A modular low-code framework for psychological assessment data management, automated scoring, and secure research workflows
Fabio Mauro Giambò, Fabiana Pia Vitiello, Maria Cristina De Cola, Maria Grazia Maggio, Angelo Quartarone, Silvia Marino, Francesca Cucinotta, Rocco Salvatore Calabrò, Viviana Lo BuonoObjective
The digital transformation of psychological assessment workflowshas become increasingly important to enhance efficiency, data quality, and secure data management in clinical and research contexts. This study aimed to develop and technically evaluate a low-code, platform-indipendent framework that enable secure digita psychological data collection, automated scoring, standardized data management, and scalable deployment across clinical and research settings without requiring dedicated software development.
Methods
Dedicated online form collections and project-specific cloud-storage folders were created for each research project. Spreadsheet files exported from the forms included automated scoring sheets and a simplified, password-protected SCORING VIEW. Data were pseudonymized and stored in the institutional digital environment, supporting secure data management through institutional acces-controll mechanism. The framework was evaluated across 22 research projects involving 10,214 assessments and 90 standardized instruments. Automated and manual scoring were compared using agreement analyses and processing-time evaluations.
Results
The framework demonstrated high agreement between automated and manual scoring procedures, with near-perfect agreement for the evaluated instruments. Automated scoring substantially reduced processing time compared with manual procedures (p < 0.0001). The system also supported standardized data organization, controlled access management, and consistent dataset generation during routine implementation.
Conclusions
The proposed frameworks provides a fast, reliable, and secure solution for digital psychological data collection, automated scoring and standardized data management supporting scalable, reproducible, and multi-center research. Its architecture facilitates multi-user collaboration offering a replicable model for multicenter and longitudinal studies.