PROPOSING A POTENTIAL SMART DIGITAL NETWORK FOR TREATMENT WITH PROBABLE ANTIBIOTICS

Background and Aim:Emerging high and variable rate of antibiotic resistance and the lack of clinical laboratories in many remote areas making it impossible to exactly determine antibiotic sensitivity of bacterial pathogens and this encounter antibiotic administration with a number of dilemmas. The aim of this project is to build a decision support system for antibiotic treatment of inpatients with moderate to severe infections. The use of this system should lead to high percentages of appropriate antibiotic treatment, reducing complications, mortality and hospital stay.Methods:This project is based on a causal probabilistic digital network and uses a cost–benefit model for antibiotic treatment in inpatients with common bacterial infections. The system works based on an artificial intelligence algorithm and use clinical and laboratory data available within hours of admitting the patient to the hospital. The system will be tested in a randomized controlled trial in different parts of our country and show to improve the percentage of appropriate antibiotic therapy.Results:Search in medical databanks revealed some recent similar studies. One project, known as TREAT was conducted in Denmark, Germany and Italy aiming to produce and test a system to improve antibiotic therapy beyond current clinical practice. The initial results were acceptable. Here, we will discuss the advantages of using this probabilistic modelConclusion:Recent studies have shown that using a smart digital network for antibiotic treatment can improve the speed of appropriate empirical antibiotics treatment and reduces therapeutic costs. We suggest to produce and apply a similar but national digital networking system in Iran.

کلیدواژه ها:

Prediction ، antibiotic treatment ، decision support system