TY  - JOUR
AU  - Cox, Samuel
AU  - Masood, Erum
AU  - Panagi, Vasiliki
AU  - Macdonald, Calum
AU  - Milligan, Gordon
AU  - Horban, Scott
AU  - Santos, Roberto
AU  - Hall, Chris
AU  - Lea, Daniel
AU  - Tarr, Simon
AU  - Mumtaz, Shahzad
AU  - Akashili, Emeka
AU  - Rae, Andy
AU  - Urwin, Esmond
AU  - Cole, Christian
AU  - Sheikh, Aziz
AU  - Jefferson, Emily
AU  - Quinlan, Philip Roy
PY  - 2025
DA  - 2025/4/2
TI  - Conversion of Sensitive Data to the Observational Medical Outcomes Partnership Common Data Model: Protocol for the Development and Use of Carrot
JO  - JMIR Res Protoc
SP  - e60917
VL  - 14
KW  - data standardization
KW  - OMOP
KW  - Observational Medical Outcomes Partnership
KW  - ETL
KW  - extract, transform, and load
KW  - data discovery
KW  - transparency
KW  - Carrot tool
KW  - common data model
KW  - data standard
KW  - health care
KW  - data model
KW  - data protection
KW  - data privacy
KW  - open-source
AB  - Background: The use of data standards is low across the health care system, and converting data to a common data model (CDM) is usually required to undertake international research. One such model is the Observational Medical Outcomes Partnership (OMOP) CDM. It has gained substantial traction across researchers and those who have developed data platforms. The Observational Health Care Data Sciences and Informatics (OHDSI) partnership manages OMOP and provides many open-source tools to assist in converting data to the OMOP CDM. The challenge, however, is in the skills, knowledge, know-how, and capacity within teams to convert their data to OMOP. The European Health Care Data Evidence Network provided funds to allow data owners to bring in external resources to do the required conversions. The Carrot software (University of Nottingham) is a new set of open-source tools designed to help address these challenges while not requiring data access by external resources. Objective: The use of data protection rules is increasing, and privacy by design is a core principle under the European and UK legislations related to data protection. Our aims for the Carrot software were to have a standardized mechanism for managing the data curation process, capturing the rules used to convert the data, and creating a platform that can reuse rules across projects to drive standardization of process and improve the speed without compromising on quality. Most importantly, we aimed to deliver this design-by-privacy approach without requiring data access to those creating the rules. Methods: The software was developed using Agile approaches by both software engineers and data engineers, who would ultimately use the system. Experts in OMOP were used to ensure the approaches were correct. An incremental release program was initiated to ensure we delivered continuous progress. Results: Carrot has been delivered and used on a project called COVID-Curated and Open Analysis and Research Platform (CO-CONNECT) to assist in the process of allowing datasets to be discovered via a federated platform. It has been used to create over 45,000 rules, and over 5 million patient records have been converted. This has been achieved while maintaining our principle of not allowing access to the underlying data by the team creating the rules. It has also facilitated the reuse of existing rules, with most rules being reused rather than manually curated. Conclusions: Carrot has demonstrated how it can be used alongside existing OHDSI tools with a focus on the mapping stage. The COVID-Curated and Open Analysis and Research Platform project successfully managed to reuse rules across datasets. The approach is valid and brings the benefits expected, with future work continuing to optimize the generation of rules. International Registered Report Identifier (IRRID): RR1-10.2196/60917 
SN  - 1929-0748
UR  - https://www.researchprotocols.org/2025/1/e60917
UR  - https://doi.org/10.2196/60917
DO  - 10.2196/60917
ID  - info:doi/10.2196/60917
ER  -