Literature DB >> 30957213

Negligible impact of highly patient-specific decision support for potassium-increasing drug-drug interactions - a cluster-randomised controlled trial.

Patrick E Beeler1, Emmanuel Eschmann2, Markus Schneemann3, Jürg Blaser4.   

Abstract

BACKGROUND AND
OBJECTIVE: Clinical decision support (CDS) might improve management of potassium-increasing drug-drug interactions (DDI). We studied CDS with five features intended to increase effectiveness: (i) focus on serious DDIs, (ii) fewer notifications, (iii) presentation of current laboratory results, (iv) timing (when adverse event becomes likelier), (v) removal of notification when appropriate.
METHODS: We conducted a 1-year, hospital-wide, cluster-randomised controlled trial in the inpatient setting at a large tertiary-care academic medical centre. Three CDS types were implemented: monitoring reminders (unknown potassium, no monitoring ordered), elevated potassium warnings (≥4.9 mEq/l), and hyperkalaemia alerts (≥5.5 mEq/l). The primary endpoint was the frequency of potassium-monitoring intervals >72 h.
RESULTS: We analysed 15,272 and 18,981 stays with 2804 and 2057 potassium-increasing DDIs in the intervention and control groups, respectively. Patient-specific notifications: displayed were 869 reminders (1 per 3.2 potassium-increasing DDIs), 356 warnings (1:7.9), and 62 alerts (1:45.2). Nevertheless, insufficiently monitored DDIs were not reduced (intervention 451 of 9686 intervals >72 h [4.66%]; control 249 of 6140 [4.06%]). The only secondary outcome improved was the length of potassium monitoring intervals (intervention group mean 22.9 h, control 23.7 h; p <0.001). However, in the intervention group, during 50 of 2804 observed potassium-increasing DDI periods (1.78%) one or more serum potassium values ≥ 5.5mEq/l were measured, in the control group, during 27 of 2057 (1.31%; p = 0.20).
CONCLUSIONS: A highly patient-specific CDS feature combination had a negligible impact on the management of potentially serious potassium-increasing DDIs and was unable to improve safety among hospitalised patients.

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Year:  2019        PMID: 30957213     DOI: 10.4414/smw.2019.20035

Source DB:  PubMed          Journal:  Swiss Med Wkly        ISSN: 0036-7672            Impact factor:   2.193


  3 in total

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Authors:  Heba Edrees; Mary G Amato; Adrian Wong; Diane L Seger; David W Bates
Journal:  J Am Med Inform Assoc       Date:  2020-06-01       Impact factor: 4.497

2.  Designing and evaluating contextualized drug-drug interaction algorithms.

Authors:  Eric Chou; Richard D Boyce; Baran Balkan; Vignesh Subbian; Andrew Romero; Philip D Hansten; John R Horn; Sheila Gephart; Daniel C Malone
Journal:  JAMIA Open       Date:  2021-03-19

3.  The effects of clinical decision support system for prescribing medication on patient outcomes and physician practice performance: a systematic review and meta-analysis.

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Journal:  BMC Med Inform Decis Mak       Date:  2021-03-10       Impact factor: 2.796

  3 in total

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