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What will ‘Behavioural Changes’ Mean for India’s Digital Health Future

We are in the middle of a global pandemic, facing a threat unlike one never seen before. COVID-19 has been a reason for global concern since it has negatively impacted economies, shut down workplaces, and forced cities into lockdowns.

But history also tells us  that times of uncertainty also foster innovation. The pandemic has forced consumers and businesses to rethink how they behave both physically and digitally. As per McKinsey, COVID-19 has speeded up the adoption of digital technologies.

India, which was on the cusp of a ‘digital health’ revolution, has now been forced to embrace innovation and emerging trends. The healthcare sector holds great promise since new-age technologies like telemedicine, robotics, artificial intelligence (AI), genomics, etc. are transforming healthcare services.

There have been unprecedented changes in consumer behaviour as well. People are now increasingly relying on using the internet to find clinical information or engage with healthcare professionals digitally. Moreover, online consultations, telemedicine, and e-pharmacies have seen a rise in popularity.

Companies will thus need to capitalize on the changing patterns of consumption and health-seeking behaviour.

This article focuses on how changing patient behaviour will affect India’s digital health future.

A growing Indian healthcare market

According to a report by Future Health Index, India is a leader in the adoption of digital health technology. As per India Brand Equity Foundation (IBEF), the Indian healthcare market is expected to grow at a compound annual growth rate of 22% to reach a valuation of USD 372 billion by 2022. This growth can be attributed to the following –

  • Growing health awareness
  • Aging population
  • Lifestyle-related diseases
  • Rising income levels
  • Growth of internet availability

The rise of digital health start-ups is also playing a role in the growth of the healthcare sector. Indian health tech startup landscape has now matured.

Over the last few years, telemedicine has emerged as a fast-growing sector in India. Prominent start-ups like Practo, mfine, and Lybrate have established themselves in the telehealth market. McKinsey estimates that India could save up to USD10 billion by 2025 by using telemedicine instead of in-person doctor appointments.

COVID-induced behavioural changes

The COVID-19 pandemic has brought about changes to patient behaviour. The fear of leaving homes to get treatment has led to the growth of virtual care and telemedicine. 

As per a report by Accenture, almost 70% of the patients canceled or postponed their treatments due to the COVID-19 pandemic. Technology, therefore, played a crucial role in helping patients continue their care. Healthcare providers were even able to improve the experience for patients by delivering them faster response time, personalized interactions, and the convenience of getting consultation from home.

The same report by Accenture highlights some key behavioural changes that are being observed in patients – 

  • Nearly half of the patients now get their treatment at their homes instead of visiting a clinic.
  • Almost 60% of patients want to continue using technology for communicating with healthcare providers.
  • About 41% of patients now use video conferencing to connect with their healthcare providers. Of these, for almost 70% of patients, it’s their first-time using video conferencing for healthcare.
  • Almost 44% of patients used new apps or devices during the pandemic to manage their health conditions.

All this highlights the need for healthcare providers to reimagine their patient engagement strategies in keeping with the changing patient behavior.

Future of digital health in India

New digital technologies and tools are making an impact across the healthcare sector. They hold great promise in improving the efficiency of healthcare services while delivering better patient care. Below are some of the technological developments that are expected to revolutionize the way we seek healthcare.

Telemedicine

About 68% of India’s population lives in rural areas where healthcare services are not usually up to the mark. This barrier can be overcome by telemedicine that offers an excellent way for patients to consult a doctor in a much shorter duration. Telemedicine can cut waiting times and allow patients to avoid traveling to a clinic or hospital. Some other benefits of telemedicine include –

  • Immediate access to specialist healthcare providers.
  • Cost-effectiveness.
  • Improved quality of care.
  • Convenience to the patients.
  • Improved patient engagement.

Internet of medical things (IoMT)

The rapid growth of IoMT devices is rapidly changing healthcare delivery by playing an important role in tracking and preventing chronic illnesses.

It not only helps eliminate the need for in-person medical visits but also helps reduce costs. Goldman Sachs estimates IoMT to save USD 300 billion annually for the healthcare industry. IoMT will benefit those patients the most who are unable to get access to quality healthcare due to remote location.

Big data in healthcare

There has been dramatic growth in the amount of medical and health data in the last few years. These massive datasets can be used to draw insights and opportunities for healthcare organizations. Analysis of healthcare data can help discover warning signs and create preventive plans.

The widespread adoption of IoT devices also makes it easier to monitor heart rate, blood pressure, etc. This can help in the early detection of diseases like hypertension, asthma, heart problems, etc.

Electronic medical records

Electronic medical records or EMRs help collect, digitalize patients’ information, and store it in a single place. EMRs store various types of medical data like medical history, prescriptions, drug allergies, etc. and allow doctors to make accurate disease prognosis in a much shorter time. Some other benefits of EMRs include – 

  • Effective medical decisions.
  • Easy data recovery.
  • Improved collaboration.
  • Portability.
  • Security of medical data.

Artificial intelligence

Artificial intelligence (AI) has a big role to play in improving healthcare since growing digitization leads to the availability of a large amount of health data. AI has the potential to transform everyday health management in the following ways –

  • Improved accessibility of healthcare services (for example – the AI-based mobile app Ada is available across 140 countries and makes it possible for anyone to have access to medical guidance).
  • Improved efficiency.
  • Accurate disease diagnosis.
  • Improved insights to reveal early disease risks (for example – a popular app Verily can forecast noncontagious and hereditary genetic diseases).
  • Time and cost savings.

mHealth

Mobile health or mHealth refers to the monitoring and sharing of health data via mobile technology like health tracking apps or wearables. 

mHealth apps can prove to be beneficial in increasing patient engagement, providing health education, and offering remote consultations to patients. It can also use the data from wearable devices to improve the quality of care. Some other benefits of mHealth include – 

  • Faster access to physicians.
  • Improved medication adherence.
  • Remote patient monitoring.
  • Increased medication reconciliation accuracy.
  • Improved coordination between healthcare providers and patients.

Conclusion

It’s quite clear that COVID-19 has significantly impacted patient behaviour. There has been a growing preference for telehealth and mHealth apps. But all of this has also compelled healthcare organizations to put in more effort in adapting to these behavioural changes. Healthcare providers are opting to rely more on new technologies to continue delivering patient care. A more affordable standard of high-quality care is in the works for India’s digital health future.

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Lake, Lakehouse, or Warehouse? Picking the Perfect Data Playground

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In 1997, the world watched in awe as IBM’s Deep Blue, a machine designed to play chess, defeated world champion Garry Kasparov. This moment wasn’t just a milestone for technology; it was a profound demonstration of data’s potential. Deep Blue analyzed millions of structured moves to anticipate outcomes. But imagine if it had access to unstructured data—Kasparov’s interviews, emotions, and instinctive reactions. Would the game have unfolded differently?

This historic clash mirrors today’s challenge in data architectures: leveraging structured, unstructured, and hybrid data systems to stay ahead. Let’s explore the nuances between Data Warehouses, Data Lakes, and Data Lakehouses—and uncover how they empower organizations to make game-changing decisions.

Deep Blue’s triumph was rooted in its ability to process structured data—moves on the chessboard, sequences of play, and pre-defined rules. Similarly, in the business world, structured data forms the backbone of decision-making. Customer transaction histories, financial ledgers, and inventory records are the “chess moves” of enterprises, neatly organized into rows and columns, ready for analysis. But as businesses grew, so did their need for a system that could not only store this structured data but also transform it into actionable insights efficiently. This need birthed the data warehouse.

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:

FeatureData WarehouseData LakeData Lakehouse
Data TypeStructuredStructured, Semi-Structured, UnstructuredBoth
Schema ApproachSchema-on-WriteSchema-on-ReadBoth
Query PerformanceOptimized for BISlower; requires specialized toolsHigh performance for both BI and AI
AccessibilityEasy for analysts with SQL toolsRequires technical expertiseAccessible to both analysts and data scientists
Cost EfficiencyHighLowModerate
ScalabilityLimitedHighHigh
GovernanceStrongWeakStrong
Use CasesBI, ComplianceAI/ML, Data ExplorationReal-Time Analytics, Unified Workloads
Best Fit ForFinance, HealthcareMedia, IoT, ResearchRetail, 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.

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