Motivation

In clinical practice, the places recently visited by the patient and their location of residence are extremely relevant. For example, a fever is investigated differently if during the previous month the patient was in a malaria endemic area. The COVID-19 pandemic only highlighted and put to the test the ability to ascertain the location of patients, their travels and movements. 

One of the main use cases of MEDDOPLACE is the association between a geographic location and health risk factors. These risk factors can be: communicable diseases (malaria, sleeping sickness, Chagas disease, Dengue, tuberculosis, etc.), non-communicable diseases (genetic diseases, obesity, cardiovascular disease) and mental health diseases (trauma from violence and of war, traumatic emigration, human trafficking, etc.). At the individual level, diagnosis of malaria based on a history of travel is crucial to administer an effective treatment. At the public health level, it is essential to detect areas of emerging diseases and epidemic outbreaks, for example, Salmonella enteritidis infection in people who have eaten in a restaurant, or a Marburg virus outbreak in Equatorial Guinea. Throughout time and particularly in this century of climate change, collecting this information will demonstrate shifting endemicity and epidemic outbreaks. 

In the case of the detection of medical departments, facilities and services, one of the main use cases is traceability of the patient’s route through the health services in unstructured text. For example, a patient enters through the Emergency Department referred by Primary Care, they are next transferred to the Cardiology Department, develop a nosocomial infection, require admission to the ICU, and are later transferred to the Internal Medicine ward before discharge. Analysis of these pathways is useful for quality control of health services, and iatrogenic control of medical care. 

There are many more use cases. Additionally, the detection of these mentions can be associated with other relevant medical entities, such as diseases, microorganisms and occupations. We anticipate that the possibility of extracting and normalizing this information and combining it with other relevant health care entities will contribute to acceleration of public health responses to orphan diseases, emergent diseases and pandemics. 

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