Conversational Artificial Intelligence in Healthcare SpringerLink

In 2018, Google CEO Sundar Pichai introduced a new Google assistant, which booked an appointment at a hair salon via phone for its user [

26

]. Using speech synthesis with natural language elements, the system was close to indistinguishable to a human voice over the phone. The system called

Google Duplex

is the first example of a conversational agent that is, indeed, a personal assistant to its user [

27

]. For people living with chronic conditions, or indeed, people experiencing a more acute illness, the ability of an agent to book a doctor’s appointment or call an ambulance could be lifesaving. We expect to see more of such autonomous systems being commonly used in the future as more health services expand their online offerings and provide online bookings.

Home assistants can employ voice recognition technology to authenticate users based on their voice and speech patterns, for example, even in a household with several people. We, thereby, need to consider the delicate interplay between household residents, conversational agents, and the home and wearable devices. For instance, our recent trial tested and showed that conversational agent ‘Laura’ could support people living with type II diabetes [

48

].

1 Towards Proactive Conversational Agents

Deloitte Insights and our research centers deliver proprietary research designed to help organizations turn their aspirations into action. Over 40% of patients and consumers believe they spend too much time and effort getting issues resolved. In the realm of AI-driven communication, a fundamental challenge revolves around elucidating the models’ decision-making processes, a challenge often denoted as the “black box” problem (25). The complex nature of these systems frequently shrouds the rationale behind their decisions, presenting a substantial barrier to cultivating trust in their application.

This aids in making more informed decisions regarding treatments and interventions. Siemens‘ AI-Rad Companion, an AI-based software assistant, supports radiologists by automating routine tasks and providing quantitative data analysis in imaging, which enhances the accuracy of diagnoses and saves significant time. 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. Some enterprises were able to manage this sudden shift since they had some form of digital customer servicing channels like live chat via instant messaging tools like WhatsApp or their web site or app. This was especially helpful in catering to customers and employees at home who saw an increased utilisation of live chat services by to 2 to 3 times the previous volumes. On-premise (private cloud or local server) deployment requires more time due to various factors.

Healthcare

Conversational AI makes healthcare more accessible, personalized, and efficient than ever before. With correct implementation, conversation AI systems can have an enormous impact on the healthcare industry. If you are wondering about the potential of this technology and how it can save the beleaguered healthcare economy, this complete guide to conversation AI for the healthcare industry is meant for you. Conversational AI can provide 24/7 service and support, and can be used to answer general patient questions, provide appointment reminders, and handle other administrative tasks that would otherwise require staff time and resources. As we touched on earlier, conversational AI can be used to solve a wide range of different healthcare challenges. To keep your strategy efficient and airtight—and to ensure that you get maximum value from your investment—focus on your key indicators of success early on.

• The characteristics of the 31 included studies are summarized in Multimedia Appendix 3 [8,9,12-15,32-56].
• AI chatbots and virtual assistants are pivotal in remote and underserved areas, offering basic medical advice and emergency guidance.
• Due to the wide variety of conversational agents, their aims and health care contexts, much of the qualitative user perception data concerned distinct aspects of the agents.
• A profile can consist of personal data, such as name and age, of interaction history, i.e., information gathered in previous conversations, and context, such as time of day, the medium through which the user converses, location, or device proximity.
• In the long term, Conversational AI can serve as a virtual ‘healthcare consultant’ at any point in time – answering questions that millions of people across the globe have about major and minor health-related issues on a daily basis.

This leads to better diagnosis and treatment planning while reducing the time patients spend in the scanner. The integration of Conversational AI in healthcare is reshaping the landscape of patient care and medical administration. This article delves into the reasons behind its rising popularity and the methods of its application, backed by real-world examples.

Digital transformation dictates the future, and the healthcare sector is ripe for an upgrade. In fact, only 7% of healthcare and pharmaceutical companies said they had gone digital. Besides this, conversational AI is more flexible than conventional chatbot and will not come up with a blank response if the symptom descriptions vary between users. It allows patients to schedule appointments without feeling frustrated to use a complicated interface. In addition, they can also reschedule or cancel appointments easily if needed to eliminate the risk of scheduling conflicts.

Similarly, there is a gap in the evidence regarding the health economics of these agents. Very few studies in this review even discussed the cost analysis of the agent in questions, let alone provide substantive evidence about its cost-effectiveness. The evaluation of costs and outcomes of new technologies and their privacy, security, and interoperability will be necessary to advance value-based health care [60]. However, there is very little evidence to suggest that the conversational agents examined in this review considered or addressed these concerns.

What is conversational AI in healthcare?

Conversational AI, by taking charge of these processes, ensures clarity and efficiency. Whether it’s generating detailed invoices or resolving claims issues, AI does so by integrating with existing healthcare systems, ensuring accuracy and a unified patient experience. By predicting patient influx and identifying peak hours, the system allows hospital administrators to allocate resources more efficiently — such as staffing, bed availability, and emergency room readiness. This optimization leads to a more balanced workload for healthcare providers and shorter wait times for patients. The AI system implemented at the Cleveland Clinic utilizes a sophisticated algorithm that analyzes incoming patient data.

By reducing wait times, the Cleveland Clinic’s AI system indirectly contributes to improved patient outcomes. Faster response times in critical situations can significantly increase the chances of successful treatment and recovery, especially in emergency and high-risk cases. GE’s AI-powered applications in medical imaging equipment, like MRI and CT scanners, help in optimizing scan protocols and improving image quality.

An intelligent conversational interface backed by AI can solve this problem and deliver engaging responses to the users. From automating parts of the patient experience to improving overall patient engagement, conversational AI presents many advantages for patients, healthcare providers, and organizations alike. Gen AI has the potential to reimagine much of the healthcare industry in ways that we have not seen to date with previously available technologies. Once gen AI matures, it could also converge with other emerging technologies, such as virtual and augmented reality or other forms of AI, to transform healthcare delivery. For example, a healthcare provider could license its likeness and voice to create a branded visual avatar with whom patients could interact. Or a physician could check, against the full corpus of a patient’s history, how their approach for that patient aligns (or deviates) from other similar patients who have experienced positive outcomes.

Traditionally, drug discovery involves a lengthy process of screening thousands of chemical compounds, which can be both time-consuming and costly. The AI can virtually screen millions of compounds in a fraction of the time it would take in a laboratory, rapidly narrowing down the list to the most promising candidates for further development. The company employs advanced machine learning algorithms that learn from vast datasets of chemical structures and their known biological activity. By training these models on existing data, Atomwise’s AI can extrapolate and predict the behavior of new, untested compounds. In a rapidly evolving technology field like artificial intelligence, it is hard to predict what the state of affairs will look like in a few months, let alone a few years. Just think back to the year 2010 (before the explosion of convolutional neural networks) and see how far we have come today.

Get a hands-on look at conversational AI in healthcare

It can streamline appointment scheduling processes by automatically screening calls and routing them to the appropriate personnel or departments. For patients, the most significant benefit is that it enables quicker access to care. These virtual assistants can help with automated appointment scheduling and knowledge sharing, and give patients quick and easy self-service options (which also reduces wait times significantly). Like clinician documentation, several cases for gen AI in healthcare are emerging, to a mix of excitement and apprehension by technologists and healthcare professionals alike. It can do so by automating tedious and error-prone operational work, bringing years of clinical data to a clinician’s fingertips in seconds, and by modernizing health systems infrastructure.

Furthermore, the semi-automated nature of the conversation can relieve some of the pressures from the healthcare system. This approach can be scalable, and people can access health services 24 hours/7 days a week and from any location. We also present some of our own work on context-aware voice assistants that are capable of proactively engaging users and delivering health information and services. In this systematic review, we examined 31 studies that evaluated the effectiveness and usability of conversational agents in health care.

The successful integration of AI with existing healthcare systems is paving the way for more efficient, accurate, and personalized patient care. Cerner, another major player in healthcare IT, has been incorporating AI into its electronic health record (EHR) systems. By using predictive analytics, their AI tools help in identifying patients at risk of deteriorating health conditions, thereby enabling early intervention. conversational ai in healthcare Just like outpatient care, we can hope to see more conversational AI systems doing the bulk of the first layer of emotional support. This could be in the form of notifications, daily check-ins and gamification of positive habits. Coupled with the growth of wearables and IoT devices, conversational AI systems will enable hospitals to care for patients in their homes before they even have a need to visit.