Chatbot breakthrough in the 2020s? An ethical reflection on the trend of automated consultations in health care PMC

Plus, a chatbot in the medical field should fully comply with the HIPAA regulation. Recently, Google Cloud launched an AI chatbot called Rapid Response Virtual Agent Program to provide information chatbot technology in healthcare to users and answer their questions about coronavirus symptoms. Google has also expanded this opportunity for tech companies to allow them to use its open-source framework to develop AI chatbots.

• And while some innovations may be too complex or expensive to implement, there is one that is highly affordable and efficient, and it’s a healthcare chatbot.
• Collect information about issues reported by users and send it to software engineers so that they can troubleshoot unforeseen problems.
• With psychiatric disorders affecting at least 35% of patients with cancer, comprehensive cancer care now includes psychosocial support to reduce distress and foster a better quality of life [80].

In another study, however, not being able to converse naturally was seen as a negative aspect of interacting with a chatbot [20]. The Physician Compensation Report states that, on average, doctors have to dedicate 15.5 hours weekly to paperwork and administrative tasks. With this in mind, customized AI chatbots are becoming a necessity for today’s healthcare businesses. The technology takes on the routine work, allowing physicians to focus more on severe medical cases. The technology helped the University Hospitals system used by healthcare providers to screen 29,000 employees for COVID-19 symptoms daily.

HealthTech Magazine

Similarly, one can see the rapid response to COVID-19 through the use of chatbots, reflecting both the practical requirements of using chatbots in triage and informational roles and the timeline of the pandemic. If you think of a custom chatbot solution, you need one that is easy to use and understand. This can be anything from nearby facilities or pharmacies for prescription refills to their business hours. 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. Let’s check how an AI-driven chatbot in the healthcare industry works by exploring its architecture in more detail.

The Rasa Core is the chatbot framework that predicts the next best action using a deep learning model. GYANT, HealthTap, Babylon Health, and several other medical chatbots use a hybrid chatbot model that provides an interface for patients to speak with real doctors. The app users may engage in a live video or text consultation on the platform, bypassing hospital visits. A user interface is the meeting point between men and computers; the point where a user interacts with the design. Depending on the type of chatbot, developers use a graphical user interface, voice interactions, or gestures, all of which use different machine learning models to understand human language and generate appropriate responses.

Chatbot Cuts Care-Related Costs

The knowledge used in the chatbot is humanly hand-coded and is organized and presented with conversational patterns [28]. A more comprehensive rule database allows the chatbot to reply to more types of user input. However, this type of model is not robust to spelling and grammatical mistakes in user input. Most existing research on rule-based chatbots studies response selection for single-turn conversation, which only considers the last input message.

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).

Template-based questions like greetings and general questions can be answered using AIML while other unanswered questions use LSA to give replies [30]. Our industry-leading expertise with app development across healthcare, fintech, and ecommerce is why so many innovative companies choose us as their technology partner. 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.

Hence, every healthcare services provider needs to think about ways of strengthening their digital environment, including chatbots. 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. Note though that a prescriptive chatbot cannot replace a doctor, and medical consultation is still needed. However, these bots can at least help patients understand what kind of treatment to request and what might be the issue, which is already a good start.

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].

In more human-like chatbots, multi-turn response selection takes into consideration previous parts of the conversation to select a response relevant to the whole conversation context [37]. Classification based on the service provided considers the sentimental proximity of the chatbot to the user, the amount of intimate interaction that takes place, and it is also dependent upon the task the chatbot is performing. Interpersonal chatbots lie in the domain of communication and provide services such as Restaurant booking, Flight booking, and FAQ bots.

Conversational Chatbots

First, the user makes a request, in text or speech format, which is received and interpreted by the chatbot. From there, the processed information could be remembered, or more details could be requested for clarification. After the request is understood, the requested actions are performed, and the data of interest are retrieved from the database or external sources [15].

Studies on the use of chatbots for mental health, in particular depression, also seem to show potential, with users reporting positive outcomes [33,34,41]. Impetus for the research on the therapeutic use of chatbots in mental health, while still predominantly experimental, predates the COVID-19 pandemic. However, the field of chatbot research is in its infancy, and the evidence for the efficacy of chatbots for prevention and intervention across all domains is at present limited.

Ready to Build Your Chatbot?

By automating the transfer of data into EMRs (electronic medical records), a hospital will save resources otherwise spent on manual entry. An important thing to remember here is to follow HIPAA compliance protocols for protected health information (PHI). Let’s take a moment to look at the areas of healthcare where custom medical chatbots have proved their worth. These health chatbots are better capable of addressing the patient’s concerns since they can answer specific questions. Healthcare chatbots help patients avoid unnecessary tests and costly treatments, guiding them through the system more effectively.

Healthcare providers must ensure that chatbots are used in conjunction with, and not as a replacement for human healthcare professionals. Artificial Intelligence (AI) and automation have rapidly become popular in many industries, including healthcare. One of the most fascinating applications of AI and automation in healthcare is using chatbots. Chatbots in healthcare are computer programs designed to simulate conversation with human users, providing personalized assistance and support.

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. Simple questions concerning the patient’s name, address, contact number, symptoms, current doctor, and insurance information can be used to extract information by deploying healthcare chatbots. A well-designed healthcare chatbot can schedule appointments based on the doctor’s availability. Also, chatbots can be designed to interact with CRM systems to help medical staff track visits and follow-up appointments for every individual patient, while keeping the information handy for future reference. Healthcare providers are relying on conversational artificial intelligence (AI) to serve patients 24/7 which is a game-changer for the industry.

Especially in healthcare education, there is a growing interest in integrating chatbots in the learning and teaching processes mostly because of their portability and affordance. 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. Also, limited examples of chatbots in European Healthcare curricula have been found.