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

Article Article scientifique Article Differential Diagnosis Assessment in Ambulatory Care With an Automated Medical History – Taking Device: Pilot Randomized Controlled Trial eng 2019-11-04 2019-11-04 author Schwitzguebel Adrien Jean-Pierre 0000-0002-8212-1812 author Jeckelmann Clarisse 0000-0002-3439-519X author Gavinio Roberto Irèneo 0000-0001-8741-2654 107681 author Levallois Cécile 0000-0002-1345-260X 893070 author Benaïm Charles 0000-0002-8999-0674 author Spechbach Hervé 0000-0002-0755-1257 516540 e14044 10.2196/14044 31682590 2291-9694 PMC6913752 http://medinform.jmir.org/2019/4/e14044/ Background: Automated 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. Objective: This study aimed to assess the effectiveness of an AMHTD to obtain an accurate differential diagnosis in an outpatient service. Methods: We 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. Results: A 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. Conclusions: The 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. Clinical applications software Computer-assisted Decision making Differential diagnosis General practitioners Hospital outpatient clinics Patient engagement Dewey 613 201 Institut de médecine de premier recours 22A9 false JMIR medical informatics 7 4 8067542b-7c3b-4515-9f70-ccdab7be9348 Article (Published version) Public document.pdf 330074 application/pdf CC BY-4.0 916c4030-4463-4e8d-9a69-c10d32b64aa1 Appendix Public medinform_v7i4e14044_app1.pdf 28542232 application/pdf 70ab4510-6014-49e7-8ff2-f93d2e428a14 Appendix Public medinform_v7i4e14044_app2.pdf 934126 application/pdf bd9d4b52-c1af-4e11-94ad-c2c6a9a22eb0 Appendix Public medinform_v7i4e14044_app3.pdf 484970 application/pdf ab0ec3f7-8f30-403f-87b1-1ef61ce656ae Appendix Public medinform_v7i4e14044_app4.pdf 19195 application/pdf 24033bcc-74e0-4ea4-8ce0-9e8a9b4a8a1d Appendix Public medinform_v7i4e14044_app5.pdf 84951 application/pdf 60bb5469-3846-4aa6-98d0-b6b604e707ff Appendix Public medinform_v7i4e14044_app6.pdf 84602 application/pdf openAccess Faculté de médecine / Section de médecine clinique / Département de santé et médecine communautaires / Institut de médecine de premier recours

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Differential Diagnosis Assessment in Ambulatory Care With an Automated Medical History – Taking Device: Pilot Randomized Controlled Trial	JMIR medical informatics			2019	128	121

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Levallois, Cécile