Why are chatbots important in healthcare?
In research, the most valuable features of using chatbots in healthcare are as follows:
Anonymity: especially for sensitive and mental health issues.
Monitoring: awareness and tracking of the user’s behavior, anxiety, weight changes to encourage the development of better habits.
- Physical, vital signs (oxygenation, heart rhythm, body temperature) via mobile sensors.
- Patient behavior via facial recognition
- Real-time interaction: instant response, notifications, and reminders.
Scalability: the ability to respond to numerous users.
What are the top use cases for chatbots in healthcare?
Healthcare chatbot use cases include:
1. Providing medical information.
Chatbot algorithms are trained on rich health data, including disease symptoms, diagnoses, markers, and readily available treatments. Public datasets are utilized to continuously train chatbots, such as COVIDx for COVID-19 diagnosis and Wisconsin Breast Cancer Diagnosis (WBCD). Chatbots with different intelligence levels can understand users’ questions and provide answers based on predefined labels in the training data.
Example: The app Healthily provides information about symptoms of various diseases, assessments of overall health, and tracks patient progress.
2. Making doctor’s appointments
Chatbots are integrated into a medical facility’s system to provide information about appropriate physicians, available appointments, clinics, and pharmacy business days. Chatbots ask patients about their current health issues, find appropriate doctors and dentists, provide available appointments and routines, reschedule, and delete appointments for patients. Chatbots are also integrated with users’ device calendars to send reminders and updates about medical appointments.
3. Collect patient data
Chatbots can collect patient data based on simple questions about name, address, symptoms, current doctor, and insurance information. Chatbots then keep this information in the medical facility’s system to facilitate patient intake, symptom tracking, doctor-patient communication, and medical record keeping.
4. Processing insurance inquiries
Chatbots can provide insurance services and health resources to patients and plan members. Integrating RPA or other automation solutions with chatbots can also automate insurance claims processing and healthcare billing.
5. Mental health support.
Chatbots that provide mental health support are trained to deliver cognitive behavioral therapy (CBT) to patients with depression, post-traumatic stress disorder (PTSD), and anxiety or train autistic patients to improve their social and interview skills. Users can interact with chatbots via text, microphones, and cameras.
6. Request medication reorders.
Chatbots capture patient information such as name, birthday, contact information, current doctor, last clinic visit, and prescription information. The chatbot forwards a request to the patient’s doctor, who makes a final decision and contacts the patient when a refill is available.
This allows physicians to batch process prescription refills or automate them in cases where physical intervention is not required.
What is the market and future of chatbots in healthcare?
Healthcare is among the top 5 industries benefiting from chatbots.
Chatbots are expected to account for more than a $1 billion market size. On the other hand, the global COVID-19 pandemic has increased the need for chatbots to support healthcare without putting healthcare workers at risk:
- Routing patients with severe symptoms to healthcare facilities with available acute care beds.
- Provide around-the-clock information on COVID-19 updates and symptoms and answer FAQs.
- Provide psychological support for coping with pandemic stress.
These global experiences will impact the healthcare industry’s reliance on chatbots and could provide new and broader opportunities for chatbot implementation in the future.
Advantages of chatbots in healthcare
The advantages of using crossbreed chatbots in healthcare are massive, and everyone involved benefits. For one, medical chatbots reduce the workload of medical staff by reducing hospital visits, cutting down on unnecessary treatments and procedures, and decreasing hospital admissions and readmissions by improving treatment adherence and symptom knowledge. For patients, this brings many benefits:
- less time spent traveling to the doctor’s office
- less money invest in unnecessary treatments and tests
- simple access to the doctor at the push of a button
Chatbots provide cost savings in healthcare. Chatbots are slowly reducing hospital wait times, appointment times, unnecessary treatments, and hospital readmissions by linking patients with the ideal healthcare providers and helping them understand their conditions and treatments, even without seeing a doctor. Experts estimate that cost savings from chatbots in healthcare will reach $3.6 billion worldwide by 2022.
In addition, hospitals and private clinics are using medical chatbots to triage and enroll patients even before they enter the treatment room. These bots ask relevant questions about patients’ symptoms, which are automatically answered to create a good medical history for the doctor. Then, these medical histories are sent via a messaging interface to the physician, who triages to identify which patients require to be seen first and which patients need a brief consultation.
The truth is that chatbots can neither replace a physician’s expertise nor take over patient treatment. However, combining the best of both globes improves the efficiency of patient care, simplifying and expediting treatment without compromising quality.
How do you develop a medical chatbot app?
Developing a healthcare chatbot can be a real challenge for someone who has no experience in the field. Follow these steps to develop an engaging, HIPAA-compliant medical chatbot.
Design a conversation path
Before chatbots, text messages provided a convenient interface for communicating with friends, loved ones, and business associates. Survey results show that more than 82 percent of people leave their text message notifications turned on. And an ordinary person has at least three messaging applications on their smartphones.
As Erika Hall describes in her book Conversational Design, people love this type of conversation not for its sophisticated features but for the convenience of maintaining and accessing social connections through short, unpredictable conversations.
App design for healthcare
But as essential as text messaging may seem, several rules govern its effectiveness: Relevance, tone, volume, speed, and context. Therefore, when using conversational paths for chatbots, AI developers must consider these principles that guide effective and productive conversations, especially in healthcare.
Choose the exemplary user interface
Chatbots are revolutionizing social interaction on a grand scale. Entrepreneurs, media companies, the automotive industry, and customer service representatives use these AI applications to ensure efficient customer communication.
However, people evaluate a process by the outcome and how easy the process is. Similarly, conversations between humans and machines are judged not only by the outcome but also by the simplicity of the interaction. This is where a well-designed user interface (UI) comes into play.
A UI is the meeting point between humans and computers when users interact with the design. Depending upon the type of chatbot, developers use a graphical user interface, voice interactions, or gestures, using different machine learning models to understand human language and generate appropriate responses.
Popular platforms for developing chatbot interfaces include Alexa API, Facebook Messenger, Skype, Slack, Google Assistant, and Telegram. These platforms have various elements that developers can create the best chatbot user interfaces. Almost all of these platforms have vibrant visual elements that provide information in text, buttons, and images to make navigation and interaction effortless.
Except for Slack, all these platforms offer a quick reply as a recommended action that disappears once clicked. Users choose fast replies to ask for a location, address, email or end the conversation. Some of these platforms, such as Telegram, also offer custom keyboards with predefined reply buttons to make the conversation seamless.
Each of them has systems has its benefits and disadvantages. So, choosing the right platform can seem daunting. However, let these simple rules guide you in choosing the best user interface for your chatbot:
- Make sure user interface elements work predictably to navigate the platform easily.
- Have the elements clearly labeled and marked to improve usability.
Design the layout to be more readable: avoid excessive colors and buttons, and use fonts, capital letters, letters, and italics appropriately.
Avoid numerous tasks on a single page: This can tire the user and confuse them. Limit the number of tasks to one per page. Also, complex tasks should be divided into subtasks to improve the usability of the bot.
Finally, the design should be easy to navigate.
An effective user interface aims to make chatbot interactions as close to a natural conversation as possible. To do this, design elements must be arranged in simple patterns to make navigation easy and convenient.
Blend the best of humans and AI
When customers interact with businesses or navigate websites, they want quick answers to their questions and an agent who interacts in real-time. This is undeniably one of the critical factors that influence customer satisfaction and a company’s brand image. Standalone chatbots have allowed companies to advance their customer support experiences, but they have been expectedly flawed.
For example, it’s nearly impossible for a healthcare chatbot to diagnose complex conditions based on symptoms accurately. Chatbots that act as symptom checkers can make accurate differential diagnoses for a range of symptoms, but in many cases, a physician must perform further testing or queries to make an accurate diagnosis.
Bots immediately advise users to see a doctor and seek treatment in emergencies. For this reason, hybrid chatbots – which combine artificial intelligence and human intellect – can achieve better results than standalone AI-powered solutions.
Using Rasa NLU for intent classification and entity extraction
For a practical chatbot application and a pleasant user experience, chatbots must be designed to make interactions as natural as possible. This requires machine learning models that allow the bot to recognize the intent and context of discussions. It is where natural language processing and understanding devices come into play.
Rasa NLU is an open-source natural language understanding library used for intent classification, response generation and retrieval, and entity extraction when designing chatbot conversations. The NLU component of Rasa used to be separate but has been merged with Rasa Core into a single framework.
The NLU is the natural language understanding library that performs intent classification and entity extraction from user input. This breaks down the user input so that the chatbot can understand the user’s intent and context. The Rasa core is the chatbot framework that uses a deep learning model to predict the best action.
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