| Literature DB >> 36258971 |
Snehil Verman1, Ashish Anjankar2.
Abstract
An adverse event is any abnormal clinical finding associated with the use of a therapy. Adverse events are classified by reporting an event's seriousness, expectedness, and relatedness. Monitoring patient safety is of utmost importance as more and more data becomes available. In reality, very low numbers of adverse events are reported via the official path. Chart review, voluntary reporting, computerized surveillance, and direct observation can detect adverse drug events. Medication errors are commonly seen in hospitals and need provider and system-based interventions to prevent them. The need of the hour in India is to develop and implement medication safety best practices to avoid adverse events. The utility of artificial intelligence techniques in adverse event detection remains unexplored, and their accuracy and precision need to be studied in a controlled setting. There is a need to develop predictive models to assess the likelihood of adverse reactions while testing novel pharmaceutical drugs.Entities:
Keywords: adverse drug reactions; adverse events; artificial intelligence techniques; computerized surveillance; detection; electronic health records; medication errors; outcomes; prevention
Year: 2022 PMID: 36258971 PMCID: PMC9564564 DOI: 10.7759/cureus.29162
Source DB: PubMed Journal: Cureus ISSN: 2168-8184
Methods for detecting adverse drug reaction
| Source | Advantages | Limitations |
| Articles in peer-reviewed journals | Simple, easy to obtain | Depending on individuals’ perceptions and judgement, may detect only everyday events. |
| Voluntary reporting (pharmacists, prescribing doctors, drug manufacturing companies) | Effortless and easily available | Reporting bias is often seen Details may be elementary, unrecorded, or underreporting. |
| Retrospective studies | Easy to perform and data already available, no need for subject’s participation | Suitable only for association and not causation; follow-up events, if any, may be missed. |
| Intensive event monitoring | Easily organized | Selected subjects studied for a limited time. |
| Case-control studies | Good for assessment | New effects are not studied; they are not economical. |
| Population statistics | A large group of people can be studied at a time. | Coordination becomes difficult; information may be of poor quality. |
| Meta-analysis | Utilizes data from a study that has already been performed | Reported studies may have a high degree of heterogeneity, unpublished data not included or need to be separately obtained for assessment. |
Figure 1Mechanism of adverse event surveillance
Figure 2Contributors to adverse drug events