JMIR Publications

ABSTRACT

Background:

Conversational agents (CAs), also known as chatbots, are digital dialog systems that enable people to have a text-based, speech-based, or nonverbal conversation with a computer or another machine on the basis of natural language via an interface. The use of CAs offers new opportunities and a variety of benefits for healthcare. However, they are not yet ubiquitous in daily practice. Nevertheless, research regarding the implementation of CAs in healthcare – with a particular focus on factors influencing implementation outcomes – has grown tremen-dously in recent years.

Objective:

This review aims to present a synthesis of the factors that facilitate or hinder the implementa-tion of CAs from the perspective of patients and healthcare professionals. In particular, it focuses on the early implementation outcomes of acceptability, acceptance, and adoption as cornerstones of later implementation success.

Methods:

We performed an integrative review (IR). To identify the relevant literature, a broad literature search was conducted in June 2021 with no date limits and by using all fields in PubMed, Cochrane Library, Web of Science, LIVIVO, and PsycINFO. To keep the IR current, a second search was conducted in March 2022. To identify as many eligible primary sources as possible, we used a snowballing approach by searching in the reference lists and conducted a hand search. Influencing factors for the acceptability, acceptance, and adoption of CAs in healthcare were coded in a parallel deductive and inductive approach, which was informed by current technology acceptance and adoption models. Finally, the factors were synthesized in a thematic map.

Results:

Seventy-six studies were included in the review. We identified influencing factors related to 4 core UTAUT and UTAUT2 factors (performance expectancy, effort expectancy, facilitating conditions, and hedonic motivation), with most studies underlining the relevance of performance and effort expectancy. To meet the particularities of the healthcare context, we redefined the 3 UTAUT2 factors social influence, habit, and price value. We identified six other influencing factors: perceived risk, trust, anthropomorphism, health issue, working alliance, and user characteristics.

Conclusions:

This review shows manifold factors influencing the acceptability, acceptance, and adoption of CAs in healthcare. Knowledge of these factors is fundamental for implementation planning. The findings of this review can therefore serve as a basis for future studies to develop appropriate implementation strategies. This review provides a further empirical test of current technology acceptance and adoption models and identifies areas where additional research is necessary.