Ordinating ChatBots in Healthcare Industry

An AI-enabled chatbot involves building and deploying web apps with Cloud Foundry, creating a DevOps toolchain for continuous delivery; using block storage and containers; and leveraging boilerplate code. Chatbots let users change passwords or schedule an appointment without human interaction. Conversational virtual assistants enable automating informational responses, improve productivity with application bots, and maximize the information trapped in transcripts. Virtual contact center agents and interactive voice responses enable self-service capabilities and improve the customer experience at scale. It helps in automating common user queries and freeing up human agents to focus on solving more complex issues.

Features of Medical Chatbots to Streamline Processes & Improve Access to Care

• Book an appointment based on the doctor’s availability
• Scales up engagement and brand offerings
• Deliver customized information to the users
• Assist patients with their medical prescriptions, reports, billing details
• Send reminders for medication timings, therapy, and routine check-ups
• Integrating backend billing and inventory
• Enabling patients with quick access to invoices
• Addressing queries on claims, procedures, coverage
• Reduces operational expenses
• Makes customer service scalable with efficient time management for higher patient satisfaction
• Understands patient’s symptoms, connects them with the right specialist, save time during consultation
• Bots are integrated with the hospital information management system and assist in pulling necessary information and complying with all the regulations.
• Chatbots help in engaging and growing brand awareness across the website and social media
• It automates all customer queries

Process of Creating a Healthcare Chatbot

Creating a chatbot needs technology and business knowledge in equal proportion.

• We start with conducting user research based on business needs and identifying the user requests or intents to focus on.
• Analyze transcripts of conversations between agents and users to track the most frequently occurring intents.
• It often takes business analysts, product owners and developers multiple weeks to analyze thousands of lines of transcripts, find the right intents while designing chatbots for their contact center flows.
• After including the missing intents, we proceed with removing ambiguity amongst intents. The user sends the text message or voice message using Google API. The users only get related answers from the chatbot. SVM algorithm is used to classify the dataset.

(The trial and error approach to identify such overlaps across intents can still be error-prone)

• The final step compiles a list of values of information required to fulfil different intents.

Which AI Tools do We Consider?

• Bluemix is a PaaS offering by IBM cloud similar to Amazon LEX to design chatbots. Tools like automated Lex automated chatbot designer reduce time and effort in designing a chatbot by automating the process using existing conversation techniques. It also uses machine learning (ML) to provide an initial bot design that you can then refine and launch conversational experiences faster.

• Google Dialogflow helps build engaging text-based conversational interfaces, using an AI-powered toolset that assists with designing, scaling, and improving your customer experience. Google Dialogflow ecosystem can be used to build chatbots for a variety of business applications. It brings in components for seamless deployment of a complete chatbot on a website using Diagflow’s platform-specific integration options.

• Amazon LEX is an AWS service for building conversational interfaces with voice and text. Amazon LEX makes the same conversational engine available to any developer, enabling building sophisticated, Natural Language chatbots into your new and existing applications. It provides deep functionality and flexibility of natural language understanding (NLU) and automatic speech recognition (ASR) to build highly engaging user experiences with lively conversational interactions, and create new categories of products.

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Conclusive: Swear on us for Chatbot Application Development

A web-based voice recognition chatbot makes use of a two-part process of capturing and analyzing an input signal required for voice recognition. It recognizes data from the server response and processes the information.

We use a black-box approach based on SOAP. It inculcates an expert system, making it possible to improve unlimited and autonomous intelligence.

If such a chatbot aims to converse between humans and machines, it stores the knowledge database to identify the sentence and make a decision to answer the question.

The input sentence gets the similarity score of input sentences using bigram.

It uses pattern comparison in which the order of the sentence is recognized and the response pattern is saved.

It uses the n-gram technique for extracting the words from the sentences. n-gram also helps in comparison and deduction of the input with case data using Moro phonemes as decision parameters.

The final expression is redirected through an expert system.

Reach out to our AI experts for Medical chatbot development!