Artificial intelligence in the field of pharmacy practice: A literature review

Prescription for Digital Evolution: Transformative Recommendations for Pharmacy Practice in the Digital Age

Increased administrative tasks, evolving expectations of care and advancement in practice scope have rapidly advanced digital health. Health policy has acknowledged the need for evaluation to determine the technological needs of clinicians, including pharmacists, to practice to full and top of scope. There is an emergent need for recommendations to address the technological transformation to enable community pharmacists' practice. This study aimed to develop digital health recommendations, through expert consensus, for the government, pharmacy professional associations, pharmacy enterprises and software vendors, to facilitate community pharmacists' practice. A modified Delphi survey was conducted online in February-March 2024. Pharmacists with digital health expertise were purposively recruited. Participants were asked to rate their level of agreement with the initial 24 research-derived statements in round 1. Consensus was defined a priori as ≥80% of participants strongly agreeing or agreeing with a statement and a standard deviation of ≤1.00. Review of participants' free-text comments progressively reduced and refined the statements. All 22 participants completed the modified Delphi study in 3 survey rounds. Participants represented every Australian jurisdiction. Eighteen participants had more than 10 years of professional experience. Sixteen recommendations emerged: 6 for government, 2 for pharmacy professional associations, 4 for pharmacy enterprises and 4 for software vendors. The majority of recommendations require financial investment and harmonization of legislation across jurisdictions. Adoption of these recommendations, with significant investment across partners in the healthcare system and technology providers, will enable pharmacists to more effectively and safely practice utilizing technology solutions.

Artificial Intelligence Tools That Improve Medication Adherence in Patients With Chronic Noncommunicable Diseases: An Updated Review

This systematic review analyzes the use of artificial intelligence (AI) tools to improve medication adherence in patients with chronic non-communicable diseases, with a specific focus on their implementation in pharmaceutical care. Medication non-adherence remains a major barrier to effective chronic disease management, contributing to poor clinical outcomes and rising healthcare costs. AI offers promising, data-driven approaches to address this challenge through tools such as conversational agents, mobile applications, smart devices, and adherence classifiers. These tools enhance patient monitoring, education, and engagement, enabling personalized interventions to promote consistent medication use. The 26 included studies were evaluated based on their methodology, type of AI tool, healthcare setting, and reported impact on adherence outcomes. Most reported improvements in adherence, though variation in assessment methods limits comparability. Ethical, legal, and accessibility issues remain key challenges to wider adoption. Overall, AI represents a valuable and emerging strategy for supporting adherence and optimizing pharmaceutical care in chronic disease management.

Community pharmacists' views towards implementing a patient self-administered screening tool designed to identify risk of medication-related problems

Objective

There is limited information regarding community pharmacists' perspectives on implementing a self-administered screening tool for identifying patients at risk of medication-related problems. This study assessed Australian pharmacists' views on introducing such a tool within the community pharmacy setting.

Methods

An online cross-sectional survey was conducted among Australian community pharmacists from March to May 2023. The survey collected relevant demographic data and responses on perceived barriers and facilitators to implementing the screening tool. Reliability statistics were computed for the responses on barriers and facilitators, and chi-square or Fisher's Exact tests were performed to assess their association with demographic variables.

Results

Two hundred thirty-one community pharmacists across Australia were surveyed. Most (78%) reported that medication-related problems are common and expressed support for a patient self-administered screening tool to identify patients at high risk of medication-related problems (88%). Over two-thirds (69%) were willing to allocate time for reviewing patient medications if flagged for medication-related problems. The most frequently anticipated barriers to implementing screening tools were time constraints for pharmacists (63%), staff shortage and limited patient interest (each accounting for 57%). In contrast, effective communication with patients (69%) and patients' appreciation of pharmacists' expertise and efforts (67%) were predominantly stated facilitators.

Conclusion

Most community pharmacists were supportive of implementing a patient self-administered screening tool to identify patients at risk of medication-related problems. The study's findings provide valuable insights for developing medication-related problems screening tools tailored to the Australian community pharmacy setting.

Pharmacist-led surgical medicines prescription optimization and prediction service improves patient outcomes - a machine learning based study

Background

Optimizing prescription practices for surgical patients is crucial due to the complexity and sensitivity of their medication regimens. To enhance medication safety and improve patient outcomes by introducing a machine learning (ML)-based warning model integrated into a pharmacist-led Surgical Medicines Prescription Optimization and Prediction (SMPOP) service.

Method

A retrospective cohort design with a prospective implementation phase was used in a tertiary hospital. The study was divided into three phases: (1) Data analysis and ML model development (1 April 2019 to 31 March 2022), (2) Establishment of a pharmacist-led management model (1 April 2022 to 31 March 2023), and (3) Outcome evaluation (1 April 2023 to 31 March 2024). Key variables, including gender, age, number of comorbidities, type of surgery, surgery complexity, days from hospitalization to surgery, type of prescription, type of medication, route of administration, and prescriber's seniority were collected. The data set was divided into training set and test set in the form of 8:2. The effectiveness of the SMPOP service was evaluated based on prescription appropriateness, adverse drug reactions (ADRs), length of hospital stay, total hospitalization costs, and medication expenses.

Results

In Phase 1, 6,983 prescriptions were identified as potential prescription errors (PPEs) for ML model development, with 43.9% of them accepted by prescribers. The Random Forest (RF) model performed the best (AUC = 0.893) and retained high accuracy with 12 features (AUC = 0.886). External validation showed an AUC of 0.786. In Phase 2, SMPOP services were implemented, which effectively promoted effective communication between pharmacists and physicians and ensured the successful implementation of intervention measures. The SMPOP service was fully implemented. In Phase 3, the acceptance rate of pharmacist recommendations rose to 71.3%, while the length of stay, total hospitalization costs, and medication costs significantly decreased (p < 0.05), indicating overall improvement compared to Phase 1.

Conclusion

SMPOP service enhances prescription appropriateness, reduces ADRs, shortens stays, and lowers costs, underscoring the need for continuous innovation in healthcare.

Evaluation of Awareness, Perception and Opinions Toward Artificial Intelligence Among Pharmacy Students

Background: Artificial intelligence (AI) helps to develop personalized medication therapy and regimens. It improves the patient care system. A cross-sectional study used and included pharmacy students, using validated survey questions. Objective: This study aimed to evaluate awareness, perception and opinion toward AI among pharmacy students. Design: This is a cross-sectional study (survey-based). Methods: A cross-sectional survey distribution among students in different levels of the college of pharmacy at National University (NU). The questions were classified to measure the variation of demographics, awareness, perceptions and opinions toward Artificial Intelligence (AI). Results: The results showed that more than 50% of pharmacy students are familiar with the uses of AI and know it's important in scientific research, 46.4% have a basic understanding of AI technologies. However more than 75% don't know the applications of AI used in pharmacy practice, 50.6 % don't know AI can support therapeutic diagnosis and 57 % don't know its importance in pharmacy education. A high perception was shown toward AI in facilitating pharmacy access to information (84.2%) and patients' access to the service (80.8%). In addition, 92% suggested that AI training is needed and 86.1 % recommended using AI in scientific research. The conclusion of this study identified the needs for awareness toward AI, and the important role of AI for education in pharmacy and health communities.

Assessment of Automation Models in Hospital Pharmacy: Systematic Review of Technologies, Practices, and Clinical Impacts

Medication management in hospitals is a complex process that encompasses every step from prescription to administration, involving multiple healthcare professionals. This process is prone to various errors that can compromise patient safety and generate significant human and financial costs. Automation in hospital pharmacies represents a major advancement, enhancing patient safety, optimizing professional practices, and reducing hospital expenses. This study aims to analyze the different types of automation systems used in hospital pharmacies, assess the impact of automation, and explore its benefits as well as the challenges and limitations associated with its implementation. A literature search was conducted using the ScienceDirect, PubMed, and Scopus databases, covering the period from 1992 to 2024. A total of 129 relevant articles related to the automation of medication preparation and distribution, as well as its challenges and perspectives were included in this study. Automated technologies significantly contribute to reducing medication errors, strengthening traceability, optimizing inventory management, and alleviating the workload of healthcare professionals. However, challenges persist, particularly in terms of costs, integration with existing processes, and staff training. The use of artificial intelligence offers promising prospects for improving the accuracy and operational efficiency of automation systems.

Allied Health Professionals' Perceptions of Artificial Intelligence in the Clinical Setting: Cross-Sectional Survey

BACKGROUND

Artificial intelligence (AI) has the potential to address growing logistical and economic pressures on the health care system by reducing risk, increasing productivity, and improving patient safety; however, implementing digital health technologies can be disruptive. Workforce perception is a powerful indicator of technology use and acceptance, however, there is little research available on the perceptions of allied health professionals (AHPs) toward AI in health care.

OBJECTIVE

This study aimed to explore AHP perceptions of AI and the opportunities and challenges for its use in health care delivery.

METHODS

A cross-sectional survey was conducted at a health service in, Queensland, Australia, using the Shinners Artificial Intelligence Perception tool.

RESULTS

A total of 231 (22.1%) participants from 11 AHPs responded to the survey. Participants were mostly younger than 40 years (157/231, 67.9%), female (189/231, 81.8%), working in a clinical role (196/231, 84.8%) with a median of 10 years' experience in their profession. Most participants had not used AI (185/231, 80.1%), had little to no knowledge about AI (201/231, 87%), and reported workforce knowledge and skill as the greatest challenges to incorporating AI in health care (178/231, 77.1%). Age (P=.01), profession (P=.009), and AI knowledge (P=.02) were strong predictors of the perceived professional impact of AI. AHPs generally felt unprepared for the implementation of AI in health care, with concerns about a lack of workforce knowledge on AI and losing valued tasks to AI. Prior use of AI (P=.02) and years of experience as a health care professional (P=.02) were significant predictors of perceived preparedness for AI. Most participants had not received education on AI (190/231, 82.3%) and desired training (170/231, 73.6%) and believed AI would improve health care. Ideas and opportunities suggested for the use of AI within the allied health setting were predominantly nonclinical, administrative, and to support patient assessment tasks, with a view to improving efficiencies and increasing clinical time for direct patient care.

CONCLUSIONS

Education and experience with AI are needed in health care to support its implementation across allied health, the second largest workforce in health. Industry and academic partnerships with clinicians should not be limited to AHPs with high AI literacy as clinicians across all knowledge levels can identify many opportunities for AI in health care.

Pharmacy students' perception and knowledge of chat-based artificial intelligence tools at a Nigerian University

BACKGROUND

Chat-based Artificial Intelligence (AI) tools, such as ChatGPT®, are becoming integral to various aspects of pharmacy education. However, their integration into the curriculum faces challenges due to students' varying levels of knowledge and perceptions. This study aimed to evaluate pharmacy students' knowledge and perception of chat-based AI tools at Afe Babalola University, Ado-Ekiti, Nigeria (ABUAD). It also assessed their familiarity with these tools and their usage patterns.

METHOD

A cross-sectional online survey was conducted from March to April 2024 among undergraduate pharmacy students, selected through random sampling. Student knowledge was categorised as good or poor while perception was grouped into positive or negative. Data analysis was conducted using Statistical Product and Service Solutions version 27.

RESULTS

A total of 252 students participated in this study with the majority being female (72.2%). Most students (88%, n = 222) were familiar with chat-based AI tools, with ChatGPT® being the most commonly used (82.8%) for assignments and studying. Students generally showed a positive perception of the tools, with 85.3% believing it enhances academic performance. Concerns were raised about potential distractions (65.7%) and the risk of academic dishonesty (65.1%). Students with prior AI education (p < 0.001), higher levels of study (p = 0.011), and prior awareness (p < 0.001) demonstrated significantly higher knowledge scores.

CONCLUSION

Pharmacy students at ABUAD demonstrated good knowledge of chat-based AI tools and generally positive perceptions towards its use. The study underscores the need to integrate AI education into the pharmacy curriculum to address knowledge gaps and better prepare students for future technological advancements.

Advancing Pharmacy Practice: The Role of Intelligence-Driven Pharmacy Practice and the Emergence of Pharmacointelligence

The field of healthcare is experiencing a significant transformation driven by technological advancements, scientific breakthroughs, and a focus on personalized patient care. At the forefront of this evolution is artificial intelligence-driven pharmacy practice (IDPP), which integrates data science and technology to enhance pharmacists' capabilities. This prospective article introduces the concept of "pharmacointelligence", a paradigm shift that synergizes artificial intelligence (AI), data integration, clinical decision support systems (CDSS), and pharmacy informatics to optimize medication-related processes. Through a comprehensive literature review and analysis, this research highlights the potential of pharmacointelligence to revolutionize pharmacy practice by addressing the complexity of pharmaceutical data, changing healthcare demands, and technological advancements. This article identifies the critical need for integrating these technologies to enhance medication management, improve patient outcomes, and streamline pharmacy operations. It also underscores the importance of regulatory and ethical considerations in implementing pharmacointelligence, ensuring patient privacy, data security, and equitable healthcare delivery.

"Using network analysis modularity to group health code systems and decrease dimensionality in machine learning models"

Background

Machine learning (ML) prediction models in healthcare and pharmacy-related research face challenges with encoding high-dimensional Healthcare Coding Systems (HCSs) such as ICD, ATC, and DRG codes, given the trade-off between reducing model dimensionality and minimizing information loss.

Objectives

To investigate using Network Analysis modularity as a method to group HCSs to improve encoding in ML models.

Methods

The MIMIC-III dataset was utilized to create a multimorbidity network in which ICD-9 codes are the nodes and the edges are the number of patients sharing the same ICD-9 code pairs. A modularity detection algorithm was applied using different resolution thresholds to generate 6 sets of modules. The impact of four grouping strategies on the performance of predicting 90-day Intensive Care Unit readmissions was assessed. The grouping strategies compared: 1) binary encoding of codes, 2) encoding codes grouped by network modules, 3) grouping codes to the highest level of ICD-9 hierarchy, and 4) grouping using the single-level Clinical Classification Software (CCS). The same methodology was also applied to encode DRG codes but limiting the comparison to a single modularity threshold to binary encoding.The performance was assessed using Logistic Regression, Support Vector Machine with a non-linear kernel, and Gradient Boosting Machines algorithms. Accuracy, Precision, Recall, AUC, and F1-score with 95% confidence intervals were reported.

Results

Models utilized modularity encoding outperformed ungrouped codes binary encoding models. The accuracy improved across all algorithms ranging from 0.736 to 0.78 for the modularity encoding, to 0.727 to 0.779 for binary encoding. AUC, recall, and precision also improved across almost all algorithms. In comparison with other grouping approaches, modularity encoding generally showed slightly higher performance in AUC, ranging from 0.813 to 0.837, and precision, ranging from 0.752 to 0.782.

Conclusions

Modularity encoding enhances the performance of ML models in pharmacy research by effectively reducing dimensionality and retaining necessary information. Across the three algorithms used, models utilizing modularity encoding showed superior or comparable performance to other encoding approaches. Modularity encoding introduces other advantages such as it can be used for both hierarchical and non-hierarchical HCSs, the approach is clinically relevant, and can enhance ML models' clinical interpretation. A Python package has been developed to facilitate the use of the approach for future research.

Ethical considerations and concerns in the implementation of AI in pharmacy practice: a cross-sectional study

BACKGROUND

Integrating artificial intelligence (AI) into healthcare has raised significant ethical concerns. In pharmacy practice, AI offers promising advances but also poses ethical challenges.

METHODS

A cross-sectional study was conducted in countries from the Middle East and North Africa (MENA) region on 501 pharmacy professionals. A 12-item online questionnaire assessed ethical concerns related to the adoption of AI in pharmacy practice. Demographic factors associated with ethical concerns were analyzed via SPSS v.27 software using appropriate statistical tests.

RESULTS

Participants expressed concerns about patient data privacy (58.9%), cybersecurity threats (58.9%), potential job displacement (62.9%), and lack of legal regulation (67.0%). Tech-savviness and basic AI understanding were correlated with higher concern scores (p < 0.001). Ethical implications include the need for informed consent, beneficence, justice, and transparency in the use of AI.

CONCLUSION

The findings emphasize the importance of ethical guidelines, education, and patient autonomy in adopting AI. Collaboration, data privacy, and equitable access are crucial to the responsible use of AI in pharmacy practice.

Social robots counselling in community pharmacies - Helping or harming? A qualitative study of pharmacists' views

Background

Welfare technological solutions such as social robots attempt to meet the growing needs of the healthcare sector. Social robots may be able to respond to the shortage of pharmaceutical personnel at community pharmacies. However, there is a lack of previous studies regarding the use of social robots for medication counselling purposes in a pharmacy setting.

Objectives

The objective of this qualitative study was to explore pharmacists' views on the potential role of social robots in medication counselling.

Methods

Pharmacists, purposively sampled based on having recent experience of counselling customers in community pharmacies in Finland, first acted as customers interacting with the social robot in a simulated setting, before taking part in a focus group where their perspectives were explored. The focus group discussions were conducted in October and November 2022. The qualitative data was transcribed and analysed using reflexive thematic analysis.

Results

The number of participants was eight in total. A main theme of how the robot may either help or harm concerning medication safety within a pharmacy setting was identified. The six sub-themes found, according to pharmacists' views on robot counselling in community pharmacies, are context, digital competence, customer integrity, interaction, pharmacists' professional role and human skills.

Conclusions

According to the study findings, pharmacists experience that the social robot can offer a potential complement to a human pharmacist. The robot is seen as beneficial with respect to certain customer groups and in the light of personnel shortages, and may in the future add to trust, equality, freedom of choice and multilingualism, among other things, in the customer service situation at community pharmacies, thus improving medication safety.

Advancements and Applications of Artificial Intelligence in Pharmaceutical Sciences: A Comprehensive Review

Artificial intelligence (AI) has revolutionized the pharmaceutical industry, improving drug discovery, development, and personalized patient care. Through machine learning (ML), deep learning, natural language processing (NLP), and robotic automation, AI has enhanced efficiency, accuracy, and innovation in the field. The purpose of this review is to shed light on the practical applications and potential of AI in various pharmaceutical fields. These fields include medicinal chemistry, pharmaceutics, pharmacology and toxicology, clinical pharmacy, pharmaceutical biotechnology, pharmaceutical nanotechnology, pharmacognosy, and pharmaceutical management and economics. By leveraging AI technologies such as ML, deep learning, NLP, and robotic automation, this review delves into the role of AI in enhancing drug discovery, development processes, and personalized patient care. It analyzes AI's impact in specific areas such as drug synthesis planning, formulation development, toxicology predictions, pharmacy automation, and market analysis. Artificial intelligence integration into pharmaceutical sciences has significantly improved medicinal chemistry, drug discovery, and synthesis planning. In pharmaceutics, AI has advanced personalized medicine and formulation development. In pharmacology and toxicology, AI offers predictive capabilities for drug mechanisms and toxic effects. In clinical pharmacy, AI has facilitated automation and enhanced patient care. Additionally, AI has contributed to protein engineering, gene therapy, nanocarrier design, discovery of natural product therapeutics, and pharmaceutical management and economics, including marketing research and clinical trials management. Artificial intelligence has transformed pharmaceuticals, improving efficiency, accuracy, and innovation. This review highlights AI's role in drug development and personalized care, serving as a reference for professionals. The future promises a revolutionized field with AI-driven methodologies.

Prescription Precision: A Comprehensive Review of Intelligent Prescription Systems

Intelligent Prescription Systems (IPS) represent a promising frontier in healthcare, offering the potential to optimize medication selection, dosing, and monitoring tailored to individual patient needs. This comprehensive review explores the current landscape of IPS, encompassing various technological approaches, applications, benefits, and challenges. IPS leverages advanced computational algorithms, machine learning techniques, and big data analytics to analyze patient-specific factors, such as medical history, genetic makeup, biomarkers, and lifestyle variables. By integrating this information with evidence-based guidelines, clinical decision support systems, and real-time patient data, IPS generates personalized treatment recommendations that enhance therapeutic outcomes while minimizing adverse effects and drug interactions. Key components of IPS include predictive modeling, drug-drug interaction detection, adverse event prediction, dose optimization, and medication adherence monitoring. These systems offer clinicians invaluable decision-support tools to navigate the complexities of medication management, particularly in the context of polypharmacy and chronic disease management. While IPS holds immense promise for improving patient care and reducing healthcare costs, several challenges must be addressed. These include data privacy and security concerns, interoperability issues, integration with existing electronic health record systems, and clinician adoption barriers. Additionally, the regulatory landscape surrounding IPS requires clarification to ensure compliance with evolving healthcare regulations. Despite these challenges, the rapid advancements in artificial intelligence, data analytics, and digital health technologies are driving the continued evolution and adoption of IPS. As precision medicine gains momentum, IPS is poised to play a central role in revolutionizing medication management, ultimately leading to more effective, personalized, and patient-centric healthcare delivery.