Evidence map›Paper›PMID 34337980›Full record

ArticleJournal of health services research & policy2022

Patient preferences for use of virtual consultations in an orthopaedic rehabilitation setting: Results from a discrete choice experiment.

Anthony W Gilbert, Emmanouil Mentzakis, Carl R May, Maria Stokes, Jeremy Jones

Open access · hybridAbstract read
In one paragraph

Article in Journal of health services research & policy, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed, 2 pooled it
3.8field-weighted citation impact, top 7% 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, 2 syntheses or guidelines pooled it, 21 citations in OpenAlex.

  1. Pooled it
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  6. Patient Preferences for Telerehabilitation Compared to In-Person Physiotherapy: A Binary Discrete Choice Experiment.Physiotherapy research international : the journal for researchers and clinicians in physical therapy · 2025
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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

5 authors at 3 institutions in 1 country.

Anthony W GilbertClinical Research Physiotherapist, Therapies Department, Royal National Orthopaedic Hospital, UK and PhD Student, School of Health Sciences, 7423University of Southampton, UK.ORCID 0000-0003-2526-8057
Emmanouil MentzakisAssociate Professor in Economics, Economics Department, Faculty of Economic, Social and Political Sciences, University of Southampton, UK.
Carl R MayProfessor of Medical Sociology, Faculty of Public Health and Policy, London School of Hygiene and Tropical Medicine, London, UK, Professor of Medical Sociology, NIHR Applied Research Collaboration, North Thames, UK.
Maria StokesProfessor of Musculoskeletal Rehabilitation, School of Health Sciences, University of Southampton, UK, Professor of Musculoskeletal Rehabilitation, Southampton NIHR Biomedical Research Centre, Southampton, UK and Professor of Musculoskeletal Rehabilitation, NIHR Applied Research Collaboration, Wessex, UK.
Jeremy JonesPrincipal Research Fellow in Health Economics, School of Health Sciences, 7423University of Southampton, UK.
University of Southampton · GBLondon School of Hygiene & Tropical Medicine · GBRoyal National Orthopaedic Hospital · GB

Funding

Department of Health
6 · The paper itself

Abstract

objectiveVirtual Consultations may reduce the need for face-to-face outpatient appointments, thereby potentially reducing the cost and time involved in delivering health care. This study reports a discrete choice experiment (DCE) that identifies factors that influence patient preferences for virtual consultations in an orthopaedic rehabilitation setting.

methodsPrevious research from the CONNECT (Care in Orthopaedics, burdeN of treatmeNt and the Effect of Communication Technology) Project and best practice guidance informed the development of our DCE. An efficient fractional factorial design with 16 choice scenarios was created that identified all main effects and partial two-way interactions. The design was divided into two blocks of eight scenarios each, to reduce the impact of cognitive fatigue. Data analysis were conducted using binary logit regression models.

resultsSixty-one paired response sets (122 subjects) were available for analysis. DCE factors (whether the therapist is known to the patient, duration of appointment, time of day) and demographic factors (patient qualifications, access to equipment, difficulty with activities, multiple health issues, travel costs) were significant predictors of preference. We estimate that a patient is less than 1% likely to prefer a virtual consultation if the patient has a degree, is without access to the equipment and software to undertake a virtual consultation, does not have difficulties with day-to-day activities, is undergoing rehabilitation for one problem area, has to pay less than £5 to travel, is having a consultation with a therapist not known to them, in 1 weeks' time, lasting 60 minutes, at 2 pm. We have developed a simple conceptual model to explain how these factors interact to inform preference, including patients' access to resources, context for the consultation and the requirements of the consultation.

conclusionsThis conceptual model provides the framework to focus attention towards factors that might influence patient preference for virtual consultations. Our model can inform the development of future technologies, trials, and qualitative work to further explore the mechanisms that influence preference.

Indexed as

OrthopedicsPatient PreferenceHumansInformation TechnologyReferral and ConsultationResearch Designdiscrete choice experimentorthopaedicsVirtual consultation

Identifiers

PMID34337980
PMCPMC8772015
OpenAlexW3190071437

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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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.