Assessing the Use of Data Analytics in Detecting Adverse Drug Events from Medical Records: (Review Artical)
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- Category: Vol. 89, December 2021
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Assessing the Use of Data Analytics in Detecting Adverse Drug Events from Medical Records: (Review Artical)
Abstract
Background: Electronic health records (HER) use has in-creased in recent years, but there is a lack of detailedresearch on identifying asymptomatic diseases (ADEs) in ambulatory set-tings. This study aims to address this gap by examining meth-ods of ADE identification in the ambulatory setting, as patients often have less contact with their physicians and maintain less thorough records. This will ensure accurate measurement of ADEs and reproducibility in future research. Aim of Study: The objectives of our study was to analyze the techniques used and establish the functions of electronic health records (EHRs) in the identification and evaluation of adverse drug events (ADEs) in the ambulatory environment. Methods: Our research included doing a systematic lit-erature review by searching PubMed and Google Scholar for papers published before June 2017. These studies focused on adverse drug events (ADEs) that were discovered in the ambu-latory environment and used the use of electronic health records (EHRs). We collected information on the features of the studies included in our analysis about the procedures used to identify adverse drug events (ADEs). Results: Researchers used the Electronic Health Record (EHR) as a data source and utilized it to produce patient safe-ty reports, which were then employed in the identification of Adverse Drug Events (ADEs). The identification methods used included manual record review conducted by skilled nurses, pharmacists, and/or doctors, prescription review, computer monitors, electronic triggers, International Classification of Diseases codes, natural language analysis of clinical notes, and patient phone calls and surveys. Seven investigations included instances of search keywords, laboratory results, and criteria used to detect adverse drug events (ADEs). Conclusion: Overall, most of the studies analyzed used Electronic Health Records (EHRs) as the primary data source for detecting Adverse Drug Events (ADEs). This retrospective methodology is suitable for quantifying the occurrence rates of Adverse Drug Events (ADEs), but it is insufficient for iden-tifying preventive ADEs before damage is inflicted upon the patient. Researchers will be able to detect and address avoida-ble adverse drug events (ADEs) using advanced techniques that use computer monitors and electrical triggers.