Modern Healthcare Apps on AWS

Modern healthcare applications built on AWS, are transforming data management and utilization in the healthcare sector. This article will examine how AWS technologies enable healthcare providers, payers, and healthtech organizations to improve patient care, streamline operations, and maintain data security through advanced cloud solutions.

Why is a modern data strategy important for healthcare?

We live in a world that is generating data at an exponential rate, with healthcare data accounting for 36% of all data. With no end to this growth in sight, data holds immense opportunity for healthcare and life science organizations to tap into to meet their goals of better health population outcomes, better patient experiences, and better cost realization. 

Creating value from healthcare data requires rigorous processing, capturing, and storing capabilities to comply with its strict usability criteria. Understanding the importance of managing high-quality data is essential for creating a competitive advantage and positioning your organization to anticipate and react to changing market dynamics. 

When dealing with healthcare data, the stakes are high. At an average investment of $2 billion to bring a drug to market, poor-quality data can jeopardize the successful development or launch of therapy, potentially denying patients access to essential treatments. Conversely, high-quality data can lead to personalized treatments, exemplified by the groundbreaking services offered by cancer genomics companies. 

Use cases for healthcare

Next-gen solutions, like those powered by artificial intelligence (AI) and machine learning (ML), leverage models that can digest large amounts of historical data to find patterns that reveal meaningful insights. These models perform better with larger amounts of high-quality training data because they can be sensitive to outliers and anomalies. 

These insights can revolutionize patient care experiences by contributing to:

• Personalized treatment: Machine learning allows healthcare organizations to deliver personalized care by analyzing patient data and tailoring treatment plans accordingly. 
• Disease diagnosis and early detection: Machine learning algorithms can analyze large clinical datasets to identify hidden patterns and flag patients who may have health problems, enabling early detection of diseases such as diabetes, liver disease, and cancer. 
• Drug discovery and development: Machine learning significantly speeds up the analysis of healthcare data, expediting drug discovery and development, and ultimately driving potential advancements in pharmaceutical research. 
• Predictive analytics and disease prevention: Machine learning algorithms can be employed for predictive modeling, which involves training models on historical data to identify patterns and make predictions, thus contributing to disease prevention and better clinical decision-making. 
• Automated image diagnosis: Machine learning can automate the analysis of medical images, such as X-rays and MRIs, to assist in diagnosing various conditions.

AWS solutions for healthcare

With over 150 HIPAA-eligible services and purpose-built solutions, AWS offers a secure platform for HCLS companies to reinvent their digital systems to improve patient experiences. These services are HIPAA-eligible and integrated into the AWS ecosystem, creating a seamless interaction between data and services. Some key services built specifically to address the needs of HCLS use cases include: 

• Amazon Comprehend Medical: Extract health data from unstructured medical text, such as doctors’ notes, clinical trial reports, or radiology reports, using machine learning and natural language processing models. 
• AWS HealthLake: Gain a complete view of individual and patient population health data using API-based transactions and securely store, transform, query, and analyze healthcare data at petabyte scale. 
• AWS HealthImaging: Efficiently retrieves medical imaging data on a large scale with sub-second latency using cloud-native APIs. This service enables the storage, analysis, and sharing of medical images in the cloud at petabyte-scale, while reducing costs by up to 40%. 
• AWS HealthScribe: Develop clinical applications utilizing speech recognition and generative AI, automating the generation of preliminary clinical documentation, including speaker role identification, dialogue classification, medical term extraction, detailed clinical transcripts and notes.

Common challenges

Healthcare organizations face significant challenges when building and maintaining applications due to the vast scale of their data and strict regulatory requirements. Managing large volumes of healthcare data drives up storage and processing costs, while on-premises systems can increase expenses. Additionally, ensuring data security and compliance with regulations like HIPAA and GDPR is critical, due to the sensitive nature of the data. The fragmented nature of healthcare data across multiple platforms also makes it difficult to derive reliable insights, further complicating app development and maintenance.

Healthcare data is huge

Medical applications generate and utilize enormous volumes of data, including electronic health records, medical images, genomic data, and health behavior information. Managing and storing this vast amount of data can be a substantial challenge for companies - specifically around the complexity of transferring and managing large volumes of data. 

Modern cloud architectural principles offer cost-saving benefits through pay-as-you-go pricing, where businesses only pay for the resources they use. This scalable model optimizes cost efficiency by aligning expenditures with actual usage, avoiding unnecessary expenses. Cloud providers handle infrastructure management, eliminating the need for dedicated IT data center teams, and ultimately reducing labor costs. 

In order to support data lifecycle management, services like Amazon S3 provide a rich set of tiers for storing data effectively based on the expected usage pattern of that data. The best solution is often to use Intelligent Tiering (IT), where AWS automatically moves unused files from standard to lower-cost storage based on detected access patterns.

Service interruptions and inability to scale

The limited scalability of on-premises data centers increases the risk of insufficient capacity to handle unexpected workload spikes, potentially resulting in application crashes or performance degradation. Service interruptions during the scaling process can impact customer experience and business performance, requiring careful consideration of the various implications.

Cloud elasticity allows businesses to scale resources according to demand, preventing overprovisioning and minimizing wasted resources. Amazon S3 provides a variety of tools for managing the vast amount of data it can store, including:

• Intelligent-tiering: The Amazon S3 Intelligent-Tiering storage class automatically stores objects in three access tiers: frequent access, infrequent access, and archive access to reduce costs by over 70%. This feature monitors access patterns and moves objects between tiers to optimize costs.
• Inventory: Amazon S3 Inventory provides scheduled reports listing objects and their corresponding metadata for an Amazon S3 bucket or shared prefix. This feature helps in verifying the access tier of objects and can be used for compliance, data discovery, and analytics.
• Lifecycle rules: Amazon S3 Lifecycle rules allow users to define actions for objects based on their lifecycle. This includes transitioning objects between storage classes, such as moving objects to the Amazon S3 Intelligent-Tiering class after a certain period of no access and eventually expiring or deleting objects. These automated transitions can delete, preserve, or optimize storage based on business rules specific to the individual organization and application requirements.
• Batch operations: Amazon S3 Batch Operations allow users to execute large-scale batch operations such as copying objects or running AWS Lambda functions across millions or billions of objects. This feature simplifies managing vast data by providing a way to perform tasks in bulk. 

Data breaches

Due to the sensitive nature of healthcare data, it is often a prime target for ransomware due to its importance to an organization and the potential impact of its loss. Ransomware restricts access to data by either removing or encrypting it. Perpetrators exploit this to threaten organizations with public exposure or release of the data, pressuring them to comply with their threats.

AWS offers a broad range of services and tools to help prevent data security breaches. Firstly, AWS provides comprehensive data protection and privacy controls, allowing you to manage how your data is used, who has access to it, and how it is encrypted. AWS has purpose-built services, such as AWS Key Management Service, designed to generate and manage encryption keys securely and provide monitoring and logging features for compliance and threat detection. 

Additionally, AWS offers continuous monitoring and threat detection services, such as Amazon GuardDuty, Amazon Macie, Amazon Inspector, and AWS Artifact, which use machine learning, anomaly detection, and integrated threat intelligence to identify and prioritize potential threats, helping to prevent malicious activity and unauthorized behavior. 

Regulatory non-compliance and penalties

It’s crucial for companies to prioritize compliance to avoid penalties and fines. As the regulatory landscape becomes more complex, you must continuously improve your compliance programs to address the growing number of laws and regulations. This involves documenting all compliance efforts, anticipating challenges, assessing risks, and presenting the business case for the compliance program. 

By moving to a cloud-based data infrastructure, organizations can benefit from AWS services built for security, scalability, and reliability, essential for ensuring compliance with evolving regulations. Services such as data lakes, purpose-built data stores, and managed analytics services, enable companies to store and analyze data in a scalable, cost-effective, and compliant manner. 

They help companies analyze data by providing scalable storage for diverse data types, specialized databases for specific needs, and on-demand analytics supporting diverse workloads. Additionally, AWS provides governance capabilities to manage access to data across various data stores, further supporting compliance efforts.

Poor data quality and unreliable insights

Data reigns supreme as a catalyst for strategic decision-making, but generating actionable insights can be complex without proper data governance, storage management, and data preparation. Data governance is vital for companies to derive reliable insights from their data. It encompasses well-defined policies and procedures for data access, usage, and interpretation, supporting business users in exploring and analyzing data while upholding data quality and security. 

Without proper data governance, organizations may struggle to respond to market trends and make informed decisions, potentially leading to financial losses and decreased market share. In addition, given that medical decisions can have life-altering consequences, it’s crucial to uphold the highest possible standards for maintaining data quality.

AWS provides governance capabilities to manage access to data across data lakes and allows for centralized security, governance, and auditing policies, resulting in uniform access control for enterprise-wide data sharing. This paves the way for enhanced data management, unified governance, and scalable data analysis, ultimately generating actionable insights.

What AWS has to offer

With a modern, cloud-native governed data platform, you can access a range of powerful benefits to enhance your data management and monetization use cases: 

• Securely democratize data, providing appropriate access while maintaining control and compliance. 
• Make data a reliable, trusted asset, delivering accurate and high-quality information to support critical operations. 
• Drive innovation and gain the agility to extract immediate insights through self-service analytics. 
• Streamline workflows and reduce operational bottlenecks with mature DataOps capabilities and end-to-end automation. 

Data migration

AWS Database Migration Service (DMS) simplifies database migrations, allowing businesses to smoothly transition their databases to AWS with minimal downtime and zero data loss.

Ingestion

Data ingestion patterns and tools enable organizations to ingest data from diverse sources. For batch ingestion, AWS provides tools like AWS Glue and AWS Lambda, which allow organizations to process and load large volumes of data at scheduled intervals. 

Stream ingestion, on the other hand, is facilitated by services like Amazon Kinesis, enabling data streaming from applications and devices, allowing businesses to capture and process data in real-time for immediate insights and quicker decision-making. Additionally, API ingestion allows organizations to collect data from external sources using secure and well-documented APIs, further expanding the available data sources and enriching existing data for analysis. 

Storage

Storing data efficiently and securely lies at the heart of a governed data platform. Using Amazon Simple Storage Service (S3) as a versatile and cost-effective data lake storage solution, and Amazon Redshift as a high-performance and fully managed data warehouse solution, organizations can build a comprehensive and agile data platform. 

Amazon S3 provides scalable and secure storage for vast amounts of unstructured data, making it the foundation for advanced analytics, including Al. On the other hand, Amazon Redshift offers lightning-fast query performance and scalability for structured data, empowering data analysts and business users to derive valuable insights and empower data-driven decision-making.

Data governance

AWS offers a range of governance tools, such as AWS Identity and Access Management (IAM) and AWS Key Management Service (KMS), to enforce strict access controls and encryption, safeguarding data from unauthorized access or breaches. 

In addition to security services, AWS offers the Glue Data Catalog, which provides a centralized metadata repository, enabling organizations to maintain a consistent and comprehensive view of their data assets.

Democratization

Storing data efficiently and securely lies at the heart of a governed data platform. Using Amazon S3, democratizing data within the platform enables a broader audience of users to access and explore data insights, empowering business teams across the organization to make informed decisions. 

• With AWS Glue, it is easier to discover, prepare, move, and integrate data from multiple sources for analytics, machine learning, and application development. As a serverless, scalable data integration service, it supports both ETL functionality and the creation of “data catalogs”, describing the meta-data found by running Glue Crawlers on various data sources. 
• Amazon DataZone is a data management service that makes it faster and easier for customers to catalog, discover, share, and govern data stored across AWS, on-premises, and third-party sources. This service essentially supports safe data sharing across organizations. 
• Amazon QuickSight, a business intelligence service, provides interactive dashboards and reporting, enabling self-service analytics and data visualization. Additionally, Amazon Athena, a serverless query service, empowers users to run ad-hoc SQL queries directly against data stored in Amazon S3, making it easier to access and analyze large datasets in real-time without complex data transformations. 

By implementing well-defined governance principles and democratizing data access, data transforms into a reliable and valuable asset. 

Data analytics

AWS offers a wealth of AI/ML services, including Amazon SageMaker, which enables data scientists and developers to build, train, and deploy machine learning models at scale. 

The introduction of AWS generative Al capabilities using SageMaker JumpStart or Amazon Bedrock makes it easier for developers to create generative Al applications, which opens up further possibilities for creativity and innovation. 

The Caylent approach to healthcare apps on AWS

In today's data-driven healthcare landscape, your organization's success depends on its ability to harness vast amounts of data, ensure ironclad security, and drive continuous innovation. Caylent empowers payers, healthcare systems, and healthtech organizations to revolutionize their digital infrastructure on AWS. Our approach encompasses strategic assessment, data-centric architecture, intelligent governance, advanced analytics integration, and continuous optimization that can transform your healthcare applications to meet modern demands.

With Caylent's HealthLake solution, you can efficiently manage extensive healthcare data, enable personalized treatments through AI and ML, ensure HIPAA compliance leveraging AWS's eligible services, optimize costs with flexible pricing models, and seamlessly scale to meet fluctuating demands. Our deep AWS expertise, coupled with comprehensive healthcare industry knowledge and a proven methodology, provides the end-to-end support you need to stay at the forefront of healthcare innovation. Caylent can partner with you to unlock the full potential of your data on AWS, driving improved patient outcomes and operational efficiency in an ever-evolving healthcare ecosystem.