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The Value of Monitoring Requisition Forms in the Laboratory

2019·0 Zitationen·American Journal of Clinical Pathology
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4

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2019

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Abstract

Abstract Objectives Insufficient or extraneous information on requisition forms may delay turnaround time and become a source of error in diagnosis. Monitoring the completeness and accuracy of clinical information in the requisition forms provided to pathologists is a potential target in laboratory quality improvement, but its effectiveness is currently not known. Here, we describe a pilot study at our institution’s anatomic pathology laboratory to determine the effect of monitoring the completeness of information filled in the requisition forms by clinicians. Methods We randomly selected and reviewed records in each quarter from August 2017 to January 2019. All records were obtained from one hospital site and included requisition forms for specimens from inpatient and outpatient surgical operating rooms. The percentage of incomplete or improperly filled forms was tracked each quarter and feedback is provided to clinicians with insufficient information in the forms. Results A total of 70 requisition forms from 7 quarters were reviewed. Five different physicians were responsible for all the requisition forms without complete information. One physician was responsible for 33.3% of all requisition forms with incomplete information. Commonly recurring missing information in requisition forms were preoperative diagnosis (40%), postoperative diagnosis (35%), type of procedure (15%), and indications for procedure (10%). There was a remarkable improvement on completion of requisition forms from 60% at the beginning of the review period to 100% at the end of the review period (P < .0001). Conclusion Long-term review of requisition forms submitted to the laboratory and provisional feedback to clinicians are associated with sustainable improvement on information provided to pathologists to ensure specimen identification, integrity, and accurate interpretation.

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Clinical Laboratory Practices and Quality ControlMeta-analysis and systematic reviewsArtificial Intelligence in Healthcare and Education
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