Systematic review of the safety of medication use in inpatient, outpatient and primary care settings in the Gulf Cooperation Council countries

Background Errors in medication use are a patient safety concern globally, with different regions reporting differing error rates, causes of errors and proposed solutions. The objectives of this review were to identify, summarise, review and evaluate published studies on medication errors, drug related problems and adverse drug events in the Gulf Cooperation Council (GCC) countries. Methods A systematic review was carried out using six databases, searching for literature published between January 1990 and August 2016. Research articles focussing on medication errors, drug related problems or adverse drug events within different healthcare settings in the GCC were included. Results Of 2094 records screened, 54 studies met our inclusion criteria. Kuwait was the only GCC country with no studies included. Prescribing errors were reported to be as high as 91% of a sample of primary care prescriptions analysed in one study. Of drug-related admissions evaluated in the emergency department the most common reason was patient non-compliance. In the inpatient care setting, a study of review of patient charts and medication orders identified prescribing errors in 7% of medication orders, another reported prescribing errors present in 56% of medication orders. The majority of drug related problems identified in inpatient paediatric wards were judged to be preventable. Adverse drug events were reported to occur in 8.5–16.9 per 100 admissions with up to 30% judged preventable, with occurrence being highest in the intensive care unit. Dosing errors were common in inpatient, outpatient and primary care settings. Omission of the administered dose as well as omission of prescribed medication at medication reconciliation were common. Studies of pharmacists’ interventions in clinical practice reported a varying level of acceptance, ranging from 53% to 98% of pharmacists’ recommendations. Conclusions Studies of medication errors, drug related problems and adverse drug events are increasing in the GCC. However, variation in methods, definitions and denominators preclude calculation of an overall error rate. Research with more robust methodologies and longer follow up periods is now required.Peer reviewe

Computer-Assisted versus Oral-and-Written History Taking for the Prevention and Management of Cardiovascular Disease: a Systematic Review of the Literature

Background and objectives: CVD is an important global healthcare issue; it is the leading cause of global mortality, with an increasing incidence identified in both developed and developing countries. It is also an extremely costly disease for healthcare systems unless managed effectively. In this review we aimed to: – Assess the effect of computer-assisted versus oral-and-written history taking on the quality of collected information for the prevention and management of CVD. – Assess the effect of computer-assisted versus oral-and-written history taking on the prevention and management of CVD. Methods: Randomised controlled trials that included participants of 16 years or older at the beginning of the study, who were at risk of CVD (prevention) or were either previously diagnosed with CVD (management). We searched all major databases. We assessed risk of bias using the Cochrane Collaboration tool. Results: We identified two studies. One comparing the two methods of history-taking for the prevention of cardiovascular disease n = 75. The study shows that generally the patients in the experimental group underwent more laboratory procedures, had more biomarker readings recorded and/or were given (or had reviewed), more dietary changes than the control group. The other study compares the two methods of history-taking for the management of cardiovascular disease (n = 479). The study showed that the computerized decision aid appears to increase the proportion of patients who responded to invitations to discuss CVD prevention with their doctor. The Computer-Assisted History Taking Systems (CAHTS) increased the proportion of patients who discussed CHD risk reduction with their doctor from 24% to 40% and increased the proportion who had a specific plan to reduce their risk from 24% to 37%. Discussion: With only one study meeting the inclusion criteria, for prevention of CVD and one study for management of CVD we did not gather sufficient evidence to address all of the objectives of the review. We were unable to report on most of the secondary patient outcomes in our protocol. Conclusions: We tentatively conclude that CAHTS can provide individually-tailored information about CVD prevention. However, further primary studies are needed to confirm these findings. We cannot draw any conclusions in relation to any other clinical outcomes at this stage. There is a need to develop an evidence base to support the effective development and use of CAHTS in this area of practice. In the absence of evidence on effectiveness, the implementation of computer-assisted history taking may only rely on the clinicians’ tacit knowledge, published monographs and viewpoint articles

Harnessing Machine Learning to Improve Healthcare Monitoring with FAERS

This research study investigates the potential of machine learning techniques to improve healthcare monitoring through the utilization of data from the FDA Adverse Event Reporting System (FAERS). The objective is to explore specific applications of machine learning in healthcare monitoring with FAERS and highlight their findings. The study reveals several significant ways in which machine learning can contribute to enhancing healthcare monitoring using FAERS.Machine learning algorithms can detect potential safety signals at an early stage by analyzing FAERS data. By employing anomaly detection and temporal pattern analysis techniques, these models can identify emerging safety concerns that were previously unknown or underreported. This early detection enables timely action to mitigate risks associated with medications or medical products.Machine learning models can assist in pharmacovigilance triage, addressing the challenge posed by the large number of adverse event reports within FAERS. By developing ranking and classification models, adverse events can be prioritized based on severity, novelty, or potential impact. This automation of the triage process enables pharmacovigilance teams to efficiently identify and investigate critical safety concerns.Machine learning models can automate the classification and coding of adverse events, which are often present in unstructured text within FAERS reports. Through the application of Natural Language Processing (NLP) techniques, such as named entity recognition and text classification, relevant information can be extracted, enhancing the efficiency and accuracy of adverse event coding.Machine learning algorithms can refine and validate signals generated from FAERS data by incorporating additional data sources, such as electronic health records, social media, or clinical trials data. This integration provides a more comprehensive understanding of potential risks and helps filter out false positives, facilitating the identification of signals requiring further investigation.Machine learning enables real-time surveillance of FAERS data, allowing for the identification of safety concerns as they occur. Continuous monitoring and real-time analysis of incoming reports enable machine learning models to trigger alerts or notifications to relevant stakeholders, promoting timely intervention to minimize patient harm.The study demonstrates the use of machine learning models to conduct comparative safety analyses by combining FAERS data with other healthcare databases. These models assist in identifying safety differences between medications, patient populations, or dosing regimens, enabling healthcare providers and regulators to make informed decisions regarding treatment choices.While machine learning is a powerful tool in healthcare monitoring, its implementation should be complemented by human expertise and domain knowledge. The interpretation and validation of results generated by machine learning models necessitate the involvement of healthcare professionals and pharmacovigilance experts to ensure accurate and meaningful insights.This research study illustrates the diverse applications of machine learning in improving healthcare monitoring using FAERS data. The findings highlight the potential of machine learning in early safety signal detection, pharmacovigilance triage, adverse event classification and coding, signal refinement and validation, real-time surveillance and alerting, and comparative safety analysis. The study emphasizes the importance of combining machine learning with human expertise to achieve effective and reliable healthcare monitoring