Systematic Review Identification of Methods to Find Medication Errors and Poor Adherence

Abstract

Patient Health is the foremost responsibility for any provider or hospital. If there is any lack of providing consent to those information and challenges in updating the treatment plan in the right way leads to serious problems in patient health. Lack of proper system in capturing the human made errors leads to the misinterpretation of data. Although all time of traditional methods of taking prescriptions and capturing the results of individual patient information is crucial as an outmost responsibility. Lot of medical data is captured in traditional methods like handwritten, system entry. If these are misinterpreted or incorrectly captured, then it leads to serious troubles for the patients. We found the methods on how we can improve the problem of integrating the AI in our medical data, which results proper use of MEDAI in understanding the drugs and patient health chart. This kind of MEDAI report results of capturing the right amount of accuracy in developing the patient health and will be known for its training of the drug detection and recommendation to the doctor, whenever doctor captures their prescription into the model. Although the doctor written notes in the traditional methods are prone to lead into misinterpretation, if anyone assumes it as different drug the chances of the accuracy in maintaining the patient health will be moved from 70% to 93% in recommending the poor adherence of healthcare data.