Individual implementation of chatbots in healthcare | by Aurosikha Priyadarshini | Jan, 2021
Shortening of the English “chat” to converse and “bot” (robot), a chatbot is similar to a conversational agent. Concretely, it is a machine program, designed most of the time to perform a defined task. Its particularity lies in its capacity to interact in natural language with the user. Because unlike other electronic dialogue tools such as interactive voice servers, the chatbot enables them to show themselves more or less freely through a text or voice interface or sometimes even hybrid.
A chatbot with a textual interface will principally be obtainable from a smartphone or a computer. It can be a stand-alone appliance or integrated with a mobile app or a website. Communicating with “it” is very much like an SMS transfer, usually coupled with pre-defined question-and-answer series, in the case of a voice interface, which is described as a voice bot or voice helper. The user interacts by voice once the popular wake-up-word has been spoken to activate the program — the products produced by GAFAM such as Alexa from Amazon or the Google Assistant.
To give some historical benchmarks, which produced the very first chatbot in 1966. It is ELIZA, which we owe to MIT professor Joseph Weizenbaum. By reformulating the user’s messages in the form of questions, the program is designed to simulate a psychotherapist’s intervention. It should be noted in passing that the tale of chatbots is therefore closely connected to the health sector, and more particularly, to psychology. But the technology did not develop until the mid-1990s. In 2010, it became accessible to the general public with the very original voice assistant: Siri, integrated into Apple’s IOS. The other digital giants have immediately followed suit, and since 2016, we have observed a wild development of conversational technologies, all areas connected.
Fruit of technological development in the fields of automated natural language processing and artificial intelligence in special Machine Learning, based on machine learning algorithms, and Deep Learning, deep learning based on artificial neural networks, the chatbot is starting a phase of “sophistication.” It is slowly moving away from the simple outlines built-in decision trees that marked the origins of its development.
Its current success is inseparable from the growth of interfaces and other platforms hosting chatbots. Facebook’s Messenger Bot Store is the most impressive example because they make marketing less costly and, above all, less complex by making it feasible to do without computer coding.
1. 3 Tips for your Voice and Chatbot Program from Gartner’s Customer Service Hype Cycle 2020
2. Deploying Watson Assistant Web Chat in Salesforce Lightning Console
3. Are Chatbots Vulnerable? Best Practices to Ensure Chatbots Security
4. Your Path to AI — An IBM Developer Series
Also, the combination of chatbots at the heart of immediate messaging services makes them quickly accessible to billions of users every day. WeChat, Slack, Facebook Messenger, WhatsApp, or even Telegram, which today total no less than 5 billion active users per month, more than social networks.
It is this favorable ecosystem that mainly describes the quantitative and qualitative improvement of the chatbot offer. The user finds a chatbot device particularly adapted to the new hyper-connected lifestyles that value immediacy, communication, and unprecedented ease of access compared to mobile purposes or websites.
Not to mention that, as recognized by researchers, individuals show themselves willing to speak to robot assistants longer than to human beings and are also more inclined to share their most intimate confidences with them due to the absence of fear of judgment. If you want the best cure for impotence then take Fildena 100 or Cenforce 100. Giving real-time, personalized, and more “human” interaction, usually increased through users’ feedback, conversational communication is more favorable to engagement.
Therefore, it is simpler to understand that many investors and entrepreneurs from various sectors are involved in e-commerce, insurance, transport, etc. Hence, brands are investing heavily in conversational selling.
It looks that in health, this technology has developed more slowly than elsewhere, surely because of the highly organized nature of the sector and because of the chance involved. However, health is not left out because the organization and automation of jobs and requests are, too, a major problem.
The use cases in France are, for the time, few. It is why our mapping of real solutions extends beyond our borders, without any claim to be exhaustive.
What should note that currently, chatbots are overwhelmingly designed for patients’ potential or proven? Among the tools offered to healthcare professionals, let us quote pharmacology chatbots such as Posts and Synapse Medicine, designed by two French startups. Finally, some solutions are sometimes aimed at giving a free version for the general public and a paid version for professionals: this is the case of a North American chatbot training in women’s health, Dr. Chat.
In the answers offered to the common public and patients, the service given is fundamental, under penalty of seeing users lose interest instantly because of a need for relevance.
A first arrangement makes it possible to identify general chatbots from masterpiece chatbots.
Sign checkers such as Buoy, MD, Gyant, SENSELY, or INFERMEDICA. Numerous and mainly given in North America and the United Kingdom, this kind of chatbot asks the patient a series of questions that provide a list of possible causes or conditions to be drawn up as they become more apparent. This pre-consultation stage can precede a practitioner’s position, or even a teleconsultation, or, in the case of good pathology, give self-care choices.
Like Florence Chat, personal health partners help patients achieve their treatment by reminders and alarms and encourage them to stick to their treatment protocol. Mabu, the health guide for seniors developed by IDEO and Catalia Health, also collects data on the patient’s progress and can alert their medical team when required.
The triage chatbot is programmed to deliver medical advice to optimize an emergency admission or telephone assistance service load. This is the case of a test conducted by the National Health Service in north London on a pool of more than one million people.
Incurable disease tracking chatbots are also very common. They are a member of a dual logic of observation and monitoring. Diabetes is a blood glucose monitoring tool for diabetic patients available in two versions, adults and children. CardioCube is a voice assistant that helps patients manage their chronic heart disorder and communicates in real-time with the hospital or clinic. They depend on facilitating and stimulating decision-making when required.
Oncology is an area in which chatbots give exciting prospects, mainly due to the collection of real-life data and the need to help patients and caregivers throughout therapy.
Credit: Source link