Literature DB >> 25911673

Tailoring of alerts substantially reduces the alert burden in computerized clinical decision support for drugs that should be avoided in patients with renal disease.

David Czock1, Michael Konias2, Hanna M Seidling3, Jens Kaltschmidt2, Vedat Schwenger4, Martin Zeier4, Walter E Haefeli2.   

Abstract

OBJECTIVE: Electronic alerts are often ignored by physicians, which is partly due to the large number of unspecific alerts generated by decision support systems. The aim of the present study was to analyze critical drug prescriptions in a university-based nephrology clinic and to evaluate the effect of different alerting strategies on the alert burden.
METHODS: In a prospective observational study, two advanced strategies to automatically generate alerts were applied when medication regimens were entered for discharge letters, outpatient clinic letters, and written prescriptions and compared to two basic reference strategies. Strategy A generated alerts whenever drug-specific information was available, whereas strategy B generated alerts only when the estimated glomerular filtration rate of a patient was below a drug-specific value. Strategies C and D included further patient characteristics and drug-specific information to generate even more specific alerts.
RESULTS: Overall, 1012 medication regimens were entered during the observation period. The average number of alerts per drug preparation in medication regimens entered for letters was 0.28, 0.080, 0.019, and 0.011, when using strategy A, B, C, or D (P<0.001, for comparison between the strategies), leading to at least one alert in 87.5%, 39.3%, 13.5%, or 7.81 % of the regimens. Similar average numbers of alerts were observed for medication regimens entered for written prescriptions.
CONCLUSIONS: The prescription of potentially hazardous drugs is common in patients with renal impairment. Alerting strategies including patient and drug-specific information to generate more specific alerts have the potential to reduce the alert burden by more than 90 %.
© The Author 2015. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Entities:  

Keywords:  clinical decision support systems; contraindications; medical order entry systems; renal insufficiency

Mesh:

Year:  2015        PMID: 25911673     DOI: 10.1093/jamia/ocv027

Source DB:  PubMed          Journal:  J Am Med Inform Assoc        ISSN: 1067-5027            Impact factor:   4.497


  8 in total

Review 1.  Electronic Alerts for Acute Kidney Injury.

Authors:  Michael Haase; Andreas Kribben; Walter Zidek; Jürgen Floege; Christian Albert; Berend Isermann; Bernt-Peter Robra; Anja Haase-Fielitz
Journal:  Dtsch Arztebl Int       Date:  2017-01-09       Impact factor: 5.594

2.  Computerized Clinical Decision Support: Contributions from 2015.

Authors:  V Koutkias; J Bouaud
Journal:  Yearb Med Inform       Date:  2016-11-10

3.  Reducing Interruptive Alert Burden Using Quality Improvement Methodology.

Authors:  Juan D Chaparro; Cory Hussain; Jennifer A Lee; Jessica Hehmeyer; Manjusri Nguyen; Jeffrey Hoffman
Journal:  Appl Clin Inform       Date:  2020-01-15       Impact factor: 2.342

4.  Impact of a clinical decision support system for drug dosage in patients with renal failure.

Authors:  Sophie Desmedt; Anne Spinewine; Michel Jadoul; Séverine Henrard; Dominique Wouters; Olivia Dalleur
Journal:  Int J Clin Pharm       Date:  2018-05-21

Review 5.  Modulators Influencing Medication Alert Acceptance: An Explorative Review.

Authors:  Janina A Bittmann; Walter E Haefeli; Hanna M Seidling
Journal:  Appl Clin Inform       Date:  2022-08-18       Impact factor: 2.762

6.  Determining Inappropriate Medication Alerts from "Inaccurate Warning" Overrides in the Intensive Care Unit.

Authors:  Christine A Rehr; Adrian Wong; Diane L Seger; David W Bates
Journal:  Appl Clin Inform       Date:  2018-04-25       Impact factor: 2.342

7.  The use of a clinical decision support tool to assess the risk of QT drug-drug interactions in community pharmacies.

Authors:  Florine A Berger; Heleen van der Sijs; Teun van Gelder; Patricia M L A van den Bemt
Journal:  Ther Adv Drug Saf       Date:  2021-02-24

8.  An e-Delphi study to obtain expert consensus on the level of risk associated with preventable e-prescribing events.

Authors:  Jude Heed; Stephanie Klein; Ann Slee; Neil Watson; Andy Husband; Sarah Patricia Slight
Journal:  Br J Clin Pharmacol       Date:  2022-03-08       Impact factor: 3.716

  8 in total

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