Thinking out of the box: revisiting health surveillance based on medical records

Abstract

Abstract Despite the considerable advances in the last years, the health information systems for health surveillance still need to overcome some critical issues so that epidemic detection can be performed in real time. For instance, despite the efforts of the Brazilian Ministry of Health (MoH) to make COVID-19 data available during the pandemic, delays due to data entry and data availability posed an additional threat to disease monitoring. Here, we propose a complementary approach by using electronic medical records (EMRs) data collected in real time to generate a system to enable insights from the local health surveillance system personnel. As a proof of concept, we assessed data from São Caetano do Sul City (SCS), São Paulo, Brazil. We used the “fever” term as a sentinel event. Regular expression techniques were applied to detect febrile diseases. Other specific terms such as “malaria,” “dengue,” “Zika,” or any infectious disease were included in the dictionary and mapped to “fever.” Additionally, after “tokenizing,” we assessed the frequencies of most mentioned terms when fever was also mentioned in the patient complaint. The findings allowed us to detect the overlapping outbreaks of both COVID-19 Omicron BA.1 subvariant and Influenza A virus, which were confirmed by our team by analyzing data from private laboratories and another COVID-19 public monitoring system. Timely information generated from EMRs will be a very important tool to the decision-making process as well as research in epidemiology. Quality and security on the data produced is of paramount importance to allow the use by health surveillance systems.