Conversational AI in Healthcare: Use Cases, Benefits & Challenges

This is where conversational AI tools can be put to use to check symptoms and suggest a step-by-step diagnosis. It can lead a patient through a series of questions in a logical sequence to understand their condition that may require immediate escalation. At times, getting an accurate diagnosis following appointment scheduling is what a patient needs for further review. Gen AI’s ability to generate and synthesize language could also improve how EHRs work. EHRs allow providers to access and update patient information but typically require manual inputs and are subject to human error.

Innovative conversational artificial intelligence (AI) powered systems have been gaining momentum in the healthcare industry in recent years. Automated artificial intelligence programs are built with the purpose of allowing effective communication by providing an interface between the computer and the user. Conversational AI are making a significant impact on the healthcare industry for both medical health providers and patients. Several natural language processing (NLP) platforms, in particular using natural language understanding (NLU), such as Google Dialogflow, IBM Watson and Rasa are used in conversational AI.

Revolutionizing Healthcare with Conversational AI: A Comprehensive Guide

With phone, mobile, and online platforms being widely accessible, conversational agents can support populations with limited access to health care or poor health literacy [16,17]. They also have the potential to be affordably scaled up to reach large proportions of a population [3]. Due to this accessibility, conversational agents are also a promising tool for the advancement of patient-centered care and can support users’ involvement in the management of their own health [17,18].

Patient Data Privacy and SecurityProtecting customer data and ensuring privacy is an important consideration in any technology adoption, irrespective of the industry. Moreover, the terms that a bot most frequently encounters could vary between geographical regions, societies, and even among conversational ai in healthcare individual healthcare institutions. For example, in some conservative societies, people may want to consult a doctor as soon as they discover symptoms. In other societies, they might be inclined to wait to see if the symptoms subside before even thinking about reaching out to a hospital.

Methods

In the above example of booking a health screening appointment, the 4 variations correspond to 4 examples. All 4 are different variations of the same essential question or action that the user wants to be answered – to book a health screening appointment. Conversational AI refers to solutions that employ a variety of AI techniques like Natural Language Processing (NLP) and Machine Learning (ML) to automate conversations with users. For each app, data on the number of downloads were abstracted for five countries with the highest numbers of downloads over the previous 30 days. Chatbot apps were downloaded globally, including in several African and Asian countries with more limited smartphone penetration.

The studies that evaluated only individual components of natural language understanding and CAs’ automatic speech recognition, dialogue management, response generation, and text-to-speech synthesis were excluded. The last exclusion criteria were studies using “Wizard of Oz” methods, where dialogue generated by a human operator rather than the CAs, were excluded [1,6,9]. Conversational agents first emerged as a tool in health care in 1966, with the development of a virtual psychotherapist (ELIZA) that could provide predetermined answers to text-based user input [6]. In the decades since, the capabilities of NLP have significantly progressed and aided the development of more advanced AI agents. Many different types of conversational agents that use NLP have been developed, including chatbots, embodied conversational agents (ECAs), and virtual patients, and are accessible by telephone, mobile phones, computers, and many other digital platforms [7-10].

Note that in hospitals such critical data might be stored on premise, on the cloud or in a hybrid model. This directly dictates where the conversational AI platform will need to be hosted. The consumer devices – iPhone, iPad, Macbook and the Apple Watch – by themselves may be of immense convenience.

• Leaders must also assess their AI tech stack—including the applications, models, APIs, and other tech infrastructure they currently use—to determine where their technological capabilities will need to be augmented to leverage large language models at scale.
• You’re already having conversations with patients and prospective patients every day.
• Learn about Deloitte’s offerings, people, and culture as a global provider of audit, assurance, consulting, financial advisory, risk advisory, tax, and related services.
• It should also be noted that the definitions of effectiveness were highly varied and, as evidenced by the methodological limitations identified in the quality assessment, rarely evaluated with the scrutiny expected for medical devices.
• Companies that are compliant have written policies, conduct training, and monitor and enforce standards.

A total of 8 studies were coded as reporting positive or mixed evidence for 10 or more of the 11 outcomes specified in the SF/HIT; the analysis for this review was limited to the interpretation of impact as reported by study authors to reflect evaluation outcomes. Excluding 1 study, which was an acceptability study only and did not assess the other outcomes, the average number of outcomes that were coded as positive or mixed was 67% (7.4/11, SD 2.5). However, the number of outcomes met per study ranged from 1/11 to 11/11 (9-100%).

These objectives build on previous systematic reviews while widening the scope of included studies to update the body of knowledge on conversational agents in health care and to inform future research and development. Many of these agents are designed to use NLP so that users can speak or write to the agent as they would to a human. The agent can then analyze the input and respond appropriately in a conversational manner [5]. Despite the large body of research concerning the application of conversational agents in health care, most reviews have limited their focus to a particular health area, agent type, or function [10,19-22]. Although there are a few recent systematic reviews that have examined a more comprehensive scope, they have presented an overall synthesis of the body of knowledge. One review developed a taxonomy that described the architecture and functions of conversational agents in health care and the state of the field but did not evaluate the effectiveness, usability, or implications for users [5].

Other than the in-person consultation with health experts, what they need is easy access to information and tools to take control of their health. Next to answering patients’ queries, appointment management is one of the most challenging yet critical operations for a healthcare facility. While it is easy to find appointment scheduling software, they are quite inflexible, leading patients to avoid using them in favor of scheduling an appointment via a phone call.