Healthcare Chatbots: Benefits, Use Cases, and Top Tools

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. For healthcare institutions when it comes to increasing enrollment for different types of programs, raising awareness, medical chatbots are the best option. Research indicates chatbots improve retention of health education content by over 40% compared to traditional written materials. For instance, the startup Sense.ly provides a chatbot specifically focused on managing care plans for chronic disease patients.

Many are finding that adding an automation component to the innovation strategy can be a game-changer by cost-effectively improving operations throughout the organization to the benefit of both staff and patients. Embracing new technologies – such as robotic process automation enabled with chatbots – is key to achieving the interdependent goals of reducing costs and serving patients better. Integrate REVE Chatbot into your healthcare business to improve patient interactions and streamline operations. As healthcare continues to rapidly evolve, health systems must constantly look for innovative ways to provide better access to the right care at the right time. Applying digital technologies, such as rapidly deployable chat solutions, is one option health systems can use in order to provide access to care at a pace that commiserates with patient expectations.

An overview of chatbots in healthcare: numbers and facts

Concerns over the unknown and unintelligible “black boxes” of ML have limited the adoption of NLP-driven chatbot interventions by the medical community, despite the potential they have in increasing and improving access to healthcare. Further, it is unclear how the performance of NLP-driven chatbots should be assessed. The framework proposed as well as the insights gleaned from the review of commercially available healthbot apps will facilitate a greater understanding of how such apps should be evaluated.

Studies show they can improve outcomes by 15-20% for chronic disease management programs. Chatbots in healthcare industry are awesome – but as any other great technology, they come with several concerns and limitations. It is important to know about them before implementing the technology, so in the future you will face little to no issues. Such fast processing of requests also adds to overall patient satisfaction and saves both doctors’ and patients’ time.

They can offer educational resources about the condition, provide tips for self-care, and answer common questions related to managing chronic illnesses. This support, facilitated by the doctor using AI technology, empowers patients to take control of their health and promotes better adherence to treatment plans. Use cases for healthcare chatbots vary from diagnosis and mental health support to more routine tasks like scheduling and medication reminders. In a world where an anxiety attack can happen at any time, you can rest easy knowing that you have AI-powered chatbots in healthcare to rely on. 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. Another review conducted by Montenegro et al. developed a taxonomy of healthbots related to health32.

Physicians worry about how their patients might look up and try cures mentioned on dubious online sites, but with a chatbot, patients have a dependable source to turn to at any time. Healthcare Chatbot is an AI-powered software that uses machine learning algorithms or computer programs to interact with leads in auditory or textual modes. The ways in which users could message the chatbot were either by choosing from a set of predefined options or freely typing text as in a typical messaging app.

These chatbots also streamline internal support by giving these professionals quick access to information, such as patient history and treatment plans. The CancerChatbot by CSource is an artificial intelligence healthcare chatbot system for serving info on cancer, cancer treatments, prognosis, and related topics. This chatbot provides users with up-to-date information on cancer-related topics, running users’ questions against a large dataset of cancer cases, research data, and clinical trials.

Similar content being viewed by others

These AI technologies leverage both machine learning and deep learning—different elements of AI, with some nuanced differences—to develop an increasingly granular knowledge base of questions and responses informed by user interactions. This sophistication, drawing upon recent advancements in large language models (LLMs), has led to increased customer satisfaction and more versatile chatbot applications. Chatbots are software programs that use artificial intelligence and natural language processing to have personalized conversations with human users, either by text or voice. In healthcare, chatbots are being applied to automate conversations with patients for numerous uses – we‘ll cover the major ones shortly. Chatbots are becoming a trend in many fields such as medical, service industry and more recently in education.

Organizations that strategically adopt conversational AI will gain an advantage in costs, quality of care and patient satisfaction over competitors still relying solely on manual processes. Most chatbots (we are not talking about AI-based ones) are rather simple and their main goal is to answer common questions. Hence, when a patient starts asking about a rare condition or names symptoms that a bot was not trained to recognize, it leads to frustration on both sides. A bot doesn’t have an answer and a patient is confused and annoyed as they didn’t get help.

Mental health research has a continued interest over time, with COVID-19–related research showing strong recent interest as expected. Due to the small numbers of papers, percentages must be interpreted with caution and only indicate the presence of research in the area rather than an accurate distribution of research. One of the authors screened the titles and abstracts of the studies identified through the database search, selecting the studies deemed to match the eligibility criteria.

Offloading simple use cases to chatbots can help healthcare providers focus on treating patients, increasing facetime, and substantially improving the patient experience. It does so efficiently, effectively, and economically by enabling and extending the hours of healthcare into the realm of virtual healthcare. An AI-enabled chatbot is a reliable alternative for patients looking to understand the cause of their symptoms. On the other hand, bots help healthcare providers to reduce their caseloads, which is why healthcare chatbot use cases increase day by day. As the name implies, prescriptive chatbots are used to provide a therapeutic solution to a patient by learning about their needs and symptoms through a conversation. Such chatbot for medical diagnosis usually asks questions and encourages patients to share their symptoms in order to understand their current condition and what kind of treatment is recommended.

Information can be customized to the user’s needs, something that’s impossible to achieve when searching for COVID-19 data online via search engines. What’s more, the information generated by chatbots takes into account users’ locations, so they can access only information useful to them. We built the chatbot as a progressive web app, rendering on desktop and mobile, that interacts with users, helping them identify their mental state, and recommending appropriate content. That chatbot helps customers maintain emotional health and improve their decision-making and goal-setting. Users add their emotions daily through chatbot interactions, answer a set of questions, and vote up or down on suggested articles, quotes, and other content.

Let’s create a contextual chatbot called E-Pharm, which will provide a user – let’s say a doctor – with drug information, drug reactions, and local pharmacy stores where drugs can be purchased. The first step is to create an NLU training file that contains various user inputs mapped with the appropriate intents and entities. The more data is included in the training file, the more “intelligent” the bot will be, and the more positive customer experience it’ll provide. The NLU is the library for natural language understanding that does the intent classification and entity extraction from the user input. This breaks down the user input for the chatbot to understand the user’s intent and context.

Regulatory standards have been developed to accommodate for rapid modifications and ensure the safety and effectiveness of AI technology, including chatbots. With the growing number of AI algorithms approved by the Food and Drug Administration, they opened public consultations for setting performance targets, monitoring performance, and reviewing when performance strays from preset parameters [102]. The American Medical Association has also adopted the Augmented Intelligence in Health Care policy for the appropriate integration of AI into health care by emphasizing the design approach and enhancement of human intelligence [109]. An area of concern is that chatbots are not covered under the Health Insurance Portability and Accountability Act; therefore, users’ data may be unknowingly sold, traded, and marketed by companies [110].

Improved Patient Engagement and Treatment Adherence

For example, providers can use bots to create a link between their doctors and patients. Such a bot can provide a detailed record of the tracked health conditions and help assess the effects of prescribed management medication. At the end of the day, regular people aren’t trained medically to understand the severity of their diseases.

Engaging in open conversations about health with medical professionals can be challenging for individuals who anticipate encountering stigma or embarrassment upon revealing their symptoms and experiences of health. This predicament can lead to missed opportunities for early treatment, ultimately impacting overall health and well-being. By facilitating preliminary conversations about embarrassing and stigmatized symptoms, medical chatbots can play a pivotal role in influencing whether or not someone seeks medical guidance. Patients who need healthcare support regularly can get advantages from chatbots also.

So in case you have a simple bot and don’t want your patients to complain about its insufficient knowledge, either invest in a smarter bot or simply add an option to connect with a medical professional for more in-depth advice. Yes, there are mental health chatbots like Youper and Woebot, which use AI and psychological techniques to provide emotional support and therapeutic exercises, helping users manage mental health challenges. Medication adherence is a crucial challenge in healthcare, and chatbots offer a practical solution. By sending timely reminders and tracking medication schedules, they ensure that patients follow their prescribed treatments effectively. This consistent medication management is particularly crucial for chronic disease management, where adherence to medication is essential for effective treatment. Chatbots in healthcare contribute to significant cost savings by automating routine tasks and providing initial consultations.

• The ethical dilemmas this growth presents are considerable, and we would do well to be wary of the enchantment of new technologies [59].
• Chatbot interfaces with generative AI can recognize, summarize, translate, predict and create content in response to a user’s query without the need for human interaction.
• Unfortunately, according to a study in the journal Evidence Based Mental Health, the true clinical value of most apps was ‘impossible to determine’.
• Future studies should consider refining the search strategy to identify other potentially relevant sources that may have been overlooked and assign multiple reviews to limit individual bias.

As per Statista’s report, the global AI health market size was $15.1 billion in 2022, and it is expected to reach around $187.95 billion by 2030, increasing at a CAGR of 37% from 2022 to 2030. Twenty of these apps (25.6%) had faulty elements such as providing irrelevant responses, frozen chats, and messages, or broken/unintelligible English. Three of the apps were not fully assessed because their healthbots were non-functional. Healthily is an AI-enabled health-tech platform that offers patients personalized health information through a chatbot. From generic tips to research-backed cures, Healthily gives patients control over improving their health while sitting at home.

Using a third-party solution is cheaper and easier, especially if you are a beginner. You can try out the chatbot cost calculator to find the estimated costs of running a bot on your website. To recognize the meaning of messages automatically, all you have to do is define the language and topic of the conversation.

This capability is crucial during health crises or peak times when healthcare systems are under immense pressure. The ability to scale up rapidly allows healthcare providers to maintain quality care even under challenging circumstances. The language processing capabilities of chatbots enable them to understand user queries accurately. Through natural language understanding algorithms, these virtual assistants can decipher the intent behind the questions posed by patients. This allows them to provide relevant responses tailored to the specific needs of each individual.

This allows doctors to process prescription refills in batch or automate them in cases where doctor intervention is not necessary. Microsoft’s Bing also allows advanced searches thanks to the implementation of ChatGPT. The tool, which was first introduced under the name Bing Chat and has since been renamed Copilot, can be accessed from the Microsoft Edge browser use of chatbots in healthcare by selecting the AI plugin icon in the toolbar. Learn about how the COVID-19 pandemic rocketed the adoption of virtual agent technology (VAT) into hyperdrive. Whatever the case or project, here are five best practices and tips for selecting a chatbot platform. The costs of developing a bot from scratch are very prohibitive if you want to hire developers.

The Indian government also launched a WhatsApp-based interactive chatbot called MyGov Corona Helpdesk that provides verified information and news about the pandemic to users in India. Another point to consider is whether your medical AI chatbot will be integrated with existing software systems and applications like EHR, telemedicine platforms, etc. Rasa stack provides you with an open-source framework to build highly intelligent contextual models giving you full control over the process flow. Conversely, closed-source tools are third-party frameworks that provide custom-built models through which you run your data files. With these third-party tools, you have little control over the software design and how your data files are processed; thus, you have little control over the confidential and potentially sensitive patient information your model receives. Just as effective human-to-human conversations largely depend on context, a productive conversation with a chatbot also heavily depends on the user’s context.

A chatbot helps in providing accurate information about COVID-19 in different languages. By being aware of these possible risks, medical experts and patients can reap the maximum benefits of this technology. By understanding the needs and detecting any issue, medical experts will become better at distributing this technology and getting the types of results they are seeking. A considerable risk presents around the probability of danger being caused by the wrong provision of medical data.

Healthcare providers can overcome this challenge by investing in a dedicated team to manage bots and ensure they are up-to-date with the latest healthcare information. Our industry-leading expertise with app development across healthcare, fintech, and ecommerce is why so many innovative companies choose us as their technology partner. As is the case with any custom mobile application development, the final cost will be determined by how advanced your chatbot application will end up being. For instance, implementing an AI engine with ML algorithms in a healthcare AI chatbot will put the price tag for development towards the higher end.

Answering patient questions

One study found that there was no effect on adherence to a blood pressure–monitoring schedule [39], whereas another reported a positive improvement medication adherence [35]. The HIPAA Security Rule requires that you identify all the sources of PHI, including external sources, and all human, technical, and environmental threats to the safety of PHI in your company. The Rule requires that your company design a mechanism that encrypts all electronic PHI when necessary, both at rest or in transit over electronic communication tools such as the internet. Furthermore, the Security Rule allows flexibility in the type of encryption that covered entities may use.

These intelligent virtual assistants automate various administrative tasks, allowing health systems, hospitals, and medical professionals to focus more on providing quality care to patients. We identified 78 healthbot apps commercially available on the Google Play and Apple iOS stores. Healthbot apps are being used across 33 countries, including some locations with more limited penetration of smartphones and 3G connectivity. The healthbots serve a range of functions including the provision of health education, assessment of symptoms, and assistance with tasks such as scheduling.

However, in the case of chatbots, ‘the most important factor for explaining trust’ (Nordheim et al. 2019, p. 24) seems to be expertise. People can trust chatbots if they are seen as ‘experts’ (or as possessing expertise of some kind), while expertise itself requires maintaining this trust or trustworthiness. 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’. Conversely, automation errors have a negative effect on trust—‘more so than do similar errors from human experts’ (p. 25).

There are three primary use cases for the utilization of chatbot technology in healthcare – informative, conversational, and prescriptive. These chatbots vary in their conversational style, the depth of communication, and the type of solutions they provide. Cancer has become a major health crisis and is the second leading cause of death in the United States [18]. The exponentially increasing number of patients with cancer each year may be because of a combination of carcinogens in the environment and improved quality of care. The latter aspect could explain why cancer is slowly becoming a chronic disease that is manageable over time [19].

Privacy threats may break the trust that is essential to the therapeutic physician–patient relationship and inhibit open communication of relevant clinical information for proper diagnosis and treatment [96]. In terms of cancer diagnostics, AI-based computer vision is a function often used in chatbots that can recognize subtle patterns from images. This would increase physicians’ confidence when identifying cancer types, as even highly trained individuals may not always agree on the diagnosis [52]. Studies have shown that the interpretation of medical images for the diagnosis of tumors performs equally well or better with AI compared with experts [53-56]. In addition, automated diagnosis may be useful when there are not enough specialists to review the images. This was made possible through deep learning algorithms in combination with the increasing availability of databases for the tasks of detection, segmentation, and classification [57].

The first step is to set up the virtual environment for your chatbot; and for this, you need to install a python module. Once this has been done, you can proceed with creating the structure for the chatbot. All these platforms, except for Slack, provide a Quick Reply as a suggested action that disappears once clicked.

You can foun additiona information about ai customer service and artificial intelligence and NLP. However, machines do not have the human capabilities of prudence and practical wisdom or the flexible, interpretive capacity to correct mistakes and wrong decisions. As a result of self-diagnosis, physicians may have difficulty convincing patients of their potential preliminary, chatbot-derived misdiagnosis. This level of persuasion and negotiation increases the workload of professionals and creates new tensions between patients and physicians. Following Pasquale (2020), we can divide the use of algorithmic systems, such as chatbots, into two strands. First, there are those that use ML ‘to derive new knowledge from large datasets, such as improving diagnostic accuracy from scans and other images’.

By streamlining workflows across different departments within hospitals or clinics, chatbots contribute significantly to cost savings for healthcare organizations. They ensure that communication between medical professionals is seamless and efficient, minimizing delays in patient care. For example, when a physician prescribes medication, a chatbot can automatically send an electronic prescription directly to pharmacies, eliminating the need for manual intervention. During emergencies or when seeking urgent medical advice, chatbot platforms offer immediate assistance. Patients can rely on these conversational agents for quick access to help and guidance.

Apart from our sponsor Zoho SalesIQ, chatbots are sorted by category and functionality. These categories can be divided into general health advice and chatbots working in specific areas (mental, cancer). Overall, though, Copilot appeared to be unhinged from the start of the conversation. When Fraser began talking to the chatbot, he asked it to please refrain from using emojis, as they caused him panic attacks. Copilot proceeded to use emojis in all six of its responses, even though it swore it wouldn’t.

If you’re looking to get a personalized consultation and diagnosis validation from a doctor, it will cost $99 for each consultation. Second, they eliminate geographic barriers, bringing access to expert medical advice to anyone that has access to the internet globally. Get a free excess of our exclusive research and tech strategies to level up your knowledge about the digital realm. She is a project leader at Mindinventory having the past experience as an iOS developer who loves exploring everything that’s in trend! In her free time, she likes to delve into various project management tools and apply her findings in her projects.

When testing is complete and this product hits the market, it will be an amazing alternative medical advice tool. Similarly, several health conditions are often connected with experiences of societal stigma, including diabetes, eating disorders, human immunodeficiency virus, and sexually transmitted infections. These conditions frequently trigger public misconceptions, discriminatory attitudes, and feelings of societal stigmatization. A company can utilize chatbots for sending files to new employees whenever required, reminding new employees automatically for finishing their forms, and automating several other jobs like requests for maternity leave, vacation time, and more. 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 application should be in line with up-to-date medical regulations, ethical codes and research data. Studies on the use of chatbots for mental health, in particular anxiety and depression, also seem to show potential, with users reporting positive outcomes on at least some of the measurements taken [33,34,41]. Surprisingly, there is no obvious correlation between application domains, chatbot purpose, and mode of communication (see Multimedia Appendix 2 [6,8,9,16-18,20-45]). Some studies did indicate that the use of natural language was not a necessity for a positive conversational user experience, especially for symptom-checking agents that are deployed to automate form filling [8,46]. In another study, however, not being able to converse naturally was seen as a negative aspect of interacting with a chatbot [20].

This is particularly relevant in today’s situation, where the spread of COVID-19 puts an unprecedented burden on healthcare providers. With the support of this technology, medical centers could ease the pressure on their systems significantly. Chatbots streamline patient data collection by gathering essential information like medical history, current symptoms, and personal health data. For example, chatbots integrated with electronic health records (EHRs) can update patient profiles in real-time, ensuring that healthcare providers have the latest information for diagnosis and treatment. Healthcare professionals can now efficiently manage resources and prioritize clinical cases using artificial intelligence chatbots.