Healthcare Data Migration Challenges and Solutions with AWS
In the lifetime of any healthcare organization, there comes a time when they must inevitably replace existing legacy infrastructure in favor of modern solutions that meet the demands, such as faster data access and compliance with different data privacy and security standards.
However, data migration is a big decision for any healthcare provider, given the number of challenges faced. While other businesses can get away with a turn-key SaaS solution, healthcare providers like yours cannot do so, as these solutions are usually not HIPAA compliant.
This is where AWS’ healthcare-focused services bridge the gap. The cloud platform provides HIPAA-eligible services and enables customizable migrations to tackle downtime, corrupted data, and other common migration challenges while ensuring financial efficiency.
In this article, we will take a detailed look at the common healthcare data migration challenges and how AWS services help optimize data security, migration time, accessibility, downtime, running costs, and more.
What is healthcare data migration?
In its most rudimentary form, healthcare data migration is the process of transferring patient and non-patient data from healthcare providers from one system to another. The purpose of healthcare data migration can be anything from data consolidation to the additional security and performance granted by newer systems.
Why is healthcare data migration needed?
The ever-evolving performance and security requirements of data storage systems make data migration inevitable for healthcare organizations. However, the advantages of data migration enable the following improvements to take effect on regular operations.
- Better data security
Cloud database services such as Amazon S3 keep your data safe from malicious forces by staying updated with the latest security protocols. Unlike most legacy systems, these services encrypt your data, which helps with healthcare compliance policies such as HIPAA.
- Enhanced data analytics
Getting accurate analytics out of data stored on legacy systems is a slow and clunky process due to the varying data sources and formats. Modern cloud data storage systems have the ability to integrate advanced analytical services such as Amazon QuickSight and Amazon Forecast to help you decipher the trends and patterns in your data in real-time.
- Universally faster access to data
Users can take advantage of the global infrastructure of cloud storage services to get faster access to data regardless of their geographical location. Such low-latency data access can make all the difference when it comes to retrieving patient data during emergencies.
- Data recovery
In cases of data deletion or corruption, healthcare providers can rely on cloud database storage services for instant data recovery. This is because they constantly store additional backups of your data to help you recover it at any given moment and make the possibility of losing vital data very rare.
- Autonomous scalability
Scaling infrastructure is a challenging and costly aspect of managing healthcare data due to its dynamic and unpredictable nature. Most modern cloud computing services are serverless, meaning they scale automatically according to your needs.
Phases of data migration
The data migration process is essentially a complete reconstruction of databases on the target migration systems. Understanding how intensive the process is, it is carried out in multiple phases to ensure a smooth transition. The phases are as follows:
- Planning and assessment: During planning and assessment, you decide the data sources to be migrated and determine the overall scope of the migration. Considerations include estimating downtimes and selecting mediums for data migration.
- Data profiling and cleansing: Here, you profile data according to its format and interactions during migration and actively rectify or remove duplicate, corrupted, and inconsistent data to ensure compatibility with the upcoming stages of migration.
- Data transformation and mapping: During data transformation and mapping, you transform data in incompatible formats to make it compatible with the new formats of the target system. Additionally, you remap the data structure to ensure that the source and target databases share the same schema.
- Data migration execution: Data migration is initiated after all the planning and preformatting are done. The actual transfer and synchronization between the source and target systems is executed in this stage.
- Monitoring and optimization: After migration and synchronization, observation of the systems for compatibility and performance starts. The target system is then amplified accordingly to increase security, resource consumption, and performance.
Challenges faced with migrating healthcare data (solutions with AWS)
The implementation of new technologies in previously established settings is often riddled with challenges related to security, compatibility, compliance, logistics, and so on. Healthcare data migration is no different, but fortunately, there are solutions from AWS that address these challenges effectively.
Let us take a closer look at these challenges and how they are resolved using AWS services.
|Healthcare data migration challenge
|AWS services and tools
|Meeting compliance standards
|Risk of data breaches
|AWS Data Migration Service
|AWS Trusted Advisor
1. Meeting compliance standards
One of the best-known challenges for implementing new technologies in the healthcare sector is complying with data privacy regulations such as HIPAA. These regulations are especially taxing during migrations as it is a process that involves the movement of protected health information (PHI).
Such compliance standards demand that the transferred data is kept secure using encryption and vendor compliance assurance. This means your data needs to be fully encrypted, and the database vendor should be HIPAA compliant. You also need to keep track of the users who have accessed the data by using logging measures that record all user interactions with the data.
Solution: AWS KMS
The track record of AWS HIPAA compliance is pretty clear, as most of their services are HIPAA compliant from the get-go. However, adding an additional layer of security is always advised.
AWS Key Management Service (KMS) integrates with the majority of AWS services, such as Amazon EC2, Amazon S3, and AWS Lambda, and encrypts the data within those systems. It also serves as a hub to access all your encryption keys at once for quick access to data. When integrated with AWS CloudTrail, it logs instances of encryption key access to increase accountability and make monitoring access easier.
2. Risk of data breaches
Data breaches happen in multiple ways, with unauthorized access to resources being one of the leading causes of healthcare data breaches. Even a single data breach can rack up millions of dollars in regulatory fines and legal fees for a healthcare provider.
Implementing measures to restrict users’ access requires careful consideration and planning. You also need to ensure that user IDs with greater access are authenticated to safeguard your data from malicious attacks.
Solution: AWS IAM
AWS Identity and Access Management (IAM) authenticates the identity of users before they are given access to your cloud computing and storage infrastructure. Along with a standard username and password, you can have your authorized users complete multi-factor authentication and restrict their access to the cloud systems based on their IP address and access time slots.
AWS IAM also follows a strict least-privilege principle that grants users the minimum necessary access to perform their tasks and ensure data safety. You may also attach custom policies to each user, which dictate all the resources they are allowed and denied access to.
3. Data interoperability
The diversity of data types and formats is large in a healthcare environment, as many different devices, such as billing or imaging devices, constantly output different forms of data. While legacy systems usually have separate repositories for different types of output devices, migrating data to a singular cloud network may present data interoperability issues.
Usually, to mitigate interoperability issues from showing up, data preformatting is implemented. However, this process not only takes a lot of time to implement but may also compromise the integrity of the data being formatted.
Solution: AWS Data Migration Service
The AWS Data Migration Service supports both homogeneous and heterogeneous migrations. This flexibility allows data transfer from the source database to multiple endpoints based on your storage needs.
For example, you can have your data migrated to different cloud database services, such as Amazon RDS, Amazon Aurora, and Amazon Redshift, simultaneously. AWS DMS also allows ongoing replication that ensures changes to the source database are reflected on the target endpoint at scheduled intervals.
4. Cost optimization
An issue that persists regardless of whether you use legacy systems or cloud-based systems is the operational costs. On legacy systems, the issue stems from overspending on resource storage or computing capacity, but it is not as if serverless cloud computing services are immune to this issue.
On serverless systems, the cost problem has more to do with the system’s resource utilization, which is a lot harder to solve. Fixing the resource utilization of your system requires long-term monitoring of operations to determine the source of recourse overutilization.
Solution: AWS Trusted Advisor
AWS Trusted Advisor is a service specifically designed to optimize all facets of your cloud computing infrastructure, such as security, performance, and costs. It continuously monitors your operations and systems to identify areas where resources are being underutilized to help you optimize your running costs.
Since it simultaneously monitors multiple areas, it suggests measures to bolster the performance and security of your infrastructure, which directly optimizes the consumption of existing resources. You can view the insights from Trusted Advisor instantly with a convenient dashboard that highlights all the areas of improvement.
5. Planned downtime
The transition from legacy infrastructure to the cloud requires scheduled downtimes for uninterrupted data transfers and quality checks. Since the volume of transferred data is high, the time taken to transfer it completely is prolonged.
To maintain data consistency, both the source and target databases have to be in sync to ensure that the data availability is same on both systems until migration finishes. Downtimes are necessary, even during synchronization, to prevent the data from being corrupted during transfers.
Solution: AWS Datasync
AWS Datasync automates many of the processes in the data transfer phase of migration. It automatically validates that the data transferred to the target system is the same as the source.
DataSync also enables incremental data transfers, only moving new data from the source database to the target system. This saves a lot of time, reducing the chances of unnecessary transfers and duplication. Users can schedule intervals for data synchronization and transfers to maintain availability during peak demand periods.
6. Data analysis
In most healthcare institutions, data is fragmented across multiple storage systems from various data sources, such as EHRs, billing software, and lab systems. Due to these scattered data sources and formats, it is difficult to analyze data and get refined analytics.
Even after consolidating healthcare data using on-site infrastructure, it may be difficult to find analytical models that are compatible with the data format. All of these factors combined compromise the quality of the final analysis and slow down the entire process.
Solution: Amazon Athena
Amazon Athena is a serverless service that makes data analysis from fragmented sources faster and easier. This is because of its capability to query multiple data sources, be it data warehouses, S3 data lakes, or transactional systems.
Knowing how diverse the data sources in healthcare applications are, you can also integrate Amazon Athena with ETL (Extract, Transform, Load) services such as AWS Glue to transform data from other sources into a format compatible with Amazon Athena. Even data without a predefined schema can be read and analyzed by Athena as it has a Schema-on-Read approach.
Effortless and secure healthcare data migration with Simform
Modernizing infrastructure for healthcare demands careful planning and AWS expertise to build optimized solutions. As a trusted Advanced AWS Consulting Partner, Simform assists in migrating healthcare data while complying with all the data privacy regulations.
Plus, with our AWS Migration Competency status, we make data migration easier for healthcare providers. Our approach includes:
- Following the best cloud architecting practices
- Prioritizing compliance with healthcare regulations
- Optimizing the performance of cloud systems and applications
- Developing custom healthcare software to fit your requirements
- Implementing proactive strategies to ensure a smooth transition from legacy infrastructure to cloud-based infrastructure.
Contact us today to learn more about how we can overhaul your healthcare data infrastructure.