Healthcare Chatbots: Innovative, Efficient, and Low-cost Care

Published on Apr 2, 2018

Updated on Sep 30, 2020

A new report from Juniper Research has found that annual cost savings derived from the adoption of chatbots in healthcare will reach $3.6 billion (€3.04 billion) globally by 2022, up from an estimated $2.8 million (€2.36 million) in 2017. This growth will average 320% per annum, as AI (artificial intelligence) powered chatbots will drive improved customer experiences for patients.- IoTnow

Experts believe that medical chatbots will play an important role in the healthcare industry soon. While the world is facing the problem of information overload, bots can help in segregating good and bad/right and wrong data. Healthcare chatbots solve the most pressing medicine industry problems-

Accessibility: The human workforce might not be available in every vital situation. But bots are. Thus making appointments and scheduling simple.
SOS: Healthcare chatbots can also act as an alarm in the instances of life-threatening symptoms as described by the patients.
Customization: Bots with NLP capabilities can understand voice and text-based queries efficiently. This is especially helpful for people with visual or speech impairments.
Integrations: Chatbots are compatible with IoT devices like Google Home, Alexa, etc.
Personalization: Patient-specific bots can place a call, advise first-aid, and even send notifications/messages to physicians.

Before we delve deep into medical chatbots, let’s quickly look at what exactly is a chatbot.

Chatbots Brief History

The first-ever computer program that could communicate with humans was Eliza, developed by MIT in 1966. Subsequently, with programs passing Turing tests, e-commerce, messaging, healthcare, and other enterprises indicated a deep interest in using chatbots.

A chatbot is an artificial intelligence program that can interact, respond, advise, assist, and converse with humans. It can mimic a two-way communication between two individuals. In the initial phases of chatbot implementation, tasks like — scheduling an appointment and answering fundamental queries, were accomplished. Today, the scope of chatbots is much broader with recommendations, references, diagnosis, and even preliminary treatments.

Snapshot of Your.MD healthcare chatbot illustrating the services it provides

How are Medical Chatbots Reshaping the Healthcare Industry?

Chatbots can help in improving Patients’ engagement and experiences with the hospital/physician. The following are the proven benefits of healthcare chatbots.

Accessible Anytime and Anywhere

Today, it is possible to embed chatbots on websites, mobile apps, and even third-party apps like Facebook and WhatsApp. There’s no need for downloading them explicitly and registration/activation. For instance, Religare, a leading health insurer has its chatbot integrated into WhatsApp.

Generally, hospitals and other healthcare organizations provide chatbot in-built in their app. Apart from usual support, it also helps to secure your medical records in one place. This record can also be shared with the doctor whenever it is required.

There are mental health and therapy chatbots available that provide continuous support to patients with mental illness, depression, or sleeplessness. Wysa — a mental therapy chatbot is one such example.

Chatbots for SOS (Emergency Alarms)

During the instances of life-threatening events/symptoms, chatbots can help in raising alarm automatically. For example, if a person who complains about chest pain does not respond to messages within a stipulated time, an emergency call made by the bot to healthcare/family/friends might help in attending the needful.

Customization

Organizations can customize chatbots to decipher different languages, voice accents, and text patterns. With different modes of conversations, chatbots can simplify communications. For instance, for a person with a visual disability, computer vision could be altered. For people with speech problems, chatbots with NLP capabilities can be beneficial.

Another remarkable development in chatbots that we’re going to witness soon is that of Emotion AI. Soon the bots would be able to understand the user’s emotions based on text, voice, or sentence structure. This feature will be a great help for understanding the sentiments of people suffering from depression or any other kind of mental illness.

Integration with Different Platforms and Personalization

Because of their omnichannel nature, one can easily integrate bots with the web, mobile, or third-party apps, and APIs. They can even work with voice assistants like Alexa and Google Home. With seamless integration, a bot can place a call, advise you on first aid, analyze your medical history and send notifications to your doctor.

Voice-enabled chatbots and multilingual chatbots are disrupting the way customers engage with chatbots. Voice-enabled chatbots increase accessibility and speed up the query process, as the user does not have to type. More than 70% of Indians face challenges while using English keyboard, and approximately 60% of them find the language to be the key barrier in adopting digital tools. Vernacular language support can personalize the services for native users and make the whole process of maintaining medical records a lot easier. For instance, to automate help desk tasks and respond to customer queries, voice-driven chatbots can be integrated.

Indian chatbots like Hitee (designed for Indian SMEs) support several Indian regional languages including Hindi, Tamil, Bengali, Telugu, Gujarati, Kannada and Malayalam.

Video conferencing chatbots can be used by private clinics and healthcare practitioners to converse with their patients.

5 Popular AI Healthcare Chatbots

mfine: It is a digital health platform for on-demand healthcare services. It offers online consultation (text, audio, video), medicine delivery, follow-ups, and patient record management services.
Wysa: Developed by Touchkin, Wysa is AI-powered stress, depression, and anxiety therapy chatbot. It helps people practice CBT (Cognitive Behavioral Therapy) and DBT (Dialectical behavior therapy) techniques to build resilience. For additional support, it connects people with real human coaches.
Mediktor: It is an accurate AI-based symptoms checker with great NLP (Natural Language Processing) capabilities.
ZINI: It provides every user with a Unique Global Health ID that can be used for managing one’s healthcare information all over the world. It also creates an emergency medical profile for the patient for urgent medical requirements.
Your.MD: It provides personalized information, guidance, and healthcare support. With an in-built symptom checker, vast medical database, health plans, and journals, it is a certified application for digital healthcare support.

Multilingual AI Chatbot with Video – Hitee

To know more about how AI is innovating the healthcare industry in bringing the digital health era, check out our webinar on ‘Digital Health Beyond COVID-19: Bringing the Hospital to the Customer’ on our YouTube channel.

We specialize in building custom AI-powered chatbots specific to your business requirements. Feel free to drop us a word at hello@mantralabsglobal.com to know more.

Why was Data Warehouse the Best Move on the Board?

Data warehouses act as the strategic command centers for enterprises. By employing a schema-on-write approach, they ensure data is cleaned, validated, and formatted before storage. This guarantees high accuracy and consistency, making them indispensable for industries like finance and healthcare. For instance, global banks rely on data warehouses to calculate real-time risk assessments or detect fraud—a necessity when billions of transactions are processed daily, tools like Amazon Redshift, Snowflake Data Warehouse, and Azure Data Warehouse are vital. Similarly, hospitals use them to streamline patient care by integrating records, billing, and treatment plans into unified dashboards.

The impact is evident: according to a report by Global Market Insights, the global data warehouse market is projected to reach $30.4 billion by 2025, driven by the growing demand for business intelligence and real-time analytics. Yet, much like Deep Blue’s limitations in analyzing Kasparov’s emotional state, data warehouses face challenges when encountering data that doesn’t fit neatly into predefined schemas.

The question remains—what happens when businesses need to explore data outside these structured confines? The next evolution takes us to the flexible and expansive realm of data lakes, designed to embrace unstructured chaos.

The True Depth of Data Lakes

While structured data lays the foundation for traditional analytics, the modern business environment is far more complex, organizations today recognize the untapped potential in unstructured and semi-structured data. Social media conversations, customer reviews, IoT sensor feeds, audio recordings, and video content—these are the modern equivalents of Kasparov’s instinctive reactions and emotional expressions. They hold valuable insights but exist in forms that defy the rigid schemas of data warehouses.

Data lake is the system designed to embrace this chaos. Unlike warehouses, which demand structure upfront, data lakes operate on a schema-on-read approach, storing raw data in its native format until it’s needed for analysis. This flexibility makes data lakes ideal for capturing unstructured and semi-structured information. For example, Netflix uses data lakes to ingest billions of daily streaming logs, combining semi-structured metadata with unstructured viewing behaviors to deliver hyper-personalized recommendations. Similarly, Tesla stores vast amounts of raw sensor data from its autonomous vehicles in data lakes to train machine learning models.

However, this openness comes with challenges. Without proper governance, data lakes risk devolving into “data swamps,” where valuable insights are buried under poorly cataloged, duplicated, or irrelevant information. Forrester analysts estimate that 60%-73% of enterprise data goes unused for analytics, highlighting the governance gap in traditional lake implementations.

Is the Data Lakehouse the Best of Both Worlds?

This gap gave rise to the data lakehouse, a hybrid approach that marries the flexibility of data lakes with the structure and governance of warehouses. The lakehouse supports both structured and unstructured data, enabling real-time querying for business intelligence (BI) while also accommodating AI/ML workloads. Tools like Databricks Lakehouse and Snowflake Lakehouse integrate features like ACID transactions and unified metadata layers, ensuring data remains clean, compliant, and accessible.

Retailers, for instance, use lakehouses to analyze customer behavior in real time while simultaneously training AI models for predictive recommendations. Streaming services like Disney+ integrate structured subscriber data with unstructured viewing habits, enhancing personalization and engagement. In manufacturing, lakehouses process vast IoT sensor data alongside operational records, predicting maintenance needs and reducing downtime. According to a report by Databricks, organizations implementing lakehouse architectures have achieved up to 40% cost reductions and accelerated insights, proving their value as a future-ready data solution.

As businesses navigate this evolving data ecosystem, the choice between these architectures depends on their unique needs. Below is a comparison table highlighting the key attributes of data warehouses, data lakes, and data lakehouses:

Feature	Data Warehouse	Data Lake	Data Lakehouse
Data Type	Structured	Structured, Semi-Structured, Unstructured	Both
Schema Approach	Schema-on-Write	Schema-on-Read	Both
Query Performance	Optimized for BI	Slower; requires specialized tools	High performance for both BI and AI
Accessibility	Easy for analysts with SQL tools	Requires technical expertise	Accessible to both analysts and data scientists
Cost Efficiency	High	Low	Moderate
Scalability	Limited	High	High
Governance	Strong	Weak	Strong
Use Cases	BI, Compliance	AI/ML, Data Exploration	Real-Time Analytics, Unified Workloads
Best Fit For	Finance, Healthcare	Media, IoT, Research	Retail, E-commerce, Multi-Industry

Conclusion

The interplay between data warehouses, data lakes, and data lakehouses is a tale of adaptation and convergence. Just as IBM’s Deep Blue showcased the power of structured data but left questions about unstructured insights, businesses today must decide how to harness the vast potential of their data. From tools like Azure Data Lake, Amazon Redshift, and Snowflake Data Warehouse to advanced platforms like Databricks Lakehouse, the possibilities are limitless.

Ultimately, the path forward depends on an organization’s specific goals—whether optimizing BI, exploring AI/ML, or achieving unified analytics. The synergy of data engineering, data analytics, and database activity monitoring ensures that insights are not just generated but are actionable. To accelerate AI transformation journeys for evolving organizations, leveraging cutting-edge platforms like Snowflake combined with deep expertise is crucial.

At Mantra Labs, we specialize in crafting tailored data science and engineering solutions that empower businesses to achieve their analytics goals. Our experience with platforms like Snowflake and our deep domain expertise makes us the ideal partner for driving data-driven innovation and unlocking the next wave of growth for your enterprise.

Healthcare Chatbots: Innovative, Efficient, and Low-cost Care

Chatbots Brief History