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Emerging Healthcare Delivery Models

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5 minutes, 30 seconds read

Wearables, IoT devices, healthcare apps have significantly increased digital health access points for customers. The evolving nature of the digital health industry in India can be witnessed through the improved personalization and value-added services delivered to the consumers. To further multiply the digital health touchpoints, new healthcare delivery models are coming up to cater to specific healthcare needs and deliver a satisfying experience. Below are a few emerging healthcare delivery models: 

1. On-Demand Healthcare

The healthcare industry is beginning to welcome on-demand services for consumers who are already accustomed to receiving services anytime and anywhere from demand-driven brands such as Zomato, Uber, etc. Among the various digital healthcare services, e-pharmacies in India such as Medlife, Pharmeasy, have made a significant profit by making medicines and drugs available to customers in no time.

Quick booking of appointments, receiving digital medical reports, settling medical fees online, scheduling doctor visits, all of this can be done through smartphones. On-demand healthcare is gathering attention among consumers, especially millennials, needing mental health counseling, follow-up appointments, and quick recovery from wounds and minor illnesses. Due to the provision of healthcare anytime and anywhere, reduced expenses due to lessened hospital visits, deep interaction with patients, the on-demand healthcare model is growing. Express Care offered by Cleveland Clinic allows patients to consult virtual doctors in matters of non-life-threatening conditions like allergies, rashes, or back pain.   

This model also allows medical practitioners to work flexibly, depending on their schedule. It also lets the doctors appoint and cancel the dates of visits depending on their availability. For instance, besides being a clinical app for patients, Doctor on Demand has features such as email, payment system, messaging, etc. that help doctors to manage their patients.

2. Social Health Networks or Peer-based health networks

This model allows people and medical professionals to share views and hold discussions on health-related issues. This model also allows the doctors to address people’s health issues without any geographical barrier, motivates people to share their health experiences, and facilitates health literacy. As people belonging to different age groups are well versed and active on social media, social health network serves as an effective solution to encourage people to look after their health and provide emotional support to others. Any platform based on this model can also push people to create awareness through health-related campaigns. 

This model also facilitates maximizing health-related conversations and allows the exchanging of information among patients, medical professionals, and doctors- one-to-many and many-to-many conversations between patients-patients, doctors-patients, doctors-doctors. Even though this model is still at its developing stage, it is sure to gain momentum because people are starting to be upfront about their health problems, especially mental health issues. Organizations following this model can also leverage health data and provide effective health solutions. HealthUnlocked is a social networking service that empowers patients and promotes relevant health-related content. It focuses on building online support groups where patients can give health recommendations and insights to other patients. Medikoe launched We, a social network that publishes health-related content from qualified doctors and allows patients to connect with doctors. It is like any other social media where people can follow health professionals, and search, share, like health-related articles. It also encourages people to take up health challenges and updates people on upcoming health campaigns. 

3. Proactive Intervention

Chronic diseases account for more than 55% of total deaths in India. Increasing awareness and the use of healthcare technologies are prompting people to opt for preventive healthcare services to manage unexpected health issues. Proactive care lessens the chances of health deterioration through active dialogues between doctors and patients. Telemedicine is one such way through which health outcomes can be improved. As it facilitates easier transmission of patient data and increased access to HCPs, it holds the potential to reduce the mortality rate in India. This instant healthcare service provider model also allows remote monitoring of patients as health records can be transmitted in no time. 

As the model is compatible with wearable sensors, medical information such as blood pressure, heart rate, etc. can be monitored easily which helps in the early detection of diseases such as asthma, hypertension, heart-related diseases. Fitness trackers that are compatible with mobile applications help people in monitoring their health data as well. Health Care Originals developed ADAMM, an intelligent asthma monitoring wearable system that is attached to the upper body. It detects symptoms of asthma attacks- body temperature, heartbeat, cough rate, etc. Rubi Life is a MedTech company that uses nanotechnology in an elastic maternity band to monitor fetal activity in high-risk pregnancies. It sends alerts to the mother’s phone in order to prevent premature births, stillbirths, and to also avoid negative outcomes.  

4. Personalised Medicine & genomics

This model aims to improve the effectiveness of medicine by leveraging the patient’s health history, genetic characteristics, and lifestyle. Personalized medicine, also known as precision medicine improves health outcomes without any side effects. The unique genetic composition of patients helps in predicting disease and curing it before it starts affecting the body. This model disrupts the one-size-fits-all model as it takes into consideration the genomic composition and forms a pattern by observing a body’s reaction to drug dosages, thus promising accuracy. 

This model also reduces the trial and error inefficiencies and proves to be beneficial for medical professionals as it can reduce the failure and cost of pharmaceutical trials.  

K&H Personalised Medicine Clinic is the only healthcare facility in Hyderabad, India that provides personalized healthcare based on genomics, DNA analysis, and a patient’s medical history. As the genes are affected by diet, exercise, stress levels, and environmental factors, the K&H clinic takes into consideration all these factors to form a proper treatment strategy.

Conclusion

The emerging healthcare delivery models are aiming for cost-effective solutions that can save time and instantly cater to patient needs digitally. The digital healthcare market is growing at a compound annual growth of 27.41% during 2019-2024, and according to a recent report by McKinsey Global Institute, telemedicine services in India have the potential to replace in-person consultations by 30-40%. This shows that people are appreciating contactless solutions and are used to immediate gratification. While some of the healthcare delivery models are still at their nascent stage in India, some are experiencing good growth. As digital health consumers are starting to demand more, the emerging healthcare models have to make sure that they cater to their diverse needs instantly and efficiently.  Moreover, as healthcare facilities are unevenly distributed, the upcoming healthcare delivery systems should also make sure to maximize their touchpoints in order to reach every corner of the country.

Know 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.

Further Readings:

  1. Building Consumer Trust in the Digital Healthcare Era
  2. HealthTech 101: How are Healthcare Technologies Reinventing Patient Care
  3. Virtual health: Delivering care through technology
  4. How Mobile Micro-Health Insurance can unlock ‘Digital for Bharat’?

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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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