Clinical, Legal, and Ethical Aspects of Artificial Intelligence Assisted Conversational Agents in Health Care Health Policy JAMA

Dr. Bhatt continues to practice medicine in the Chicago area while serving in his leadership role at Deloitte. This may include information about diseases, symptoms, medications, and general health advice. However, the benefits can be felt for humans directly as they engage through Conversational AI with inquiries regarding their health, insurance, and general information. Navigating healthcare can be complex, and your members have increasingly high expectations. They want to be able to look up coverage and have questions answered without dealing with long hold times or multiple transfers.

On the other hand, the same system can be used to streamline the patient onboarding process and guide them through the process in an easy way. On a related note, conversational AI can also help speed up time-consuming processes like billing and insurance processing. Whether these are through phone calls, video calls, SMS/MMS messaging, live chat, or social media channels, a key consideration when you’re implementing conversational AI is which of these channels you’ll want the AI to analyze insights and answer questions for. Before you start looking at different vendors or types of conversational AI technology, you should first have an idea of what problems you’re trying to solve with AI. To train gen-AI models, organizations should also ensure that they are processing data within secure firewalls.

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In the future, as AI systems get better at automating repetitive tasks with better accuracy, the next frontier will be in perfecting the humanity part of these bots. Conversational AI platform vendors, especially those experienced in working with multiple healthcare institutions, will generally have built up a specialised knowledge database in this domain. Leveraging this extended domain knowledge may help the bot cover a larger scope of queries and achieve a higher accuracy. Healthcare institutions and other smaller enterprises may not have such a level of technology expertise in-house. In fact, hospitals may already have a large and complex ecosystem of mission critical systems to maintain and may not want to take further technology risks with AI R&D and software development.

• KeyReply is an AI-powered patient engagement orchestrator that is revolutionizing the healthcare space by enabling Healthcare Providers and Insurers to engage with their customers across a variety of online platforms.
• 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.
• Woebot, a chatbot therapist developed by a team of Stanford researchers, is a successful example of this.
• All studies retrieved from the databases were stored in the reference management software EndNote (version X9, Clarivate Analytics), which automatically eliminated duplicates.

Many studies in this review showed some positive evidence for the usefulness and usability of AI CAs to support the management of different chronic diseases. The overall acceptance of CAs by users for the self-management of their chronic conditions is promising. Users’ feedback shows helpfulness, conversational ai in healthcare satisfaction, and ease of use in more than half of the included studies. Although the users in many studies appear to feel more comfortable with CAs, there is still a lack of reliable and comparable evidence to determine the efficacy of AI-enabled CAs for chronic health conditions.

Considerations for healthcare practices that are interested in conversational AI

Recently, AI-based CAs have demonstrated multiple benefits in many domains, especially in healthcare. It is used to deliver scalable, less costly medical support solutions that can help at any time via smartphone apps or online [8,9]. For example, support and follow-up for adults after cancer treatment via chatbot reduced the patients’ anxiety without needing a psychiatrist [10,11,12]. Hence, CAs can play an useful role in health care, improving consultations by assisting clinicians and patients, supporting consumers with behavior change, and assisting older people in their living environments [13,14,15]. They can also help in completing specific tasks such as self-monitoring and overcoming obstacles for self-management, which is important in chronic disease management and in the fight against pandemics [6,16]. To our knowledge, our study is the first comprehensive review of healthbots that are commercially available on the Apple iOS store and Google Play stores.

This could be due to the emphasis on human to human interaction (patients expect to be treated in person by doctors), the higher levels of risk and compliance regulations. The terms virtual assistants and conversational AI agents are often used interchangeably. While they are all related and refer to the same technology in general, it is useful to distinguish them clearly for clarity. The authors would like to thank the outreach librarians Liz Callow (University of Oxford) and Kirsten Elliot (Imperial College London), for their assistance in developing search terms and reviewing search strategies. EM’s work on digital health solutions is currently supported by the Sir David Cooksey Fellowship in Healthcare Translation at the University of Oxford. The conclusions drawn in this paper were made by the authors and are not necessarily supported by the University of Oxford.

A user can ask a virtual assistant and receive an automated reply with no human intervention. In fact, the first incarnations of virtual assistants and even most of today’s bots use pre-defined, rule-based programming to deliver replies to queries. The Cochrane Collaboration risk-of-bias tool was used to evaluate the risk of bias in randomized controlled trials (RCTs) [28].

From offering patients faster and more efficient care to improving productivity and reducing costs for clinics and hospitals, there are many possible applications of conversational AI in healthcare—and we’re only scratching the surface. To weigh the value of gen-AI applications in healthcare against the risks, leaders should create risk and legal frameworks that govern the use of gen AI in their organizations. Data security, bias and fairness, and regulatory compliance and accountability should all be considered as part of these frameworks. Member services offer many ways for gen AI to improve the quality and efficiency of interactions. For example, many member inquiries relate to benefits, which require an insurance specialist to manually confirm the scope of a member’s plan. With gen AI, digital resources and call-center specialists can quickly pull relevant information from across dozens of plan types and files.

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Summary of the quality assessment and judgments of the cross-sectional studies using the Appraisal tool for Cross-Sectional Studies tool. Summary of the quality assessment and judgments of qualitative studies using the CASP (Critical Appraisal Skills Programme) Qualitative Study Checklist. Summary of the quality assessment and judgments of cohort studies using the CASP (Critical Appraisal Skills Programme) Cohort Study Checklist. And I.M.; writing—original draft preparation, A.B.S.; writing—review and editing, A.B.S., B.N., A.A., A.M., I.M., J.S., B.Y., D.P., M.P. And A.B.K. All authors have read and agreed to the published version of the manuscript.

Subject matter experts and business stakeholders will also have the flexibility of updating dialogs and correcting responses as and when necessary. Machine learning refers to a more general set of techniques to enable machines to look at past and current data and optimise for the best processes that lead to the right results. In supervised learning, the training data is labelled, while in unsupervised learning, it is not and the system has to study the data set to discover an underlying structure in order to make predictions. Rapid growth in computing capabilities and data storage has led to new and ingenious artificial intelligence (AI) techniques that enable machines to learn with minimal human supervision.

These efforts aim to strike a balance between leveraging the power of AI chatbots for improved healthcare outcomes while safeguarding the privacy and confidentiality of sensitive patient information. Notably, the integration of chatbots into healthcare information websites, exemplified by platforms such as WebMD, marked an early stage where chatbots aimed to swiftly address user queries, as elucidated by Goel et al. (2). Subsequent developments saw chatbots seamlessly integrated into electronic health record (EHR) systems, streamlining administrative tasks and enhancing healthcare professional efficiency, as highlighted by Kocakoç (3). The busy nurse can let the bot schedule appointments and manage medication reminders. From ancient syringes to the advanced telemedicine of today, healthcare technology has come a long way and has conversational AI as a part of the next exciting developments.

• This resulted in the drawback of not being able to fully understand the geographic distribution of healthbots across both stores.
• After treatment, patients can also often relapse into a condition and end up back at the hospital in a worse condition than before, for more intensive treatment.
• A low-code approach can accomplish the same basic appointment feature integration in 2 days, and will also bring down the timeline for a full-fledged solution.
• An example is the Ada Health app, which assists users in understanding their symptoms and guides them towards appropriate care.
• Several studies reported user feedback that was specific to that conversational agent.