. Department of Anesthesiology and Perioperative Medicine, Mayo Clinic, Rochester, MN, USA

2. Anesthesia Clinical Research Unit (ACRU), Department of Anesthesiology and Perioperative Medicine, Mayo Clinic, Rochester, MN, USA

3. Division of Pulmonary and Critical Care Medicine, Mayo Clinic, Rochester, MN, USA

Abstract

Objectives: The objective of this derivation and validation study was to develop and validate a search strategy algorithm to detect patients who used professional interpreter services. Methods: We identified all adults who had at least one intensive care unit admission during their hospital stay across the Mayo Clinic Enterprise between 1 January 2015 and 30 June 2020. Three random subsets of 100 patients were extracted from 60,268 patients to develop the search strategy algorithm. Two physician reviewers conducted gold standard manual chart review and any discrepancies were resolved by a third reviewer. These results were compared with the search strategy algorithm each time it was refined. Sensitivity and specificity were calculated during each phase by comparing the search strategy results to the reference gold standard for both derivation cohorts and the final validation cohort. Results: The first search strategy resulted in a sensitivity of 100% and a specificity of 89%. The second revised search strategy achieved a sensitivity of 100% and a specificity of 87%. The final version of the search strategy was applied to the validation subset and sensitivity and specificity were 100% and 89%, respectively. Conclusion: We derived and validated a search strategy algorithm to assess interpreter use among hospitalized patients. Using a search strategy algorithm with high sensitivity and specificity can reduce the time required to abstract data from the electronic medical records compared with manual data abstraction.

Publisher

SAGE Publications

Subject

General Medicine

Reference48 articles.

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