ASHP Statement on the Use of Artificial Intelligence in Pharmacy

Schutz, Nick; Olsen, Casey A; McLaughlin, Andrew J; Yi, Whitley; Nelson, Scott D.; Kalichira, Asha L; Smith, Andrew H.; Miller, Katherine A.; Le, Trinh; Chaffee, Bruce W.; Woodbury, C D R Kendra Worthy; Patel, Hardik

doi:10.1093/ajhp/zxaa249

Cited by 16 publications

(9 citation statements)

References 10 publications

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“…The study was designed to test if the use of artificial intelligence, namely machine learning, could automate currently manual methods for detecting diversion in large datasets and thereby offload time-consuming pharmacy operations tasks, allowing pharmacists to focus on more high-value patient care activities, consistent with ASHP’s statement on the use of artificial intelligence in pharmacy. 32 …”

Section: Methodsmentioning

confidence: 99%

Detecting drug diversion in health-system data using machine learning and advanced analytics

Knight

May

Tyson

et al. 2022

American Journal of Health-System Pharmacy

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Disclaimer In an effort to expedite the publication of articles related to the COVID-19 pandemic, AJHP is posting these manuscripts online as soon as possible after acceptance. Accepted manuscripts have been peer-reviewed and copyedited, but are posted online before technical formatting and author proofing. These manuscripts are not the final version of record and will be replaced with the final article (formatted per AJHP style and proofed by the authors) at a later time. Purpose The theft of drugs from healthcare facilities, also known as drug diversion, occurs frequently but is often undetected. This paper describes a research study to develop and test novel drug diversion detection methods. Improved diversion detection and reduction in diversion improves patient safety, limits harm to the person diverting, reduces the public health impact of substance use disorder, and mitigates significant liability risk to pharmacists and their organizations. Methods Ten acute care inpatient hospitals across 4 independent health systems extracted 2 datasets from various health information technology systems. Both datasets were consolidated, normalized, classified, and sampled to provide a harmonious dataset for analysis. Supervised machine learning methods were iteratively used on the initial sample dataset to train algorithms to classify medication movement transactions as involving a low or high risk of diversion. Thereafter, the resulting machine learning model classified the risk of diversion in a historical dataset capturing 8 to 24 months of history that included 27.9 million medication movement transactions by 19,037 nursing, 1,047 pharmacy, and 712 anesthesia clinicians and that included 22 known, blinded diversion cases to measure when the model would have detected the diversion compared to when the diversion was actually detected by existing methods. Results The machine learning model had 95.9% specificity and 96.6% sensitivity in detecting transactions involving a high risk of diversion using the initial sample dataset. In subsequent testing using the much larger historical dataset, the analytics detected known diversion cases (n = 22) in blinded data faster than existing detection methods (a mean of 160 days and a median of 74 days faster; range, 7-579 days faster). Conclusion The study showed that (1) consolidated datasets and (2) supervised machine learning can detect known diversion cases faster than existing detection methods. Users of the technology also noted improved investigation efficiency.

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Section: Methodsmentioning

confidence: 99%

Detecting drug diversion in health-system data using machine learning and advanced analytics

Knight

May

Tyson

et al. 2022

American Journal of Health-System Pharmacy

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“…Healthcare providers are generally positive regarding the uptake of ML [119]. For instance, the American Society of Hospital Pharmacists has stated that forms of artificial intelligence can be employed provided 'they perform as well or better than pharmacists' [120]. Moreover, the UK's National Health Service (NHS) has pledged to modernise its systems by integrating emerging technology to support its service needs, driven by its partner NHS Digital v .…”

Section: A Clinical Future For 3dp and MLmentioning

confidence: 99%

Disrupting 3D printing of medicines with machine learning

Elbadawi

McCoubrey

Gavins

et al. 2021

Trends in Pharmacological Sciences

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“…Recent examples include drug repurposing for identifying effective medications for coronavirus disease 2019 (COVID‐19), such as baricitinib and dexamethasone 1,2 . Within pharmacy practice, the use of AI can enhance various aspects such as medication safety, pharmacy operations, and patient‐specific recommendations that are supported by AI models 3 . A benefit of AI is the ability to analyze large amounts of data to generate insights and provide more tailored patient recommendations, based on a medication's efficacy and safety profile.…”

Section: Introductionmentioning

confidence: 99%

“…The 2023 American Society of Health‐System Pharmacists (ASHP) Foundation Pharmacy Forecast specifically asked panelists on how prepared their institution is in being able to validate safety and efficacy of AI algorithms, with 63% indicating they expect to be very to somewhat unprepared by 2027 6 . This would include understanding the types of questions that AI may be able to help answer, methodology of AI‐based models, and how to evaluate published literature in this area 3 . An ASHP perspective identified five steps to prepare for integrating machine learning (ML), a type of AI using mathematical functions to allow for a computer to develop algorithms without direct guidance, into health‐system pharmacy, which include: Engage, Study, Prioritize, Collaborate, and Share 4 .…”

Section: Introductionmentioning

confidence: 99%

“…6 This would include understanding the types of questions that AI may be able to help answer, methodology of AI-based models, and how to evaluate published literature in this area. 3 An ASHP perspective identified five steps to prepare for integrating machine learning (ML), a type of AI using mathematical functions to allow for a computer to develop algorithms without direct guidance, into health-system pharmacy, which include: Engage, Study, Prioritize, Collaborate, and Share. 4 When medications are used, it is still difficult to predict both efficacy and safety, which are affected by multiple patient-specific aspects, such as drug interactions, pharmacogenomics, and comorbidities.…”

Section: Introductionmentioning

confidence: 99%

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Role of artificial intelligence in pharmacy practice: A narrative review

Wong,

Wentz,

Palisano

et al. 2023

J Am Coll Clin Pharm

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Artificial intelligence (AI) has emerged as a potentially useful tool in transforming healthcare. Roles for AI include drug safety, operations, as well as precision medicine to improve patient outcomes. Within pharmacy practice, limited guidance is available describing the purpose of AI. This gap is important to address given the current growth of AI in medicine. The purpose of this narrative review was to review the literature evaluating the potential role of AI in three relevant areas of pharmacy practice. A review of Medline and Embase from January 2004 to June 2022 evaluating search terms involving AI and drug safety, pharmacy operations, or precision medicine. Articles had to be focused on human subjects, be either an observational cohort study or randomized control trial, and involve medication use to be included in this review. Data collection included the study design, AI methodology, and primary outcome and associated AI model performance measures. A total of 24 232 articles were identified from the search strategy, with 403 articles meeting inclusion criteria (1.7%). A total of 22 articles were included in this review. Articles focused on drug safety included safe use of opioids and the role of social media in identifying adverse drug reactions. In terms of pharmacy operations, articles included the identification of medications at risk for shortage and visual identification of medications. Finally, for precision medicine, articles included determining likelihood of beneficial effects of medications for new diagnoses and dosing of warfarin. The role of AI in improving patient outcomes is numerous. However, challenges that remain include transparency of generated AI models, generalizability, and lack of external validation. Future areas of focus include education of pharmacy clinicians on the role of AI and the increased use of open‐source models/data to allow for external validation of AI.

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ASHP Statement on the Use of Artificial Intelligence in Pharmacy

Cited by 16 publications

References 10 publications

Detecting drug diversion in health-system data using machine learning and advanced analytics

Detecting drug diversion in health-system data using machine learning and advanced analytics

Disrupting 3D printing of medicines with machine learning

Role of artificial intelligence in pharmacy practice: A narrative review

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