Evolution of Healthcare in USA: From Passive Care to Active Patient Engagement

Published on Sep 12, 2023

Updated on Mar 20, 2024

Imagine walking into a bustling hospital several decades ago. You’d probably feel like just another face in the crowd, a number on a chart, waiting for your turn to see a busy doctor. There’s no denying that, back then, healthcare was all about treatment. The personal touch, understanding, and overall patient experience took a back seat. But as the years rolled on, a transformation was brewing. Today’s healthcare paints a very different picture, and this article aims to journey through that evolution, showcasing how healthcare in the U.S. has shifted from passive care to a deeply engaging, patient-centric approach.

What was Passive Care?

At its core, passive care was a one-way street. Patients came in, got treated, and left. Little room existed for understanding their experiences, emotions, or concerns. Here’s a closer look:

It was all “Number” mentality. Patients often felt they were just numbers in a system. Personal stories and individual concerns? They often got lost amidst the rush to move on to the next patient.

Very limited channels for feedback. If you had a suggestion or a concern, where would you go? Back in the day, feedback mechanisms were few and far between. This meant patients had little say in shaping their own care experiences.

However, things started to change gradually until the year 1999 brought with it a jolt. The Institute of Medicine unveiled a report that estimated a staggering 44,000 to 98,000 people die annually in hospitals from preventable medical errors. It was more than a statistic; it was a clear sign that the system needed change.

As we entered the 21st century, a wind of change began to blow through the corridors of hospitals and clinics across the U.S. What sparked this shift?

Digital Information Wave

The internet changed the game. Suddenly, patients weren’t solely relying on doctors for medical information.

A 2013 Pew Research study found that 72% of internet users sought health information online. This was a significant shift, one that empowered patients to ask questions and demand better care.

Think about the last time you visited a coffee shop or booked a hotel. Chances are, you experienced personalized service. Other sectors were setting the bar high for customer experience, and healthcare couldn’t stay behind.

With platforms like online forums, reviews, and patient communities, individual stories and experiences started echoing louder than ever before. A poor hospital review could now reach thousands, urging institutions to listen and adapt.

Transitioning to Active Engagement

With the foundation laid, healthcare began its transformative journey:

If there’s one thing that streamlined healthcare, it’s technology. Electronic Health Records (EHRs) became pivotal. From being a novelty in 2008, the adoption rate for EHRs in U.S. hospitals jumped to an impressive 96% by 2017. It was clear that healthcare was turning a new leaf, one that was digital and efficient.
With the onset of the COVID-19 pandemic, another trend gained momentum – telemedicine. The convenience of consulting a doctor from one’s living room became not just preferred but essential.
A report from the CDC highlighted a 154% surge in telehealth visits during March 2020 compared to the previous year. It’s undeniable; that healthcare was evolving rapidly, focusing more on patient comfort and safety.

As we navigate through this narrative, it’s clear that the push for change in healthcare wasn’t just internal. External factors, technological advancements, and the rise of patient voices played a massive role in redefining the healthcare experience in the U.S.

Pillars of Modern Healthcare Engagement

As the healthcare landscape shifted, certain principles started standing out as beacons of modern patient care:

Tailored to You: Today, healthcare isn’t just about one-size-fits-all solutions.
Genomic Medicine: Imagine treatments crafted based on your unique genetic blueprint. This isn’t sci-fi; it’s happening now. Genomic medicine is revolutionizing how ailments are treated, ensuring that care is personalized and effective.
Feedback Loop: Hospitals today aren’t just places of healing; they’re learning institutions.
Patient Surveys & Feedback Systems: Clinics and hospitals actively seek out feedback, using it as a tool to continuously evolve and better their services.
Wellness Beyond Medicine: The definition of health has expanded. It’s not just about curing ailments but fostering overall well-being.
Mental and Emotional Health: More than ever, there’s an emphasis on addressing mental health concerns and emotional well-being alongside physical health. A holistic approach is at the forefront.

In U.S. healthcare, the emphasis on customer experience has grown significantly, underscoring the vital role tech companies play in developing digital tools to enhance this experience.

A survey conducted shows that 72% of patients would like to have access to a patient portal, and 64% would like to use a mobile app to manage their health.

We have a vivid example of Manipal Hospital’s mHealth app developed by Mantra Labs. It’s a self-service healthcare mobile application that enables users to – book appointments (OPD, Lab tests, home collection), buy health packages, track health improvement reports, and self-check-in to avoid hospital queues.

Looking back, it’s truly remarkable to trace the journey of the U.S. healthcare system. From crowded waiting rooms where patients were mere numbers to a contemporary era where every individual’s health story is heard and valued—it’s a testament to the resilience and adaptability of the sector. As we move forward, with technological advancements on one hand and a commitment to patient-centric care on the other, the future of healthcare in the U.S. is not just bright—it’s luminous.

The progression from past to present-day healthcare showcases the incredible strides made in patient care, all geared towards creating an ecosystem where every patient feels valued, heard, and cared for.

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:

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.

Evolution of Healthcare in USA: From Passive Care to Active Patient Engagement