Evidence map›Paper›PMID 37093626›Full record

ArticleJMIR perioperative medicine2023

Toward Enhanced Clinical Decision Support for Patients Undergoing a Hip or Knee Replacement: Focus Group and Interview Study With Surgeons.

Sabrina Grant, Emma Tonkin, Ian Craddock, Ashley Blom, Michael Holmes, Andrew Judge, Alessandro Masullo, Miquel Perello Nieto, Hao Song, Michael Whitehouse and 2 more

Abstract read
In one paragraph

Article in JMIR perioperative medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
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

1 citing paper in PubMed.

  1. 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

12 authors.

Sabrina Grant *Musculoskeletal Research Unit, University of Bristol, Southmead Hospital, Bristol Medical School, Bristol, United Kingdom.ORCID https://orcid.org/0000-0003-0148-9103
Emma Tonkin *Digital Health, Faculty of Engineering, Bristol, United Kingdom.ORCID https://orcid.org/0000-0001-7405-4982
Ian CraddockDigital Health, Faculty of Engineering, Bristol, United Kingdom.ORCID https://orcid.org/0000-0001-6552-8541
Ashley BlomFaculty of Medicine, Dentistry and Health, University of Sheffield, Sheffield, United Kingdom.ORCID https://orcid.org/0000-0002-9940-1095
Michael HolmesDigital Health, Faculty of Engineering, Bristol, United Kingdom.ORCID https://orcid.org/0000-0003-1067-5282
Andrew JudgeMusculoskeletal Research Unit, University of Bristol, Southmead Hospital, Bristol Medical School, Bristol, United Kingdom.ORCID https://orcid.org/0000-0003-3015-0432
Alessandro MasulloDigital Health, Faculty of Engineering, Bristol, United Kingdom.ORCID https://orcid.org/0000-0002-6510-835X
Miquel Perello NietoDigital Health, Faculty of Engineering, Bristol, United Kingdom.ORCID https://orcid.org/0000-0001-8925-424X
Hao SongDigital Health, Faculty of Engineering, Bristol, United Kingdom.ORCID https://orcid.org/0000-0003-0876-485X
Michael WhitehouseMusculoskeletal Research Unit, University of Bristol, Southmead Hospital, Bristol Medical School, Bristol, United Kingdom.ORCID https://orcid.org/0000-0003-2436-9024
Peter FlachIntelligent Systems Laboratory, Department of Computer Science, University of Bristol, Bristol, United Kingdom.ORCID https://orcid.org/0000-0001-6857-5810
Rachael Gooberman-HillMusculoskeletal Research Unit, University of Bristol, Southmead Hospital, Bristol Medical School, Bristol, United Kingdom.ORCID https://orcid.org/0000-0003-3353-2882

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe current assessment of recovery after total hip or knee replacement is largely based on the measurement of health outcomes through self-report and clinical observations at follow-up appointments in clinical settings. Home activity-based monitoring may improve assessment of recovery by enabling the collection of more holistic information on a continuous basis.

objectiveThis study aimed to introduce orthopedic surgeons to time-series analyses of patient activity data generated from a platform of sensors deployed in the homes of patients who have undergone primary total hip or knee replacement and understand the potential role of these data in postoperative clinical decision-making.

methodsOrthopedic surgeons and registrars were recruited through a combination of convenience and snowball sampling. Inclusion criteria were a minimum required experience in total joint replacement surgery specific to the hip or knee or familiarity with postoperative recovery assessment. Exclusion criteria included a lack of specific experience in the field. Of the 9 approached participants, 6 (67%) orthopedic surgeons and 3 (33%) registrars took part in either 1 of 3 focus groups or 1 of 2 interviews. Data were collected using an action-based approach in which stimulus materials (mock data visualizations) provided imaginative and creative interactions with the data. The data were analyzed using a thematic analysis approach.

resultsEach data visualization was presented sequentially followed by a discussion of key illustrative commentary from participants, ending with a summary of key themes emerging across the focus group and interview data set.

conclusionsThe limitations of the evidence are as follows. The data presented are from 1 English hospital. However, all data reflect the views of surgeons following standard national approaches and training. Although convenience sampling was used, participants' background, skills, and experience were considered heterogeneous. Passively collected home monitoring data offered a real opportunity to more objectively characterize patients' recovery from surgery. However, orthopedic surgeons highlighted the considerable difficulty in navigating large amounts of complex data within short medical consultations with patients. Orthopedic surgeons thought that a proposed dashboard presenting information and decision support alerts would fit best with existing clinical workflows. From this, the following guidelines for system design were developed: minimize the risk of misinterpreting data, express a level of confidence in the data, support clinicians in developing relevant skills as time-series data are often unfamiliar, and consider the impact of patient engagement with data in the future. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.1136/bmjopen-2018-021862.

Indexed as

arthroplastyclinical decision-makinghip replacementhome monitoringknee replacementorthopedic surgerypostoperative follow-upvideowearables

Identifiers

PMID37093626
PMCPMC10167586

What OpenQuestion holds

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LicenceCC BY
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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.