Computerized Predictive Analysis of Accidents (FOCOS/PRONTOS System): Study of Rare Cases

Introduction: We report here a series of ten rare cases of work accidents that occurred in five Brazilian companies using the Computerized Evaluation of Work Readiness (FOCOS/Prontos System), aiming to assess the correlation between instrument performance and factors influencing the accidents.

Materials and Methods: Before joining the company, employees access the system with a login and password, and in less than a minute, they perform the test, which consists of identifying pre-known figures as correct or incorrect. The test assesses impulse control (the ability to avoid errors by touching during the evaluation), attention (discriminating the figure that represents the correct stimulus in the test and reacting as quickly as possible by touching the screen every time it appears), and reaction time (speed to react to the correct stimulus by touching the screen). The test result is immediately released after its completion. The test result contributes to work clearance, referral for medical or psychological evaluation, or activity suspension.

Results: The ten cases occurred in 2022 and early 2023, equally distributed across morning, afternoon, and night shifts. In the 300 readiness evaluations, 218 alterations associated with impulse control (66), attention (39), reaction time (40), concentration (27), and risk predictor (46) were identified. The average deviation level was 80.1±22.62%, ranging from 27% to 100%, with a median of 88.5%. The main reasons for reporting deviations were falls from heights (leading to death), improper activity performed in restricted areas (leading to death), collisions with heavy vehicles (6 cases), upper limb injuries (1), and fire (1). Two fatalities occurred.

Conclusion: The Computerized Test for Readiness Evaluation (FOCOS/Prontos System) as a risk assessment tool identified alterations in readiness parameters in 100% of the workers involved in the analyzed cases, demonstrating its predictive capacity in identifying accident risks.