Extending Patient-Chatbot Experience with Internet-of-Things and Background Knowledge: Case Studies with Healthcare Applications PMC

Still, they’re especially helpful in medicine because they make it easier for doctors to access their patient records, cases, health and appointments data and update them in real time whenever necessary. Acting as 24/7 virtual assistants, healthcare chatbots efficiently respond to patient inquiries. This immediate interaction is crucial, especially for answering general health queries or providing information about hospital services. A notable example is an AI chatbot, which offers reliable answers to common health questions, helping patients to make informed decisions about their health and treatment options. In the future, healthcare chatbots will get better at interacting with patients. The industry will flourish as more messaging bots become deeply integrated into healthcare systems.

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]. One response to these issues involved the deployment of chatbots as a scalable, easy to use, quick to deploy, social-distanced solution. Chatbots are algorithms that interact with users using natural language, either text or voice,4,5 with their distinguishing feature being natural language conversational interactions. Given the use of chatbots in a variety of settings prior to Covid-19, the existing infrastructure and abundance of prebuilt modules resulted in their rapid development and deployment to address Covid-19 needs.

Assess your needs, considering desired chatbot healthcare use cases

To discover how Yellow.ai can revolutionize your healthcare services with a bespoke chatbot, book a demo today and take the first step towards an AI-powered healthcare future. Many patients find making appointments with their preferred mental health practitioners difficult due to waiting times and costs. Going in person to speak to someone can also be an insurmountable hurdle for those who feel uncomfortable discussing their mental health needs in person. The QliqSOFT chatbot provides patients with care information and guidelines for recovery, allowing them to access information and ask questions at any time. Tars offers clinics and diagnostic centers a smoother alternative to the traditional contact form, collecting patient information for healthcare facilities through their chatbots.

Regulatory standards have been developed to accommodate for rapid modifications and ensure the safety and effectiveness of AI technology, including chatbots. 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 chatbot use cases in healthcare 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]. On the other hand, overregulation may diminish the value of chatbots and decrease the freedom for innovators.

Instant access to medical knowledge

Consequently, balancing these opposing aspects is essential to promote benefits and reduce harm to the health care system and society. Hesitancy from physicians and poor adoption by patients is a major barrier to overcome, which could be explained by many of the factors discussed in this section. A cross-sectional web-based survey of 100 practicing physicians gathered the perceptions of chatbots in health care [6].

Thus, algorithms are an actualisation of reason in the digital domain (e.g. Finn 2017; Golumbia 2009). However, it is worth noting that formal models, such as game-theoretical models, do not completely describe reality or the phenomenon in question and its processes; they grasp only a slice of the phenomenon. Reaching beyond the needs of the patients, hospital staff can also benefit from chatbots. A chatbot can be used for internal record- keeping of hospital equipment like beds, oxygen cylinders, wheelchairs, etc. Whenever team members need to check the availability or the status of equipment, they can simply ask the bot. The bot will then fetch the data from the system, thus making operations information available at a staff member’s fingertips.

The number of studies assessing the development, implementation, and effectiveness are still relatively limited compared with the diversity of chatbots currently available. Further studies are required to establish the efficacy across various conditions and populations. Nonetheless, chatbots for self-diagnosis are an effective way of advising patients as the first point of contact if accuracy and sensitivity requirements can be satisfied. One of the consequences can be the shift from operator to supervisor, that is, expert work becomes more about monitoring and surveillance than before (Zerilli et al. 2019).

Chatbots are unable to efficiently cope with these errors because of the lack of common sense and the inability to properly model real-world knowledge [105]. 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.

Providing mental health support

This enabled swift response to potential cases and eased the burden on clinicians. Many chatbots are also equipped with natural language processing (NLP) technology, meaning that through careful conversation design, they can understand a range of questions and process healthcare-related queries. They then generate an answer using language that the user is most likely to understand, allowing users to have a smooth, natural-sounding interaction with the bot. There were 47 (31%) apps that were developed for a primary care domain area and 22 (14%) for a mental health domain.

Since the bot records the appointments for all patients, it can also be programmed to send reminder notifications and things to carry before the appointment. It eliminates the need for hospital administrators to do the same manually over a call. This healthcare chatbot use case is reliable because it reduces errors and is intuitive since the user gets a quick overview of the available spots. Witnessing the success of this, a lot of major healthcare institutions followed suit and deployed a healthcare chatbot during the pandemic that provided information about common diseases, their symptoms, and other precautionary methods. Studies show that chatbots in healthcare are expected to grow at an exponential rate of 19.16% from 2022 to 2030. This growth can be attributed to the fact that chatbot technology in healthcare is doing more than having conversations.

These chatbots engage users in therapeutic conversations, helping them cope with anxiety, depression, and stress. The accessibility and anonymity of these chatbots make them a valuable tool for individuals hesitant to seek traditional therapy. By quickly assessing symptoms and medical history, they can prioritize patient cases and guide them to the appropriate level of care. This efficient sorting helps in managing patient flow, especially in busy clinics and hospitals, ensuring that critical cases get timely attention and resources are optimally utilized. Healthcare chatbots revolutionize patient interaction by providing a platform for continuous and personalized communication.

• To do that, the application must employ NLP algorithms and have the latest knowledge base to draw insights.
• The systematic literature review and chatbot database search includes a few limitations.
• Chatbots not only automate the process of gathering patient data but also follows a more engaging experience for the patients since they’re conversational in their approach.
• With so many algorithms and tools around, knowing the different types of chatbots in healthcare is key.
• A patient can open the chat window and self-schedule a visit with their doctor using a bot.

Chatbot for healthcare help providers effectively bridges the communication and education gaps. Automating connection with a chatbot builds trust with patients by providing timely answers to questions and delivering health education. Undoubtedly, chatbots have great potential to transform the healthcare industry. They can substantially boost efficiency and improve the accuracy of symptom detection, preventive care, post-recovery care, and feedback procedures. Chatbots can help doctors communicate with patients more conveniently than ever before.

Chatbots experience the Black

Box problem, which is similar to many computing systems programmed using ML that are trained on massive data sets to produce multiple layers of connections. Although they are capable of solving complex problems that are unimaginable by humans, these systems remain highly opaque, and the resulting solutions may be unintuitive. This means that the systems’ behavior is hard to explain by merely looking inside, and understanding exactly how they are programmed is nearly impossible. For both users and developers, transparency becomes an issue, as they are not able to fully understand the solution or intervene to predictably change the chatbot’s behavior [97]. 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.

Customized chat technology helps patients avoid unnecessary lab tests or expensive treatments. Patients can use text, microphones, or cameras to get mental health assistance to engage with a clinical chatbot. AI chatbots in the healthcare industry are great at automating everyday responsibilities in the healthcare setting. They assist users in identifying symptoms and guide individuals to seek professional medical advice if needed. AI-powered chatbots have been one of the year’s top topics, with ChatGPT, Bard, and other conversational agents taking center stage. For healthcare businesses, the adoption of chatbots may become a strategic advantage.

With healthcare chatbots, a healthcare provider can quickly respond to patient queries and provide follow-up care, improving healthcare outcomes. Chatbots can also provide healthcare advice about common ailments or share resources such as educational materials and further information about other healthcare services. This means that they are incredibly useful in healthcare, transforming the delivery of care and services to be more efficient, effective, and convenient for both patients and healthcare providers. Input modality, or how the user interacts with the chatbot, was primarily text-based (96%), with seven apps (9%) allowing for spoken/verbal input, and three (4%) allowing for visual input. 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.

• Machine learning (ML) is a subset of AI that improves its performance based on the data provided to a generic algorithm from experience rather than defining rules in traditional approaches [1].
• If the person wants to keep track of their weight, bots can help them record body weight each day to see improvements over time.
• You can also leverage outbound bots to ask for feedback at their preferred channel like SMS or WhatsApp and at their preferred time.
• Chatbot for healthcare help providers effectively bridges the communication and education gaps.
• About 67% of all support requests were handled by the bot and there were 55% more conversations started with Slush than the previous year.
• This efficient sorting helps in managing patient flow, especially in busy clinics and hospitals, ensuring that critical cases get timely attention and resources are optimally utilized.

The ability to scale up rapidly allows healthcare providers to maintain quality care even under challenging circumstances. They will be equipped to identify symptoms early, cross-reference them with patients’ medical histories, and recommend appropriate actions, significantly improving the success rates of treatments. This proactive approach will be particularly beneficial in diseases where early detection is vital to effective treatment. Chatbots with access to medical databases retrieve information on doctors, available slots, doctor schedules, etc. Patients can manage appointments, find healthcare providers, and get reminders through mobile calendars. This way, appointment-scheduling chatbots in the healthcare industry streamline communication and scheduling processes.