Evidence map›Paper›PMID 34978662›Full record

Trial reportInternational journal of clinical pharmacy2022

Assessment of a hybrid decision support system using machine learning with artificial intelligence to safely rule out prescriptions from medication review in daily practice.

Clara Levivien, Pauline Cavagna, Annick Grah, Anne Buronfosse, Romain Courseau, Yvonnick Bézie, Jennifer Corny

Abstract readRandomized Controlled Trial
PubMed Publisher
In one paragraph

Trial report in International journal of clinical pharmacy, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed, 1 pooled it
4.1field-weighted citation impact, top 5% of its field
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

13 citing papers in PubMed, 1 synthesis or guideline pooled it, 33 citations in OpenAlex.

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4 · The record

Corrections and comments

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 at 1 institution in 1 country.

Clara LevivienPharmacy Department, Paris Saint Joseph Hospital Group, 185 rue Raymond Losserand, 75014, Paris, France.ORCID https://orcid.org/0000-0003-1259-2982
Pauline CavagnaPharmacy Department, Paris Saint Joseph Hospital Group, 185 rue Raymond Losserand, 75014, Paris, France.
Annick GrahPharmacy Department, Paris Saint Joseph Hospital Group, 185 rue Raymond Losserand, 75014, Paris, France.
Anne BuronfosseMedical Information Department, Paris Saint-Joseph Hospital Group, Paris, France.
Romain CourseauOrthopaedics Department, Paris Saint Joseph Hospital Group, Paris, France.
Yvonnick BéziePharmacy Department, Paris Saint Joseph Hospital Group, 185 rue Raymond Losserand, 75014, Paris, France.
Jennifer CornyPharmacy Department, Paris Saint Joseph Hospital Group, 185 rue Raymond Losserand, 75014, Paris, France. jcorny@ghpsj.fr.ORCID http://orcid.org/0000-0003-4303-6479
Hôpital Paris Saint-Joseph · FR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background Medication review is time-consuming and not exhaustive in most French hospitals. We routinely use an innovative hybrid decision support system using Artificial Intelligence to prioritize medication review by scoring prescriptions by their risk of containing at least one drug related problem (DRP). Aim Our aim was to attest that the prescriptions with low risk of DRPs ruled out by the tool in everyday practice were effectively free of any DRPs with potentially severe clinical impact. Methods We conducted a randomized single-blinded study to compare the rate of pharmaceutical interventions (PI) between low and high-risk prescriptions defined by the tool's calculated score. Prescriptions were reviewed daily by a clinical pharmacist. Proportion of prescriptions with at least one severe DRP was calculated in both groups. Severe DRPs were characterized through a multidisciplinary approach. Results Four hundred and twenty (107 low score and 313 high score) prescriptions were analyzed. The percentage of prescriptions with severe DRPs was dramatically decreased in low score prescriptions (2.8% vs. 15.3% for high-risk; p = 0.0248). A significant difference was found (94% vs. 20%; p < 0.001) in the percentage of severe DRPs detected by the hybrid approach compared to a CDSS. During the study period, the hybrid tool allowed to rule out 55% of all prescriptions in our hospital.Conclusion This hybrid decision support tool has shown to be accurate to detect DRPs in daily practice. Despite some limitations, it offers the best possible solution to prioritized medication review, considering the shortage of clinical pharmacists in France and considerably improves the safety of patients' care.

Indexed as

Artificial IntelligenceDrug-Related Side Effects and Adverse ReactionsHumansMachine LearningMedication ReviewPharmacistsPrescriptionsClinical pharmacy information systemsDecision Support systemsDeep learningElectronic prescribingMedication errors

Identifiers

PMID34978662
OpenAlexW4206772752

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.