Evidence map›Paper›PMID 31682590›Full record

ArticleJMIR medical informatics2019

Differential Diagnosis Assessment in Ambulatory Care With an Automated Medical History-Taking Device: Pilot Randomized Controlled Trial.

Adrien Jean-Pierre Schwitzguebel, Clarisse Jeckelmann, Roberto Gavinio, Cécile Levallois, Charles Benaïm, Hervé Spechbach

Open access · goldAbstract read
In one paragraph

Article in JMIR medical informatics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.

0numbers the graph read from it
0cells of the map it votes in
16citing papers in PubMed
2.5field-weighted citation impact, top 11% 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

16 citing papers in PubMed, 24 citations in OpenAlex.

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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.

Adrien Jean-Pierre Schwitzguebel *Division of Physical Medicine and Rehabilitation, Department of Rheumatology, Lausanne University Hospital, Lausanne, Switzerland.ORCID https://orcid.org/0000-0002-8212-1812
Clarisse Jeckelmann *Faculty of Medicine, University of Lausanne, Lausanne, Switzerland.ORCID https://orcid.org/0000-0002-3439-519X
Roberto GavinioAmbulatory Emergency Care Unit, Department of Primary Care Medicine, Geneva University Hospitals, Geneva, Switzerland.ORCID https://orcid.org/0000-0001-8741-2654
Cécile LevalloisAmbulatory Emergency Care Unit, Department of Primary Care Medicine, Geneva University Hospitals, Geneva, Switzerland.ORCID https://orcid.org/0000-0002-1345-260X
Charles Benaïm *Division of Physical Medicine and Rehabilitation, Department of Rheumatology, Lausanne University Hospital, Lausanne, Switzerland.ORCID https://orcid.org/0000-0002-8999-0674
Hervé Spechbach *Ambulatory Emergency Care Unit, Department of Primary Care Medicine, Geneva University Hospitals, Geneva, Switzerland.ORCID https://orcid.org/0000-0002-0755-1257
University Hospital of Geneva · CHUniversity of Lausanne · CH

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundAutomated medical history-taking devices (AMHTDs) are emerging tools with the potential to increase the quality of medical consultations by providing physicians with an exhaustive, high-quality, standardized anamnesis and differential diagnosis.

objectiveThis study aimed to assess the effectiveness of an AMHTD to obtain an accurate differential diagnosis in an outpatient service.

methodsWe conducted a pilot randomized controlled trial involving 59 patients presenting to an emergency outpatient unit and suffering from various conditions affecting the limbs, the back, and the chest wall. Resident physicians were randomized into 2 groups, one assisted by the AMHTD and one without access to the device. For each patient, physicians were asked to establish an exhaustive differential diagnosis based on the anamnesis and clinical examination. In the intervention group, residents read the AMHTD report before performing the anamnesis. In both the groups, a senior physician had to establish a differential diagnosis, considered as the gold standard, independent of the resident's opinion and AMHTD report.

resultsA total of 29 patients were included in the intervention group and 30 in the control group. Differential diagnosis accuracy was higher in the intervention group (mean 75%, SD 26%) than in the control group (mean 59%, SD 31%; P=.01). Subgroup analysis showed a between-group difference of 3% (83% [17/21]-80% [14/17]) for low complexity cases (1-2 differential diagnoses possible) in favor of the AMHTD (P=.76), 31% (87% [13/15]-56% [18/33]) for intermediate complexity (3 differential diagnoses; P=.02), and 24% (63% [34/54]-39% [14/35]) for high complexity (4-5 differential diagnoses; P=.08). Physicians in the intervention group (mean 4.3, SD 2) had more years of clinical practice compared with the control group (mean 5.5, SD 2; P=.03). Differential diagnosis accuracy was negatively correlated to case complexity (r=0.41; P=.001) and the residents' years of practice (r=0.04; P=.72). The AMHTD was able to determine 73% (SD 30%) of correct differential diagnoses. Patient satisfaction was good (4.3/5), and 26 of 29 patients (90%) considered that they were able to accurately describe their symptomatology. In 8 of 29 cases (28%), residents considered that the AMHTD helped to establish the differential diagnosis.

conclusionsThe AMHTD allowed physicians to make more accurate differential diagnoses, particularly in complex cases. This could be explained not only by the ability of the AMHTD to make the right diagnoses, but also by the exhaustive anamnesis provided.

Indexed as

clinical applications softwarecomputer-assisteddecision makingdifferential diagnosisgeneral practitionershospital outpatient clinicspatient engagement

Identifiers

PMID31682590
PMCPMC6913752
OpenAlexW2971458922

What OpenQuestion holds

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Registered trials

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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.