Conversational agents in healthcare have many jobs. They aim to improve how patients interact and to support self-care. These systems can understand natural language, so users do not need to follow strict commands or scripts when they talk or type. This helps collect information, give health advice, and manage appointments more easily.
Many conversational agents use ways to guide the conversation. The most common are finite-state and frame-based methods. Finite-state methods follow set steps where the agent leads the user through the talk. Frame-based methods gather information to fill in specific data parts but allow more flexibility. Few agents use agent-based methods, which are more complex and manage talks more independently.
A review looked at 17 articles about conversational agents in healthcare from 1,513 citations. These studies checked 14 different agents that use natural language to help patients or healthcare workers.
Most of these 17 studies were quasi-experimental, meaning they did not randomly assign people to groups. This can make their results less certain. Only two studies were randomized controlled trials (RCTs), which randomly put people in groups and are the best for testing how well something works.
The review also included one cross-sectional study. It pointed out that many studies used weaker methods, so more careful research is needed.
The two RCTs gave useful information about conversational agents in healthcare:
The other studies, which were quasi-experimental, looked at tasks like booking appointments, sending medication reminders, giving health education, and checking symptoms. These studies were less controlled but gave helpful ideas about how practical and accepted these agents are.
For example, some reports showed that agents can guide patients in self-care, like changing diets, doing exercises, and managing long-term illnesses. These results suggest conversational AI can reduce the work for healthcare staff by automating routine messages and helping patients manage their health.
Still, since these studies do not use random groups or control groups, they cannot fully prove how safe or effective the agents are.
One big concern from the review is that most studies paid little attention to patient safety. Few looked at bad events or wrong advice from the agents. This is an important issue that healthcare providers should think about before widely using these systems.
For administrators and IT managers, this means while agents can make workflows better, safety measures are needed. These include testing the systems well before use, having clear rules to pass on difficult cases to human providers, and watching carefully for mistakes that could harm patients.
NLP is the technology that lets conversational agents understand how people talk in many different ways. Unlike systems that only follow strict commands, NLP agents accept natural speech or writing. This is important in healthcare because patient communication can be complex and full of emotion or slang.
With NLP, agents can make conversations personal, clear up unclear information, and give answers that fit the situation. This makes them useful in primary care, mental health, and managing chronic diseases.
Besides talking directly to patients, AI-powered conversational agents help automate tasks in healthcare offices. This includes call centers, managing appointments, checking insurance, and pre-visit screenings.
Here are ways AI automation helps with administrative goals:
For IT and healthcare managers in the U.S., using conversational agents for office automation can improve productivity and patient communication.
Medical practice administrators and IT managers should look carefully at the evidence when choosing conversational AI tools. The technology shows promise to improve patient talks and reduce office work, but there are important points to consider:
Researchers like Bickmore TW and others have provided strong data supporting conversational agents. Their RCTs showed agents can help lower depression symptoms and encourage healthier actions. This research helps us understand how conversational AI can work not just as office helpers but also in treatment.
Other studies by Watson A, Bickmore T, and colleagues show how virtual coaches motivate overweight adults to exercise more. These projects show growing interest in AI tools for health behavior changes in the U.S.
Healthcare groups in the U.S. wanting to update how they talk with patients can find conversational AI useful for making work smoother while improving patient experience. Companies like Simbo AI focus on phone automation and automatic answering, using AI to handle everyday patient communication with natural language.
By using these systems, medical offices can get benefits like:
When choosing conversational AI, medical leaders should pick ones that use proven conversation methods—mainly finite-state and frame-based models—and allow customization to fit their needs.
Studies of conversational agents in U.S. healthcare, including RCTs and quasi-experimental work, show the field is growing. Early results suggest agents can help improve clinical results, like lowering depression symptoms, and support patient self-care. But most research so far uses weaker designs, so stronger studies and better safety checks are needed.
The AI and NLP technologies behind these agents also give healthcare offices chances to automate front-desk tasks, cut inefficiencies, and keep patient communication focused. Healthcare managers must consider evidence, safety, customization, training, and privacy rules when adding conversational agents to get benefits and prevent harm.
Good conversational agents may lead to healthcare delivery and administration that are more responsive and easier to access, meeting the need for better patient care and using resources well in busy U.S. medical offices.
The primary objective was to review the characteristics, current applications, and evaluation measures of conversational agents with unconstrained natural language input capabilities used for health-related purposes.
Studies were included if they focused on consumers or healthcare professionals, involved a conversational agent using any unconstrained natural language input, and reported evaluation measures from user interaction. Independent reviewers screened studies with Cohen’s kappa used to measure inter-coder agreement.
Out of 1513 citations retrieved, 17 articles describing 14 different conversational agents met the inclusion criteria.
Dialogue management strategies were mostly finite-state and frame-based, with 6 and 7 conversational agents using these types respectively, while agent-based strategies were present in only one system.
Two studies were randomized controlled trials (RCTs), one was cross-sectional, and the remaining were quasi-experimental designs.
Half of the conversational agents supported consumers with health tasks such as self-care and management of health-related activities.
The only RCT evaluating a conversational agent found a significant effect in reducing depression symptoms, with an effect size d = 0.44 and p = .04.
Patient safety was rarely evaluated in the studies included in the review.
Future studies should employ more robust experimental designs and standardized reporting to better evaluate efficacy and safety.
NLP enables conversational agents to interpret and respond to unconstrained natural language inputs from users, facilitating interactive, personalized, and task-oriented healthcare support.
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