Evidence map›Paper›PMID 41726438›Full record

ArticleAMIA ... Annual Symposium proceedings. AMIA Symposium2024

Right Patient, Right Specialist, Right Time: Retrieval Augmented Generation for Specialty Referral Routing.

Fateme Nateghi Haredasht, Ethan Goh, Vishnu Ravi, Pooya Ashtari, Yixing Jiang, Nodir Yuldashev, François Grolleau, Robert J Gallo, Aaryan Shah, Evelyn Hur and 10 more

Abstract read
PubMed Full text at PMC
In one paragraph

Article in AMIA ... Annual Symposium proceedings. AMIA Symposium, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

20 authors.

Fateme Nateghi HaredashtStanford Center for Biomedical Informatics Research, Stanford University, Stanford, CA, USA.
Ethan GohStanford Center for Biomedical Informatics Research, Stanford University, Stanford, CA, USA.
Vishnu RaviStanford Mussallem Center for Biodesign, Stanford University, 318 Campus Drive, Stanford, 94305, CA, USA.
Pooya AshtariDepartment of Electrical Engineering (ESAT), STADIUS Center, KU Leuven, Belgium.
Yixing JiangDepartment of Biomedical Data Science, Stanford University, Stanford, CA, USA.
Nodir YuldashevStanford Center for Biomedical Informatics Research, Stanford University, Stanford, CA, USA.
François GrolleauStanford Center for Biomedical Informatics Research, Stanford University, Stanford, CA, USA.
Robert J GalloCenter for Innovation to Implementation, VA Palo Alto Health Care System, Palo Alto, CA, USA.
Aaryan ShahDepartment of Biomedical Data Science, Stanford University, Stanford, CA, USA.
Evelyn HurDepartment of Computer Science, Stanford University, Stanford, CA, USA.
Kanav ChopraDepartment of Computer and Information Sciences, Webster University, St. Louis, MO, USA.
Olivia JeeDivision of Primary Care and Population Health, Stanford University School of Medicine, Palo Alto, CA, USA.
Julie J LeeDivision of Primary Care and Population Health, Stanford University School of Medicine, Palo Alto, CA, USA.
Leah RosengausStanford Health Care, Stanford Medicine, Palo Alto, CA, USA.
Lena GiangStanford Health Care, Stanford Medicine, Palo Alto, CA, USA.
Kevin SchulmanStanford Clinical Excellence Research Center, Stanford University, Stanford, CA, USA.
Jason HomStanford University, School of Medicine, Stanford, CA, USA.
Arnold MilsteinStanford Clinical Excellence Research Center, Stanford University, Stanford, CA, USA.
Andrew Y NgDepartment of Computer Science, Stanford University, Stanford, CA, USA.
Jonathan H ChenStanford Center for Biomedical Informatics Research, Stanford University, Stanford, CA, USA.

Funding

Measuring and Predicting Appropriate Antibiotic Use to Combat Resistant BacteriaR01AI178121 · NIAID · STANFORD UNIVERSITY · PI JONATHAN H. CHEN · 2023 to 2026
$3.1M
NIAID NIH HHS R01 AI178121
6 · The paper itself

Abstract

We present an embedding-based retrieval system that automatically directs physician clinical questions to the most relevant specialist-curated question template, which is necessary for the specialist to provide a clinically relevant response. The system utilizes MPNet, a transformer-based model, to generate dense vector representations of both clinical queries and 24 predefined clinical templates. Given a clinical question, the system computes cosine similarity between the query and template embeddings to retrieve the most relevant matches. When validated against real-world, retrospective eConsults across five specialties, the system accurately identified the most relevant template in 87% of cases (success@1) and included it in the top three results 99% of the time (success@3). Automating specialty selection and clinical question referrals reduces the administrative burden on physicians, minimizes care delivery delays, and improves specialist responses by providing proper context.

Indexed as

Information Storage and RetrievalMedicineNatural Language ProcessingReferral and ConsultationSpecializationHumans

Identifiers

PMID41726438
PMCPMC12919621

What OpenQuestion holds

Textmetadata
Read underepoch 390

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.