Semi-automated data collection from electronic health records in a stroke unit in Brazil - Zambom Valencio, Raquel Franco [UNESP]; Souza, Juli Thomaz de [UNESP]; Winckler, Fernanda Cristina [UNESP]; Modolo, Gabriel Pinheiro [UNESP]; Ferreira, Natalia Cristina [UNESP]; Zanati Bazan, Silmeia Garcia [UNESP]; Lange, Marcos Christiano [UNESP]; Macedo de Freitas, Carlos Clayton [UNESP]; Rupp de Paiva, Sergio Alberto [UNESP]; Oliveira, Rogerio Carvalho de [UNESP]; Luvizutto, Gustavo Jose; Bazan, Rodrigo [UNESP] (Assoc Arquivos Neuro- Psiquiatria, 2021)
Background: There is a high demand for stroke patient data in the public health systems of middle and low-income countries. Objective: To develop a stroke databank for integrating clinical or functional data and benchmarks from stroke patients. Methods: This was an observational, cross-sectional, prospective study. A tool was developed to collect all clinical data during hospitalizations due to stroke, using an electronic editor of structured forms that was integrated with electronic medical records. Validation of fields in the electronic editor was programmed using a structured query language (SQL). To store the results from SQL, a virtual table was created and programmed to update daily. To develop an interface between the data and user, the Embarcadero Delphi software and the DevExpress component were used to generate the information displayed on the screen. The data were extracted from the fields of the form and also from cross-referencing of other information from the computerized system, including patients who were admitted to the stroke unit. Results: The database was created and integrated with the hospital electronic system, thus allowing daily data collection. Quality indicators (benchmarks) were created in the database for the system to track and perform decision-making in conjunction with healthcare service managers, which resulted in improved processes and patient care after a stroke. An intelligent portal was created, in which the information referring to the patients was accessible. Conclusions: Based on semi-automated data collection, it was possible to create a dynamic and optimized Brazilian stroke databank.
Keywords: Stroke; Benchmarking; Artificial Intelligence; Supervised Machine Learning; Emergency Service; Hospital