AMAR - MONITORING, TRACKING AND ATTENDANCE APPLICATION FOR CHILDREN AT RISK OF DEVELOPING CEREBRAL PARALYSIS AND AUTISM SPECTRUM DISORDER
Child development, Neurodevelopmental disorders, patient identification systems
Introduction: Despite scientific advances and increased investment in maternal and childcare, early diagnosis of developmental disorders in Brazil is not yet common, and in socially and economically unfavorable regions, this process is even more difficult. In Rio Grande do Norte, for example, the access to magnetic resonance imaging, used in the Cerebral Palsy (CP) diagnosis, or multiprofessional assessments for the diagnosis of Autistic Spectrum Disorder (ASD), are extremely restricted. Objective: To develop and implement a primary care surveillance system to identify early children at risk for the development of CP and ASD. Methodology: This study will take place in Macaíba, in three units that accompany children. It will be divided into three phases: The first phase - Software/App Development: The software and content creation for the app will be developed. The software will have two versions, one for families and one for professionals, and in both, there will be the accompaniment of the child. At this step, a systematic review will be carried out on standardized instruments for assessing child development (CD) in Brazil, following the recommendations of COSMIN. Then, based on the systematic review, the instrument that will track CD frameworks will be built. The second phase - Software/App Validation: it will be the process of concurrent and content validation and reliability of the software and its content. In the third phase - App Implementation: the predictive validation process will be done along with the implementation process, where families and professionals will use the software/app. At this phase, the knowledge of professionals and family about CD will be evaluated, and if the child has any risk factor for ASD or CP, it will be referred to the team of the Specialized Center for Rehabilitation in Macaíba, for diagnosis by a multiprofessional team. Expected results: This tool will be able to monitor and store data on maternal conditions, birth and child development involving indirect parental assessment, prospectively capturing data with the future possibility of algorithms generating growth trajectories and providing information tracking, supporting the development of strategies that improve integrated health care. The results of this project are expected to create a primary care surveillance network to early track children at risk for CP and ASD.