Applications of AI in pharmacy practice: a look at hospital and community settings

Alrobaian M. Strengths and weaknesses of current and future prospects of artificial intelligence-mounted technologies applied in the development of pharmaceutical products and services

Starting from drug discovery, through research and development, to clinical trials and FDA approval, artificial intelligence (AI) plays a vital role in planning, developing, assessing modelling, and optimization of product attributes. In recent decades, machine-learning algorithms integrated into artificial neural networks, neuro-fuzzy logic and decision trees have been applied to tremendous domains related to drug formulation development. Optimized formulations were transformed from lab to market based on optimized properties derived from AI Technologies. Research and development in pharmaceutical industry rely upon computer-driven equipment and machine learning technology to extract data, perform simulations, modelling, and optimization to get optimum solutions. Merging AI technologies in various steps of pharmaceutical manufacture is a major challenge due to lack of in-house technologies. In silico studies based on artificial intelligence are widely applied as effective tools to screen the market needs of medications and pharmaceutical services through inspecting scientific literature and prioritizing medicines for specific illnesses or a particular patient. Specialized personnel who excel in scientific and data science with analytical knowledge are essential for transformation to smart manufacturing and offering services. However, privacy, cybersecurity, AI-dependent unemployment, and ownership rights of AI technologies require proper regulations to gain the benefits and minimize the drawbacks.

Assessment of clinical pharmacist interventions using a web-based application in a Saudi Arabian Tertiary Hospital

Objectives

Medication-related problems are a top concern of clinical pharmacists. Medication-related problems can cause patient harm and increase the number of visits, hospital admissions, and length of hospital stay. The objective was to assess clinical pharmacy medication-related problem-related interventions in a tertiary care setting.

Methods

A retrospective cohort study was conducted at King Fahad Armed Forces Hospital in Jeddah (Saudi Arabia) between June 2021 and June 2022. The data were extracted monthly from a new web-based Microsoft Excel application documenting medication-related problems during any stage of the medication use process.

Results

A total of 5310 medication-related problem-related interventions in 1494 patients were performed. The departments associated with the highest frequency of medication-related problem-related interventions were the critical care unit (26.9%), intensive care unit (23.8%), anticoagulation clinic (17.1%), medical ward (11.3%), and nephrology unit (6.8%). The most common type of medication-related problem-related interventions included inappropriate dosage regimens (25.6%), monitoring drug effect or therapeutic drug monitoring (24.4%), requirement of additional drug therapy (21.9%), and inappropriate drug selection (14.1%). The proposed interventions were accepted by physicians in 97% of the incidents. The most frequent medication classes associated with medication-related problem-related interventions were cardiovascular agents (47.6%), antimicrobial agents (27.2%), and nutrition and blood substitute agents (11.4%). The most frequent medication groups associated with medication-related problem-related interventions were anticoagulants (25.6%) and antibiotics (25.2%).

Conclusions

The current findings characterize the medication-related problem-related interventions addressed in clinical pharmacy at a tertiary care setting. The high rate of physician acceptance emphasizes the integral patient safety role of clinical pharmacy services.

How AI is advancing asthma management? Insights into economic and clinical aspects

Asthma, an increasingly prevalent chronic respiratory condition, incurs significant economic costs worldwide. Artificial Intelligence (AI), particularly Machine Learning (ML), has been widely recognized as transformative when applied to asthma care. This commentary investigates how AI and ML may improve clinical outcomes while alleviating some of the costs associated with asthma care. AI's powerful analytical abilities could usher in an unprecedented era of preventive measures, particularly by identifying at-risk populations and anticipating environmental triggers. ML shows promise for enhancing real-time monitoring, early detection, and tailored treatment strategies in paediatric asthma, potentially reducing hospitalizations and emergency care costs. Emerging AI-powered wearable technologies are catalysing a revolutionary shift in patient monitoring, providing proactive interventions. Although optimistic, this commentary highlights a gap in empirical studies evaluating the cost-effectiveness of AI in asthma care and stresses the need for larger datasets to accurately represent the economic benefits of AI solutions. Additionally, this paper emphasizes the ethical considerations surrounding data privacy and algorithmic bias, which are vital for the successful and equitable integration of AI into healthcare settings. This editorial underscores the urgent necessity of conducting thorough analyses to assess all economic implications, facilitate optimized resource allocation, and foster a nuanced understanding of AI/ML technologies in asthma management that may reduce costs to healthcare systems.