Evidence map›Paper›PMID 34674190›Full record

ArticleDrug safety2021

Identifying Actionability as a Key Factor for the Adoption of 'Intelligent' Systems for Drug Safety: Lessons Learned from a User-Centred Design Approach.

George I Gavriilidis, Vlasios K Dimitriadis, Marie-Christine Jaulent, Pantelis Natsiavas

Abstract read
In one paragraph

Article in Drug safety, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–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

2 citing papers in PubMed.

  1. Article
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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

4 authors.

George I GavriilidisInstitute of Applied Biosciences, Centre for Research and Technology Hellas, 6th Km. Charilaou, Thermi Road, PO Box 60361, 57001, Thermi, Thessaloniki, Greece.
Vlasios K DimitriadisInstitute of Applied Biosciences, Centre for Research and Technology Hellas, 6th Km. Charilaou, Thermi Road, PO Box 60361, 57001, Thermi, Thessaloniki, Greece.
Marie-Christine JaulentSorbonne Université, INSERM, Univ Paris 13, Laboratoire d'Informatique Médicale et d'Ingénierie des Connaissances pour la e-Santé, LIMICS, 75006, Paris, France.
Pantelis NatsiavasInstitute of Applied Biosciences, Centre for Research and Technology Hellas, 6th Km. Charilaou, Thermi Road, PO Box 60361, 57001, Thermi, Thessaloniki, Greece. pnatsiavas@certh.gr.ORCID 0000-0002-4061-9815

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionInformation technology (IT) plays an important role in the healthcare landscape via the increasing digitization of medical data and the use of modern computational paradigms such as machine learning (ML) and knowledge graphs (KGs). These 'intelligent' technical paradigms provide a new digital 'toolkit' supporting drug safety and healthcare processes, including 'active pharmacovigilance'. While these technical paradigms are promising, intelligent systems (ISs) are not yet widely adopted by pharmacovigilance (PV) stakeholders, namely the pharma industry, academia/research community, drug safety monitoring organizations, regulatory authorities, and healthcare institutions. The limitations obscuring the integration of ISs into PV activities are multifaceted, involving technical, legal and medical hurdles, and thus require further elucidation.

objectiveWe dissect the abovementioned limitations by describing the lessons learned during the design and implementation of the PVClinical platform, a web platform aiming to support the investigation of potential adverse drug reactions (ADRs), emphasizing the use of knowledge engineering (KE) as its main technical paradigm.

resultsTo this end, we elaborate on the related 'business processes' (i.e. operational processes) and 'user goals' identified as part of the PVClinical platform design process based on Design Thinking principles. We also elaborate on key challenges restricting the adoption of such ISs and their integration in the clinical setting and beyond.

conclusionsWe highlight the fact that beyond providing analytics and useful statistics to the end user, 'actionability' has emerged as the operational priority identified through the whole process. Furthermore, we focus on the needs for valid, reproducible, explainable and human-interpretable results, stressing the need to emphasize on usability.

Indexed as

Drug-Related Side Effects and Adverse ReactionsPharmacovigilanceDelivery of Health CareHumansInformation TechnologyMachine Learning

Identifiers

PMID34674190
PMCPMC8553681

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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.