Artificial intelligence (AI) is a transformative technology used in various industrial sectors including healthcare. In pharmacy practice, AI has the potential to significantly improve medication management and patient care. This review explores various AI applications in the field of pharmacy practice.

High-alert medications (HAMs) potentiate heightened risk of causing patient harm ranging from 0.24 to 89.6 errors per 100 prescriptions. High-alert medications are crucially utilized in the intensive care settings (ICUs) due to their excellent potential in delivering therapeutic efficacy, yet these medications could cause severe harm if used inappropriately. Despite the cautious use of these medications, medication safety issues persist, which compromises patient safety. A prospective interventional study was conducted in ICUs for a period of 6 months. The HAMs were adopted from the Institute for Safe Medication Practices (ISMP) list of HAMs that were used. A suitably designed medication error assessment form was used to capture the necessary data, including demographics, medications, medication error, and the contributing factors. The National Coordinating Council for Medication Error Reporting and Prevention (NCCMERP) index was used to categorize the medication errors (MEs). The error rate was calculated using error rate formula. Continuous variables were expressed as mean ± standard deviation, whereas categorical variables were presented in frequencies and percentages. A total of 165 patients were enrolled during the study period, with 98 (59.4%) being male and 67 (40.6%) female. The majority [54 (32.73%)] of the study participants belonged to the 61–70 age range. A total of 204 MEs were reported, of which [92 (41.5%)] errors were prescribing errors, followed by documentation errors [69 (33.82%)] and administration errors [43 (21.08%)]. The baseline medication error rate was noted to be 160.12/1,000 patient days. Potassium chloride, tramadol, propranolol, aspirin, insulin, and metoprolol were identified as the most common HAMs to cause errors. According to NCC MERP classification, 41.18% were categorized as category B, followed by category C (35.78%). An overall of 666 contributing factors (CFs) were identified for 204 errors. Stress (24.32%) was the most common factor that contributed to the MEs, followed by workload (21.47%). While great strides have been adopted in error prevention, yet the goal of making HAM errors “never” event has not been achieved. Thus, an active surveillance by a clinical pharmacist could support the healthcare team in promoting patient care. Aradhya PJ, Ravi R, Subhash Chandra BJ, Ramesh M, Chalasani SH. Assessment of Medication Safety Incidents Associated with High-alert Medications Use in Intensive Care Setting: A Clinical Pharmacist Approach. Indian J Crit Care Med 2023;27(12):917–922.

Neurological disorders are common in the general population and the majority of patients have other chronic diseases, necessitating the use of multiple medications, which increases the incidence of drug-related problems (DRPs). Studies from different countries discovered an average of 0.29–1.45 DRPs per patient admitted into the neurology unit. To identify common DRPs and to evaluate the impact of clinical pharmacist's interventions in resolving the identified DRPs in patients with neurological disorders. A prospective interventional study was conducted in the Department of Neurology in a tertiary care teaching hospital in Southern India, for a period of six months. Patients aged ≥18 years and had been hospitalized for >24 h, were intensively monitored until discharge for the occurrence of any DRPs and pharmacist interventions were provided. The identified DRPs were classified according to Hepler and Strand's Classification. A total of 310 prescriptions were reviewed, of which 174 patients (mean age 45.93 ± 2.49 years) experienced at least one DRP during their hospital stay. The average DRP per patient was found to be 1.75, with drug-drug interactions [254 (83%)] being the predominant DRPs, followed by adverse drug reactions [13 (4%)], and drug duplications [9 (3%)]. Most of the drug-drug interactions were pharmacokinetic [144 (56.69%)]. Hyponatremia [2 (15%)]; and nausea and vomiting [2 (15%)] were most commonly reported ADRs. All 306 DRPs involved active clinical pharmacist intervention, of which [275 (89.87%)] of pharmacists' interventions were accepted, which led to modification of the therapy. Monitoring the use of drugs allowed the clinical pharmacist to detect DRPs and to suggest interventions that promote rational drug prescribing, therapy optimization and enhanced patient safety.

Emamectin Benzoate (EB) is a semi-synthetic insecticide which was primarily created to combat lepidopteron insects. EB disrupts the neurotransmitters Gamma Amino Butyric Acid (GABA) through enhancing permeability of membrane chloride ion, resulting in the loss of cell function progressing to irreversible paralysis in invertebrates.

Background: Medical devices are the vital part of healthcare system. The use of medical devices is higher in the intensive care units leading to increased exposure rendering the exponential rise in incidence of medical device associated adverse events (MDAEs). Timely detection and reporting of MDAEs can help reduce the disease and associated liabilities. Objective: To determine the rate, patterns, and predictors of MDAEs. Methods: An active surveillance was carried out in the intensive care units (ICUs) of a tertiary care teaching hospital located in southern India. The patients were monitored for MDAEs which were reported based on MvPI guidance document 1.2. The predictors were calculated using an odds ratio at 95% confidence interval. Results: A total of 185 MDAEs were reported amongst 116 patients, of which the majority [74 (63.7%)] were males. Most of the MDAEs were attributed to urethral-catheters [42 (22.7%)] among which a high majority of 34 were associated with urinary tract infections (UTI), followed by ventilators [35 (18.9%)] with all events causing pneumonia. Urethral catheters and ventilators are both classified as categories B and C respectively based on device risk classification provided by the Indian Pharmacopoeia Commission (IPC). Over 58% of MDAEs were reported among the elderly. The causality assessment was possible for 90 (48.6%) MDAEs whereas 86 (46.4%) were probable. The majority of the MDAEs reported were serious [165 (89.2%)] and only [20 (10.8%)] were found to be non-serious on the severity scale. Most [104 (56.2%)] of the devices attributed to MDAEs were single-use devices, of which [103 (55.6%)] were destroyed and only [81 (43.7%)] were retained in healthcare facilities. Conclusions: Despite the best possible care in the intensive care units (ICUs), MDAEs are inevitable, adding to the burden of patients in terms of suffering, disease, extended hospital stay, and increased costs. MDAEs require rigorous monitoring of patients, especially in the elderly population and patients with increased exposure to multiple devices.

Background: Medication dosing calculation errors can cause significant harm to patients, especially in the pediatric population. Crushing tablets for dose division purposes may increase the risk of calculation errors, which can lead to incorrect dosing and compromised patient safety. This study aimed to develop a calculator to eliminate calculation errors associated with dose division. Methods: Using the Wix platform, a group of pharmacists created a user-friendly webpage “Dose 4 You.” To enable accurate dose division calculations, the advanced language model Chat GPT and Visual Studio were used. The tool assists healthcare professionals through a step-by-step process, allowing them to enter the necessary dose and medication requirements. The Dose 4 You web page’s reliability and feasibility were assessed using retrospective data and validated questionnaires, including the System Usability Scale (SUS), respectively and a Likert scale-based acceptance questionnaire. Results: The Dose 4 You website calculated the required amount of powdered tablet to achieve the desired dose with 100% accuracy. The obtained SUS score was 88.38, indicating excellent usability. The average score of all questions for acceptance was found to be 4.7 ± 0.15 indicating a strong agreement on the tool’s usefulness and effectiveness. Conclusion: Dose 4 You is a reliable tool that improves patient safety by streamlining dose calculations and lowering calculation errors. The tool’s ease of use, practicality in daily clinical practice, and potential to reduce medication errors are highlighted by the positive perception among healthcare professionals. Dose 4 You’s successful implementation demonstrates the power of technology and collaboration in transforming medication administration and improving patient outcomes. Similar innovative solutions to optimize healthcare practices can be explored in future health informatics endeavors.