Evidence map›Paper›PMID 38827075›Full record

ArticleAMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science2024

Voice-Enabled Response Analysis Agent (VERAA): Leveraging Large Language Models to Map Voice Responses in SDoH Survey.

Rishivardhan Krishnamoorthy, Vishal Nagarajan, Hayden Pour, Supreeth P Shashikumar, Aaron Boussina, Emilia Farcas, Shamim Nemati, Christopher S Josef

Abstract read
PubMed Full text at PMC
In one paragraph

Article in AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed, 1 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

1 citing paper in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

8 authors.

Rishivardhan KrishnamoorthyUniversity of California San Diego Health, Department of Biomedical Informatics, San Diego, CA.
Vishal NagarajanUniversity of California San Diego Health, Department of Biomedical Informatics, San Diego, CA.
Hayden PourUniversity of California San Diego Health, Department of Biomedical Informatics, San Diego, CA.
Supreeth P ShashikumarUniversity of California San Diego Health, Department of Biomedical Informatics, San Diego, CA.
Aaron BoussinaUniversity of California San Diego Health, Department of Biomedical Informatics, San Diego, CA.
Emilia FarcasQualcomm Institute, the San Diego division of the California Institute for Telecommunications and Information Technology (Calit2).
Shamim NematiUniversity of California San Diego Health, Department of Biomedical Informatics, San Diego, CA.
Christopher S JosefHealcisio, Inc., La Jolla, CA.

Funding

San Diego Biomedical Informatics Education & Research (SABER)T15LM011271 · NLM · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI SHAMIM NEMATI · 2012 to 2026
$9.7M
GeneRAlizable Sepsis Phenotyping (GRASP) using Electronic Health Records and Continuous Monitoring SensorsR35GM143121 · NIGMS · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI NEMATI, SHAMIM · 2021 to 2025
$2.2M
NIGMS NIH HHS R35 GM143121NLM NIH HHS T15 LM011271
6 · The paper itself

Abstract

Social Determinants of Health (SDoH) have been shown to have profound impacts on health-related outcomes, yet this data suffers from high rates of missingness in electronic health records (EHR). Moreover, limited English proficiency in the United States can be a barrier to communication with health care providers. In this study, we have designed a multilingual conversational agent capable of conducting SDoH surveys for use in healthcare environments. The agent asks questions in the patient's native language, translates responses into English, and subsequently maps these responses via a large language model (LLM) to structured options in a SDoH survey. This tool can be extended to a variety of survey instruments in either hospital or home settings, enabling the extraction of structured insights from free-text answers. The proposed approach heralds a shift towards more inclusive and insightful data collection, marking a significant stride in SDoH data enrichment for optimizing health outcome predictions and interventions.

Identifiers

PMID38827075
PMCPMC11141834

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.