Healthcare Chatbots: Benefits, Use Cases, and Top Tools

Chatbot for Health Care and Oncology Applications Using Artificial Intelligence and Machine Learning: Systematic Review PMC

Conversational chatbots with different intelligence levels can understand the questions of the user and provide answers based on pre-defined labels in the training data. This can help the facility avoid cases where bills were sent to patients with no coverage. A chatbot can also help a healthcare facility determine what types of insurance plans they accept and how much they will reimburse for specific services or procedures.

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. Gathering user feedback is essential to understand how well your chatbot is performing and whether it meets user demands. Collect information about issues reported by users and send it to software engineers so that they can troubleshoot unforeseen problems.

Solutions

References to case numbers below refer to the corresponding chatbots in Appendix 1. Statista reports that approximately 92% of students globally express interest in receiving personalized support and information regarding their degree progress. The majority (28/32, 88%) of the studies contained very little description of the technical implementation of the chatbot, which made it difficult to classify the chatbots from this perspective. Most (19/32, 59%) of the included papers included screenshots of the user interface.

Chatbots for mental health can help patients feel better by having a conversation with the person. Patients can talk about their stress, anxiety, or any other feelings they’re experiencing at the time. This can provide people with an effective outlet to discuss their emotions and deal with them better. Chatbots can collect the patients’ data to create fuller medical profiles you can work with. And this is one of the chatbot use cases in healthcare that can be connected with some of the other medical chatbot’s features. Chatbots can help physicians, patients, and nurses with better organization of a patient’s pathway to a healthy life.

Associated Data

Not only can customers book through the chatbot, but they can also ask questions about the tests that will be conducted and get answers in real time. Finally, the issue of fairness arises with algorithm bias when data used chatbot healthcare use cases to train and test chatbots do not accurately reflect the people they represent [101]. As the AI field lacks diversity, bias at the level of the algorithm and modeling choices may be overlooked by developers [102].

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].

What is a healthcare chatbot, and what does it do?

Integration with a hospital’s internal systems is required to run administrative tasks like appointment scheduling or prescription refill request processing. If you want your company to benefit financially from AI solutions, knowing the main chatbot use cases in healthcare is the key. Furthermore, you can also contact us if you need assistance in setting up healthcare or a medical chatbot. But the problem arises when there are a growing number of patients and you’re left with a limited staff.

In a study using 2 cases, differences in prediction accuracy were shown concerning gender and insurance type for intensive care unit mortality and psychiatric readmissions [103]. On a larger scale, this may exacerbate barriers to health care for minorities or underprivileged individuals, leading to worse health outcomes. Identifying the source of algorithm bias is crucial for addressing health care disparities between various demographic groups and improving data collection. 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. Issues to consider are privacy or confidentiality, informed consent, and fairness.

The bottom line

Insurance bots offer a wide range of valuable chatbot use cases for both insurance providers and customers. These AI-powered chatbot can efficiently provide policy information, generate personalized insurance quotes, and compare various insurance products to help customers make informed decisions. This result is possibly an artifact of the maturity of the research that has been conducted in mental health on the use of chatbots and the massive surge in the use of chatbots to help combat COVID-19. The graph in Figure 2 thus reflects the maturity of research in the application domains and the presence of research in these domains rather than the quantity of studies that have been conducted. Studies were included if they used or evaluated chatbots for the purpose of prevention or intervention and for which the evidence showed a demonstrable health impact.

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. Further refinements and large-scale implementations are still required to determine the benefits across different populations and sectors in health care [26]. Although overall satisfaction is found to be relatively high, there is still room for improvement by taking into account user feedback tailored to the patient’s changing needs during recovery. In combination with wearable technology and affordable software, chatbots have great potential to affect patient monitoring solutions. 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.

This enabled swift response to potential cases and eased the burden on clinicians. But if the issue is serious, a chatbot can transfer the case to a human representative through human handover, so that they can quickly schedule an appointment. Livi, a conversational AI-powered chatbot implemented by UCHealth, has been helping patients pay better attention to their health. The use case for Livi started with something as simple as answering simple questions.

• Everyone wants a safe outlet to express their innermost fears and troubles and Woebot provides just that—a mental health ally.
• These numerous use cases for chatbots have contributed to their widespread adoption as virtual assistants.
• They can answer reactions to your Instagram stories, communicate with your Facebook followers, and chat with people interested in specific products.
• This provides you with relevant data and ensures your customers are happy with their experience on your site.
• This psychiatric counseling chatbot was effective in engaging users and reducing anxiety in young adults after cancer treatment [40].

Motivational interview–based chatbots have been proposed with promising results, where a significant number of patients showed an increase in their confidence and readiness to quit smoking after 1 week [92]. No studies have been found to assess the effectiveness of chatbots for smoking cessation in terms of ethnic, racial, geographic, or socioeconomic status differences. Creating chatbots with prespecified answers is simple; however, the problem becomes more complex when answers are open.

Evidence for the Efficacy of Chatbot-Based Health Interventions

Chatbots, also known as chatter robots, smart bots, conversational agents, digital assistants, or intellectual agents, are prime examples of AI systems that have evolved from ML. The Oxford dictionary defines a chatbot as “a computer program that can hold a conversation with a person, usually over the internet.” They can also be physical entities designed to socially interact with humans or other robots. Predetermined responses are then generated by analyzing user input, on text or spoken ground, and accessing relevant knowledge [3]. Problems arise when dealing with more complex situations in dynamic environments and managing social conversational practices according to specific contexts and unique communication strategies [4]. The evidence cited in most of the included studies either measured the effect of the intervention or surface and self-reported user satisfaction.

A patient can open the chat window and self-schedule a visit with their doctor using a bot. Just remember that the chatbot needs to be connected to your calendar to give the right dates and times for appointments. After they schedule an appointment, the bot can send a calendar invitation for the patient to remember about the visit. It used a chatbot to address misunderstandings and concerns about the colonoscopy and encourage more patients to follow through with the procedure. This shows that some topics may be embarrassing for patients to discuss face-to-face with their doctor. Zalando uses its chatbots to provide instant order tracking straight after the customer makes a purchase.