Evidence map›Paper›PMID 30898118›Full record

ArticleOrphanet journal of rare diseases2019

Can a decision support system accelerate rare disease diagnosis? Evaluating the potential impact of Ada DX in a retrospective study.

Simon Ronicke, Martin C Hirsch, Ewelina Türk, Katharina Larionov, Daphne Tientcheu, Annette D Wagner

Abstract read
In one paragraph

Article in Orphanet journal of rare diseases, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 66 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
66citing papers in PubMed, 2 pooled it
–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

66 citing papers in PubMed, 2 syntheses or guidelines pooled it.

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  8. GEN-KnowRD: Reframing AI for Rare Disease Recognition.medRxiv : the preprint server for health sciences · 2026
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6 more citing papers are in PubMed but not listed here.

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.

Simon RonickeOutpatient clinic for rare inflammatory systemic diseases, Department of Nephrology, Hannover Medical School, Carl-Neuberg-Straße 1, Hannover, 30625, Germany. simon@ronicke.de.ORCID 0000-0001-8822-4268
Martin C HirschAda Health GmbH, Adalbertstraße 20, Berlin, 10997, Germany.
Ewelina TürkAda Health GmbH, Adalbertstraße 20, Berlin, 10997, Germany.
Katharina LarionovOutpatient clinic for rare inflammatory systemic diseases, Department of Nephrology, Hannover Medical School, Carl-Neuberg-Straße 1, Hannover, 30625, Germany.
Daphne TientcheuOutpatient clinic for rare inflammatory systemic diseases, Department of Nephrology, Hannover Medical School, Carl-Neuberg-Straße 1, Hannover, 30625, Germany.
Annette D WagnerOutpatient clinic for rare inflammatory systemic diseases, Department of Nephrology, Hannover Medical School, Carl-Neuberg-Straße 1, Hannover, 30625, Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundRare disease diagnosis is often delayed by years. A primary factor for this delay is a lack of knowledge and awareness regarding rare diseases. Probabilistic diagnostic decision support systems (DDSSs) have the potential to accelerate rare disease diagnosis by suggesting differential diagnoses for physicians based on case input and incorporated medical knowledge. We examine the DDSS prototype Ada DX and assess its potential to provide accurate rare disease suggestions early in the course of rare disease cases.

resultsAda DX suggested the correct disease earlier than the time of clinical diagnosis among the top five fit disease suggestions in 53.8% of cases (50 of 93), and as the top fit disease suggestion in 37.6% of cases (35 of 93). The median advantage of correct disease suggestions compared to the time of clinical diagnosis was 3 months or 50% for top five fit and 1 month or 21% for top fit. The correct diagnosis was suggested at the first documented patient visit in 33.3% (top 5 fit), and 16.1% of cases (top fit), respectively. Wilcoxon signed-rank test shows a significant difference between the time to clinical diagnosis and the time to correct disease suggestion for both top five fit and top fit (z-score -6.68, respective -5.71, α=0.05, p-value <0.001).

conclusionAda DX provided accurate rare disease suggestions in most rare disease cases. In many cases, Ada DX provided correct rare disease suggestions early in the course of the disease, sometimes at the very beginning of a patient journey. The interpretation of these results indicates that Ada DX has the potential to suggest rare diseases to physicians early in the course of a case. Limitations of this study derive from its retrospective and unblinded design, data input by a single user, and the optimization of the knowledge base during the course of the study. Results pertaining to the system's accuracy should be interpreted cautiously. Whether the use of Ada DX reduces the time to diagnosis in rare diseases in a clinical setting should be validated in prospective studies.

Indexed as

AdultDecision Support Systems, ClinicalFemaleHumansMaleMiddle AgedRare DiseasesReproducibility of ResultsRetrospective StudiesSoftwareTime FactorsAda DXArtificial intelligenceDiagnostic decision support systemProbabilistic reasoningRare disease diagnosisTime to diagnosis

Identifiers

PMID30898118
PMCPMC6427854

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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.