Toward Efficiency and Accuracy: Implementation of a Semiautomated Data Capture and Processing Model for the Construction of a Hospital-based Tumor Registry in Chile

Abstract

Introduction: The innovative implementation of a Hospital-based cancer registry (HBCR) at the Arturo López Pérez Oncology Institute (FALP), showcasing the transition from a manual data extraction model to a semi-automation of the process. The purpose of this publication is to compare both methodologies by assessing their efficiency and accuracy. Methods: The analysis was conducted by comparing the complete dataset of the FALP HBCR from 2017 to 2021. The efficiency variable is analyzed, taking into account the total execution time of the registration process, and the precision variable was measured through the internal data consistency method using the IARCcrg Tools Software. Results: In terms of efficiency, the analysis reveals that in 2017, employing a manual approach without automation, it was necessary to analyze 13,061 cases over 144 weeks with an average of 4 registrars to achieve a total of 3,211 cases fully registered. In contrast, over the subsequent 4 years (2018 to 2021), with varying degrees of automation, 65,088 cases were analyzed within 115 weeks, employing an average of 8 registrars, resulting in 13,537 fully registered. This method demonstrated to be 3 times more efficient. Regarding precision, the manual approach exhibited a 5% error rate in registered cases, whereas the automated approach showed a 0.6% error rate during the 2018-2021 period. Conclusion: The obtained results highlight the significant impact of semi-automating the tumor registration process through the utilization of tools for data capture and processing, achieving a threefold increase in efficiency and reducing errors to 0.6%.