Dear members of openEHR Clinical List,
I am much honored in joining this mailing list.
My name is Luciana Tricai Cavalini, and at this moment I occupying the position as the Director of the of at in .
My research field is social epidemiology, that is located on a triple boundary among health sciences, social sciences and biostatistics, and the critical concept for this knowledge area is information.
Thus, our research group has been developing some critical approach abour how information systems are incomplete, and we have been trying to reflect on and develop some solutions for our legate systems in a peripheral country such as (please see an iceberg of our research products on http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0034-89102007000100012&lng=pt&nrm=iso&tlng=en).
But we have realized, since we started our research in 1999, that there is a lot to do at the point of care as far as health information systems goes, and being an epidemiologist, that it is critical for health surveillance. Probably outside this healthcare field is more known as epidemiological surveillance, but for this moment, I will consider it as a semantic issue.
Patients with suspected cases of epidemic and endemic diseases, with high potential of transmission, both to healthcare professionals and to the community, show up every day on healthcare settings, especially emergency rooms and primary care settings, where healthcare professionals are not completely prepared to deal with all biohazard and disease control issues. I will give you two examples:
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A patient with fever and cough for 2-3 weeks is by definition suspected of having tuberculosis, and if this patient is admitted at the hospital and stays there outside the respiratory isolation, other patients (and especially healthcare professionals, dealing with similar cases every day) might be infected. In an average healthcare setting in a country like Brazil, this case will be kept at the end of the line, because victims of traffic accidents and open violence are showing up all the time, but for the entire period the suspected tuberculosis patient is there he is likely to release tuberculosis bacilli to the environment, what, together with another similar cases every day, it increases the risk of hospital tuberculosis infection for everyone sharing that environment. Besides that, there is the fact that family/workplace contacts could be in risk of infection even not being there at the hospital, which sets up a typical slow non-scale network topology for the tuberculosis endemic. This topology has a defined space-time configuration, that can be set into an exponential relationship if one thinks about an acute disease such as meningococcal meningitis;
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A patient shows up at the emergency room with a dog bite. An average physician in Brazil will know everything about how to deal surgically with the wound, and hopefully how to deal with tetanus prevention, but rabies prevention for post-aggression events is a little bit more complex algorithm, that includes: (a) the coverage of anti-rabies vaccination for cats and dogs in the region; (b) the clinical and vaccination condition of the animal; (c) the definition of severity, that is different for clinicians and epidemiologists, because for us, epidemiologists (OK, I admit, we are picky), a single wound deeper than epidermis is severe, because it reaches neural terminals, and that is the way rabies virus infects, and not by the blood stream. But we are not picky just because this is charming: let us remember that we are dealing with a disease with 100% of case fatality, but 100% of vulnerability, if the post-aggression event is managed correctly. And there is (d), (e) and (f) that you can check on CDC website if you want, but I do not want to bother you anymore with this complexity. But the fact is that our physicians are dealing with that all the time and they must be empowered in their decision-making process.
So, since 2003, our research group is developing the concept of a decision support for epidemiological surveillance to be implemented at every point of care, no matter what is the level of complexity. Because for us, epidemiologists, it is always good when we can identify a suspected case of an epidemic/endemic disease at the primary care setting, but it is better to identify that case at the high-complexity level (e.g., pulmonary surgery) than on the death certificate. But in fact our group started working on the case that is not even identified on the death certificate (what we call ill-defined cause of death, or even under-registration, the worst case), but for us now this is a done deal.
We want to develop a decision support system for epidemiological surveillance based on openEHR specifications. Our technological decision-making is based on the fact that openEHR standards are computable and interoperable, something that is badly needed for disease prevention and control on a national/worldwide level (just think about SARS). So, we need to start building archetypes that must contain the specific context needed for epidemiologic surveillance, but it must make sense to physicians filling the information at the point of care. Our idea is to feedback physicians at the point of care with some information such as: This patient is a suspected case of tuberculosis. He must be kept in respiratory isolation, etc etc, or This patient needs 5 doses of rabies vaccination, etc etc. I am not discussing with you the layout architecture of this DSS, because this is not the proper forum.
So, my question to this honorable working group is: how can I build archetypes that can fulfill the needs of epidemiologic surveillance field, and at the same time being meaningful for healthcare professionals at every level of care?
I know my question has a high level of sensitivity, but if it is needed, I can go down (as we epidemiologists say) to a higher specificity, in order to bring light to this room that, at this moment, is full of heat for our research group.
Thank you (in advance) for your attention,
Luciana Tricai Cavalini, MD, PhD
Department of Epidemiology and Biostatistics
Health
