Evidence map›Paper›PMID 38182935›Full record

ArticleThe patient2024

Attitudes Toward the Adoption of Remote Patient Monitoring and Artificial Intelligence in Parkinson's Disease Management: Perspectives of Patients and Neurologists.

Carlos Antonio Godoy Junior, Francesco Miele, Laura Mäkitie, Eleonora Fiorenzato, Maija Koivu, Lytske Jantien Bakker, Carin Uyl-de Groot, William Ken Redekop, Welmoed Kirsten van Deen

Open access · hybridAbstract read
In one paragraph

Article in The patient, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed, 1 pooled it
6.3field-weighted citation impact, top 3% 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

10 citing papers in PubMed, 1 synthesis or guideline pooled it, 17 citations in OpenAlex.

  1. Pooled it
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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

9 authors at 4 institutions in 3 countries.

Carlos Antonio Godoy JuniorErasmus School of Health Policy and Management, Erasmus University Rotterdam, Burgemeester Oudlaan 50, 3062 PA, Rotterdam, Netherlands. godoyjunior@eshpm.eur.nl.ORCID 0009-0008-0871-459X
Francesco MieleDepartment of Political and Social Sciences, University of Trieste, Trieste, Italy.ORCID 0000-0003-1337-9112
Laura MäkitieDepartment of Neurology, Brain Center, Helsinki University Hospital, Helsinki, Finland.ORCID 0000-0002-4063-6674
Eleonora FiorenzatoDepartment of Neuroscience (DNS), University of Padova, Padua, Italy.ORCID 0000-0001-7947-5903
Maija KoivuDepartment of Neurology, Brain Center, Helsinki University Hospital, Helsinki, Finland.ORCID 0000-0002-7921-5412
Lytske Jantien BakkerErasmus School of Health Policy and Management, Erasmus University Rotterdam, Burgemeester Oudlaan 50, 3062 PA, Rotterdam, Netherlands.ORCID 0000-0001-8583-0115
Carin Uyl-de GrootErasmus School of Health Policy and Management, Erasmus University Rotterdam, Burgemeester Oudlaan 50, 3062 PA, Rotterdam, Netherlands.ORCID 0000-0001-5808-302X
William Ken RedekopErasmus School of Health Policy and Management, Erasmus University Rotterdam, Burgemeester Oudlaan 50, 3062 PA, Rotterdam, Netherlands.ORCID 0000-0001-9538-4083
Welmoed Kirsten van DeenErasmus School of Health Policy and Management, Erasmus University Rotterdam, Burgemeester Oudlaan 50, 3062 PA, Rotterdam, Netherlands.ORCID 0000-0002-4836-9849
Erasmus University Rotterdam · NLUniversity of Helsinki · FIUniversity of Padua · ITUniversity of Trieste · IT

Funding

H2020 European Research Council 101016902
6 · The paper itself

Abstract

objectiveEarly detection of Parkinson's Disease (PD) progression remains a challenge. As remote patient monitoring solutions (RMS) and artificial intelligence (AI) technologies emerge as potential aids for PD management, there's a gap in understanding how end users view these technologies. This research explores patient and neurologist perspectives on AI-assisted RMS.

methodsQualitative interviews and focus-groups were conducted with 27 persons with PD (PwPD) and six neurologists from Finland and Italy. The discussions covered traditional disease progression detection and the prospects of integrating AI and RMS. Sessions were recorded, transcribed, and underwent thematic analysis.

resultsThe study involved five individual interviews (four Italian participants and one Finnish) and six focus-groups (four Finnish and two Italian) with PwPD. Additionally, six neurologists (three from each country) were interviewed. Both cohorts voiced frustration with current monitoring methods due to their limited real-time detection capabilities. However, there was enthusiasm for AI-assisted RMS, contingent upon its value addition, user-friendliness, and preservation of the doctor-patient bond. While some PwPD had privacy and trust concerns, the anticipated advantages in symptom regulation seemed to outweigh these apprehensions. DISCUSSION: The study reveals a willingness among PwPD and neurologists to integrate RMS and AI into PD management. Widespread adoption requires these technologies to provide tangible clinical benefits, remain user-friendly, and uphold trust within the physician-patient relationship.

conclusionThis study offers insights into the potential drivers and barriers for adopting AI-assisted RMS in PD care. Recognizing these factors is pivotal for the successful integration of these digital health tools in PD management.

Indexed as

Artificial IntelligenceNeurologistsParkinson DiseaseAdultAgedAttitude of Health PersonnelDisease ProgressionFemaleFinlandFocus GroupsHumansInterviews as TopicItalyMaleMiddle AgedPhysician-Patient Relations

Identifiers

PMID38182935
PMCPMC11039525
OpenAlexW4390603631

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.