Abstract
Information technology, which uses the computer for data processing and decision-making, has invaded all kinds of sciences. Health informatics is one of these important areas. The Objective of this study is to develop a mathematical model that calculates the percentage of ADR occurrence using the data available from primary literature for a specific medication. A mathematical model was developed to calculate the percentage of adverse drug reaction occurrence for a specific patient, using specific patient factors. The expected effect is that these different patient factors will produce different adverse drug reactions in different percentages. The adverse drug reaction prediction model is developed using Microsoft access, forms are built and ready for the data to be collected and utilized from primary literature. Pharmacists and other medical specialists should give information technology more concern in order to develop a comprehensive decision support systems able to predict medication errors before they actually happen.
Highlights
Information technology which uses the computer for data processing and decision making has invaded all kinds of sciences
The objectictive of this study is to develop a mathematical model that calculates the percentage of ADR occurrence using the data available in the previously done literature for specific medication
The objective in the case of health information technology is to improve health care systems through optimal information support, i.e. it is a meaningful collection of facts or data that help in the proper treatment
Summary
Information technology which uses the computer for data processing and decision making has invaded all kinds of sciences. Information technology has been applied indirectly in the drug discovery process and patient care for a long period of time. Morimoto et al.[1] illustrated that patients with a history of angiotensin-converting enzyme inhibitorinduced cough were 29 times more likely to develop a cough than those without this history. These factors were used to develop a model stratifying patients into 4 risk groups. They suggested developing a system able to predict adverse drug reactions before they happen using the factors collected from patients histories
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