Towards a Domain Expert Evaluation Framework for Conversational Search in Healthcare

The rise of large language models for client-facing conversational search in healthcare necessitates evaluation frameworks that enable the assessment and comparison of these tools.Most such frameworks centre around the automated calculation of performancerelated metrics and benchmarks.Though necessary, this focus fails to account for the human factors that impact the development, use, and adoption of these systems, as well as the factors specific to the healthcare context.Human evaluation frameworks attempt to address these drawbacks, but few such frameworks have been developed so far, and even fewer are those based on expert insight.In this work, we conduct semi-structured interviews with eleven healthcare professionals in health lifestyle care.From these interviews, we contribute a two-part healthcare domain expert evaluation framework, (K) Knowledge and (I) Interaction, which organises seven evaluation metrics.Our results reveal key understudied metrics for evaluation like (I1) Context-Seeking, (I2) Empathy, and (I3) Trustworthiness. CCS Concepts Human-centered computing HCI theory, concepts and models.

Authors

Institutions

Publication Details

Published
2025-04-25
DOI
https://doi.org/10.1145/3706599.3719675
Citations
1
Primary Topic
AI in Service Interactions
Type
article
Field-Weighted Citation Impact
2.48

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Towards a Domain Expert Evaluation Framework for Conversational Search in Healthcare

Evangelos Niforatos, Chadha Degachi, Gerd Kortuem, Ujjayan Dhar
1 citations
AI in Service Interactions
2.48
article

Towards a Domain Expert Evaluation Framework for Conversational Search in Healthcare

Evangelos Niforatos, Chadha Degachi, Gerd Kortuem, Ujjayan Dhar
article en
1 citations

Abstract

The rise of large language models for client-facing conversational search in healthcare necessitates evaluation frameworks that enable the assessment and comparison of these tools.Most such frameworks centre around the automated calculation of performancerelated metrics and benchmarks.Though necessary, this focus fails to account for the human factors that impact the development, use, and adoption of these systems, as well as the factors specific to the healthcare context.Human evaluation frameworks attempt to address these drawbacks, but few such frameworks have been developed so far, and even fewer are those based on expert insight.In this work, we conduct semi-structured interviews with eleven healthcare professionals in health lifestyle care.From these interviews, we contribute a two-part healthcare domain expert evaluation framework, (K) Knowledge and (I) Interaction, which organises seven evaluation metrics.Our results reveal key understudied metrics for evaluation like (I1) Context-Seeking, (I2) Empathy, and (I3) Trustworthiness. CCS Concepts Human-centered computing HCI theory, concepts and models.

Delft University of Technology (NL)
Nederlandse Organisatie voor Wetenschappelijk Onderzoek
Quality Education
Openalex Percentile: Top 11%
AI in Service Interactions
2.48
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.