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Automated Robotic Liquid Handling Assembly of Modular DNA Devices

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Modernizing Registry Operations: Implementing File*Pro Automation with Groovy Scripting to Streamline Data Processing

Nishant Jain^1,2, Deborah Carey^1,2, Lucinda Ham^1,2

¹Missouri Cancer Registry and Research Center, University of Missouri, Columbia, MO.

Journal of Registry Management

|May 11, 2026

Summary

This summary is machine-generated.

Automating out-of-state (OOS) cancer registry file processing with File*Pro and groovy scripts significantly reduced turnaround times. Oncology data specialists (ODS) gained independence, improving efficiency for cancer registries.

Keywords:

File*Pro automation cancer registry groovy scripts workflow efficiency

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Area of Science:

Oncology Data Management
Health Informatics
Cancer Registry Operations

Background:

Processing out-of-state (OOS) cancer registry files traditionally involves extensive manual work and programmer reliance.
This bottleneck hinders timely data submission and analysis in cancer research.

Purpose of the Study:

To evaluate the effectiveness of integrating File*Pro with custom groovy configuration files at the Missouri Cancer Registry and Research Center (MCR).
To streamline OOS file preparation, reduce processing time, and enhance the independence of oncology data specialists (ODS).

Main Methods:

Developed custom groovy scripts to automate OOS file alignment with PrepPlus requirements.
Implemented scripts for default value population and correction of state-specific fields, such as county codes.
Evaluated processing times, staff autonomy, and quality assurance (QA) metrics pre- and post-implementation.

Main Results:

Processed 180 cases across 9 OOS files with minimal programmer intervention between January and April 2025.
Automated county code corrections for 3,476 cases across 31 files.
ODS staff independently processed up to 13 OOS files, achieving an average time saving of ~5 days per file and a 5-day improvement in QA turnaround times.

Conclusions:

File*Pro with groovy configurations provides a scalable and adaptable solution for cancer registries.
This approach empowers ODS staff, reduces programmer dependency, and accelerates data processing workflows.
The methodology is replicable in other central cancer registries with minimal training, enhancing overall efficiency.