Kelli Keats’s research while affiliated with Society of Critical Care Medicine and other places

Purpose of Review Sleep medicine focuses on diagnosing and managing disorders like obstructive sleep apnea (OSA), which affects nearly one billion people worldwide, yet remains underdiagnosed in many cases. This review explores how artificial intelligence (AI) and machine learning (ML) can address these gaps by improving diagnostic tools, enhancing treatment strategies, and advancing patient outcomes. We also examine key challenges, including data privacy concerns, algorithmic bias, and the lack of standardized methods for integrating AI into clinical practice, while highlighting recent advancements and the evolving role of AI in sleep healthcare. Recent Findings Recent advancements in AI for sleep medicine include the use of deep learning models for automated sleep stage classification, wearable devices for real-time sleep monitoring, and AI-powered tools that increase the accuracy of home testing for disorders like OSA. ML algorithms enhance diagnostic accuracy by analyzing large datasets to identify patterns that traditional methods might miss. These advancements also enable more precise endotyping, allowing tailored treatment based on specific physiological drivers of sleep disorders, such as airway collapsibility or ventilatory control abnormalities. In the realm of pharmacology, AI is also helping predict drug efficacy, repurpose existing medications for sleep-related disorders, and explore how genetic variations influence individual responses to treatment. These innovations offer new opportunities for personalized medicine in sleep healthcare. Summary AI holds immense promise in revolutionizing sleep medicine by refining diagnostic tools, optimizing management protocols, and reducing healthcare costs. Recent advancements, such as deep learning models for sleep stage classification, wearable devices for real-time monitoring, and AI-driven pharmacological insights, highlight its potential to transform patient care. Addressing the challenges of data standardization, ethical implementation, and clinician education will be critical to unlocking its full potential. With continued advancements, AI can provide more personalized and efficient care, improving outcomes for patients with sleep-related disorders.

Background: Large language models (LLMs) have shown capability in diagnosing complex medical cases and passing medical licensing exams, but to date, only limited evaluations have studied how LLMs interpret, analyze, and optimize complex medication regimens. The purpose of this evaluation was to test four LLMs ability to identify medication errors and appropriate medication interventions on complex patient cases from the intensive care unit (ICU). Methods: A series of eight patient cases were developed by critical care pharmacists including history of present illness, laboratory values, vital signs, and medication regimens. Then, four LLMs (ChatGPT (GPT-3.5), ChatGPT (GPT-4), Claude2, and Llama2-7b) were prompted to develop a medication regimen for the patient. LLM generated medication regimens were then reviewed by a panel of seven critical care pharmacists to assess for presence of medication errors and clinical relevance. For each medication regimen recommended by the LLM, clinicians were asked to assess for if they would continue a medication, identify perceived medication errors in the medications recommended, identify the presence of life-threatening medication choices, and rank overall agreement on a 5-point Likert scale. Results: The clinician panel rated to continue therapies recommended by the LLMs between 55.8-67.9% of the time. Clinicians perceived between 1.57-4.29 medication errors per recommended regimen, and life-threatening recommendations were present between 15.0-55.3% of the time. Level agreement was between 1.85-2.67 for the four LLMs. Conclusions: LLMs demonstrated potential to serve as clinical decision support for the management of complex medication regimens with further domain specific training; however, caution should be used when employing LLMs for medication management given the present capabilities.

Purpose Critical care pharmacists (CCPs) are essential members of the multidisciplinary critical care team. Professional activities of the CCP are outlined in a 2020 position paper on critical care pharmacy services. This study looks to characterize CCP perspectives for priorities in optimizing pharmacy practice models and professional activities. Methods This was a cross-sectional survey conducted from July 24 to September 20, 2023. A 41-question survey instrument was developed to assess 7 domains: demographics, CCP resource utilization, patient care, quality improvement, research and scholarship, training and education, and professional development. This voluntary survey was sent to members of the American College of Clinical Pharmacy’s Critical Care Practice and Research Network. The survey was open for a total of 6 weeks. Results There was a response rate of 20.7% (332 of 1,605 invitees), with 66.6% of respondents (n = 221) completing at least 90% of the survey questions. Most respondents were clinical specialists (58.2%) and/or practiced at an academic medical center (58.5%). Direct patient care, quality improvement and medication safety, and teaching and precepting were identified as the CCP activities of highest importance to CCPs. The CCP-to-patient ratios considered ideal were 1:11-15 (selected by 49.8% of respondents) and 1:16-20 (33.9% of respondents). The ideal percentage of time dedicated to direct patient care activities, as identified by survey respondents, was 50% (interquartile range, 40-50). Conclusion These findings highlight the professional activities viewed as having the highest priority by CCPs. Future research is needed to define optimal CCP practice models for the delivery of patient care in real-world settings.

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Objective Common data models provide a standard means of describing data for artificial intelligence (AI) applications, but this process has never been undertaken for medications used in the intensive care unit (ICU). We sought to develop a common data model (CDM) for ICU medications to standardize the medication features needed to support future ICU AI efforts. Materials and Methods A 9-member, multi-professional team of ICU clinicians and AI experts conducted a 5-round modified Delphi process employing conference calls, web-based communication, and electronic surveys to define the most important medication features for AI efforts. Candidate ICU medication features were generated through group discussion and then independently scored by each team member based on relevance to ICU clinical decision-making and feasibility for collection and coding. A key consideration was to ensure the final ontology both distinguished unique medications and met Findable, Accessible, Interoperable, and Reusable (FAIR) guiding principles. Results Using a list of 889 ICU medications, the team initially generated 106 different medication features, and 71 were ranked as being core features for the CDM. Through this process, 106 medication features were assigned to 2 key feature domains: drug product-related (n = 43) and clinical practice-related (n = 63). Each feature included a standardized definition and suggested response values housed in the electronic data library. This CDM for ICU medications is available online. Conclusion The CDM for ICU medications represents an important first step for the research community focused on exploring how AI can improve patient outcomes and will require ongoing engagement and refinement.

Purpose: Critical care pharmacists are considered essential members of the healthcare team; however, justification and recruitment of new positions, especially in the evening or weekend shifts, remains a significant challenge. The purpose of this study was to investigate the number of interventions, type of interventions, and associated cost savings with the addition of 1 board certified critical care clinical pharmacist to evening shift. Methods: This was a prospective collection and characterization of 1 evening shift critical care pharmacist’s clinical interventions over a 12-week period. Interventions were collected and categorized daily from 13:00 to 22:00 Monday through Friday. After collection was complete, cost savings estimates were calculated using pharmacy wholesaler acquisition cost. Results: Interventions were collected on 52 of 60 weekdays. A total of 510 interventions were collected with an average of 9.8 interventions accepted per day. The most common interventions included transitions of care, medication dose adjustment, and antibiotic de-escalation and the highest proportion of interventions occurred in the medical intensive care unit. An estimated associated cost avoidance of $66 537.80 was calculated for an average of$1279.57 saved per day. Additionally, 22 (4.1%) of interventions were considered high yield interventions upon independent review by 2 pharmacists. Conclusion: The addition of 1 board-certified critical care pharmacist to evening shift resulted in multiple interventions across several categories and a significant cost avoidance when calculated using conservative measures.

INTRODUCTION: Intravenous (IV) medications are a fundamental cause of fluid overload (FO) in the intensive care unit (ICU); however, the association between IV medication use (including volume), administration timing, and FO occurrence remains unclear. METHODS: This retrospective cohort study included consecutive adults admitted to an ICU ≥72 hours with available fluid balance data. FO was defined as a positive fluid balance ≥7% of admission body weight within 72 hours of ICU admission. After reviewing medication administration record (MAR) data in three-hour periods, IV medication exposure was categorized into clusters using principal component analysis (PCA) and Restricted Boltzmann Machine (RBM). Medication regimens of patients with and without FO were compared within clusters to assess for temporal clusters associated with FO using the Wilcoxon rank sum test. Exploratory analyses of the medication cluster most associated with FO for medications frequently appearing and used in the first 24 hours was conducted. RESULTS: FO occurred in 127/927 (13.7%) of the patients enrolled. Patients received a median (IQR) of 31 (13-65) discrete IV medication administrations over the 72-hour period. Across all 47,803 IV medication administrations, ten unique IV medication clusters were identified with 121-130 medications in each cluster. Among the ten clusters, cluster 7 had the greatest association with FO; the mean number of cluster 7 medications received was significantly greater in patients in the FO cohort compared to patients who did not experience FO (25.6 vs.10.9. p<0.0001). 51 of the 127 medications in cluster 7 (40.2%) appeared in > 5 separate 3-hour periods during the 72-hour study window. The most common cluster 7 medications included continuous infusions, antibiotics, and sedatives/analgesics. Addition of cluster 7 medications to a prediction model with APACHE II score and receipt of diuretics improved the ability for the model to predict fluid overload (AUROC 5.65, p =0.0004). CONCLUSIONS: Using ML approaches, a unique IV medication cluster was strongly associated with FO. Incorporation of this cluster improved the ability to predict development of fluid overload in ICU patients compared with traditional prediction models. This method may be further developed into real-time clinical applications to improve early detection of adverse outcomes.