Medical Chatbots Use Cases, Examples and Case Studies of Generative Conversational AI in Medicine and Health

If you’d like to know more about our healthcare chatbots and how we can enhance your patient experience, simply get in touch with our customer experience experts here. When a patient checks into a hospital with a time-sensitive ailment the chatbot can offer information about the relevant doctor, the medical condition and history and so on. When a patient checks into a hospital with a time-sensitive ailment, the chatbot can offer information about the relevant doctor, the medical condition and history, and so on. With a messaging interface, the website/app visitors can easily access a chatbot. Chatbots may even collect and process co-payments to further streamline the process.

However, more conversational bots, for example, those that strive to help with mental illnesses and conditions, cannot be constructed—at least not easily—using these thought models. This requires the same kind of plasticity from conversations as that between human beings. The division of task-oriented and social chatbots requires additional elements to show the relation among users, experts (professionals) and chatbots.

Providing basic mental healthcare

Another top use of chatbots in healthcare is in the sphere of appointment scheduling. This way, you don’t need to call your healthcare provider to get an appointment anymore. The best healthcare chatbots available today have different missions, and consequently, different pros and cons. If you’re interested in learning about an alternative source of medical advice or simply want to learn about the top health chatbots that exist today, let us show you the way.

Furthermore, accessibility via both smartphones and personal computers makes such chatbots widely available. Though a minority, we highlight the importance of SMS-based and phone-call-based chatbots to bridge the digital divide and reach people who lack access to smartphones or reliable internet connections or lack the skills to use technology. The proven chatbot use cases we have explored demonstrate the significant impact these AI-driven tools can have on businesses and organizations. From enhancing customer service to optimizing sales and streamlining various processes, chatbots have shown their ability to deliver efficient and personalized results.

Integrate your Healthcare Chatbot with a CRM

Some experts also believe doctors will recommend chatbots to patients with ongoing health issues. In the future, we might share our health information with text bots to make better decisions about our health. Companies are actively developing clinical chatbots, with language models being constantly refined. As technology improves, conversational agents can engage in meaningful and deep conversations with us. Healthcare providers can handle medical bills, insurance dealings, and claims automatically using AI-powered chatbots. Chatbots also support doctors in managing charges and the pre-authorization process.

Questions like these are very important, but they may be answered without a specialist. A chatbot is able to walk the patient through post-op procedures, inform him about what to expect, and apprise him when to make contact for medical help. The chatbot also remembers conversations and can report the nature of the patient’s questions to the provider. This type of information is invaluable to the patient and sets-up the provider and patient for a better consultation. LeadSquared’s CRM is an entirely HIPAA-compliant software that will integrate with your healthcare chatbot smoothly. The world witnessed its first psychotherapist chatbot in 1966 when Joseph Weizenbaum created ELIZA, a natural language processing program.

Chatbot users (patients) need to see and experience the bots as ‘providing answers reflecting knowledge, competence, and experience’ (p. 24)—all of which are important to trust. In practice, ‘chatbot expertise’ has to do with, for example, giving a correct answer (provision of accurate and relevant information). The importance of providing correct answers has been found in previous studies (Nordheim et al. 2019, p. 25), which have ‘identified the perceived ability of software agents as a strong predictor of trust’.

Another factor that contributes to errors and inaccurate predictions is the large, noisy data sets used to train modern models because large quantities of high-quality, representative data are often unavailable [58]. In addition to the concern of accuracy and validity, addressing clinical utility and effectiveness of improving patients’ quality of life is just as important. With the increased use of diagnostic chatbots, the risk of overconfidence and overtreatment may cause more harm than benefit [99]. There is still clear potential for improved decision-making, as diagnostic deep learning algorithms were found to be equivalent to health care professionals in classifying diseases in terms of accuracy [106]. These issues presented above all raise the question of who is legally liable for medical errors.

Evaluation of Chatbot Design

With the novelty and complexity of chatbots, obtaining valid informed consent where patients can make their own health-related risk and benefit assessments becomes problematic [98]. Without sufficient transparency, deciding how certain decisions are made or how errors may occur reduces the reliability of the diagnostic process. The Black Box problem also poses a concern to patient autonomy by potentially undermining the shared decision-making between physicians and patients [99].

In an industry where uncertainties and emergencies are persistently occurring, time is immensely valuable. It allows you to integrate your patient information system and calendar into an AI chatbot system. Minmed, a multifaceted healthcare group, uses a chatbot on its website that offers comprehensive information on several health screening packages, COVID-19 detection tests, clinic locations, operating hours, and so much more. But, ever since the pandemic hit, a larger number of people now understand the importance of such practices and this means that healthcare institutions are now dealing with higher call volumes than ever before. Although scheduling systems are in use, many patients still find it difficult to navigate the scheduling systems.

Included Studies

Here are five ways the healthcare industry is already using chatbots to maximize their efficiency and boost standards of patient care. Emerging trends like increasing service demand, shifting focus towards 360-degree wellbeing, and rising costs of quality care are propelling the adoption of new technologies in the healthcare sector. By harnessing the power of Generative Conversational AI, medical institutions are rewriting the rules of patient engagement.

From Docus.ai to MedPaLM 2, these chatbots improve almost every aspect of patient care. They streamline workflows for healthcare staff, engage patients in their own health, and give 24/7 assistance to virtually anyone in the world. For healthcare chatbots, this comes in the form of ethical issues, data privacy, and the requirement for human oversight. Currently, and for the foreseeable future, these chatbots are meant to assist healthcare providers – not replace them altogether. At the end of the day, human oversight is required to minimize the risk of inaccurate diagnoses and more.

Hence, it’s very likely to persist and prosper in the future of the healthcare industry. According to an MGMA Stat poll, about 49% of medical groups said that the rates of ‘no-shows‘ soared since 2021. No-show appointments result in a considerable loss of revenue and underutilize the physician’s time.

• This frees up healthcare and public health workers to deal with more critical and complicated tasks and addresses capacity bottlenecks and constraints.
• Serving as the lead content strategist, Snigdha helps the customer service teams to leverage the right technology along with AI to deliver exceptional and memorable customer experiences.
• Chatbots like Docus.ai can even validate these diagnoses with top healthcare professionals from the US and Europe.
• Few of the included studies discussed how they handled safeguarding issues, even if only at the design stage.
• This will help you to choose the right tools or find the right experts to build a chat agent that suits your users’ needs.
• They gather and process information while interacting with the user and increase the level of personalization.

They built one of the most highly intuitive AI-powered chatbots in healthcare, which could come up with possible diagnoses for a patient’s symptoms by asking around 20 questions. They can automate bothersome and time-consuming tasks, like appointment scheduling or consultation. An AI chatbot can be integrated with third-party software, enabling them to deliver proper functionality. The rapid adoption of AI chatbots in healthcare leads to the rapid development of medical-oriented large language models. This particular healthcare chatbot use case flourished during the Covid-19 pandemic. Since the pandemic became a global concern, it became essential to reach billions of people at once and have personalized conversations about what the disease is, what are the common symptoms, and what are the treatments and medications available.

Cem’s work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE, NGOs like World Economic Forum and supranational organizations like European Commission. Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised businesses on their enterprise software, automation, cloud, AI / ML and other technology related chatbot healthcare use cases decisions at McKinsey & Company and Altman Solon for more than a decade. He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem’s work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.

Many state or regional governments also developed their own chatbots; for instance, Spain has 9 different chatbots for different regions. For instance, in Brazil, a chatbot designed to support LGBT teenagers added Covid-19 information.36 Others were developed by healthcare institutions and a diverse range of organizations for their customers, patients, students, and employees. An interesting use case—mHero33,34—involves facilitating coordination between distributed frontline healthcare workers and health organizations or the Ministry of Health in areas of poor technology infrastructure (1 case).

To further advance medicine and knowledge, the use of chatbots in education for learning and assessments is crucial for providing objective feedback, personalized content, and cost-effective evaluations [113]. For example, the development of the Einstein app as a web-based physics teacher enables interactive learning and evaluations but is still far from being perfect [114]. Given chatbots’ diverse applications in numerous aspects of health care, further research and interdisciplinary collaboration to advance this technology could revolutionize the practice of medicine.