How Smart Technology in the USA is Improving Efficiency in Healthcare

Smart technology in healthcare marks is set to make a significant improvement in patient care. In the US, advancements in digital health technologies such as AI, the Internet of Things (IoT), telehealth, and wearable devices are already revolutionizing how healthcare providers approach diagnosis, treatment, and patient management.

According to recent studies, medical errors are the third leading cause of death in the United States causing up to 251,000 deaths annually. Thankfully, these digital health trends will diminish human errors, enhance the quality of care, streamline operations, reduce costs, and improve access to healthcare services across the population.

As the healthcare industry embraces these exciting innovations, understanding the role and impact of smart technology is essential in every area, from audiology and eye care to dental and diagnostics.

The Role of Smart Technology in Healthcare

Hospitals worldwide are suffering from staff shortages and lack of treatment access. Advanced digital tools and systems will tackle these issues by leveraging data, connectivity, and automation to improve healthcare delivery and, ultimately, patient outcomes. These technologies have various applications, from electronic health records and telemedicine to wearables and AI-powered diagnostic tools. Thanks to the power of these innovations we’re about to see a more equitable society with personalized, efficient, and effective healthcare for all.

Telehealth and telemedicine

The rise of telehealth and telemedicine has been one of the most significant shifts in healthcare delivery, especially during the pandemic. Patients can receive medical consultations and follow-up care remotely, significantly expanding access to healthcare services, particularly for individuals in rural or underserved areas.

Wearable health monitoring devices

Wearables such as fitness trackers, glucose monitors, and smartwatches enable continuous monitoring of health metrics in real-time. These devices are typically worn by people with chronic diseases and studies show that 30% of hospital visits by patients with common conditions are unnecessary. Luckily, ongoing monitoring can alert healthcare providers to potential health issues before they become serious, allowing for proactive management of chronic conditions and overall health without overwhelming hospitals.

Artificial intelligence and machine learning

AI in healthcare offers exceptional precision in diagnostics, treatment plan development, and even in predicting patient outcomes. The benefits of AI in healthcare include analyzing vast amounts of data to identify patterns and insights that would be difficult for humans to discern, leading to more accurate diagnoses and personalized treatment plans.

Blockchain in healthcare

Blockchain technology enhances the security, privacy, and efficiency of health data exchange. Providing secure records of transactions, blockchain can facilitate a transparent and reliable exchange of medical information among healthcare providers, payers, and patients.

Electronic health records

Fast and accurate data access has transformed how patient records are stored, accessed, and used across the healthcare system. By digitizing these records, healthcare providers no longer have to waste time sourcing and authenticating patient information, facilitating better-informed decision-making and more coordinated care.

Benefits of Smart Technology in Healthcare

Thanks to smart technology, the world is becoming more advanced in many areas – including the healthcare industry. Patients and healthcare workers alike can enjoy a multitude of benefits, including efficient and effective medical services. With improved patient care and optimized everyday operations, these benefits lead to significant cost savings and increased satisfaction overall.

Fast, efficient operations

AI in healthcare provides better administrative and clinical processes, reducing the time and resources typically required for routine tasks. Electronic health records, for example, have eliminated paper documentation, making patient data easily accessible and shareable among healthcare professionals. The automation of scheduling, billing, and inventory management also reduces administrative strain, giving medical staff more time to focus more on patient care. Another way healthcare providers can minimize costs and maximize efficiency is by monitoring overall equipment effectiveness. OEE assesses the effectiveness of medical equipment and technologies, ensuring they are available, reliable, and performing at their optimal capacity. By monitoring and improving OEE, healthcare facilities can reduce equipment downtime, enhance patient care, decrease delays in diagnosis or treatment, and ensure a higher return on investment in medical technology.

Improved patient care and satisfaction

Access to healthcare is something that the world has struggled with for decades. But thanks to telehealth services and wearable health monitoring devices, providers can now give personalized patient care outside of traditional clinical settings. Quality of care, increased patient engagement and satisfaction are some benefits of AI in healthcare, providing patients with greater control over their health and well-being. While trained human healthcare workers offer excellent services, smart technology can greatly enhance them. AI-driven diagnostics and treatment produce high-degree precision and personalization, leading to better health outcomes.

Reduced healthcare costs

One of the barriers to receiving correct, quality healthcare is finances. Luckily, smart technology is significantly reducing healthcare costs in many ways. For instance, telehealth decreases the need for in-person visits, saving money for healthcare providers and patients. Predictive analytics and real-time health monitoring can prevent costly emergency interventions by identifying potential health issues before they escalate. Plus, AI and machine learning optimize resource allocation, ensuring that healthcare services are delivered more cost-effectively.

Improved accuracy in diagnostics

Traditionally, coming to an official diagnosis can take a long time due to inspecting data and cross-examinations. Sometimes, this can lead to inaccuracies or late diagnosis with little room for treatment. Fortunately, AI in healthcare and machine learning algorithms can easily analyze large amounts of medical data with incredible accuracy and speed, outperforming the usual methods we use to identify diseases and predict outcomes. For instance, AI algorithms can more accurately interpret imaging results, leading to earlier and more precise diagnoses of conditions like cancer, cardiovascular diseases, and neurological disorders. Using this accurate data, healthcare providers can plan personalized treatments and provide better overall patient care.

Challenges and Considerations

While smart technology in healthcare brings many advantages, it also presents some challenges. Privacy and security concerns are critical, as the increased digitization of health information raises the risk of data breaches. Ensuring the equitable distribution of these technologies is crucial to avoid widening the health disparity gap. But, there’s a need for comprehensive training for healthcare professionals to adapt to these new technologies effectively. Overcoming these barriers requires thoughtful policy-making, strong security measures, and ongoing education.

Optimizing Healthcare

As digital trends suggest, smart technology will boost efficiency in the USA by improving every area of healthcare, from diagnostics to disease prevention. Despite challenges, the potential benefits are vast and exciting. To learn more about smart technology in healthcare and other industries, speak to our professional team at Mantra Labs for more information today.

Published on Dec 19, 2024

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.

How Smart Technology in the USA is Improving Efficiency in Healthcare