Knowledge of relevant regulatory considerations related to quality data collection Adequate knowledge of the use of MedDRA for coding purposes, as applicable. This is particularly important for concepts such as coding of definitive versus provisional diagnoses (with or without symptoms) and not inferring diagnoses A thorough understanding of and compliance with an organisation’s agreed-upon process to clarify information “data The Quality, Coding and MedDRA” presentation in the section of “Data ‘General/Basics’ the Materials” page of the MedDRA website () is another useful resource. This “Training customisable slide set is intended for use at investigator meetings and for training personnel involved with data collection (such as clinical research associates and clinical coordinators). It provides an overview of the importance and benefits of good quality data as it relates to MedDRA. Quality assurance checks A thoughtful and thorough quality assurance (QA) process supports the goal of maximising data quality. QA checks during the data management process ensure compliance with established organisational procedures and metrics. Examples of inaccurate MedDRA coding which QA checks could identify include: Reported Inaccurately QA Review Outcome Selected LLT This inaccurate LLT was selected by an autoencoder which matched the Allergic to CAT scan Allergic to cats words to CAT scan” from “Allergic the reported term This inaccurate LLT refers to the name of the test for intraocular Feels pressure in eye Intraocular pressure pressure; the appropriate term to reflect the symptom being described in the report would be LLT Sensation of pressure in eye These checks can identify coding errors with MedDRA before the database is locked and erroneous data become part of a data analysis.
Knowledge of relevant regulatory considerations related to quality data collection Adequate knowledge of the use of MedDRA for coding purposes, as applicable. This is particularly important for concepts such as coding of definitive versus provisional diagnoses (with or without symptoms) and not inferring diagnoses A thorough understanding of and compliance with an organisation’s agreed-upon “data process to clarify information The “Data Quality, Coding and MedDRA” presentation in the ‘General/Basics’ section of the “Training Materials” page of the MedDRA website () is another useful resource. This customisable slide set is intended for use at investigator meetings and for training personnel involved with data collection (such as clinical research associates and clinical coordinators). It provides an overview of the importance and benefits of good quality data as it relates to MedDRA. Quality assurance checks A thoughtful and thorough quality assurance (QA) process supports the goal of maximising data quality. QA checks during the data management process ensure compliance with established organisational procedures and metrics. Examples of inaccurate MedDRA coding which QA checks could identify include: Reported Inaccurately Selected LLT QA Review Outcome Allergic to CAT scan Allergic to cats This inaccurate LLT was selected by an autoencoder which matched the words “Allergic to CAT scan” from the reported term Feels pressure in eye Intraocular pressure This inaccurate LLT refers to the name of the test for intraocular pressure; the appropriate term to reflect the symptom being described in the report would be LLT Sensation of pressure in eye These checks can identify coding errors with MedDRA before the database is locked and erroneous data become part of a data analysis.
000199_companionptc_r1_01_jun2018.docx