Novel opportunities for clinical pharmacy research: development of a machine learning model to identify medication related causes of delirium in different patient groups

The advent of artificial intelligence (AI) technologies has taken the world of science by storm in 2023. The opportunities of this easy to access technology for clinical pharmacy research are yet to be fully understood. The development of a custom-made large language model (LLM) (DELSTAR) trained on a wide range of internationally recognised scientific publication databases, pharmacovigilance sites and international product characteristics to help identify and summarise medication related information on delirium, as a proof-of-concept model, identified new facilitators and barriers for robust clinical pharmacy practice research. This technology holds great promise for the development of much more comprehensive prescribing guidelines, practice support applications for clinical pharmacy, increased patient and prescribing safety and resultant implications for healthcare costs. The challenge will be to ensure its methodologically robust use and the detailed and transparent verification of its information accuracy.

Enhancing the clinical pharmacy service of a large teaching hospital: Development of a new clinical prioritisation tool

Background

The number and complexity of patients being admitted to hospitals is rising and some patients may not receive a full clinical pharmacy review or be reviewed as regularly as needed during their inpatient stay. This is a risk factor for medication errors. Clinical prioritisation identifies patients who are high-risk and most in need of a pharmacist review, targeting finite pharmacy resources to patients who will benefit the most.

Objectives

Assess and enhance clinical prioritisation within a hospital pharmacy department.

Methods

The study was conducted in a large urban academic teaching hospital. A cross-sectional survey of clinical pharmacists in the hospital was conducted to establish the patient clinical criteria they prioritise in their work. A clinical prioritisation tool was developed based on survey findings and was integrated into an existing electronic pharmacy care interface. A pre- and post-intervention study was conducted, consisting of data collection for five days pre- and five days post-implementation of the tool. Quantitative data were analysed using descriptive and inferential statistics. Qualitative data were analysed by thematic analysis.

Results

Of 39 eligible pharmacists, 37 (95%) responded to the survey. The top-rated prioritisation criteria, including medicines reconciliation tasks and high-risk medicines, helped to inform the content of the clinical prioritisation tool. Post-intervention, there were more Level 1 complex patients reviewed by pharmacists and fewer Level 3 stable patients compared to pre-intervention. Tool sensitivity ranged from 51 to 88%, depending on the experience of the pharmacist using the tool. High levels of satisfaction with clinical prioritisation were reported by those using the tool.

Conclusion

This newly developed clinical prioritisation tool has the potential to support pharmacists in identifying and reviewing patients in a more targeted manner than practice prior to tool development. Continued development and validation of the tool is essential, with a focus on developing a fully automated tool.

Assessment of a hybrid decision support system using machine learning with artificial intelligence to safely rule out prescriptions from medication review in daily practice

Background Medication review is time-consuming and not exhaustive in most French hospitals. We routinely use an innovative hybrid decision support system using Artificial Intelligence to prioritize medication review by scoring prescriptions by their risk of containing at least one drug related problem (DRP). Aim Our aim was to attest that the prescriptions with low risk of DRPs ruled out by the tool in everyday practice were effectively free of any DRPs with potentially severe clinical impact. Methods We conducted a randomized single-blinded study to compare the rate of pharmaceutical interventions (PI) between low and high-risk prescriptions defined by the tool's calculated score. Prescriptions were reviewed daily by a clinical pharmacist. Proportion of prescriptions with at least one severe DRP was calculated in both groups. Severe DRPs were characterized through a multidisciplinary approach. Results Four hundred and twenty (107 low score and 313 high score) prescriptions were analyzed. The percentage of prescriptions with severe DRPs was dramatically decreased in low score prescriptions (2.8% vs. 15.3% for high-risk; p = 0.0248). A significant difference was found (94% vs. 20%; p < 0.001) in the percentage of severe DRPs detected by the hybrid approach compared to a CDSS. During the study period, the hybrid tool allowed to rule out 55% of all prescriptions in our hospital.Conclusion This hybrid decision support tool has shown to be accurate to detect DRPs in daily practice. Despite some limitations, it offers the best possible solution to prioritized medication review, considering the shortage of clinical pharmacists in France and considerably improves the safety of patients' care.

Pharmacy informatics: A call to action for educators, administrators, and residency directors

Pharmacy informatics involves the customization and application of information technology to improve medication-related processes. It is a critical function given the recent expansion of technology and prevalence of medication use throughout healthcare. Despite the necessity for pharmacy involvement, many pharmacists and student pharmacists are unaware of how to get started in informatics. Ideally, training should start early with student pharmacists being enrolled in introductory courses taught by leaders in the field. Students especially interested in informatics can build upon their classroom experience with postgraduate year two (PGY2) residencies in several informatics-related areas. Additionally, current pharmacists can gather information from national pharmacy organizations and local information technology pharmacists to prepare for projects in the field. These approaches provide opportunities for all pharmacists to expand their knowledge and establish the basis for highly-motivated pharmacists to become experts in informatics.

Clinical decision support for drug related events: Moving towards better prevention

Clinical decision support (CDS) systems with automated alerts integrated into electronic medical records demonstrate efficacy for detecting medication errors (ME) and adverse drug events (ADEs). Critically ill patients are at increased risk for ME, ADEs and serious negative outcomes related to these events. Capitalizing on CDS to detect ME and prevent adverse drug related events has the potential to improve patient outcomes. The key to an effective medication safety surveillance system incorporating CDS is advancing the signals for alerts by using trajectory analyses to predict clinical events, instead of waiting for these events to occur. Additionally, incorporating cutting-edge biomarkers into alert knowledge in an effort to identify the need to adjust medication therapy portending harm will advance the current state of CDS. CDS can be taken a step further to identify drug related physiological events, which are less commonly included in surveillance systems. Predictive models for adverse events that combine patient factors with laboratory values and biomarkers are being established and these models can be the foundation for individualized CDS alerts to prevent impending ADEs.

Utilization of a Pharmacy Clinical Surveillance System for Pharmacist Alerting and Communication at a Tertiary Academic Medical Center

The objective of this analysis is to describe the utilization metrics of a pharmacy clinical surveillance system (PCSS) at a tertiary, academic medical center.We performed a retrospective database analysis assessing rule-based alerts (RBA), interventions and pharmacist communication notes documented in the PCSS from January 1, 2014 to December 31, 2014. Reports were generated on 92 unique RBAs sent to clinicians for evaluation. Metrics assessed included the number of RBAs that were triggered, clinically evaluated, intervened on by pharmacists, and therapeutic category of interventions. Pharmacy communication notes were also evaluated.A total of 399,979 RBAs were triggered through the PCSS. During that time, pharmacists documented a total of 17,733 interventions. The most common RBAs were related to lab abnormalities (132,487; 33 %) and anticoagulation/antiplatelet therapy (126,425; 32.1 %). Interventions were most frequently related to RBAs regarding anticoagulation/antiplatelet therapy (6412; 36 %) and antimicrobial therapy (3320; 19 %). Pharmacist communication was most commonly related to clarification of medication and lab orders, and therapeutic drug monitoring.Based on utilization metrics presented, the implementation of a PCSS has successfully generated RBAs to aid pharmacists in clinical practice and improved departmental documentation and communication. Further analysis is warranted to assess the impact of the RBAs, interventions, and communication notes on outcomes such as hospital cost and adverse drug events.