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Drug Repurposing Hypothesis Generation Using the "RE:fine Drugs" System

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Published on: December 11, 2016

Assessing real-world medication data completeness.

Laura Evans¹, Jack W London², Matvey B Palchuk¹

¹TriNetX, LLC, Boston, USA.

Journal of Biomedical Informatics

|June 23, 2021

Summary

This summary is machine-generated.

Analyzing medication data completeness in Real-World Data (RWD) is crucial. Five diagnosis-medication pairs, like Type 1 diabetes mellitus and insulin, reliably indicate overall medication data completeness for healthcare organizations.

Keywords:

Data quality Electronic health record Real-world data Secondary use

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Drug Repurposing Hypothesis Generation Using the "RE:fine Drugs" System

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Published on: December 11, 2016

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Published on: April 23, 2019

Area of Science:

Health Informatics
Data Science
Clinical Research

Background:

Real-World Data (RWD) offers insights into patient care but may have limitations.
Incomplete RWD can significantly impact the validity of research conclusions.
Assessing the completeness of medication data within RWD is essential for reliable analysis.

Purpose of the Study:

To investigate the completeness of medication data in Real-World Data (RWD).
To identify reliable indicators for assessing medication data completeness in healthcare organizations.
To establish a proxy method for evaluating the overall completeness of medication RWD.

Main Methods:

Analyzed the incidence of diagnosis-medication couplets across 61 U.S. healthcare organizations in the TriNetX network.
Calculated the percentage of completeness for various diagnosis-medication pairs.
Validated potential indicator couplets based on their status as standards of care.

Main Results:

Identified five diagnosis-medication couplets as reliable proxies for medication data completeness.
These reliable couplets include Type 1 diabetes mellitus and insulin, asthma and albuterol, congestive heart failure and diuretics, cardiovascular disease and aspirin, and hypothyroidism and levothyroxine.
Observed peak completeness of at least 87% for these indicator couplets across participating organizations.

Conclusions:

The completeness of specific indicator diagnosis-medication couplets can serve as a proxy for overall medication data completeness.
This method allows healthcare organizations to assess and potentially improve the quality of their medication RWD.
Utilizing these validated couplets enhances the reliability of RWD for clinical research and decision-making.