A Near Field Communication Detection System for Drug-Drug Interactions

One of the main problems that requires the increase of the worldwide awareness is the Adverse Drugs Reactions (ADRs) and more importantly their subset Drug-Drug Interactions (DDIs). These DDIs which occur with the elderly people in a higher rate were the focus of some recent studies and researches which showed that their prevalence is high causing serious side effects. Prescription errors and the use of Over-the-Counter (OTC) drugs are the main reasons behind having these DDIs. A lot of studies came with solutions to detect these DDIs using reporting systems, or online databases, but none of them came with an easy convenient solution that can be used at the consumer level without the need to have internet access, or the knowledge and experience in the medical field to decipher drug information. This thesis presents an oriented solution to help detecting DDIs. The solution was based on the Near Field Communication technology (NFC) as its core. Drugs will be identified by means of NFC tags attached to their containers, and encoded in the pharmacies or by the drug suppliers. Afterwards, these drugs can be checked by the consumer using a Patient Terminal which is a device designed to detect any potential DDI among the purchased drugs by scanning the tags that is attached to their containers, along with the severity levels of these DDIs. This design used an existing unified identifier to solve the problem of having different names for the same drug. The reason is to be able to detect any possible DDI among drugs from different brands and manufacturers. Due to the large amount of DDIs data, several steps have been taken to compress them in order to store them reliably in the NFC tags. The Consumer Terminal is designed with a simple user friendly interface that does not require internet access, which will be a great benefit for the elderly people and those who do not have internet connection. Finally, the system from the pharmacy and patient ends, has been tested and worked successfully with samples of real drugs data.