TY - JOUR
T1 - Methods used to identify and classify medication-related admissions and readmissions to hospitals
T2 - A systematic review
AU - Krogh, Linda
AU - Carter, Stephen
AU - Liu, Shania
AU - Moles, Rebekah Jane
AU - Chen, Jenny
AU - Englezos, Klaudia
AU - Yeung, Kingston
AU - Elliott, Rohan Andrew
AU - Angley, Manya
AU - Criddle, Deirdre Thelma
AU - Rigby, Deborah
AU - Sanfilippo, Frank Mario
AU - Budgeon, Charley Ann
AU - Nguyen, Kim-Huong
AU - Yates, Paul Andrew
AU - Phillips, Katie Maree
AU - Yik, Jerry
AU - McMillan, Faye
AU - Hawthorne, Deborah
AU - Fleming, Cristen
AU - Packer, Anna Louise
AU - Poon, Simon
AU - Chambers, Brett
AU - Ratnanayagam, Ganga
AU - Emadi, Tara
AU - Shakib, epehr
AU - Penm, onathan
PY - 2026/3/31
Y1 - 2026/3/31
N2 - Background: Medication-related hospital admissions, including readmissions, are common and often preventable. Identifying these admissions is essential for implementing effective interventions, yet no consensus exists on the most appropriate identification method. Objective: This systematic review aimed to evaluate the methodologies used to classify medication-related hospital admissions, summarize the tools employed, identify validated tools, and assess their usability in clinical settings. Methods: A systematic search was conducted in Scopus, PubMed, and Embase following PRISMA guidelines. The review was registered on Open Science Framework (https://doi.org/10.17605/OSF.IO/WEK2D). Full-text English-language articles published between October 2013 and October 2023 were included if they focused on the development or evaluation of a tool to identify medication-related hospital admissions. Systematic reviews, conference abstracts, editorials, and commentaries were excluded. Studies were screened and selected using Covidence by two authors, with disagreements resolved by a third party. Risk of bias and validity of evidence were assessed using the QUADAS-2 tool and the JBI Critical Appraisal Checklist for Diagnostic Test Accuracy Studies. Data was extracted and evaluated based on usability and if validated, the validation measures. Results: Twenty-three studies were included which describe three methods for identifying medication-related admissions: trigger tools and indicators (n = 8), questionnaires (n = 4), and author-selected ICD-9 or ICD-10 codes (n = 10). Four studies included validated tools, which were further assessed using QUADAS-2 for risk of bias. The AT-HARM10 tool demonstrated the strongest evidence of validity, with good inter-rater reliability and practical usability (average completion time 5.7 min, useable by pharmacy students). However, most studies showed limitations, including risk of bias, inconsistent definitions, and concentrated in older populations, reducing generalizability. While ICD codes were frequently used, their retrospective design limited their applicability in real-time clinical decisionmaking. These findings highlight the need for standardized, validated tools that are feasible for routine use to improve identification of medication-related admissions and support targeted interventions. Conclusion: A range of methodologies exists for identifying medication-related hospital admissions, but few are both validated and feasible for clinical use. ATHARM10 was the only tool meeting both criteria, making it the most suitable option for real-time application in clinical settings. These findings underscore the need for standardized, validated tools that are practical for routine use to improve detection and enable targeted interventions. Future research should prioritize validation across diverse populations to improve generalizability and support widespread implementation.
AB - Background: Medication-related hospital admissions, including readmissions, are common and often preventable. Identifying these admissions is essential for implementing effective interventions, yet no consensus exists on the most appropriate identification method. Objective: This systematic review aimed to evaluate the methodologies used to classify medication-related hospital admissions, summarize the tools employed, identify validated tools, and assess their usability in clinical settings. Methods: A systematic search was conducted in Scopus, PubMed, and Embase following PRISMA guidelines. The review was registered on Open Science Framework (https://doi.org/10.17605/OSF.IO/WEK2D). Full-text English-language articles published between October 2013 and October 2023 were included if they focused on the development or evaluation of a tool to identify medication-related hospital admissions. Systematic reviews, conference abstracts, editorials, and commentaries were excluded. Studies were screened and selected using Covidence by two authors, with disagreements resolved by a third party. Risk of bias and validity of evidence were assessed using the QUADAS-2 tool and the JBI Critical Appraisal Checklist for Diagnostic Test Accuracy Studies. Data was extracted and evaluated based on usability and if validated, the validation measures. Results: Twenty-three studies were included which describe three methods for identifying medication-related admissions: trigger tools and indicators (n = 8), questionnaires (n = 4), and author-selected ICD-9 or ICD-10 codes (n = 10). Four studies included validated tools, which were further assessed using QUADAS-2 for risk of bias. The AT-HARM10 tool demonstrated the strongest evidence of validity, with good inter-rater reliability and practical usability (average completion time 5.7 min, useable by pharmacy students). However, most studies showed limitations, including risk of bias, inconsistent definitions, and concentrated in older populations, reducing generalizability. While ICD codes were frequently used, their retrospective design limited their applicability in real-time clinical decisionmaking. These findings highlight the need for standardized, validated tools that are feasible for routine use to improve identification of medication-related admissions and support targeted interventions. Conclusion: A range of methodologies exists for identifying medication-related hospital admissions, but few are both validated and feasible for clinical use. ATHARM10 was the only tool meeting both criteria, making it the most suitable option for real-time application in clinical settings. These findings underscore the need for standardized, validated tools that are practical for routine use to improve detection and enable targeted interventions. Future research should prioritize validation across diverse populations to improve generalizability and support widespread implementation.
U2 - 10.1016/j.sapharm.2025.12.009
DO - 10.1016/j.sapharm.2025.12.009
M3 - Article
SN - 1551-7411
VL - 22
SP - 397
EP - 406
JO - Research in Social and Administrative Pharmacy
JF - Research in Social and Administrative Pharmacy
IS - 3
ER -