Using computer agents to explain medical documents to patients with low health literacy

Abstract

Objective

Patients are commonly presented with complex documents that they have difficulty understanding. The objective of this study was to design and evaluate an animated computer agent to explain research consent forms to potential research participants.

Methods

Subjects were invited to participate in a simulated consent process for a study involving a genetic repository. Explanation of the research consent form by the computer agent was compared to explanation by a human and a self-study condition in a randomized trial. Responses were compared according to level of health literacy.

Results

Participants were most satisfied with the consent process and most likely to sign the consent form when it was explained by the computer agent, regardless of health literacy level. Participants with adequate health literacy demonstrated the highest level of comprehension with the computer agent-based explanation compared to the other two conditions. However, participants with limited health literacy showed poor comprehension levels in all three conditions. Participants with limited health literacy reported several reasons, such as lack of time constraints, ability to re-ask questions, and lack of bias, for preferring the computer agent-based explanation over a human-based one.

Conclusion

Animated computer agents can perform as well as or better than humans in the administration of informed consent.

Practice implications

Animated computer agents represent a viable method for explaining health documents to patients.

Introduction

Face-to-face encounters with a health provider – in conjunction with written instructions – remains one of the best methods for communicating health information to patients in general, but especially those with low health literacy [1], [2], [3], [4]. Face-to-face consultation is effective because providers can use verbal and nonverbal behaviors, such as head nods, hand gesture, eye gaze cues and facial displays to communicate factual information to patients, as well as to communicate empathy [5] and immediacy [6] to elicit patient trust. Face-to-face conversation also allows providers to make their communication more explicitly interactive by asking patients to do, write, say, or show something that demonstrates their agreement and understanding [7]. Finally, face-to-face interaction allows providers to dynamically assess a patient's level of understanding based on the patient's verbal and nonverbal behavior and to repeat or elaborate information as necessary [8].

However, there are several pervasive problems that limit a clinician's capacity to communicate effectively. Providers can only spend a limited amount of time with each patient [9]. Time pressures can result in patients feeling too intimidated to ask questions. Another problem is that of “fidelity”: providers do not always perform in accordance with recommended guidelines, resulting in significant variation in the delivery of health information.

Given the efficacy of face-to-face consultation, a promising approach for conveying health information to patients with limited health literacy is the use of computer animated agents that simulate face-to-face conversation with a provider [10]. The benefits of using conversational agents include: use of verbal and nonverbal conversational behaviors that signify understanding and mark significance, and convey information in redundant channels of information (e.g., hand gestures, such as pointing, facial display of emotion, and eye gaze); use of of verbal and nonverbal communicative behaviors to maximize comprehension; use of verbal and nonverbal communicative behaviors used by providers to establish trust and rapport with their patients in order to increase satisfaction and adherence to treatment regimens [11]; adaptation of their messages to the particular needs of patients and to the immediate context of the conversation; and provision of health information in a consistent manner and in a low-pressure environment in which patients are free to take as much time as they need to thoroughly understand it. This latter point is particularly important as health providers frequently fail to elicit patients’ questions, and patients with limited health literacy are even less likely than others to ask questions [12].

According to the 2004 National Assessment of Adult Literacy, fully 36% of American adults have limited health literacy skills, with even higher rates of prevalence among patients with chronic diseases, those who are older, minorities, and those who have lower levels of education [13], [14]. Seminal reports about the problem of health literacy include a sharp critique of current norms for overly complex documents in health care such as informed consent [15], [16]. Indeed, a significant and growing body of research has brought attention to the ethical and health impact of overly complex documents in healthcare [17], [18]. Computer agents may provide a particularly effective solution for addressing this problem, by having the agents describe health documents to patients using exemplary communication techniques for patients with limited health literacy and by providing this information in a context unconstrained by time pressures.

Informed consent agreements for individuals to participate in medical research represent a particular challenge for individuals with limited health literacy to understand, since they typically encode many subtle and counter-intuitive legal and medical concepts. They are often written at a reading level that is far beyond the capacity of most subjects [19], [20]. Researchers may not have the resources to ensure that participants understand all the terms of the consent agreement. Indeed, many potential research subjects sign consent forms that they do not understand [21], [22], [23].

Consequently, we modified an existing computer agent framework designed for health counseling [10], [11] to provide explanation of health documents such as research informed consent forms. In this paper we describe the development of this agent, and then present a preliminary evaluation of the computer agent in a three-arm randomized trial in which the agent explains an informed consent document for participation in a genetic repository.