Objective: To develop an objective tool designed to standardize the identification of high-alert medications (HAMs) according to patient safety risk.
Methods: Medications were evaluated using the High-Alert Medication Stratification Tool (HAMST). Tool revision occurred through assessing medications on an organization-approved HAM list and comparing scores with control medications not included on the list. Because of variations in HAMST interpretation by end users in interdisciplinary committees, a revision of the scoring tool was completed to create the High-Alert Medication Stratification Tool-Revised (HAMST-R), and the assessment was repeated. Both tools range from 0 to 10, with 10 describing agents with highest risk.
Results: The median (interquartile range [IQR]) initial HAM (n = 44) score using HAMST was 6 (5-7). The median (IQR) control (n = 45) score was 1 (0-2). Using the modified tool, HAMST-R, the median (IQR) HAM score was 4 (4-6) versus 1 (0-1) for controls. Scores for HAMs were significantly higher than controls using both tools (P < 0.001). A HAMST-R score of 4 or higher defines medications as high alert, as this score includes 75% of HAMs and no controls.
Conclusions: Through this exploratory study, clarification of the tool was required to increase its concurrent validity, interrater reliability, and implementation among other health systems. The revised tool, HAMST-R, is a newly developed, objective tool for standardized identification of HAMs. The tool may also be used for prospective identification of medications as high risk to patient safety during formulary review.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.1097/PTS.0000000000000445 | DOI Listing |
Explor Res Clin Soc Pharm
March 2025
Laboratory of Teaching and Research in Social Pharmacy (LEPFS), Department of Pharmacy, Federal University of Sergipe, Av. Marcelo Déda Chagas, São Cristóvão, Sergipe, Brazil.
Objective: To identify new drugs that present an increased risk of causing significant damage to critically ill patients due to failure in the administration process.
Method: The systematic literature review was conducted in the PubMed, Lilacs, Scopus, Web of Science and gray literature. The year in which the study was conducted was not restricted.
Syst Rev
January 2025
Pharmacy Department, Hamad Medical Corporation, Doha, Qatar.
Introduction: Medication errors occur at any point of the medication management process and are a major cause of death and harm globally. The perioperative environment introduces challenges in identifying medication errors due to the frequent use of time-sensitive, high-alert medications in a dynamic and intricate setting. Pharmacists could potentially reduce the occurrence of these errors because of their training and expertise.
View Article and Find Full Text PDFJPEN J Parenter Enteral Nutr
January 2025
Department of Pharmacy, Mississippi Baptist Medical Center, Jackson, Mississippi, USA.
Parenteral nutrition (PN), a high-alert medication, is an important lifesaving modality. The American Society for Parenteral and Enteral Nutrition (ASPEN) has historically provided guidelines and recommendations for the safe and efficacious use of PN. These recommendations have included detailed guidance regarding the compounding of this highly complex medication.
View Article and Find Full Text PDFDrugs Real World Outcomes
December 2024
Division of Pharmacology and Pharmacotherapy, Faculty of Pharmacy, University of Helsinki, Viikinkaari 5 E, P.O. Box 56, 00014, Helsinki, Finland.
Background: Paediatric patients are prone to medication errors, but an in-depth understanding of errors involving high-alert medications remains limited.
Objective: We aimed to investigate incident reports involving high-alert medications to describe medication errors, error chains and stages of the medication management and use process where the errors occur in paediatric hospitals.
Methods: A retrospective document analysis of self-reported medication safety incidents in a paediatric university hospital in 2018-20.
Kidney Med
December 2024
Division of Pediatric Nephrology, Dialysis, and Transplantation, University of Iowa Stead Family Children's Hospital, Iowa City, IA.
Rationale And Objective: Acute kidney injury (AKI) is a common complication among hospitalized adults, but AKI prediction and prevention among adults has proved challenging. We used machine learning to update the nephrotoxic injury negated by just-in time action (NINJA), a pediatric program that predicts nephrotoxic AKI, to improve accuracy among adults.
Study Design: A retrospective cohort study.
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!