ArticleApplied clinical informatics2021
Development of a Perioperative Medication-Related Clinical Decision Support Tool to Prevent Medication Errors: An Analysis of User Feedback.
Article in Applied clinical informatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.
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Who cites it
10 citing papers in PubMed, 1 synthesis or guideline pooled it, 20 citations in OpenAlex.
- Understanding Medication Errors in Intensive Care Settings and Operating Rooms-A Systematic Review.Medicina (Kaunas, Lithuania) · 2025Pooled it
- Usability of a perioperative medication-related clinical decision support software application: a randomized controlled trial.Journal of the American Medical Informatics Association : JAMIA · 2022Trial
- Prescribing Errors in Iraqi Intensive and Cardiac Care Units: A Qualitative Investigations of Pharmacists' Perceptions, Determining Factors, Outcomes and Management Strategies.Journal of multidisciplinary healthcare · 2026Article
- ConciliaMed: an interactive mobile and web tool to reconcile chronic medications of patients undergoing elective surgery.BMJ health & care informatics · 2025Article
- Factors Influencing Integration and Usability of Model-Informed Precision Dosing Software in the Intensive Care Unit.Applied clinical informatics · 2024Article
- Road map for clinicians to develop and evaluate AI predictive models to inform clinical decision-making.BMJ health & care informatics · 2023Review
- Use of a Medication Safety Audit and Feedback Tool in the Emergency Department Is Affected by Prescribing Characteristics.Applied clinical informatics · 2023Article
- Clinical Decision Support for Fall Prevention: Defining End-User Needs.Applied clinical informatics · 2022Article
- Evaluating a Prototype Clinical Decision Support Tool for Chronic Pain Treatment in Primary Care.Applied clinical informatics · 2022Article
- Clinician Acceptance of Order Sets for Pain Management: A Survey in Two Urban Hospitals.Applied clinical informatics · 2022Article
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Authors and funding
7 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesMedication use in the perioperative setting presents many patient safety challenges that may be improved with electronic clinical decision support (CDS). The objective of this paper is to describe the development and analysis of user feedback for a robust, real-time medication-related CDS application designed to provide patient-specific dosing information and alerts to warn of medication errors in the operating room (OR).
methodsWe designed a novel perioperative medication-related CDS application in four phases: (1) identification of need, (2) alert algorithm development, (3) system design, and (4) user interface design. We conducted group and individual design feedback sessions with front-line clinician leaders and subject matter experts to gather feedback about user requirements for alert content and system usability. Participants were clinicians who provide anesthesia (attending anesthesiologists, nurse anesthetists, and house staff), OR pharmacists, and nurses.
resultsWe performed two group and eight individual design feedback sessions, with a total of 35 participants. We identified 20 feedback themes, corresponding to 19 system changes. Key requirements for user acceptance were: Use hard stops only when necessary; provide as much information as feasible about the rationale behind alerts and patient/clinical context; and allow users to edit fields such as units, time, and baseline values (e.g., baseline blood pressure).
conclusionWe incorporated user-centered design principles to build a perioperative medication-related CDS application that uses real-time patient data to provide patient-specific dosing information and alerts. Emphasis on early user involvement to elicit user requirements, workflow considerations, and preferences during application development can result in time and money efficiencies and a safer and more usable system.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.