An observational study of root-cause analysis of medication errors in elderly with methotrexate toxicity.

INTRODUCTION: Medication errors are inherent in a healthcare system. This results in both time and cost burdens for both the patient and the health system. The aim of this study was to conduct a root-cause analysis of medication errors in elderly patients with methotrexate toxicity, analyze associated factors, and propose solutions. METHODS: This single-center prospective study was designed to identify medication errors in cases of methotrexate toxicity between November 2022 to May 2023. Categorical data and free-text data are used to describe incidents. Harm assessment, factors related to medication errors, and preventability were evaluated for each case. Possible strategies to prevent similar occurrences are discussed. RESULTS: Out of a total of 15 patients who presented during the study period, nine suffered from methotrexate toxicity due to medication errors. Most medication errors occurred during prescribing or dispensing (seven cases). Inadequate knowledge about medication and dosage, inadequate communication was identified as a contributing factor for all medication errors. Patients on long-term methotrexate treatment are at high risk of methotrexate toxicity. CONCLUSION: This study highlights the challenges of health literacy and lacking communication between healthcare providers and patients that can be met through community pharmacy programs for the elderly in lower-middle-income countries.

Artificial Intelligence and Machine Learning Technology Driven Modern Drug Discovery and Development.

The discovery and advances of medicines may be considered as the ultimate relevant translational science effort that adds to human invulnerability and happiness. But advancing a fresh medication is a quite convoluted, costly, and protracted operation, normally costing USD ~2.6 billion and consuming a mean time span of 12 years. Methods to cut back expenditure and hasten new drug discovery have prompted an arduous and compelling brainstorming exercise in the pharmaceutical industry. The engagement of Artificial Intelligence (AI), including the deep-learning (DL) component in particular, has been facilitated by the employment of classified big data, in concert with strikingly reinforced computing prowess and cloud storage, across all fields. AI has energized computer-facilitated drug discovery. An unrestricted espousing of machine learning (ML), especially DL, in many scientific specialties, and the technological refinements in computing hardware and software, in concert with various aspects of the problem, sustain this progress. ML algorithms have been extensively engaged for computer-facilitated drug discovery. DL methods, such as artificial neural networks (ANNs) comprising multiple buried processing layers, have of late seen a resurgence due to their capability to power automatic attribute elicitations from the input data, coupled with their ability to obtain nonlinear input-output pertinencies. Such features of DL methods augment classical ML techniques which bank on human-contrived molecular descriptors. A major part of the early reluctance concerning utility of AI in pharmaceutical discovery has begun to melt, thereby advancing medicinal chemistry. AI, along with modern experimental technical knowledge, is anticipated to invigorate the quest for new and improved pharmaceuticals in an expeditious, economical, and increasingly compelling manner. DL-facilitated methods have just initiated kickstarting for some integral issues in drug discovery. Many technological advances, such as "message-passing paradigms", "spatial-symmetry-preserving networks", "hybrid de novo design", and other ingenious ML exemplars, will definitely come to be pervasively widespread and help dissect many of the biggest, and most intriguing inquiries. Open data allocation and model augmentation will exert a decisive hold during the progress of drug discovery employing AI. This review will address the impending utilizations of AI to refine and bolster the drug discovery operation.