Evidence map›Paper›PMID 38114970›Full record

ArticleBMC medical informatics and decision making2023

Acceptance and use of a clinical decision support system in musculoskeletal pain disorders - the SupportPrim project.

Fredrik Granviken, Ingebrigt Meisingset, Ottar Vasseljen, Kerstin Bach, Anita Formo Bones, Nina Elisabeth Klevanger

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in BMC medical informatics and decision making, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed, 2 pooled it
2.5field-weighted citation impact, top 10% 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

7 citing papers in PubMed, 2 syntheses or guidelines pooled it, 10 citations in OpenAlex.

  1. Pooled it
  2. Pooled it
  3. Trial
  4. Article
  5. 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

6 authors at 2 institutions in 1 country.

Fredrik GranvikenDepartment of Public Health and Nursing, Norwegian University of Science and Technology (NTNU), Postboks 8905, Trondheim, 7491, Norway. fredrik.granviken@ntnu.no.
Ingebrigt MeisingsetDepartment of Public Health and Nursing, Norwegian University of Science and Technology (NTNU), Postboks 8905, Trondheim, 7491, Norway.
Ottar VasseljenDepartment of Public Health and Nursing, Norwegian University of Science and Technology (NTNU), Postboks 8905, Trondheim, 7491, Norway.
Kerstin BachDepartment of Computer Science, Norwegian University of Science and Technology (NTNU), Trondheim, Norway.
Anita Formo BonesDepartment of Public Health and Nursing, Norwegian University of Science and Technology (NTNU), Postboks 8905, Trondheim, 7491, Norway.
Nina Elisabeth KlevangerDepartment of Public Health and Nursing, Norwegian University of Science and Technology (NTNU), Postboks 8905, Trondheim, 7491, Norway.
Norwegian University of Science and Technology · NOSt Olav's University Hospital · NO

Funding

Fond til etter- og videreutdanning av fysioterapeuter 118231Norges Forskningsråd 303331
6 · The paper itself

Abstract

backgroundWe have developed a clinical decision support system (CDSS) based on methods from artificial intelligence to support physiotherapists and patients in the decision-making process of managing musculoskeletal (MSK) pain disorders in primary care. The CDSS finds the most similar successful patients from the past to give treatment recommendations for a new patient. Using previous similar patients with successful outcomes to advise treatment moves management of MSK pain patients from one-size fits all recommendations to more individually tailored treatment. This study aimed to summarise the development and explore the acceptance and use of the CDSS for MSK pain patients.

methodsThis qualitative study was carried out in the Norwegian physiotherapy primary healthcare sector between October and November 2020, ahead of a randomised controlled trial. We included four physiotherapists and three of their patients, in total 12 patients, with musculoskeletal pain in the neck, shoulder, back, hip, knee or complex pain. We conducted semi-structured telephone interviews with all participants. The interviews were analysed using the Framework Method.

resultsOverall, both the physiotherapists and patients found the system acceptable and usable. Important findings from the analysis of the interviews were that the CDSS was valued as a preparatory and exploratory tool, facilitating the therapeutic relationship. However, the physiotherapists used the system mainly to support their previous and current practice rather than involving patients to a greater extent in decisions and learning from previous successful patients.

conclusionsThe CDSS was acceptable and usable to both the patients and physiotherapists. However, the system appeared not to considerably influence the physiotherapists' clinical reasoning and choice of treatment based on information from most similar successful patients. This could be due to a smaller than optimal number of previous patients in the CDSS or insufficient clinical implementation. Extensive training of physiotherapists should not be underestimated to build understanding and trust in CDSSs.

Indexed as

Decision Support Systems, ClinicalMusculoskeletal PainArtificial IntelligenceHumansPhysical Therapy ModalitiesAcceptanceCDSSClinical Decision Support SystemInterviewMusculoskeletal pain disordersPhysiotherapyPrimary CareQualitativeUsability

Identifiers

PMID38114970
PMCPMC10731802
OpenAlexW4389949211

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.