ArticleJMIR formative research2025
Differential Diagnosis Assessment in Ambulatory Care With a Digital Health History Device: Pseudorandomized Study.
Article in JMIR formative research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT03901495 (Differential Diagnosis Assessment in Ambulatory Care With an Automated Medical History-Taking Device), which is not on this map. Not yet cited in PubMed.
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The trial behind it
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Differential Diagnosis Assessment in Ambulatory Care With an Automated Medical History-Taking Device: A Confirmatory Pseudo-Randomized Study
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3 authors.
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Abstract
backgroundDigital health history devices represent a promising wave of digital tools with the potential to enhance the quality and efficiency of medical consultations. They achieve this by providing physicians with standardized, high-quality patient history summaries and facilitating the development of differential diagnoses (DDs) before consultation, while also engaging patients in the diagnostic process.
objectiveThis study evaluates the efficacy of one such digital health history device, diagnosis and anamnesis (DIANNA), in assisting with the formulation of appropriate DDs in an outpatient setting.
methodsA pseudorandomized controlled trial was conducted with 101 patients seeking care at the University Hospital Geneva emergency outpatient department. Participants presented with various conditions affecting the limbs, back, and chest. The first 51 patients were assigned to the control group, while the subsequent 50 formed the intervention group. In the control group, physicians developed DD lists based on traditional history-taking and clinical examination. In the intervention group, physicians reviewed DIANNA-generated DD reports before interacting with the patient. In both groups, a senior physician independently formulated a DD list, serving as the gold standard for comparison.
resultsThe study findings indicate that DIANNA use was associated with a notable improvement in DD accuracy (mean 79.3%, SD 24%) compared with the control group (mean 70.5%, SD 33%; P=.01). Subgroup analysis revealed variations in effectiveness based on case complexity: low-complexity cases (1-2 possible DDs) showed 8% improvement in the intervention group (P=.08), intermediate-complexity cases (3 possible DDs) showed 17% improvement (P=.03), and high-complexity cases (4-5 possible DDs) showed 15% improvement (P=.92). The intervention was not superior to the control in low-complexity cases (P=.08) or high-complexity cases (P=.92). Overall, DIANNA successfully determined appropriate DDs in 81.6% of cases, and physicians reported that it helped establish the correct DD in 26% of cases.
conclusionsThe study suggests that DIANNA has the potential to support physicians in formulating more precise DDs, particularly in intermediate-complexity cases. However, its effectiveness varied by case complexity and further validation is needed to assess its full clinical impact. These findings highlight the potential role of digital health history devices such as DIANNA in improving clinical decision-making and diagnostic accuracy in medical practice.
trial registrationClinicalTrials.gov NCT03901495; https://clinicaltrials.gov/study/NCT03901495.
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