Evidence map›Paper›PMID 38035302›Full record

ArticleFrontiers in public health2023

Patient safety discourse in a pandemic: a Twitter hashtag analysis study on #PatientSafety.

Olena Litvinova, Farhan Bin Matin, Maima Matin, Bogumila Zima-Kulisiewicz, Cyprian Tomasik, Bodrun Naher Siddiquea, Jivko Stoyanov, Atanas G Atanasov, Harald Willschke

Open access · goldAbstract read
In one paragraph

Article in Frontiers in public health, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
0.9field-weighted citation impact, top 29% 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

3 citing papers in PubMed, 4 citations in OpenAlex.

  1. Article
  2. Pharmacovigilance in the digital age: gaining insight from social media data.Experimental biology and medicine (Maywood, N.J.) · 2025
    Review
  3. Article
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

9 authors at 6 institutions in 6 countries.

Olena LitvinovaDepartment of Management and Quality Assurance in Pharmacy, National University of Pharmacy of the Ministry of Health of Ukraine, Kharkiv, Ukraine.
Farhan Bin MatinDepartment of Pharmacy, East West University, Aftabnagar, Dhaka, Bangladesh.
Maima MatinInstitute of Genetics and Animal Biotechnology of the Polish Academy of Sciences, Jastrzebiec, Poland.
Bogumila Zima-KulisiewiczInstitute of Genetics and Animal Biotechnology of the Polish Academy of Sciences, Jastrzebiec, Poland.
Cyprian TomasikInstitute of Genetics and Animal Biotechnology of the Polish Academy of Sciences, Jastrzebiec, Poland.
Bodrun Naher SiddiqueaDepartment of Epidemiology and Preventive Medicine, School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC, Australia.
Jivko StoyanovSwiss Paraplegic Research, Nottwil, Switzerland.
Atanas G AtanasovLudwig Boltzmann Institute Digital Health and Patient Safety, Medical University of Vienna, Vienna, Austria.
Harald WillschkeLudwig Boltzmann Institute Digital Health and Patient Safety, Medical University of Vienna, Vienna, Austria.
Institute of Genetics and Animal Biotechnology of the Polish Academy of Sciences · PLAustrian Institute for Health Technology Assessment GmbH · ATEast West University · BDMedical University of Vienna · ATMonash University · AUSwiss Paraplegic Research · CH

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: The digitalization of medicine is becoming a transformative force in modern healthcare systems. This study aims to investigate discussions regarding patient safety, as well as summarize perceived approaches to mitigating risks of adverse events expressed through the #PatientSafety Twitter hashtag during the COVID-19 pandemic. Methods: This research is grounded in the analysis of data extracted from Twitter under the hashtag #PatientSafety between December 1, 2019 and February 1, 2023. Symplur Signals, which represents a tool offering a method to monitor tweets containing hashtags registered with the Symplur Healthcare Hashtag Project, was used for analyzing the tweets shared in the study period. For text analytics of the relevant data, we further used the word cloud generator MonkeyLearn, and VOSviewer. Results: The analysis encompasses 358'809 tweets that were shared by 90'079 Twitter users, generating a total of 1'183'384'757 impressions. Physicians contributed to 18.65% of all tweets, followed by other healthcare professionals (14.31%), and health-focused individuals (10.91%). Geographically, more than a third of tweets (60.90%) were published in the United States. Canada and India followed in second and third positions, respectively. Blocks of trending terms of greater interest to the global Twitter community within the hashtag #PatientSafety were determined to be: "Patient," "Practical doctors," and "Health Care Safety Management." The findings demonstrate the engagement of the Twitter community with COVID-19 and problems related to the training, experience of doctors and patients during a pandemic, communication, the vaccine safety and effectiveness, and potential use of off-label drugs. Noteworthy, in the field of pharmacovigilance, Twitter has the possibility of identifying adverse reactions associated with the use of drugs, including vaccines. The issue of medical errors has been also discussed by Twitter users using the hashtag #PatientSafety. Conclusion: It is clear that various stakeholders, including students, medical practitioners, health organizations, pharmaceutical companies, and regulatory bodies, leverage Twitter to rapidly exchange medical information, data on the disease symptoms, and the drug effects. Consequently, there is a need to further integrate Twitter-derived data into the operational routines of healthcare organizations.

Indexed as

COVID-19Patient SafetySocial MediaCommunicationHumansPandemicsadverse eventsCOVID-19hashtaghealthcarepatient safetypharmacovigilanceTwittervaccine

Identifiers

PMID38035302
PMCPMC10687459
OpenAlexW4388722017

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.