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. 2022:31:100996.
doi: 10.1016/j.imu.2022.100996. Epub 2022 Jun 25.

Use of automatic SQL generation interface to enhance transparency and validity of health-data analysis

Kavishwar B Wagholikar  1   2 , David Zelle  3 , Layne Ainsworth  4 , Kira Chaney  3 , Alexander J Blood  1   3 , Angela Miller  2 , Rupendra Chulyadyo  4 , Michael Oates  4 , William J Gordon  1   3   4 , Samuel J Aronson  4 , Benjamin M Scirica  1   3 , Shawn N Murphy  1   2
Affiliations

Use of automatic SQL generation interface to enhance transparency and validity of health-data analysis

Kavishwar B Wagholikar et al. Inform Med Unlocked. 2022.

Abstract

Analysis of health data typically requires development of queries using structured query language (SQL) by a data-analyst. As the SQL queries are manually created, they are prone to errors. In addition, accurate implementation of the queries depends on effective communication with clinical experts, that further makes the analysis error prone. As a potential resolution, we explore an alternative approach wherein a graphical interface that automatically generates the SQL queries is used to perform the analysis. The latter allows clinical experts to directly perform complex queries on the data, despite their unfamiliarity with SQL syntax. The interface provides an intuitive understanding of the query logic which makes the analysis transparent and comprehensible to the clinical study-staff, thereby enhancing the transparency and validity of the analysis. This study demonstrates the feasibility of using a user-friendly interface that automatically generate SQL for analysis of health data. It outlines challenges that will be useful for designing user-friendly tools to improve transparency and reproducibility of data analysis.

Keywords: Databases; Graphical user-interface; Reproducibility of analysis; Structured query language; Validity of analysis.

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Conflict of interest statement

Declaration of competing interest The authors declare that they have no conflict of interest.

Figures

Fig. 1.
Fig. 1.
In the conventional approach the data-analyst developed a SQL query to generate the report, while in the proposed auto-SQL approach the data-analyst first denormalized the study database and then the domain expert used the i2b2-webclient graphical-user-interface that automatically generated the SQL for performing the analysis.
Fig. 2.
Fig. 2.
Graphical query interface from the i2b2 platform. The criteria for querying can be easily constructed by dragging terms from the hierarchical tree structure on the left to the widgets on the right. The SQL query is automatically generated in the back-end, which enables clinical staff that are not familiar with SQL to perform complex queries on the data.
See this image and copyright information in PMC

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