- One of the reasons for healthcare data migration is to increase storage capacity. With the rise in healthcare data every day, a better, larger, and more robust storage space is needed.
- Planning of healthcare data migration revolves around what type of data needs to be migrated and in which format, along with storage space. The goal is to have an efficient migration plan, with minimal challenges.
- Two approaches for migration include trickle migration and the big bang. With the big bang approach data transfer is completed from one source to the other in a single operation and in a brief time span. Whereas, in trickle migration, data is transferred in phases
A study suggests that by 2025 the compound annual growth rate of healthcare data will reach 36 percent, that’s 6 percent faster than manufacturing, 11 percent faster than media and entertainment, and 10 percent faster than financial services. To accommodate rising healthcare data, and to ensure that it is seamlessly analyzed, and accessed, the healthcare data migration process is carried out.
Healthcare data migration is preferred when there’s system upgradation or a new EHR system is being incorporated. Data is also transferred for better security, mapping, and conversion from one type to another.
This article highlights the healthcare data migration process, tools, best practices, and challenges.
Reasons for Healthcare Data Migration
1. To Increase Storage Capacity
One of the reasons for healthcare data migration is to increase storage capacity. With the rise in healthcare data every day, a better, larger, and more robust storage space is needed. Accommodating health data only on on-premise spaces or on EHR systems sets limits on storage capacity. However, migrating data to a larger space such as cloud storage can be fruitful in the long run. Both physicians and patients can access data from anywhere through their servers.
2. Modernizing System
Modernizing software architecture is a common reason for data migration. Healthcare organizations are on the verge of demolishing legacy systems and adopting new ones for better workflow and care options. New software features and functionalities bring an advanced IT architecture design to manage and store data efficiently.
Legacy systems can crash at any time due to outdated functions. They also lack support, are difficult to use, improve, and are incapable of integrating with other systems. So, they have to undergo a modernization process, during which data is migrated for safekeeping.
3. For Data Analysis
Healthcare data analytics is applied to every facet of patient care. Healthcare providers make use of AI and machine learning technologies for predictive analytics, which has a significant impact on patient outcomes. To get better results, healthcare organizations tend to switch to health tech software. The process of changing software also includes healthcare data migration for analysis. Data analysis produces results for research and development, drug discovery, medication, diagnosis, and population health management to name a few.
4. To Ensure Interoperability
A well-connected healthcare system ensures high-quality data exchange or migration that can be accessed by patients and healthcare providers for enhanced care. An interoperable system benefits healthcare providers through increased patient point-of-care and fewer medical errors. It helps patients by reducing the risks of adverse drug effects and decreasing the possibility of duplication of lab tests. Migrating healthcare data through a robust connected network ensures interoperability.
5. To Comply with Regulations
The HIPAA (Health Insurance Portability and Accountability Act) requires Business Associates and Covered Entities to store healthcare data for a minimum of six years from the time of their creation date or the time they were last effective, whichever is later. So, healthcare organizations may switch to cloud storage options for organizing data and avoiding clutter. As a result, they migrate healthcare data to compliant storage space for the stated timespan.
Best Practices of Healthcare Data Migration
- Identify Healthcare Data Format, Sensitivity, and Location: Before starting with the migration process, determine what kind of data you wish to migrate, what is its format, where it is stored, and in which format it should be migrated. This information helps to identify potential threats to data before moving it, and pre-plan security measures. It also saves you from making critical errors during the healthcare data migration process.
- Plan Scope of Project: Once data is identified, define the scope and size of the migration project. Plan out resources needed during data transfer and set aside a definite budget for it. Additionally, ensure to have a flexible timeline for migration. It is also essential to consider whether data migration will affect normal healthcare functions and contribute to downtime.
- Review Applicable Standards: One of the essential steps in healthcare data migration is to assure that data is compliant and is on par with regulations. Healthcare organizations are obliged to follow multiple rules such as HIPAA, FDA, HITRUST, and specific state regulations if any. Go ahead with the data migration process only if the system is compliant, if not it can cause legal troubles for organizations.
- Choose Migration Tools and Storage Systems: It is essential to choose a safe storage option, one that is scalable. It has to be reliable enough to manage large data volumes while adhering to the organization’s security requirements. Likewise, review data migration tools to ensure that they are secure and work well with all types of data.
- Backup Data: Before migration, assure that all healthcare data is backed up. If there are any corrupt, duplicate, or incomplete files, then correct the error and restore data in its original form. Cloud backup is the safest and easiest option for data backup.
- Test Everything: Once healthcare data migration is completed, check whether there are no connectivity issues with the source and target servers. The goal is to ascertain that the migrated data is secure, correct, and at the proper location. To verify this, make sure to conduct system, unit, batch application, and web-based application tests. Further, conduct an audit of the system and data to assure that everything is correct and there are no errors.
3 Key Steps in Healthcare Data Migration
1. Planning and Data Analysis
Data analysis includes an understanding of the type of data that needs to be migrated, the timeline, and other trivial details. Data analysis is used to detect trends, relationships, and patterns in data. It is also used to assess the quality of data and to carry out exploratory data analysis.
Planning of healthcare data migration revolves around what type of data needs to be migrated and in which format, along with storage space. The goal is to have an efficient migration plan, with minimal challenges. Critical aspects to consider while planning healthcare data migration are:
- Audit: This includes carrying out a complete data audit to understand data format, quality, duplication (if any), errors, etc. This step saves unexpected issues such as compliance and low-level data quality.
- Cleanup: This step is immediately carried out after an anomaly is spotted in data while auditing. Issues are spotted and resolved for a smooth migration.
- Protection and Maintenance: Under this step, control measures can be set up to maintain data quality. Regulations and system maintenance fall under this step too.
2. Data Mapping and Profiling
Data mapping is the process of matching fields from one database to the other. It bridges the differences between two data models or systems, so when data is moved from one system to another, it is usable and accurate. Data mapping is the first step to facilitate data migration, data management tasks, and data integration.
During mapping from one system to the next, data doesn’t change over time. So, after migration the new system is the source of migrated data, and the original one is retired. Data mapping supports migration by mapping source fields to destination fields.
Furthermore, during migration data profiling can detect data quality problems. It also uncovers new requirements for the target system.
3. Data Migration
Two major approaches can be used for healthcare data migration: big bang and trickle migration.
With the big bang approach data transfer is completed from one source to the other in a single operation and in a brief time span. Whereas, in trickle migration, data is transferred in phases, it is broken into small migration processes, each with its own goals, scope, and deadlines. Moreover, the big bang approach is not performed during off days because the entire data is transferred in a single sitting. The downside of this approach is that it may experience downtime during the migration process.
Challenges of Healthcare Data Migration
One of the aspects that poses challenges for data migration is poor planning as it results in a lot of issues such as loss of data due to incorrect choice of data migration tools. Poor planning is also a cause for downtime and high costs, along with data duplication, alteration, and loss.
Next, the source of healthcare data can be boiled down to EHRs, IoMT, monitoring devices, healthcare apps, and wearables. All of these data are available in multiple formats that have to be structured for a smooth transition. Failure to structure datasets is an obstacle to a seamless data migration process.
Apart from this, stringent security regulations pose challenges as organizations may miss out on crucial, yet small rules mentioned in HIPAA, HITECH, etc. These regulations must be taken into consideration while transferring healthcare data. Also, not all data migration tools available in the market are compliant, and this hampers data security. It is recommended to hire specialists who are aware of all the ins and outs of healthcare data migration and avert unnecessary risks encountered during a relocation.
If you’re looking for specialists who can manage and develop HIPAA-compliant healthcare software for your organization, then connect with Arkenea, a leading healthcare software development company. With over 12 years of experience in the industry, the Arkenea team delivers top-notch software for their clients. Connect to know more.