Notoriously known for being slow to innovate due to strict government regulations to standardized processes, the big data in the healthcare industry is now on a journey to leveraging increased digitization and digital technology to support innovative and modern industry-specific models. Healthcare organizations that fail to deliver data capabilities are bound to impair their organization's journey to digital transformation. After the COVID-19 pandemic, it’s imperative to think of the many repercussions the healthcare sector experienced to learn from it, but also look at how different the big data on the healthcare landscape looks like now.

Why is big data important for health care?

From overwhelming hospitalization rates, intensifying cybersecurity threats, and an increasing number of direct or indirect illnesses, health care organizations are desperate for improvements. Big data in health care proactively provides meaningful, recent insights that help them make data-driven decisions for uncertain, volatile, and pressing issues. Also, it helps them prepare for what’s coming. Operational data in big data health care poses a wealth of information as evidenced by hospitals’ increasing shift to big data development service providers to help make sense of it all. Health care Weekly stated that the global big data market in the healthcare industry is set to reach $34.3 billion at the end of 2022. But, what is the true value of big data analytics in healthcare? What tools are there to extract and act on said value? How do you set your organization up for success? Amazon Web Services (AWS) is a top player in big data health care services that aim to support and further drive the use of big data analytics in healthcare. In this blog post, we’ll be focusing on the positive impact AWS big data solutions bring to the health care sector.

Amazon is one of the best cloud solutions for Big Data for health care

More often than we’d like, big data, especially in the health care industry, is incomplete and unstructured. Typically, this is a result of data being stored in a multitude of formats and systems such as insurance claims, medical images, recorded conversations, clinical notes, lab reports, and more. Making informed decisions in big data health care goes hand in hand with making sense of big data once it’s structured. It also helps design more comprehensive clinical trials and overall, complete tasks more efficiently. Most healthcare organizations leverage automating the conversion of unstructured data and categorizing clinical data with the use of rule-based technologies. These solutions frequently fail since data must be normalized across diverse systems as mentioned earlier Furthermore, available tools are sometimes unable to account for all possible spelling variations, accidental typos, and grammatical problems. In 2020, AWS announced a new HIPAA-eligible health care data lake service to store, transform, and analyze data in the cloud. Harnessing big data at such levels helps achieve critical goals for the healthcare industry, such as:

• Care improvement. Patient engagement and healthcare initiatives need to ve personalized to improve the quality and efficiency of care. Understanding patients on an individual basis helps design tailored yet effective healthcare models.
• Sustainable health care systems. The healthcare industry is fierce and riddled with obstacles that prevent it from adopting new technologies more quickly. There’s constant legislative pressure to reduce the costs of care while managing resources efficiently. Understanding patient data can help promote effective resource utilization, which in turn, will improve patient care.
• Increased healthcare access. One of healthcare’s major issues is that not everyone has access. Healthcare should be available, accessible, and affordable to everyone, but that’s not a reality as of yet. Understanding the health care environment and resources, as well as helping patients learn more about preventive care, can help improve health, reduce demand, and minimize the waste of resources.

Amazon Big Data software for electronic medical records

What is Amazon EMR? Amazon EMR (formerly known as Amazon Elastic MapReduce) is a managed cluster platform that makes it easier to run big data frameworks on AWS, such as Apache Hadoop and Apache Spark, to process and analyze large volumes of data. Thanks to its capabilities Amazon EMR is a prime ally when looking for software for electronic data records. You can process data for analytics and business intelligence tasks using these frameworks and related open-source projects. Amazon EMR also enables you to transform and move huge amounts of data into and out of AWS data stores and databases including Amazon Simple Storage Service (Amazon S3) and Amazon DynamoDB.

Benefits of AWS EMR

Amazon EMR also gives healthcare organizations the option to run several groups to use on-demand instances in one group to ensure processing power along with Spot instances in a separate group to complete jobs faster and cheaper. Overall, the main benefits of Amazon EMR are as follows.

Selection, speed, and agility

Speed and agility at scale allow health care organizations to meet the modern needs of their field. Resize clusters to add instances for peak workloads or remove them to better manage costs when peak seasons subside, Amazon EMR makes that process a breeze. Overall, AWS helps you buy and implement products quickly from healthcare partners that address your desire to unlock data's value and improve patient care.

Flexible pricing and terms

The price for Amazon EMR is straightforward and predictable: you pay a per-second fee for each second you use, with a one-minute minimum. It costs the same to run a 10-node cluster for 10 hours as it does to run a 100-node cluster for one hour. The cost of Amazon EMR is determined by how you deploy your EMR apps. You can use Amazon Elastic Cloud Compute (Amazon EC2) instances, AWS Outposts, Amazon Elastic Kubernetes Service (Amazon EKS), or EMR Serverless to execute them on EMR clusters. You can use EC2 or AWS Fargate to run Amazon EKS on AWS.

Control and governance

To debug cluster issues such as failures or errors, you can use the Amazon EMR management interfaces and log files. Amazon EMR allows you to archive log files on Amazon S3, allowing you to save records and troubleshoot issues even after your cluster has ended. The Amazon EMR console also has an optional debugging tool that allows you to examine log files based on steps, jobs, and tasks. With AWS, organizations can streamline administration, improve quickly, and distribute health care programs or third-party data items that adapt to your strategies and compliance requirements.

Big Data on Amazon EMR

As stated, Amazon EMR is a cloud big data platform that uses open-source analytics frameworks like Apache Spark, Apache Hive, and Presto to conduct large-scale distributed data processing jobs, interactive SQL queries, and machine learning (ML) applications. Next, we’ll give you more details about Apache Spark, Apache Hive, and Presto.

Apache Spark

Amazon EMR and Apache Spark are a match made in technology heaven. You can easily and quickly build Spark clusters from the AWS Management Console, AWS CLI, or the Amazon EMR API. Thanks to powerful features like fast connectivity, the Amazon EMR File System, EC2 Spot integration, AWS Glue data catalog, and more, there are plenty of capabilities to leverage out of these two technologies. The main benefits of Apache Spark with Amazon EMR are:

• Fast performance thanks to its performance-optimized runtime environment active by default on Amazon EMR clusters. Amazon EMR runtime for Apache Spark is up to 3x faster than clusters without EMR runtime and its API is 100% compatible with Apache Spark.
• Diverse workflows thanks to a multitude of tightly-integrated libraries that come as a great ally in building apps for machine learning, stream processing, and graph processing.
• Quick collaboration. Apache Spark supports Java, Scala, Python, and SQL to give users several language options to build applications.
• Feature set integration thanks to capabilities like EMR Step API, data access in S3, EC2 Spot capacity, EMR Managed Scaling and more.

Apache Hive

is a distributed, fault-tolerant open-source system with data warehouse-like query capabilities. It uses a SQL-like interface to allow users to read, write, and manage petabytes of data. Here's where you can learn more about Apache Hive. In Amazon EMR, Apache Hive is natively supported, and you can construct managed Apache Hive clusters fast and easily using the AWS Management Console, AWS CLI, or the Amazon EMR API. You can also use Amazon EMR features like direct storage connectivity to Amazon DynamoDB or Amazon S3, external metastore configuration with the AWS Glue Data Catalog, AWS Lake Formation, Amazon RDS, or Amazon Aurora, and EMR Managed Scaling to add or remove instances from your cluster. The main benefits of Apache Hive are:

• Managed scaling by giving users the ability to define EMR Managed Scaling for Apache Hive clusters and thus, optimize resource usage.
• High availability with multiple master nodes to run Apache Hive on EMR clusters without a hiccup or interruption.
• Fast performance with an average performance speedup of 2x as it uses Amazon EMR 6.0.0 which comes with support for Hive LLAP.

Presto

is an open-source distributed SQL query engine designed for ad hoc data analysis with low latency. Complex queries, aggregations, joins, and window functions are all supported by the ANSI SQL standard. Presto can process data from a variety of sources, including Hadoop Distributed File System (HDFS) and Amazon Simple Storage Service (S3). PrestoDB and PrestoSQL are two community initiatives for Presto. Both projects are supported by Amazon EMR. PrestoDB can be found here. Using the AWS Management Console, AWS CLI, or the Amazon EMR API, you can rapidly and simply construct managed Presto clusters. You can also use Amazon EMR's other features, such as quick Amazon S3 connectivity, integration with Amazon EC2 Spot instances, a wide range of Amazon EC2 instances, including memory-optimized instances, and resize commands to easily resize your instances. The main benefits of Presto are:

• Interactive query performance thanks to its custom engine with operators especially built to handle SQL semantics workloads.
• Ease of use as Presto can users can launch an Amazon EMR cluster in a matter of minutes.

Amazon EMR Studio

is an integrated development environment (IDE) for data scientists and engineers that makes it simple to create, visualize, and debug data engineering and data science applications written in R, Python, Scala, and PySpark. EMR Studio offers fully managed Jupyter Notebooks as well as debugging tools like Spark UI and YARN Timeline Service.

Amazon EMR Notebooks

make it simple to explore with Spark and build applications. If you want, you can construct interactive and collaborative notebooks for Spark data analysis with Apache Zeppelin.

Amazon healthcare data visualizations

Data Visualization and Business Analytics are partner concepts. You must be able to graph, chart, and plot your data. Graphics are just as valuable as pictures, both giving a clear visualization of patterns to help us make sense of a specific use case scenario. The right visualization option will yield the most value to what you are looking for. Some patterns may be lost in a table, but maybe a picture will be too ambiguous to interpret, it all depends on the specific use case. In short, the right visualization will assist you in gaining better knowledge in a shorter amount of time. There’s a direct relationship between the way information is presented and how we understand it. Particularly with COVID-19, geospatial displays helped us see variations in how the virus was spreading worldwide, helping report a narrative of how quickly and dangerously the virus was reaching new geographies. The power of data visualizations in healthcare helps unearth health and healthcare data with profound insights that often lead to changes and improvements in our health and healthcare services.

AWS Healthlake

AWS announced Amazon HealthLake in 2020. Amazon HealthLake is a HIPAA-compliant service aimed at advancing the use of big data analytics in healthcare by supporting interoperability standards. Amazon HealthLake attempts to aggregate all of an organization's data from various silos and formats into a centralized data lake, and standardize it automatically using machine learning techniques. The service enables businesses to store, categorize, index, standardize, query, and analyze data in the cloud using machine learning techniques. To detect and tag each piece of clinical information, index events into a chronological view, and enrich the data with standardized labels so that providers can simply search all of this information, HealthLake uses machine learning taught to recognize medical terminology. The service also converts all of an organization's data into the FHIR standard, allowing information to be transferred simply and securely between health systems and third-party apps. This will help providers to collaborate more effectively while also providing patients with limitless access to their medical records.

Conclusion

By integrating cloud software solutions and capturing data insights at scale, healthcare professionals are speeding up innovation and digital transformation. AWS has a curated list of solutions that can help organizations modernize treatment, enhance patient outcomes, comply with regulations, and maximize the value of health care data. Partnering with AWS experts will make it seamless to effortlessly identify, acquire, install, and manage cloud technology and data management solutions, and more, all with the goal of improving healthcare models and patient care. At Svitla Systems, we have rich experience working in the health care sector and have participated in high-impact, high-value projects that have given us the expertise, know-how, and confidence in deploying AWS-powered big data analytics solutions that transform healthcare tasks and future-proof health care organizations for what’s next.