Beyond Boundaries: The Promise Of Conversational AI In Healthcare

This chatbot use case is all about advising people on their financial health and helping them to make some decisions regarding their investments. The banking chatbot can analyze a customer’s spending habits and offer recommendations based on the collected data. Bots can also monitor the user’s emotional health with personalized conversations using a variety of psychological techniques. The bot app also features personalized practices, such as meditations, and learns about the users with every communication to fine-tune the experience to their needs. You don’t have to employ people from different parts of the world or pay overtime for your agents to work nights anymore.

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. 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]. The first chatbot was designed for individuals with psychological issues [9]; however, they continue to be used for emotional support and psychiatric counseling with their ability to express sympathy and empathy [81].

Pick the AI methods to power the bot

QliqSOFT also offers a HIPAA-compliant method for doctors, nurses, and patients to communicate with each other, along with image and video sharing capabilities. Patients can also quickly refer to their electronic medical records, securely stored in the app. The app also helps assess their general health with its quick health checker and book medical appointments. They can even attend these appointments via video call within two hours of booking. New technologies may form new gatekeepers of access to specialty care or entirely usurp human doctors in many patient cases.

So if you’re assessing your symptoms in a chatbot, you should know that a qualified doctor has designed the flow and built the decision tree, in the same manner, that they would ask questions and reach a conclusion. Once this data is stored, it becomes easier to create a patient profile and set timely reminders, medication updates, and share future scheduling appointments. So next time, a random patient contacts the clinic or a hospital, you have all the information in front of you — the name, previous visit, underlying health issue, and last appointment.

Top 10 Chatbots in Healthcare: Insights & Use Cases in 2024

Identifying and characterizing elements of NLP is challenging, as apps do not explicitly state their machine learning approach. We were able to determine the dialogue management system and the dialogue interaction method of the healthbot for 92% of apps. Dialogue management is the high-level design of how the healthbot will maintain the entire conversation while the dialogue interaction method is the way in which the user interacts with the system. While these choices are often tied together, e.g., finite-state and fixed input, we do see examples of finite-state dialogue management with the semantic parser interaction method. Ninety-six percent of apps employed a finite-state conversational design, indicating that users are taken through a flow of predetermined steps then provided with a response.

While the industry is already flooded with various healthcare chatbots, we still see a reluctance towards experimentation with more evolved use cases. It is partially because conversational AI is still evolving and has a long way to go. As natural language understanding and artificial intelligence technologies evolve, we will see the emergence of more advanced healthcare chatbot solutions. A healthcare chatbot is an AI-powered software program designed to interact with users and provide healthcare-related information, support, and services through a conversational interface. It uses natural language processing (NLP) and Machine Learning (ML) techniques to understand and respond to user queries or requests. With chatbots in healthcare, doctors can now access this data without asking their patients questions directly.

Automating healthcare processes

Finally, the issue of fairness arises with algorithm bias when data used 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]. 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.

So, even though a bank could use a chatbot, like ManyChat, this platform won’t be able to provide for all the banking needs the institution has for its bot. Therefore, you should choose the right chatbot for the use cases that you will need it for. The virtual assistant also gives you the option to authenticate signatures in real time. The bot performs banking activities, such as checking balance, funds transfers, and bill payments.

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

These findings align with studies that demonstrate that chatbots have the potential to improve user experience and accessibility and provide accurate data collection [66]. Knowledge domain classification is based on accessible knowledge or the data used to train the chatbot. Under this category are the open domain for general topics and the closed domain focusing on more specific information.

• Acquiring patient feedback is highly crucial for the improvement of healthcare services.
• Healthcare chatbots are AI-enabled digital assistants that allow patients to assess their health and get reliable results anywhere, anytime.
• But healthcare chatbots have been on the scene for a long time, and the healthcare industry is projected to see a significant increase in market share within the artificial intelligence sector in the next decade.
• It can be via a CSAT rating or a detailed rating system where patients can rate their experience for different types of services.

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’. Second, ‘there are user-facing applications […] which interact with people in real-time’, providing advice and ‘instructions based on probabilities which the tool can derive and improve over time’ (p. 55). The latter, that is, systems such as chatbots, seem to complement and sometimes even substitute HCP patient consultations (p. 55). Healthcare chatbots are AI-enabled digital assistants that allow patients to assess their health and get reliable results anywhere, anytime.

It is also one of the most rapidly-changing industries, with new technologies being introduced annually for the patient and the customer alike. Chatbots have already been used, many a time, in various ways within this industry, but they could potentially be used in even more innovative ways. A chatbot can verify insurance coverage data for patients seeking treatment from an emergency room or urgent care facility.

Being a customer service adherent, her goal is to show that organizations can use customer experience as a competitive advantage and win customer loyalty. The data can be saved further making patient admission, chatbot use cases in healthcare symptom tracking, doctor-patient contact, and medical record-keeping easier. Saeedeh Shekarpour is an Assistant Professor of computer science with the University of Dayton, Dayton, OH, USA.

Beyond Boundaries: The Promise Of Conversational AI In Healthcare

While most people would use Google and probably misdiagnose themselves, Buoy has come up with a solution. They built one of the most highly intuitive AI-powered chatbots in healthcare, which could come up with possible diagnoses for a patient’s symptoms by asking around 20 questions. Such an interactive AI technology can automate various healthcare-related activities.