Application of Electronic Health Records in Pharmacovigilance

A special issue of Healthcare (ISSN 2227-9032). This special issue belongs to the section "Medication Management".

Deadline for manuscript submissions: 31 May 2025 | Viewed by 1494

Special Issue Editor


E-Mail Website
Guest Editor
1. UCBL · Pharmacotoxicology and Neonatal Intensive Care Unit, University of Lyon 1, EMET, LBBE, UMR CNRS 5558, Villeurbanne, France
2. Laboratoire de Biométrie et Biologie Humaine, Équipe Évaluation et Modélisation des Effets Thérapeutiques, rue Guillaume-Paradin, BP8071, CEDEX 08, 69376 Lyon, France
Interests: application AI and automatic trigger tool in pharmacovigilance

Special Issue Information

Dear Colleagues,

Medicines and vaccines have transformed the prevention and treatment of diseases. In addition to their benefits, medicinal products may also have side effects, some of which may be undesirable and / or unexpected. Pharmacovigilance (PV) is the science and activities relating to the detection, assessment, understanding and prevention of adverse effects or any other medicine/vaccine related problem (WHO).  Electronic health records (EHRs) has played an important role in pharmacovigilance. Nowadays, EHRs become a principal data source using in daily PV. Furthermore, artificial intelligence (AI) recently has emerged as a useful tool in pharmacovigilance.  AI could help in early detection and prevention of moderate and serious adverse drug reactions. However, there are a number of challenges that need to be addressed before AI can be fully adopted in this field. One of the challenge is how to use routine electronic health records with AI application in promoting pro-active monitoring of ADRs in clinical practice. This special issue aim to address the use of EHRs in PV.

We are pleased to invite you to contribute your research works related to innovated tools using EHRs in pharmacovigilance. 

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but not limited to) the following:

  1. Using EHRs in pharmacovigilance
  2. Implementation of automatic or semiautomatic trigger tool to detect ADRs using EHRs
  3. AI application in pharmacovigilance
  4. Active monitoring of adverse drug reactions using EHRs

Using trigger words in unstructured, narrative text to detect adverse drug reactions (ADRs).

I look forward to receiving your contributions.

Dr. Kim An Nguyen
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Healthcare is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • pharmacovigilance
  • drug-related side effects
  • adverse drug reactions
  • reporting systems
  • medical records systems
  • electronic health records
  • deep learning
  • machine learning
  • data mining
  • artificial intelligence.

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue polices can be found here.

Published Papers (1 paper)

Order results
Result details
Select all
Export citation of selected articles as:

Research

11 pages, 414 KiB  
Article
Unlicensed/Off-Label Drug Prescriptions at Hospital Discharge in Children: An Observational Study Using Routinely Collected Health Data
by Elham Jaberi, Inesse Boussaha, Xavier Dode, Guillaume Grenet, Behrouz Kassai and Kim An Nguyen
Healthcare 2024, 12(2), 208; https://doi.org/10.3390/healthcare12020208 - 15 Jan 2024
Cited by 1 | Viewed by 1082
Abstract
Background: Unlicensed and off-label (UL/OL) prescriptions have been associated with an increased risk of drug-related problems. Data of their prevalence at hospital discharge remain insufficient. We aimed to describe the prevalence of UL/OL drugs in outpatient prescriptions at discharge in children. Methods: We [...] Read more.
Background: Unlicensed and off-label (UL/OL) prescriptions have been associated with an increased risk of drug-related problems. Data of their prevalence at hospital discharge remain insufficient. We aimed to describe the prevalence of UL/OL drugs in outpatient prescriptions at discharge in children. Methods: We conducted a retrospective study using the routinely collected health data of children at discharge from 2014 to 2016. The primary reference source for determining licensed labelling was the summaries of product characteristics (SPCs) in a French industry-independent formulary named Thériaque. We described the characteristics of UL/OL prescriptions at discharge and looked for predictors of UL/OL prescriptions. Results: We included 2536 prescriptions of 479 children. Licensed, OL, and UL prescriptions accounted for 58.6% (95% CI: 56.7–60.5), 39.2% (95% CI: 37.3–41.1), and 2.3% (95% CI: 1.7–2.9), respectively. A total of 323 (74%) children received at least one UL/OL drug. Among the licensed drugs, bronchodilators (8.8%) and analgesics (8.6%), and among the OL drugs, antibiotics (2.8%), were the most prescribed. The younger age of the children and higher number of drugs they received increased the probability of UL/OL prescriptions (unadjusted p-value of ≤0.05). Conclusion: The prevalence of UL/OL prescriptions is about 40% at discharge from a pediatric university hospital in France. Full article
(This article belongs to the Special Issue Application of Electronic Health Records in Pharmacovigilance)
Show Figures

Figure 1

Back to TopTop