Evidence map›Paper›PMID 32996074›Full record

ArticleInternational journal of clinical pharmacy2021

The effect of medication related clinical decision support at the time of physician order entry.

Matthijs L Becker, Fatih Baypinar, Marieke Pereboom, Soufiane Lilih, Ruud T M van der Hoeven, Thijs J Giezen, Hylke Jan Kingma

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Article in International journal of clinical pharmacy, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

5 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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4 · The record

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PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Matthijs L BeckerPharmacy Foundation of Haarlem Hospitals, Boerhaavelaan 24, 2035 RC, Haarlem, The Netherlands. mbecker@sahz.nl.ORCID http://orcid.org/0000-0003-0054-7498
Fatih BaypinarPharmacy Foundation of Haarlem Hospitals, Boerhaavelaan 24, 2035 RC, Haarlem, The Netherlands.
Marieke PereboomPharmacy Foundation of Haarlem Hospitals, Boerhaavelaan 24, 2035 RC, Haarlem, The Netherlands.
Soufiane LilihPharmacy Foundation of Haarlem Hospitals, Boerhaavelaan 24, 2035 RC, Haarlem, The Netherlands.
Ruud T M van der HoevenPharmacy Foundation of Haarlem Hospitals, Boerhaavelaan 24, 2035 RC, Haarlem, The Netherlands.
Thijs J GiezenPharmacy Foundation of Haarlem Hospitals, Boerhaavelaan 24, 2035 RC, Haarlem, The Netherlands.
Hylke Jan KingmaPharmacy Foundation of Haarlem Hospitals, Boerhaavelaan 24, 2035 RC, Haarlem, The Netherlands.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background In advanced clinical decision support systems, patient characteristics and laboratory values are included in the algorithms that generate alerts. These alerts have a higher specificity than basic medication surveillance alerts. The alerts of advanced clinical decision support systems can be shown directly to the prescriber during order entry, without the risk of generating an overload of irrelevant alerts. We implemented five advanced algorithms that are shown directly to the prescriber. These algorithms are for gastrointestinal prophylaxis, folic or folinic acid prescribed with orally or subcutaneously administered methotrexate, vitamin D prescribed with bisphosphonates, hyponatremia and measuring plasma levels for vancomycin and gentamicin. Objective We evaluated the effect of the implementation of the algorithms. Setting We performed prospective intervention studies with a historical group for comparison in both inpatients and outpatients at a teaching hospital in the Netherlands. Methods We compared the time period after implementation of the algorithm with the time period before implementation, using data from the hospital information system Epic. Difference in guideline adherence were analyzed using Chi square tests. Main outcome measure The outcome measures were the number of alerts, the acceptance rate of the advice in the alert, and for the algorithm measuring plasma levels for vancomycin and gentamicin the time to the correct dose. Results For all algorithms, the implementation resulted in a significant increase in guideline adherence, varying from 11 to 36%. The acceptance rate varied from 14% for hyponatremia to 90% for methotrexate. For gastrointestinal prophylaxis the acceptance rate was 4.4% for basic drug-drug interaction alerts when no gastrointestinal prophylaxis was prescribed and increased to 44.7% after implementation of the advanced algorithm. This algorithm substantially decreased the number of alerts from 812 before implementation to 217 after implementation. After implementation of the algorithm for measuring plasma levels for vancomycin and gentamicin, the proportion of patients receiving the correct dose after 48 h increased from 73 to 84% (p = 0.03). Conclusion Implementation of advanced algorithms that take patient characteristics into account and are shown directly to the physician during order entry, result in an increased guideline adherence.

Indexed as

Decision Support Systems, ClinicalDrug Therapy, Computer-AssistedMedical Order Entry SystemsPhysiciansDrug InteractionsHumansProspective StudiesClinical decision support systemsComputer-assisted drug therapyMedical order entry systemsMedication errors

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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.