Key Takeaways from the Webinar

The outbreak of pandemic COVID-19 put the healthcare sector into a tizzy. From dealing with a rising number of patients and lack of medical supplies and infrastructure to falling in-patient and out-patient footfalls and elective surgeries; it was indeed a difficult period. The latter affected the business of private healthcare due to their inability to sustain cash flow and fixed costs. However, the healthcare system did come around and in response to the COVID-19, new care packages were introduced which included services such as Medical Kits, App to Check Vitals, Home Delivery of Medicines, Remote Doctor Consultations, Remote Nurse Consultations, Helpline for Query Resolution, etc. to give a meaningful digital health experience.

Post-COVID: Future of Indian Digital Healthcare

The pandemic has been around for quite some time now and we’re halfway through the New Normal. In India, the consumer-led healthcare ecosystem is changing to consumer-led digital health. At-home/doorstep delivery is reducing the number of physical touchpoints. The rapid growth of telemedicine and preventive healthcare apps are some of the driving forces for the Indian Healthtech market which is expected to grow up to $21bn by 2025 (which is only 3.3% of the total addressable healthcare market of $638 Bn by 2025). One of the untapped opportunities that the healthcare ecosystem could look into is providing out-patient insurance for day-to-day doctor visits or health needs.

Here are some takeaways from a very insightful and interactive webinar on Digital Health-

Evolving digital health behavior

During Pandemic: 35% of Consumers are impacted at their jobs, 25% are still saving & stockpiling essentials, 38% are in a ‘hibernating & spending’ state, while the remaining 2% are least affected and will continue their old spending behaviors. Here’s a brief overview of changing patient behavior and benefits of patient-generated data-

Patterns of Consumption and Health-seeking Behaviour

People have certainly become more health-conscious since the outbreak of the pandemic. A major shift in consumer behavior is that now they are data-oriented. Tracking health conditions and using medical records to make decisions via mobile apps is trending. Consumers want ownership of their health data. Amongst at-home consumers (in the last three months)

at least 1 in 3 have used a fitness app
at least 1 in 4 have used a telehealth service
at least 1 in 5 have consulted with a virtual doctor

Doctors’ digital behavior is evolving too

In India, doctors’ are now finding clinical information and engaging with medical reps digitally. Each general physician on their platform now consults around 100 patients. Doctors too are leaning towards digital health platforms to increase engagement with new prescription influencers and auxiliary stakeholders such as pharmacists (formularies) and insurers. Telehealth apps and e-pharmacies will see a boom in the upcoming months as both patients and doctors are getting accustomed to virtual health trends.

Rise of mHealth Apps

mHealth market in India is expected to touch $2.4B by 2024. mHealth apps are more popular in preventive healthcare space. There are 400,000 mobile health apps in India, for self-monitoring a variety of health data — heart rate, bp, sleep pattern, blood glucose level, etc. Some of the top use-cases of mhealth apps are in the fitness and nutrition areas. Now there’s increased adoption of apps in mental health, e-prescriptions, and diagnostics as more consumers demand door-step services.

Real World Use Cases for Pre and Post-Op Care

Even though the economy has slowly started to open up, the COVID scare has not yet gone. People are still being cautious about stepping out of their homes. We have seen the maximum usage of mobile apps and door-step services during this period. Demand for home care services driven over mobile applications is on the rise. This has opened up so many opportunities in the digital health space to making healthcare accessible from anywhere for both patients and doctors.

Let’s take a look some of the use cases in Digital Health-

Using AI for Doctor Consultations

Many doctors face challenges in managing high patient volume. Digital self-care tools such as an AI doctor can assist here by coming up with a basic diagnosis and treatment plan. These results are then validated by an actual doctor.

E.g. around 20% of consultations on 1mg is done by an AI doctor.

Know how Mantra Labs helped PAHOMIQ build and train AI models to enhance their Image processing techniques to allow earlier detection of abnormalities and treatment monitoring.

Digital Mental Health Therapy Chatbots

Mental Health and Emotional Wellness were not considered serious health conditions. But in the light of recent events, with depression and suicide rates going up, detecting mental health conditions early-on has become very important. An AI conversational chat tool can help users by monitoring their moods coupled with self-care exercises for dealing with mental health issues. Furthermore, AI can help give people preliminary diagnoses that open ups treatment options, freeing up resources for mental health providers. AI can even triage patients based on the data it collects from patients which will enable doctors to come up with treatment plans.

Ex: Wysa — App supports and encourages users to achieve defined mental health goals.

ORCHA, the World’s leading health app evaluation, and advisor organization awarded Wysa an overall rating of 93%, including 100% on clinical safety. Wysa has also rated the best app for COVID-19 stress and anxiety.

Digital Lab

Managing digital pathology workflows is a challenge for both patients and doctors as it requires a lot of coordination and is time-consuming. A mobile app with features of a digital lab such as case-based tracking, schedule appointments, extract diagnostic data from pathology reports, receive alerts, prescription, billing & inventory management will help both patients and doctors save time and effort.

Know more about how Mantra Labs built Manipal’s Digital 360° Patient Management app which included features such as scheduling appointments and uploading medical records. Thus, improving the quality of care and patient satisfaction.

Mobile Phlebotomist App

Tier 2 and Tier 3 markets have limited access to healthcare services. One way to make medicine and testing more accessible to people in these markets is by mHealth apps. The appointment booking feature can help a phlebotomist to manage online patients bookings. The phlebotomists will collect blood samples following a specific protocol and use bar-coded stickers with patient identification information that they stick to collection vials. This enables the field team to manage home sample collection requests, view daily collection schedules and drop-off points, with geo-location services.

Mantra Labs Five-Factor Model for Digital Health Mobile App Transformation

Building Truly Engaging Apps

There are many healthcare apps in play today running from fitness and nutrition trackers to hospital apps. But, how many truly keep the user engaged?

Personalization

A one-size-fits-all approach will not engage with the majority of the population due to diverse personas. With features like providing timely & relevant advice will make users feel privileged keeping them engaged.

Digital Nudges

One of the reasons why Health and Wellness apps are more popular is because they help users to self-monitor their progress. Apps with gentle reminders or notifications act as triggers to get desired results.

Empathy by design

A deep study into the user behavior through surveys/secondary research can help get qualitative data which is crucial to incorporate into the app. If the app matches the user needs, then they are more likely to stay on it.

Frictionless Touchpoints

Lengthy workflows, frequent pop-ups, and advertisements can create a distraction for a user while on the app. An app should not interfere with a user’s daily routine or disrupt their behavior.

Gamification

The gamified approach grabs a user’s attention and keeps him interested in the app. A plain layout will wear out a user soon. Gameplay focuses on the user’s attention better.

E.g. Using reward badges for completing virtual exercises, with an added caveat like, “This badge will expire in 6 days. Keep exercising to earn more badges.”

In a nutshell

As we move towards more digitization of the healthcare sector, it is important to determine specific KPIs for mobile apps and digital platforms. Good KPIs will inform the need to change before a material loss occurs. Digital transformation is a multi-year effort, and the return on investment is not always immediate. Data is going to be the key. Turning data into actionable information through advanced analytics will help in a long-term improvement strategy. Ultimately, setting the right business outcomes and working backward to solve the problem areas through technology will help achieve those goals.

Know more about our work in Digital Health and how we have helped clients such as Suraksha Diagnostics, Abbvie, Religare Health Insurance, and SBI Health Insurance build mobile and web applications improving their operational efficiency and customer experience.

Check out the webinar on our YouTube channel.

Further Readings:

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.

Key Takeaways from the Webinar – Digital Health Beyond COVID-19: Bringing the Hospital to the Customer

Post-COVID: Future of Indian Digital Healthcare