The I3PT is developing a system for prioritizing pharmaceutical care in the Emergency

The I3PT is developing a system for prioritizing pharmaceutical care in the Emergency 1080 608 Mireia Córcoles
Pharmacist and clinical pharmacy researcher Javier Ramos is working on a computer tool that could predict the risk of problems related to medication that take place in emergency departments

The Emergency services attend to hundreds of patients every day. Only in Parc Taulí, every day there are more than 300 people who need urgent medical attention. This volume of patients also means a logistical challenge for the hospital pharmacy service, which must respond to all the pharmaco-therapeutic needs, sometimes complex, that allow these patients to be treated.

Clinical pharmacists ensure that the medicines prescribed to patients contribute to the best possible health outcomes. To ensure this, they have the complex task of assessing the suitability, effectiveness and safety of the use of medicines for a large number of patients. For this reason, it is necessary prioritize attention based on the degree of urgency, that is, of the risk of suffer from a medication-related problem (PRM, for its acronym) during the stay in the Emergency Department. However how can this pharmaceutical care be prioritized?

Faced with this challenge, the pharmacist and researcher of the clinical pharmacy research group Javier Ramos, is working on developing one computer tool that predicts and indicates the risk of PRM and serves to prioritize pharmaceutical care in emergency services.

This project is the subject of Ramos' doctorate, which he is carrying out at the Autonomous University of Barcelona under the direction of Gema Muñoz Gamito and Susana Redondo Capafons and the tutoring of Caridad Pontes. Ramos emphasizes that this tool will be a predictive score that offers an up-to-date risk score for each patient and allows pharmaceutical care to be prioritized.

I detected the need to have a score that predicts PRMs and assesses the urgency of patient care, to know which one needs early pharmaceutical care

"The project arose a year ago when I detected the need to have a tool in the form of a score that allows predicting PRMs and assessing the urgency of patient care, to know which patient needs early pharmaceutical care and, therefore, which one can benefit the most”. There are currently some models available but they have many limitations, says Ramos. "On one hand, the level of evidence they have is very low and, on the other, they take into account patient variables that are not possible to obtain, and if you have them, it is difficult to know which one contributes more to a PRM".

A project awarded by the SEFH

The project has been distinguished with the third prize at the Innovation Forum of the Spanish Society of Clinical Pharmacy and it was nominated among the 10 Best Operational Communications of the 67th Congress of this entity, which took place last November in Barcelona.

The project has won the 3rd prize at the SEFH Innovation Forum and was nominated among the 10 best operational communications of its 67th Congress

"The communication we presented shows that using this stratification we get that the 80% of Emergency Department patients with a request for admission have pharmaceutical care before 15 p.m".

The phases of the project

The project is currently in a first phase of development, in which a multidisciplinary team of professionals—including an international group of pharmacists, emergency physicians, doctors of medical and surgical specialties, nurses, a social worker and computer scientists— works to identify and analyze the variables that can be given in patients with the aim of detecting PRM, and to demonstrate the impact of early pharmaceutical care in patients at greater risk of PRM.

Once a consensus has been reached, which will be done following the Delphi methodology, this system will be launched at Parc Taulí so that, at a later stage, it can be implemented in other hospitals. The ultimate goal is to develop with artificial intelligence and machine learning a computer tool for the electronic medical record that predicts the risk of PRM and is useful for any hospital.

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