Artificial intelligence-supported web application design and development for reducing polypharmacy side effects and supporting rational drug use in geriatric patients

Evaluating the Capabilities of Generative AI Tools in Understanding Medical Papers: Qualitative Study

BACKGROUND

Reading medical papers is a challenging and time-consuming task for doctors, especially when the papers are long and complex. A tool that can help doctors efficiently process and understand medical papers is needed.

OBJECTIVE

This study aims to critically assess and compare the comprehension capabilities of large language models (LLMs) in accurately and efficiently understanding medical research papers using the STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) checklist, which provides a standardized framework for evaluating key elements of observational study.

METHODS

The study is a methodological type of research. The study aims to evaluate the understanding capabilities of new generative artificial intelligence tools in medical papers. A novel benchmark pipeline processed 50 medical research papers from PubMed, comparing the answers of 6 LLMs (GPT-3.5-Turbo, GPT-4-0613, GPT-4-1106, PaLM 2, Claude v1, and Gemini Pro) to the benchmark established by expert medical professors. Fifteen questions, derived from the STROBE checklist, assessed LLMs' understanding of different sections of a research paper.

RESULTS

LLMs exhibited varying performance, with GPT-3.5-Turbo achieving the highest percentage of correct answers (n=3916, 66.9%), followed by GPT-4-1106 (n=3837, 65.6%), PaLM 2 (n=3632, 62.1%), Claude v1 (n=2887, 58.3%), Gemini Pro (n=2878, 49.2%), and GPT-4-0613 (n=2580, 44.1%). Statistical analysis revealed statistically significant differences between LLMs (P<.001), with older models showing inconsistent performance compared to newer versions. LLMs showcased distinct performances for each question across different parts of a scholarly paper-with certain models like PaLM 2 and GPT-3.5 showing remarkable versatility and depth in understanding.

CONCLUSIONS

This study is the first to evaluate the performance of different LLMs in understanding medical papers using the retrieval augmented generation method. The findings highlight the potential of LLMs to enhance medical research by improving efficiency and facilitating evidence-based decision-making. Further research is needed to address limitations such as the influence of question formats, potential biases, and the rapid evolution of LLM models.

Evolutionary trend analysis of the pharmaceutical management research field from the perspective of mapping the knowledge domain

Background

Pharmaceutical management is a new frontier subject between pharmacy, law and management, and related research involves the whole process of drug development, production, circulation and use. With the development of medical systems and the diversification of patients' drug needs, research in the field of pharmaceutical management is becoming increasingly abundant. To clarify the development status of this field, this study conducted a bibliometric analysis of relevant literature in the field based on the knowledge graph method for the first time and explored the evolutionary trends of research hotspots and frontiers.

Methods

Literature was obtained from the Web of Science Core Collection database. CiteSpace 6.2.R4 (Advanced), VOSViewer, Scimago Graphica, Pajek and the R programming language were used to visualize the data.

Results

A total of 12,771 publications were included in the study. The publications in the field of pharmaceutical management show an overall increasing trend. In terms of discipline evolution, early research topics tended to involve the positioning of pharmacists and pharmaceutical care and the establishment of a management system. From 2000 to 2005, this period tended to focus on clinical pharmacy and institutional norms. With the development of globalization and the market economy, research from 2005 to 2010 began to trend to the fields of drug markets and economics. From 2010 to 2015, research was gradually integrated into health systems and medical services. With the development of information technology, after 2015, research in the field of pharmaceutical management also began to develop in the direction of digitalization and intelligence. In light of the global pandemic of COVID-19, research topics such as drug supply management, pharmaceutical care and telemedicine services under major public health events have shown increased interest since 2020.

Conclusion

Based on the knowledge mapping approach, this study provides a knowledge landscape in the field of pharmaceutical management research. The results showed that the reform of pharmacy education, the challenge of drug management under the COVID-19 pandemic, digital transformation and the rise of telemedicine services were the hot topics in this field. In addition, the research frontier also shows the broad prospects of the integration of information technology and pharmaceutical management, the practical value of precision pharmaceutical services, the urgent need of global drug governance, and the ethical and legal issues involved in the application of artificial intelligence technology in drug design, which points out the direction for the future development of pharmaceutical practice.

Toward structure-multiple activity relationships (SMARts) using computational approaches: A polypharmacological perspective

In the current era of biological big data, which are rapidly populating the biological chemical space, in silico polypharmacology drug design approaches help to decode structure-multiple activity relationships (SMARts). Current computational methods can predict or categorize multiple properties simultaneously, which aids the generation, identification, curation, prioritization, optimization, and repurposing of molecules. Computational methods have generated opportunities and challenges in medicinal chemistry, pharmacology, food chemistry, toxicology, bioinformatics, and chemoinformatics. It is anticipated that computer-guided SMARts could contribute to the full automatization of drug design and drug repurposing campaigns, facilitating the prediction of new biological targets, side and off-target effects, and drug-drug interactions.

Natural language processing assisted detection of inappropriate proton pump inhibitor use in adult hospitalised patients

OBJECTIVES

To establish a clinical application monitoring system for proton pump inhibitors (PPI-MS) and to enhance the detection and intervention of inappropriate PPI use in adult hospitalised patients.

METHODS

Natural language processing technology was applied to indication recognition of therapeutic PPI applications and the assessment of admission record recognition for preventive PPI applications. Symptom judgement was based on the tense-negation model and regular expressions. Evidence-based rules for clinical PPI application were embedded for the construction of PPI-MS. A total of 9421 patient records using PPI from July 2022 to July 2023 were analysed to validate the performance of the system and to identify common issues related to inappropriate clinical PPI use.

RESULTS

Out of 9421 hospitalised patients detected using PPI, 4736 (50.27%) were used for prophylaxis and the rest for therapeutic use. Among the prophylactic medications, 2274 patients (48.02%) were identified as receiving inappropriate prophylactic PPI. The main reasons were inappropriate prophylaxis without indication. Additionally, 258 cases of inappropriate therapeutic PPI use were identified, mainly involving the use of esomeprazole for peptic ulcers and Zollinger-Ellison syndrome. The efficiency of the PPI rational medication monitoring system, when coupled with human involvement, was 32 times that of manual monitoring. Among cases of inappropriate prophylactic PPI use, 45.29% were due to lack of indications, 28.34% involved inappropriate administration routes, 15.74% were related to inappropriate dosing frequencies and 10.62% were attributed to inappropriate drug selection. There were 933 cases related to the use of antiplatelet and anticoagulant drugs and 708 cases related to the use of non-steroidal anti-inflammatory drugs. The overall accuracy of the PPI-MS system was 88.69%, with a recall rate of 99.33%, and the F1 score was 93.71%.

CONCLUSIONS

Establishing a PPI medication monitoring system through natural language processing technology, while ensuring accuracy and recall rates, improves evaluation efficiency and homogeneity. This provides a new solution for timely detection of issues relating to clinical PPI usage.

Optimizing drug therapy for older adults: shifting away from problematic polypharmacy

INTRODUCTION

The accelerated discovery and production of pharmaceutical products has resulted in many positive outcomes. However, this progress has also contributed to problematic polypharmacy, one of the rapidly growing threats to public health in this century. Problematic polypharmacy results in adverse patient outcomes and imposes increased strain and financial burden on healthcare systems.

AREAS COVERED

A review was conducted on the current body of evidence concerning factors contributing to and consequences of problematic polypharmacy. Recent trials investigating interventions that target polypharmacy and emerging solutions, including incorporation of artificial intelligence, are also examined in this article.

EXPERT OPINION

To shift away from problematic polypharmacy, a multifaceted interdisciplinary approach is necessary. Any potentially successful strategy must be adapted to suit various healthcare settings and must utilize all available resources, including artificial intelligence.

Defining, identifying and addressing problematic polypharmacy within multimorbidity in primary care: a scoping review

INTRODUCTION

Polypharmacy and multimorbidity pose escalating challenges. Despite numerous attempts, interventions have yet to show consistent improvements in health outcomes. A key factor may be varied approaches to targeting patients for intervention.

OBJECTIVES

To explore how patients are targeted for intervention by examining the literature with respect to: understanding how polypharmacy is defined; identifying problematic polypharmacy in practice; and addressing problematic polypharmacy through interventions.

DESIGN

We performed a scoping review as defined by the Joanna Briggs Institute.

SETTING

The focus was on primary care settings.

DATA SOURCES

Medline, Embase, Cumulative Index to Nursing and Allied Health Literature and Cochrane along with ClinicalTrials.gov, Science.gov and WorldCat.org were searched from January 2004 to February 2024.

ELIGIBILITY CRITERIA

We included all articles that had a focus on problematic polypharmacy in multimorbidity and primary care, incorporating multiple types of evidence, such as reviews, quantitative trials, qualitative studies and policy documents. Articles focussing on a single index disease or not written in English were excluded.

EXTRACTION AND ANALYSIS

We performed a narrative synthesis, comparing themes and findings across the collective evidence to draw contextualised insights and conclusions.

RESULTS

In total, 157 articles were included. Case-finding methods often rely on basic medication counts (often five or more) without considering medical history or whether individual medications are clinically appropriate. Other approaches highlight specific drug indicators and interactions as potentially inappropriate prescribing, failing to capture a proportion of patients not fitting criteria. Different potentially inappropriate prescribing criteria also show significant inconsistencies in determining the appropriateness of medications, often neglecting to consider multimorbidity and underprescribing. This may hinder the identification of the precise population requiring intervention.

CONCLUSIONS

Improved strategies are needed to target patients with polypharmacy, which should consider patient perspectives, individual factors and clinical appropriateness. The development of a cross-cutting measure of problematic polypharmacy that consistently incorporates adjustment for multimorbidity may be a valuable next step to address frequent confounding.

Development and validation of a machine learning model to improve precision prediction for irrational prescriptions in orthopedic perioperative patients

OBJECTIVE

Our objective was to develop a machine learning model capable of predicting irrational medical prescriptions precisely within orthopedic perioperative patients.

METHODS

A dataset comprising 3047 instances of suspected irrational medication prescriptions was collected from a sample of 1318 orthopedic perioperative patients from April 2019 to March 2022. Four machine learning models were employed to forecast irrational prescriptions, following which, the performance of each model was meticulously assessed. Subsequently, a thorough variable importance analysis was conducted on the model that performed the best predictive capabilities. Thereafter, the efficacy of integrating this optimal model into the existing audit prescription process was rigorously evaluated.

RESULTS

Of the models utilized in this study, the RF model yielded the highest AUC of 92%, whereas the NB model presented the lowest AUC of 68%. Also, the RF model boasted the most robust performance in terms of PPV, reaching 82.4%, and NPV, reaching 86.6%. The ANN and the XGBoost model were neck and neck, with the ANN slightly edging out with a higher PPV of 95.9%, while the XGBoost model boasted an impressive NPV of 98.2%. The RF model singled out the following five factors as the most influential in predicting irrational prescriptions: the type of drug, the type of surgery, the number of comorbidities, the date of surgery after hospitalization, as well as the associated hospital and drug costs.

CONCLUSION

The RF model showcased significantly high level of proficiency in predicting irrational prescriptions among orthopedic perioperative patients, outperforming other models by a considerable margin. It effectively enhanced the efficiency of pharmacist interventions, displaying outstanding performance in assisting pharmacists to intervene with irrational prescriptions.

Potential Applications of Artificial Intelligence (AI) in Managing Polypharmacy in Saudi Arabia: A Narrative Review

Prescribing medications is a fundamental practice in the management of illnesses that necessitates in-depth knowledge of clinical pharmacology. Polypharmacy, or the concurrent use of multiple medications by individuals with complex health conditions, poses significant challenges, including an increased risk of drug interactions and adverse reactions. The Saudi Vision 2030 prioritises enhancing healthcare quality and safety, including addressing polypharmacy. Artificial intelligence (AI) offers promising tools to optimise medication plans, predict adverse drug reactions and ensure drug safety. This review explores AI's potential to revolutionise polypharmacy management in Saudi Arabia, highlighting practical applications, challenges and the path forward for the integration of AI solutions into healthcare practices.

Impact of Artificial Intelligence (AI) Technology in Healthcare Sector: A Critical Evaluation of Both Sides of the Coin

The influence of artificial intelligence (AI) has drastically risen in recent years, especially in the field of medicine. Its influence has spread so greatly that it is determined to become a pillar in the future medical world. A comprehensive literature search related to AI in healthcare was performed in the PubMed database and retrieved the relevant information from suitable ones. AI excels in aspects such as rapid adaptation, high diagnostic accuracy, and data management that can help improve workforce productivity. With this potential in sight, the FDA has continuously approved more machine learning (ML) software to be used by medical workers and scientists. However, there are few controversies such as increased chances of data breaches, concern for clinical implementation, and potential healthcare dilemmas. In this article, the positive and negative aspects of AI implementation in healthcare are discussed, as well as recommended some potential solutions to the potential issues at hand.

The Prevalence of Potentially Inappropriate Prescribing in Two Family Health Units in Portugal

OBJECTIVE

Polypharmacy and potentially inappropriate prescribing (PIP) are growing concerns in the ageing population. They carry the risk of increasing adverse effects, medical interactions, and difficulties managing the medication. Few studies in Portugal evaluate the prevalence of polypharmacy and PIP in primary care. No previous studies in the primary care setting in Portugal have been conducted using the European Union (EU)(7)-PIM (potentially inappropriate medication) list tool. In this study, we aimed to estimate the prevalence of polypharmacy and PIP in two family health units in Portugal.  Methods: To answer this question, we enrolled a sample of 361 elderly patients from two family health units in a descriptive observational transversal study. We randomly selected patients, consulted their prescription records in the previous 12 months, and applied the EU(7)-PIM list tool, validated for the Portuguese population. The data was then analyzed using descriptive and inferential statistics and the Statistical Package for the Social Sciences (IBM SPSS Statistics for Windows, IBM Corp., Version 24.0, Armonk, NY).

RESULTS

Our results showed a prevalence of 79.8% of polypharmacy in the elderly population and 73.4% of PIP. These values are higher than predicted in the literature, but different screening tools have been used among papers. The mean number of prescribed drugs per patient was nine in one unit and seven in the other, and the mode was eleven per patient. The most identified PIP-associated drugs were proton pump inhibitors in 46.4% of the patients in one unit and 43.7% in the other. We also found a statistically significant higher prevalence of PIP and polypharmacy in females and patients over 75 years.

CONCLUSION

From a prevalence perspective, we found higher-than-expected prevalences of PIP and polypharmacy in our population. Contributing factors might be a higher ageing index in the Portuguese population, modern practices using combination therapy, and the use of a screening tool that does not take into account the personal clinical history of patients. Further limitations involve only including patients with follow-up in the units studied. Even so, it suggests both PIP and polypharmacy as concerns to address, and we will strive to educate both health teams on PIP, polypharmacy, and deprescribing. We also emphasize the need to widen the study to other family health units.

Enhancing Primary Care for Nursing Home Patients with an Artificial Intelligence-Aided Rational Drug Use Web Assistant

Polypharmacy can result in drug-drug interactions, severe side-effects, drug-disease interactions, inappropriate medication use in the elderly, and escalating costs. This study aims to evaluate nursing home residents' medication regimens using a rational drug use web assistant developed by researchers to mitigate unnecessary medication usage. This analytical, cross-sectional study included data from nursing home residents recently recorded in a training family health center. Sociodemographic information, medical conditions, and prescribed medications of all patients in the nursing home (n = 99) were documented. Medications were assessed using an artificial intelligence-aided rational drug use web assistant. Instances of inappropriate drug use and calculations of contraindicated drug costs were also recorded. The study revealed that 88.9% (n = 88) of patients experienced polypharmacy, with a mean value of 6.96 ± 2.94 drugs per patient. Potential risky drug-drug interactions were present in 89.9% (n = 89) of patients, contraindicated drug-drug interactions in 20.2% (n = 20), and potentially inappropriate drug use in 86.9% (n = 86). Plans to discontinue 83 medications were estimated to reduce total direct medication costs by 9.1% per month. After the assessment with the rational drug use web assistant, the number of drugs that patients needed to use and polypharmacy decreased significantly. This study concludes that the rational drug use web assistant application, which is more cost-effective than the traditional manual method, assisted by artificial intelligence, and integrated into healthcare services, may offer substantial benefits to family physicians and their geriatric patients.