Acute paediatrics tele-support for caregivers in Singapore: an initial experience with a prototype Chatbot: UPAL

Ganapathy, Sashikumar; Chang, Serena Su Ying; Tan, Joanne Mui Ching; Lim, Cynthia; Ng, Kee Chong

doi:10.11622/smedj.2021119

Cited by 2 publications

(3 citation statements)

References 17 publications

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“…Human involvement mainly focused on a single user who interacts with the CA (160/173, 92.5%). In total, 4.6% (8/173) of the CAs involved a clinician [ 30 - 35 ]. Either the clinician was enabled to access the conversation protocol or they were able to interact with the patient through the chat interface.…”

Section: Resultsmentioning

confidence: 99%

“…Only 14.5% (25/173) used sentiment analysis techniques (there was no information for 3/173, 1.7%). In the personality dimension, we recognized that 86.7% (150/173) of the CAs were implemented with a simple personality, and 11% [30][31][32][33][34][35]. Either the clinician was enabled to access the conversation protocol or they were able to interact with the patient through the chat interface.…”

Section: Agent Appearancementioning

confidence: 99%

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Developing a Technical-Oriented Taxonomy to Define Archetypes of Conversational Agents in Health Care: Literature Review and Cluster Analysis

Denecke¹,

May²

2023

J Med Internet Res

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Background The evolution of artificial intelligence and natural language processing generates new opportunities for conversational agents (CAs) that communicate and interact with individuals. In the health domain, CAs became popular as they allow for simulating the real-life experience in a health care setting, which is the conversation with a physician. However, it is still unclear which technical archetypes of health CAs can be distinguished. Such technical archetypes are required, among other things, for harmonizing evaluation metrics or describing the landscape of health CAs. Objective The objective of this work was to develop a technical-oriented taxonomy for health CAs and characterize archetypes of health CAs based on their technical characteristics. Methods We developed a taxonomy of technical characteristics for health CAs based on scientific literature and empirical data and by applying a taxonomy development framework. To demonstrate the applicability of the taxonomy, we analyzed the landscape of health CAs of the last years based on a literature review. To form technical design archetypes of health CAs, we applied a k-means clustering method. Results Our taxonomy comprises 18 unique dimensions corresponding to 4 perspectives of technical characteristics (setting, data processing, interaction, and agent appearance). Each dimension consists of 2 to 5 characteristics. The taxonomy was validated based on 173 unique health CAs that were identified out of 1671 initially retrieved publications. The 173 CAs were clustered into 4 distinctive archetypes: a text-based ad hoc supporter; a multilingual, hybrid ad hoc supporter; a hybrid, single-language temporary advisor; and, finally, an embodied temporary advisor, rule based with hybrid input and output options. Conclusions From the cluster analysis, we learned that the time dimension is important from a technical perspective to distinguish health CA archetypes. Moreover, we were able to identify additional distinctive, dominant characteristics that are relevant when evaluating health-related CAs (eg, input and output options or the complexity of the CA personality). Our archetypes reflect the current landscape of health CAs, which is characterized by rule based, simple systems in terms of CA personality and interaction. With an increase in research interest in this field, we expect that more complex systems will arise. The archetype-building process should be repeated after some time to check whether new design archetypes emerge.

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Section: Resultsmentioning

confidence: 99%

Section: Agent Appearancementioning

confidence: 99%

Developing a Technical-Oriented Taxonomy to Define Archetypes of Conversational Agents in Health Care: Literature Review and Cluster Analysis

Denecke¹,

May²

2023

J Med Internet Res

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“…Robust against cyber attacks Penetration testing Ethical hacking [12] Assessing technical errors (including language understanding and response generation [5]) Providing information on data security and processing including information on use of third part tools Patient safety Who is the user? Which safe-critical situations might occur while interacting with the system?…”

Section: System Safetymentioning

confidence: 99%

Towards Safe Conversational Agents in Healthcare

Denecke

2023

Caring Is Sharing – Exploiting the Value in Data for Health and Innovation

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Conversational agents (CA) are becoming very popular to deliver digital health interventions. These dialog-based systems are interacting with patients using natural language which might lead to misunderstandings and misinterpretations. To avoid patient harm, safety of health CA has to be ensured. This paper raises awareness on safety when developing and distributing health CA. For this purpose, we identify and describe facets of safety and make recommendations for ensuring safety in health CA. We distinguish three facets of safety: 1) system safety, 2) patient safety, and 3) perceived safety. System safety comprises data security and privacy which has to be considered when selecting technologies and developing the health CA. Patient safety is related to risk monitoring and risk management, to adverse events and content accuracy. Perceived safety concerns a user’s perception of the level of danger and user‘s level of comfort during the use. The latter can be supported when data security is guaranteed and relevant information on the system and its capabilities are provided.

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Acute paediatrics tele-support for caregivers in Singapore: an initial experience with a prototype Chatbot: UPAL

Cited by 2 publications

References 17 publications

Developing a Technical-Oriented Taxonomy to Define Archetypes of Conversational Agents in Health Care: Literature Review and Cluster Analysis

Developing a Technical-Oriented Taxonomy to Define Archetypes of Conversational Agents in Health Care: Literature Review and Cluster Analysis

Towards Safe Conversational Agents in Healthcare

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