Open AccessDesign, development, and deployment of an indication- and kidney function-based decision support tool to optimize treatment and reduce medication dosing errors
Author(s) -
Jamie S. Hirsch,
Rajdeep Brar,
Christopher Forrer,
Eunyoung Sung,
Richard Roycroft,
Pradeep Seelamneni,
Hemala Dabir,
Ambareen Naseer,
Pranisha Gautam-Goyal,
Kevin Bock,
Michael Oppenheim
Publication year - 2021
Publication title -
jamia open
Language(s) - English
Resource type - Journals
ISSN - 2574-2531
DOI - 10.1093/jamiaopen/ooab039
Subject(s) - dosing , usability , software deployment , clinical decision support system , computerized physician order entry , medicine , decision support system , patient safety , electronic prescribing , workflow , intensive care medicine , antimicrobial stewardship , point of care , health care , medical emergency , risk analysis (engineering) , computer science , medical prescription , nursing , data mining , pharmacology , antibiotics , antibiotic resistance , human–computer interaction , database , biology , microbiology and biotechnology , economics , economic growth , operating system
Delivering clinical decision support (CDS) at the point of care has long been considered a major advantage of computerized physician order entry (CPOE). Despite the widespread implementation of CPOE, medication ordering errors and associated adverse events still occur at an unacceptable level. Previous attempts at indication- and kidney function-based dosing have mostly employed intrusive CDS, including interruptive alerts with poor usability. This descriptive work describes the design, development, and deployment of the Adult Dosing Methodology (ADM) module, a novel CDS tool that provides indication- and kidney-based dosing at the time of order entry. Inclusion of several antimicrobials in the initial set of medications allowed for the additional goal of optimizing therapy duration for appropriate antimicrobial stewardship. The CDS aims to decrease order entry errors and burden on providers by offering automatic dose and frequency recommendations, integration within the native electronic health record, and reasonable knowledge maintenance requirements. Following implementation, early utilization demonstrated high acceptance of automated recommendations, with up to 96% of provided automated recommendations accepted by users.