Ensuring the preferential right to care for people assisted by the Public Defender's Office of Rio Grande do Norte through responsible data science
online scheduling, preferential care, responsible data science, predictive models, Public Defender's Office
The growing demand for services at the Public Defender’s Office in the State of Rio Grande do Norte, combined with the need to prioritize vulnerable groups such as the elderly, calls for an efficient and equitable model for distributing service appointments. In this context, this work proposes solutions based on computational intelligence to optimize the online scheduling system of the Defender’s Office through a strategic distribution model. The research employs responsible data science methods, integrating predictive models based on time series with adjustments that align with institutional guidelines, ensuring a fairer and more effective allocation of available appointments. This approach includes historical data analysis, preparation, and modeling, as well as a comparison between forecasts and actual appointments, aimed at reducing wait times and increasing the utilization rate of available slots. Initial results focus on enhancing transparency, decreasing wait times, and providing more effective services to the assisted population. Through these efforts, hope to promote greater equity in access to services, clarity in procedures, and efficiency in the management of resources within the Public Defender's Office. Furthermore, there is an expectation that this solution can be adapted to other institutions or organizations facing similar challenges.