Artificial Intelligence(AI) is innovating healthcare sector

Published on Apr 11, 2018

Updated on Sep 30, 2020

We are under the spell of the Fourth Revolution or the digital revolution. The ability of technology to help the humankind is empowering each day. With AI, Machine Learning, IoTs, and Virtual Reality we are witnessing a diminishing line between man and machine. While the machine is helping man to live luxuriously, it has also extended its help in saving lives.
The use cases of Artificial Intelligence[AI] in healthcare are fascinating – be it Robotic Surgery, digital consultation, managing medical records over a blockchain network or a virtual nurse assisting you. AI in health is assisting machines to sense, analyze, act, diagnose and help in the clinical and administrative task in a hospital.

Let’s explore in detail on how AI is helping humans to stay healthy and save lives.

Assisting Patient at Every Step

An AI app/product could effectively scan the medical records and help in diagnosing the particular disease, minimizing chances of human error. Based on the prescriptive analysis, the AI software could aid real-time case prioritization. It can precisely analyze actions and predict the risk associated with specific clinical procedures.
AI programs could also help in providing personalized services based on patient data and moods. In fact, an AI app can also recommend the best doctor as per your medical record. AI can be a helping hand for many expectant mothers, with continuous monitoring and ability of early diagnosis.

Several wearable devices and health apps are assisting customers in keeping track of their health. Health apps like Cure.fit help customers to order healthy food and keep tabs on their daily workouts. People can also book appointments and buy medicines through apps like Practo.

Reaching New Heights in Research and Development

Collecting data samples of all the patient in a clinic/hospital, applying big data techniques and deep learning technology could help in extracting meaningful information. Such information could be used to study pattern for a disease or about an individual.
Genetics and study of genes are one of the most crucial jobs in healthcare, with AI the study could be exhaustive and precise resulting in impactful drugs and medicine. Applying medical intelligence could help in understanding the connection between drug and disease at the root level.

Helping Hospitals with Pricing, Risk, and Operations

In need of a marketing strategy that highlights the pain points, lessons learned, target segment and market perception? AI could help you. It can present you a unique strategy that helps in modeling competitive pricing charts,understanding market risk and structuring market data into meaningful actions. Rehauling of your repetitive tasks or back office could be achieved by implementing Robotic Process Automation[RPA] into your system.

With voice-enabled chatbots and video conferencing chatbots, customer queries and appointment booking can be facilitated in private clinics and healthcare sectors

Virtual Nurses, Healthcare Bots

Are you in need of the second opinion from the country’s best doctor at the convenience of your home? AI can help you with Digital Consultation. Or you need a nurse who helps in keeping track of your medicines and food; Virtual Nurse is on his way. Or you need help in picking the best diagnostic center based on your health records? Or you need help in what are the side effects of a drug? Healthcare bots are in for the rescue.

All of this may sound like a sci-fi movie being watched, but now is a possibility with AI and machine learning technology.

Other significant innovation is the chatbot. Chatbots help in raising alarms during life-threatening incidents and save the needful. During an emergency situation, a call made by the chatbot to the needy’s family/ friends or a health center can help the suffering person.

Write us at hello@mantralabsglobal.com to know how we are helping healthcare businesses through AI technology.

Check out the webinar on ‘Digital Health Beyond COVID-19: Bringing the Hospital to the Customer’ on our YouTube channel to know more about how the digital health industry is disrupting the traditional ways of healthcare.

As healthcare becomes more patient-centric, the demand for efficient and personalized care continues to grow. One of the key technologies that have gained traction in this domain is Conversational UI (CUI) — a user interface where interactions occur through natural language, often with the help of chatbots. For developers, building a robust CUI in healthcare requires a balance of technical proficiency, understanding of the healthcare landscape, and empathy toward patient needs. Let’s explore how CUI can improve patient interactions through chatbots and what developers should consider during implementation.

Why Conversational UI is Gaining Popularity in Healthcare

From scheduling appointments to answering medical queries, healthcare chatbots have become vital tools for enhancing patient engagement and streamlining healthcare workflows. Conversational UIs enable these chatbots to interact with patients naturally, making them accessible even to non-tech-savvy users. By incorporating AI and NLP (Natural Language Processing), chatbots can now simulate human-like conversations, ensuring patients receive timely, relevant responses.

Image credit: https://www.analytixlabs.co.in/blog/ai-chatbots-in-healthcare/

Key Areas Where Chatbots Are Revolutionizing Healthcare

Appointment Scheduling and Reminders – Chatbots can automatically schedule appointments based on patient availability and send reminders before the visit, reducing no-show rates. For developers, this feature requires integration with hospital management systems (HMS) and calendar APIs. The challenge lies in ensuring secure and real-time data transfer while adhering to healthcare compliance standards like HIPAA.

Medical Query Resolution– Chatbots equipped with NLP can answer common patient questions related to symptoms, medications, and treatment plans. This reduces the burden on healthcare providers, allowing them to focus on more critical tasks. Developers working on this feature need to consider integrating medical databases, such as SNOMED CT or ICD-10, for accurate and up-to-date information.

Patient Monitoring and Follow-ups – Post-discharge, chatbots can monitor a patient’s condition by regularly asking for health updates (e.g., vital signs or medication adherence). Developers can integrate IoT devices, such as wearable health monitors, with chatbot platforms to collect real-time data, providing healthcare professionals with actionable insights.

Mental Health Support – Chatbots have shown promise in offering mental health support by providing patients with an outlet to discuss their feelings and receive advice. Building these chatbots involves training them on therapeutic conversational frameworks like Cognitive Behavioral Therapy (CBT), ensuring they offer relevant advice while recognizing when a human intervention is required.

Key Considerations for Developers

1. Natural Language Processing (NLP) and AI Training

NLP plays a pivotal role in enabling chatbots to understand and process patient queries effectively. Developers must focus on the following:

Training Data: Start by gathering extensive datasets that include real-life medical queries and patient conversations. This ensures that the chatbot can recognize various intents and respond appropriately.

Multi-language Support: Healthcare is global, so building multi-lingual capabilities is critical. Using tools like Google’s BERT or Microsoft’s Turing-NLG models can help chatbots understand context in different languages.

Contextual Understanding: The chatbot must not just respond to individual queries but also maintain the context across the conversation. Developers can use contextual models that preserve the state of the conversation, ensuring personalized patient interactions.

2. Security and Compliance

Healthcare chatbots handle sensitive patient information, making security a top priority. Developers must ensure compliance with regulations such as HIPAA (Health Insurance Portability and Accountability Act) in the U.S. and GDPR (General Data Protection Regulation) in Europe. Key practices include:

Data Encryption: All communication between the chatbot and the server must be encrypted using protocols like TLS (Transport Layer Security).
Authentication Mechanisms: Implement two-factor authentication (2FA) to verify patient identity, especially for sensitive tasks like accessing medical records.
Anonymization: To avoid accidental data breaches, ensure that the chatbot anonymizes data where possible.

3. Seamless Integration with EHR Systems

For chatbots to be truly effective in healthcare, they must integrate seamlessly with Electronic Health Record (EHR) systems. This requires a deep understanding of healthcare APIs like FHIR (Fast Healthcare Interoperability Resources) or HL7. Developers should aim to:

Enable Real-time Updates: Ensure that chatbot interactions (e.g., new appointment schedules, and symptom checks) are instantly reflected in the patient’s EHR.
Avoid Data Silos: Ensure that all systems (EHR, chatbot, scheduling system) can communicate with each other, eliminating data silos that can lead to fragmented patient information.

4. Scalability and Performance Optimization

In healthcare, downtime can be critical. Developers need to ensure that chatbots are scalable and capable of handling thousands of patient interactions simultaneously. Using cloud-based platforms (AWS, Google Cloud) that offer auto-scaling capabilities can help. Additionally, performance optimization can be achieved by:

Caching Responses: Store frequently used responses (such as FAQs) in memory to speed up interaction times.
Load Balancing: Implement load balancers to distribute incoming queries across servers, ensuring no single server is overwhelmed.

Image Credit: https://www.analytixlabs.co.in/blog/ai-chatbots-in-healthcare/

Tools and Platforms for Building Healthcare Chatbots

Several tools and platforms can aid developers in building healthcare chatbots with conversational UIs:

Dialogflow (Google): Offers pre-built healthcare intents and integrates with Google Cloud’s healthcare APIs.
Microsoft Bot Framework: A scalable platform that integrates with Azure services and offers AI-driven insights.
Rasa: An open-source NLP tool that provides flexibility in creating highly customized healthcare bots.

Conclusion

Conversational UI in healthcare is transforming patient care by offering real-time, scalable, and personalized interactions through chatbots. However, for developers, building these systems goes beyond programming chatbots — it involves understanding the unique challenges of healthcare, from regulatory compliance to seamless integration with hospital systems. By focusing on NLP capabilities, ensuring security and privacy, and integrating with existing healthcare infrastructure, developers can create chatbots that not only enhance patient interaction but also alleviate the burden on healthcare providers.

References

NLP in Healthcare: Opportunities and Challenges
HIPAA Compliance for Chatbots

About the Author:

Shristi is a creative professional with a passion for visual storytelling. She recently transitioned from the world of video and motion graphics to the exciting field of product design at Mantra Labs. When she’s not designing, she enjoys watching movies, traveling, and sharing her experiences through vlogs.