AI Healthcare Chatbots Everything You Need to Know

Although this may seem as an attractive option for patients looking for a fast solution, computers are still prone to errors, and bypassing professional inspection may be an area of concern. Chatbots may also be an effective resource for patients who want to learn why a certain treatment is necessary. Madhu et al [31] proposed an interactive chatbot app that provides a use of chatbots in healthcare list of available treatments for various diseases, including cancer. This system also informs the user of the composition and prescribed use of medications to help select the best course of action. The diagnosis and course of treatment for cancer are complex, so a more realistic system would be a chatbot used to connect users with appropriate specialists or resources.

Although, if you’re looking for a basic chatbot assisting your website visitors, we advise you to take a look at some existing solutions like Smith.ai, Acobot, or Botsify. Healthcare professionals can’t reach and screen everyone who may have symptoms of the infection; therefore, leveraging AI health bots could make the screening process fast and efficient. With the growing spread of the disease, there comes a surge of misinformation and diverse conspiracy theories, which could potentially cause the pandemic curve to keep rising. Therefore, it has become necessary to leverage digital tools that disseminate authoritative healthcare information to people across the globe. 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.

We recommend using ready-made SDKs, libraries, and APIs to keep the chatbot development budget under control. This practice lowers the cost of building the app, but it also speeds up the time to market significantly. Furthermore, Rasa also allows for encryption and safeguarding all data transition between its NLU engines and dialogue management engines to optimize data security. As you build your HIPAA-compliant chatbot, it will be essential to have 3rd parties audit your setup and advise where there could be vulnerabilities from their experience. For example, if a chatbot is designed for users residing in the United States, a lookup table for “location” should contain all 50 states and the District of Columbia.

Patients can trust that they will receive accurate and up-to-date information from chatbots, which is essential for making informed healthcare decisions. In the event of a medical emergency, chatbots can instantly provide doctors with patient information such as medical history, allergies, past records, check-ups, and other important details. With its seamless integration and intelligent functionalities, Voiceoc emerges as a transformative solution in the healthcare landscape, driving efficiency, satisfaction, and improved patient outcomes. Voiceoc’s comprehensive suite of AI-driven features positions it as a powerful tool for healthcare organizations looking to enhance patient care delivery and streamline operations. Chatbots are limited to handling simple queries and should not be relied upon for complex medical advice. Ensuring clear communication of chatbot capabilities and limitations is essential to avoid misinterpretation and potential harm to patients.

Patients can use chatbots to receive valuable information about their health conditions directly, empowering them with knowledge to make informed decisions about their well-being. Whether it’s explaining symptoms, treatment options, or medication instructions, chatbots serve as virtual assistants that ensure patients are well-informed about their medical concerns. Chatbots are now able to provide patients with treatment and medication information after diagnosis without having to directly contact a physician. Such a system was proposed by Mathew et al [30] that identifies the symptoms, predicts the disease using a symptom–disease data set, and recommends a suitable treatment.

Through chatbots (and their technical functions), we can have only a very limited view of medical knowledge. The ‘rigid’ and formal systems of chatbots, even with the ML bend, are locked in certain a priori models of calculation. Expertise generally requires the intersubjective circulation of knowledge, that is, a pool of dynamic knowledge and intersubjective criticism of data, knowledge and processes (e.g. Prior 2003; Collins and Evans 2007).

What chatbot building platforms do you recommend to spearhead my bot development?

The advantages of chatbots in healthcare are enormous – and all stakeholders share the benefits. Chatbots have already gained traction in retail, news media, social media, banking, and customer service. From catching up on sports news to navigating bank applications to playing conversation-based games on Facebook Messenger, chatbots are revolutionizing the way we live. Sophisticated AI-based chatbots require a great deal of human resources, for instance, experts of data analytics, whose work also needs to be publicly funded.

With chatbots implemented in cancer care, consultations for minor health concerns may be avoided, which allows clinicians to spend more time with patients who need their attention the most. For example, the workflow can be streamlined by assisting physicians in administrative tasks, such as scheduling appointments, providing medical information, or locating clinics. Many health professionals and experts have emphasised that chatbots are not sufficiently mature to be able to technically diagnose patient conditions or replace health professional assessments (Palanica et al. 2019). Although some applications can provide assistance in terms of real-time information on prognosis and treatment effectiveness in some areas of health care, health experts have been concerned about patient safety (McGreevey et al. 2020). A pandemic can accelerate the digitalisation of health care, but not all consequences are necessarily predictable or positive from the perspectives of patients and professionals.

Capacity’s conversational AI platform enables graceful human handoffs and intuitive task management via a powerful workflow automation suite, robust developer platform, and flexible database that can be deployed anywhere. Healthcare chatbot diagnoses rely on artificial intelligence algorithms that continuously learn from vast amounts of data. 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 United States had the highest number of total downloads (~1.9 million downloads, 12 apps), followed by India (~1.4 million downloads, 13 apps) and the Philippines (~1.25 million downloads, 4 apps). Details on the number of downloads and app across the 33 countries are available in Appendix 2.

The implementation of chatbots also benefits healthcare teams by allowing them to focus on more critical tasks rather than spending excessive time managing appointment schedules manually. By automating this administrative aspect, medical professionals can dedicate more attention to patient care and complex cases that require their expertise. Furthermore, chatbots contribute to enhancing patient experience in the healthcare industry by providing round-the-clock support for health systems. Unlike traditional customer service hotlines that operate within limited hours, chatbots are available 24/7. This accessibility ensures that patients in the healthcare industry can seek assistance whenever they need it most, regardless of the time zone or geographical location they are in.

Since the 1950s, there have been efforts aimed at building models and systematising physician decision-making. For example, in the field of psychology, the so-called framework of ‘script theory’ was ‘used to explain how a physician’s medical diagnostic knowledge is structured for diagnostic problem solving’ (Fischer and Lam 2016, p. 24). According to this theory, ‘the medical expert has an integrated network of prior knowledge that leads to an expected outcome’ (p. 24).

Building an AI Experience for Healthcare

This resulted in the drawback of not being able to fully understand the geographic distribution of healthbots across both stores. These data are not intended to quantify the penetration of healthbots globally, but are presented to highlight the broad global reach of such interventions. Another limitation stems from the fact that in-app purchases were not assessed; therefore, this review highlights features and functionality only of apps that are free to use. Lastly, our review is limited by the limitations in reporting on aspects of security, privacy and exact utilization of ML.

The chatbot provides users with evidence-based tips, relying on a massive patient data set, plus, it works really well alongside other treatment models or can be used on its own. Now that you have understood the basic principles of conversational flow, it is time to outline a dialogue flow for your chatbot. This forms the framework on which a chatbot interacts with a user, and a framework built on these principles creates a successful chatbot experience whether you’re after chatbots for medical providers or patients. Although still in its early stages, chatbots will not only improve care delivery, but they will also lead to significant healthcare cost savings and improved patient care outcomes in the near future. Do medical chatbots powered by AI technologies cause significant paradigm shifts in healthcare? Chatbots must be designed with the user in mind, providing patients a seamless and intuitive experience.

In addition, the development of algorithmic systems for health services requires a great deal of human resources, for instance, experts of data analytics whose work also needs to be publicly funded. A complete system also requires a ‘back-up system’ or practices that imply increased costs and the emergence of new problems. The crucial question that policy-makers are faced with is what kind of health services can be automated and translated into machine readable form.

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).

Engaging patients in their own healthcare journey is crucial for successful treatment outcomes. Chatbots play a vital role in fostering patient engagement by facilitating interactive conversations. Patients can communicate with chatbots to seek information about their conditions, medications, or treatment plans anytime they need it.

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. Both of these reviews focused on healthbots that were available in scientific literature only and did not include commercially available apps. Our study leverages and further develops the evaluative criteria developed by Laranjo et al. and Montenegro et al. to assess commercially available health apps9,32. With the rapidly increasing applications of chatbots in health care, this section will explore several areas of development and innovation in cancer care. Various examples of current chatbots provided below will illustrate their ability to tackle the triple aim of health care.

We’re app developers in Miami and California, feel free to reach out if you need more in-depth research into what’s already available on the off-the-shelf software market or if you are unsure how to add AI capabilities to your healthcare chatbot. The challenge here for software developers is to keep training chatbots on COVID-19-related verified updates and research data. As researchers uncover new symptom patterns, these details need to be integrated into the ML training data https://chat.openai.com/ to enable a bot to make an accurate assessment of a user’s symptoms at any given time. A drug bot answering questions about drug dosages and interactions should structure its responses for doctors and patients differently. Before chatbots, we had text messages that provided a convenient interface for communicating with friends, loved ones, and business partners. In fact, the survey findings reveal that more than 82 percent of people keep their messaging notifications on.

Other applications in pandemic support, global health, and education are yet to be fully explored. With a 99.9% uptime, healthcare professionals can rely on chatbots to assist and engage with patients as needed, providing answers to their queries at any time. With the eHealth chatbot, users submit their symptoms, and the app runs them against a database of thousands of conditions that fit the mold. This is followed by the display of possible diagnoses and the steps the user should take to address the issue – just like a patient symptom tracking tool. This AI chatbot for healthcare has built-in speech recognition and natural language processing to analyze speech and text to produce relevant outputs. Chatbots are software developed with machine learning algorithms, including natural language processing (NLP), to stimulate and engage in a conversation with a user to provide real-time assistance to patients.

With the advent of phenotype–genotype predictions, chatbots for genetic screening would greatly benefit from image recognition. New screening biomarkers are also being discovered at a rapid speed, so continual integration and algorithm training are required. These findings align with studies that demonstrate that chatbots have the potential to improve user experience and accessibility and provide accurate data collection [66].

Woebot is a chatbot designed by researchers at Stanford University to provide mental health assistance using cognitive behavioral therapy (CBT) techniques. People who suffer from depression, anxiety disorders, or mood disorders can converse with this chatbot, which, in turn, helps people treat themselves by reshaping their behavior and thought patterns. Although prescriptive chatbots are conversational by design, they are built not just to answer questions or provide direction, but to offer therapeutic solutions.

This chatbot solution for healthcare helps patients get all the details they need about a cancer-related topic in one place. Buoy Health was built by a team of doctors and AI developers through the Harvard Innovation Laboratory. Trained on clinical data from more than 18,000 medical articles and journals, Buoy’s chatbot for medical diagnosis provides users with their likely diagnoses and accurate answers to their health questions. Conversational chatbots use natural language processing (NLP) and natural language understanding (NLU), applications of AI that enable machines to understand human language and intent. There are three primary use cases for the utilization of chatbot technology in healthcare – informative, conversational, and prescriptive.

• From medical database integration to triage protocols and personalized patient flows, these virtual assistants are designed to adapt to diverse scenarios seamlessly.
• By using AI to offer personalized recommendations for healthy habits, such as exercise routines or dietary guidelines, they encourage patients to adopt healthier lifestyles.
• Chatbot apps were downloaded globally, including in several African and Asian countries with more limited smartphone penetration.
• This article contributes to the discussion on the ethical challenges posed by chatbots from the perspective of healthcare professional ethics.

Therapy chatbots that are designed for mental health, provide support for individuals struggling with mental health concerns. These chatbots are not meant to replace licensed Chat PG mental health professionals but rather complement their work. Cognitive behavioral therapy can also be practiced through conversational chatbots to some extent.

More simple solutions can lead to new costs and workload when the usage of new technology creates unexpected problems in practice. Thus, new technologies require system-level assessment of their effects in the design and implementation phase. Healthcare chatbots have been instrumental in addressing public health concerns, especially during the COVID-19 pandemic.

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]. The chatbot’s personalized suggestions are based on algorithms and refined based on the user’s past responses. The removal of options may slowly reduce the patient’s awareness of alternatives and interfere with free choice [100]. Most would assume that survivors of cancer would be more inclined to practice health protection behaviors with extra guidance from health professionals; however, the results have been surprising.

In this paper, we seek to explore the primary uses of chatbots in medical education, as well as how they are developed. Additionally, we examine the metrics that have been used to evaluate these chatbots, which include subjective ones like the usability and acceptability by the users, and objectives ones, like their accuracy and users’ skills evaluation. Overall, even though chatbots offer a flexible solution and a vast possibility to improve healthcare education, our literature review suggests that their efficacy has not been thoroughly tested. Healthcare chatbots are AI-powered virtual assistants that provide personalized support to patients and healthcare providers. They are designed to simulate human-like conversation, enabling patients to interact with them as they would with a real person. These chatbots are trained on healthcare-related data and can respond to many patient inquiries, including appointment scheduling, prescription refills, and symptom checking.

AI-based chatbots in customer service and their effects on user compliance

However, there are different levels of maturity to a conversational chatbot – not all of them offer the same depth of conversation. Furthermore, hospitals and private clinics use medical chat bots to triage and clerk patients even before they come into the consulting room. These bots ask relevant questions about the patients’ symptoms, with automated responses that aim to produce a sufficient medical history for the doctor. Subsequently, these patient histories are sent via a messaging interface to the doctor, who triages to determine which patients need to be seen first and which patients require a brief consultation. Neither does she miss a dose of the prescribed antibiotic – a healthcare chatbot app brings her up to speed on those details.

These intelligent systems are designed with secure data encryption protocols that safeguard sensitive patient information from unauthorized access or breaches. By adhering to strict security measures, chatbots ensure that patient privacy remains intact throughout every interaction. You can foun additiona information about ai customer service and artificial intelligence and NLP. One of the key advantages of chatbots is their ability to offer up-to-date information about testing centers, vaccination sites, and updated pandemic guidelines. With the constantly evolving nature of the virus, having access to accurate and timely information is crucial. Chatbots can provide users with a list of nearby testing centers or vaccination sites based on their location, ensuring they have easy access to these important resources.

While healthcare professionals can only attend to one patient at a time, chatbots can engage and assist multiple customers simultaneously without compromising the quality of interaction or information provided. By automating routine tasks and providing initial consultations, chatbots contribute to significant cost savings for healthcare providers. By harnessing the power of natural language processing (NLP) algorithms, conversational AI tools can decipher clinical terminology, extract relevant information from patient inputs, and offer personalized recommendations.

Similarly, conversations between men and machines are not nearly judged by the outcome but by the ease of the interaction. This concept is described by Paul Grice in his maxim of quantity, which depicts that a speaker gives the listener only the required information, in small amounts. If you look up articles about flu symptoms on WebMD, for instance, a chatbot may pop up with information about flu treatment and current outbreaks in your area. At the forefront for digital customer experience, Engati helps you reimagine the customer journey through engagement-first solutions, spanning automation and live chat.

This provides a seamless and efficient experience for patients seeking medical attention on your website. Taking healthcare assistance a step further, prescriptive chatbots offer personalized medical advice based on user-provided information. Capable of addressing specific queries, conversational chatbots engage users in meaningful interactions. Utilizing NLP algorithms, they learn and adapt to user inputs, providing tailored responses to diverse healthcare inquiries. One stream of healthcare chatbot development focuses on deriving new knowledge from large datasets, such as scans.

The key is to know your audience and what best suits them and which chatbots work for what setting. The higher the intelligence of a chatbot, the more personal responses one can expect, and therefore, better customer assistance. As a result of patient self-diagnoses, physicians may have difficulty convincing patients of their potential preliminary misjudgement. This persuasion and negotiation may increase the workload of professionals and create new tensions between patients and physicians. By leveraging chatbot technology for survey administration, hospitals and clinics can achieve higher response rates compared to traditional methods like paper-based surveys or phone interviews.

Capacity is an AI-powered support automation platform that provides an all-in-one solution for automating support and business processes. It connects your entire tech stack to answer questions, automate repetitive support tasks, and build solutions to any business challenge. New technologies may form new gatekeepers of access to specialty care or entirely usurp human doctors in many patient cases. There were only six (8%) apps that utilized a theoretical or therapeutic framework underpinning their approach, including Cognitive Behavioral Therapy (CBT)43, Dialectic Behavioral Therapy (DBT)44, and Stages of Change/Transtheoretical Model45.

While our research team assessed the NLP system design for each app by downloading and engaging with the bots, it is possible that certain aspects of the NLP system design were misclassified. Seventy-four (53%) apps targeted patients with specific illnesses or diseases, sixty (43%) targeted patients’ caregivers or healthy individuals, and six (4%) targeted healthcare providers. Although there are a variety of techniques for the development of chatbots, the general layout is relatively straightforward. As a computer application that uses ML to mimic human conversation, the underlying concept is similar for all types with 4 essential stages (input processing, input understanding, response generation, and response selection) [14].

• The first step is to create an NLU training file that contains various user inputs mapped with the appropriate intents and entities.
• Many health professionals have taken to telemedicine to consult with their patients, allay fears, and provide prescriptions.
• Regulatory standards have been developed to accommodate for rapid modifications and ensure the safety and effectiveness of AI technology, including chatbots.
• 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.
• With the chatbot remembering individual patient details, patients can skip the need to re-enter their information each time they want an update.

By leveraging advanced algorithms and natural language processing (NLP), AI chatbots ensure efficient communication between patients and healthcare providers, fostering a more responsive and patient-centric approach to care delivery. Whether it’s scheduling appointments, answering FAQs, or providing timely reminders, healthcare chatbots streamline the patient experience, relieving the burden on medical staff and enhancing accessibility to healthcare services. Medical chatbots are especially useful since they can answer questions that definitely should not be ignored, questions asked by anxious patients or their caregivers, but which do not need highly trained medical professionals to answer. Since such tools avoid the need for patients to come in for an appointment just to have their questions answered, they can prevent wastage of time for both patients and healthcare providers while providing useful information in a timely fashion. Furthermore, social distancing and loss of loved ones have taken a toll on people’s mental health.

We acknowledge the difficulty in identifying the nature of systemic change and looking at its complex network-like structure in the functioning of health organisations. Nonetheless, we consider it important to raise this point when talking about chatbots and their potential breakthrough in health care. We suggest that new ethico-political approaches are required in professional ethics because chatbots can become entangled with clinical practices in complex ways. It is difficult to assess the legitimacy of particular applications and their underlying business interests using concepts drawn from universal AI ethics or traditional professional ethics inherited from bioethics. Insufficient consideration regarding the implementation of chatbots in health care can lead to poor professional practices, creating long-term side effects and harm for professionals and their patients.

In an era dominated by digital interaction, chatbots serve as indispensable tools in meeting the evolving demands of patients. 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. This safeguard includes designating people, either by job title or job description, who are authorized to access this data, as well as electronic access control systems, video monitoring, and door locks restricting access to the data.

For the output modality, or how the chatbot interacts with the user, all accessible apps had a text-based interface (98%), with five apps (6%) also allowing spoken/verbal output, and six apps (8%) supporting visual output. Visual output, in this case, included the use of an embodied avatar with modified expressions in response to user input. Eighty-two percent of apps had a specific task for the user to focus on (i.e., entering symptoms). The use of chatbots in health care presents a novel set of moral and ethical challenges that must be addressed for the public to fully embrace this technology.

Through the rapid deployment of chatbots, the tech industry may gain a new kind of dominance in health care. AI technologies, especially ML, have increasingly been occupying other industries; thus, these technologies are arguably naturally adapted to the healthcare sector. In most cases, it seems that chatbots have had a positive effect in precisely the same tasks performed in other industries (e.g. customer service).

They could also be thought of as decision aids that deliver regular feedback on disease progression and treatment reactions to help clinicians better understand individual conditions. Preventative measures of cancer have become a priority worldwide, as early detection and treatment alone have not been effective in eliminating this disease [22]. Physical, psychological, and behavioral improvements of underserved or vulnerable populations may even be possible through chatbots, as they are so readily accessible through common messaging platforms. Health promotion use, such as lifestyle coaching, healthy eating, and smoking cessation, has been one of the most common chatbots according to our search. In addition, chatbots could help save a significant amount of health care costs and resources. Newer therapeutic innovations have come with a heavy price tag, and out-of-pocket expenses have placed a significant strain on patients’ financial well-being [23].

While chatbots offer many benefits for healthcare providers and patients, several challenges must be addressed to implement them successfully. One of the key elements of expertise and its recognition is that patients and others can trust the opinions and decisions offered by the expert/professional. 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.

While chatbots can provide personalized support to patients, they cannot replace the human touch. Healthcare providers must ensure that chatbots are used in conjunction with, and not as a replacement for human healthcare professionals. For example, chatbots can schedule appointments, answer common questions, provide medication reminders, and even offer mental health support. These chatbots also streamline internal support by giving these professionals quick access to information, such as patient history and treatment plans. 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’.

When you are ready to invest in conversational AI, you can identify the top vendors using our data-rich vendor list on voice AI or chatbot platforms. Chatbots are integrated into the medical facility database to extract information about suitable physicians, available slots, clinics, and pharmacies  working days. Enhance patient engagement and minimize no-show rates with Voiceoc’s automated appointment reminder feature.

By sending regular reminders through messaging platforms, chatbots ensure that patients adhere to their prescribed medication schedules. 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.

You do not design a conversational pathway the way you perceive your intended users, but with real customer data that shows how they want their conversations to be. Furthermore, these chatbots play a vital role in addressing public health concerns like the ongoing COVID-19 pandemic. By offering symptom checkers and reliable information about the virus, they help alleviate anxiety among individuals and ensure appropriate actions are taken based on symptoms exhibited. Moreover, chatbots simplify appointment scheduling by allowing patients to book appointments online or through messaging platforms.

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]. For example, Medical Sieve (IBM Corp) is a chatbot that examines radiological images to aid and communicate with cardiologists and radiologists to identify issues quickly and reliably [24]. Similarly, InnerEye (Microsoft Corp) is a computer-assisted image diagnostic chatbot that recognizes cancers and diseases within the eye but does not directly interact with the user like a chatbot [42]. Even with the rapid advancements of AI in cancer imaging, a major issue is the lack of a gold standard [58].

Voiceoc enables patients to securely access both their latest and historical reports and invoices. Integrated with healthcare information systems, Voiceoc ensures data privacy and accuracy, allowing patients to retrieve essential medical documents conveniently. This functionality transforms the diagnostic journey, enabling healthcare providers to deliver personalized care and suggestions based on thorough data analysis and expert insights. Healthcare chatbots play a crucial role in patient education by providing personalized, easy-to-understand information about diseases, treatments, and preventive measures. Through interactive conversations, chatbots empower patients to actively participate in their healthcare journey, leading to better health outcomes. From symptom assessment to medication management, these bots cater to a spectrum of patient needs.