Ask a layperson what healthcare data migration is, and they’ll likely say something like “moving data from one location [or source] to another.” But exactly how (and where) that data moves is more nuanced. For example, if you don’t need to use the data regularly, you might archive it. Doing so gives you continued access for auditing, legal, and regulatory purposes while reducing costs and mitigating risk.

When you require essential information for clinical care as your facility changes software platforms, however, you must ensure the data from your existing system is usable in the new system through data conversion. In an EHR or EMR conversion, providers typically need immediate access to the problem list, allergies, medications, and immunizations (commonly referred to as PAMI data) as well as other important information that supports patient safety, care coordination, and clinical decision-making.

If the PAMI or other crucial data doesn’t align with the data structure in the new system, or the old system includes unstructured data in healthcare (which makes integration more complex), data conversion transforms it so it’s functional, explains Madelaine Yue, vice president of solutions delivery at Experis Health Solutions, a Verisma partner. Madelaine and her colleague Ayesaan Jude Rebello, solutions director of process improvement, spoke with us about some of the common considerations and challenges with converting data and how to ensure success.
 

Understanding Healthcare Data Conversion Benefits and Challenges

When completed effectively, the conversion of healthcare data contributes to a more seamless patient and provider experience. “It reduces the likelihood of patient safety risks like medication errors,” says Jude, “and boosts provider satisfaction by making it easier to gather information whether it’s in the old system or current system.”

However, you’ll need to anticipate and address potential challenges as well. These may involve problems with your system or internal processes.

One common source of trouble is when the original system’s data structure doesn’t lend itself well to data conversion. For example, EMR conversion may be difficult unless your EMR complies with HL7 and FHIR data standards and includes repeatable, structured data elements.

To address this challenge, create a mapping strategy to translate unstructured data in healthcare into standardized formats that comply with HL7 and FHIR data standards. Then, you should perform data normalization to ensure that all elements are clean, consistent, and fit the standardized schema.

Data conversion can also be difficult when there’s no consistent data capture method to document key elements (e.g., users document data that combines unstructured data in healthcare and discrete fields). To overcome this issue, you may create a standardized schema to map the unstructured and semi-structured data into discrete, structured fields. You can leverage natural language processing (NLP) tools to process unstructured text and extract data elements.
 

Keeping Things Moving: What to Know About Conversion Timelines

Your data migration plan should address the time you anticipate it will take to convert your data. While it’s common for conversion to take approximately six months, this timeline depends on numerous factors, some of which may or may not be under your control. It’s therefore helpful to account for additional time depending on your specific situation. Consider the following:

• The volume of data you convert. “Organizations participating in value-based care arrangements may want to convert and migrate more than a couple years’ worth of data because they may need access to historical information to support specific quality measures,” says Madelaine. However, it’s a balancing act. “There’s a cost associated with converting all that data,” she adds. “You also don’t want bad data triggering quality measures. Organizations must be tactical in how they approach a data conversion.”
• Your relationship with the legacy system provider. As you retire legacy systems and migrate your data, your legacy system provider must be on board with releasing data. “Sometimes organizations have to bring legal counsel into the conversation because they have significant difficulties pulling their data,” says Madelaine “If your legacy system vendor puts up walls—even unintentionally—it can throw off your entire timeline.”
• Resource constraints of a hosted environment. If you use another healthcare organization’s EMR, it’s different from working directly with a software vendor. You’re at the mercy of that organization and its resources (or lack thereof) for ingesting the data during an EMR conversion.
• Source system delays or the inability to pull data. If you can’t pull the data out of a source system efficiently, your entire data conversion project may take longer. For example, you may face an extended timeline if your source system doesn’t comply with information-blocking regulations.
• Internal resource constraints for evaluating and validating the data. You must have enough internal resources to assess and validate the data to move into the final stages of the process.
• Maintaining a single source of truth. Identifying and correcting duplication errors to create an enterprise master patient index (EMPI) is crucial, otherwise the conversion process may become burdensome and time-consuming.
• Patient matching. If you’re unable to match patients easily across systems, the conversion process may also be delayed.
• Scope creep. Failing to monitor the scope of a project can be costly, says Jude. “Sometimes additional asks can make things less straightforward and increase the price” in addition to the time it takes to deliver, he notes.

Safeguarding Your Data and Organization During Conversion

Alongside its people, data is one of the most important assets that a healthcare system or hospital holds. Because it’s also highly sensitive and any corruption could be catastrophic, it must be protected during data conversion.
 

Ensuring Data Quality and Integrity

Any degradation of data quality and integrity during data conversion can endanger patient care and your operations and organization’s reputation. That’s why you should dedicate resources (e.g., informaticists or medical records specialists) to the important task of comparing a statistically significant sample of patient records in the source system to the transformed data categories in the new system – with the goal of identifying whether any errors (e.g., specification errors, mismatches, or data gaps) occurred during the data conversion process. Once the destination system ingests the data, it’s important to perform additional spot checks for accuracy.

“We’re currently looking at ways to create a low-cost, automated approach to do a full assessment of the files and not just spot checks,” says Madelaine. “We want to streamline the process and hopefully reduce some of the FTE lift because we know healthcare is really tight on resources.”
 

Following Healthcare Data Protection Best Practices

Data security and privacy are also critical during data conversion. One best practice for protecting healthcare data is to set up Secure File Transfer Protocols (SFTP) properly, so everyone (including offshore vendors) has proper access to perform file transfers. Another is to ensure your vendor meets security protocols. Ask your vendor to articulate how it ensures HIPAA compliance and healthcare data protection.
 

Heeding Expert Advice: Guidance from a Pro

As you plan for data conversion and migration, Madelaine recommends following these best practices:

• Have a plan and a strong partner. Converting data is complex, but the right partner(s) can help you simplify the process. Ask lots of questions to understand each one and what it brings to the table.
• Keep end-users informed. Let users know what data is being converted versus archived and when so that they know how to access the information they need.
• Keep going, even if you hit a wall. Engage your vendor partners to brainstorm new approaches for data conversion.
• Reiterate the importance of validation. “The reason you’re converting your data is that you need it to be usable,” says Madelaine. “You do this through data validation.”

Support Effective Data Conversion With a Robust Archiving Solution

Data conversion is a critical step in the data migration process, but it’s just the first of many. During the process, you’ll identify information that should be archived to optimize storage costs, improve system performance, facilitate historical data access, and ensure compliance with data security and privacy regulations.

Verisma streamlines healthcare data migration with Olah™, our simple, fast, and complete enterprise archiving solution. Request a demo to see how Olah can help you modernize your data strategy.

Like many healthcare organizations, yours may be embarking on a clinical data migration project to enhance efficiency, modernize your healthcare organization, and leverage the data in new ways.

Unfortunately, moving healthcare data from one system to another isn’t as simple as pressing a button. It requires a multi-step process that prioritizes data accuracy, reliability, and security. You may undermine your efforts if you fail to ensure data integrity.

Before we explore data integrity’s importance, let’s clarify some related and often-confused terms. 

Data Quality vs. Data Integrity

Data integrity is often used synonymously with data quality, even though the two terms are technically distinct concepts.

Data quality considers whether the data is accurate, comprehensive, consistent, well-defined, relevant, and timely. It’s the assurance that the data retains its value to whoever uses it, and it’s widely considered a subset of data integrity.

Data integrity is the measure of data’s reliability and trustworthiness. It ensures that the data hasn’t been corrupted or changed from its original state.

Data protection in healthcare directly impacts data integrity by guarding against threats like hackers accessing and altering the information. When information is private and secure, its integrity is less likely to be compromised throughout the data lifecycle, including during data migration. 

Why Data Integrity Is Essential in Healthcare

Data integrity is important because it impacts every aspect of healthcare provision in the digital health era, starting with clinical decisions and care delivery. Data of poor integrity might lead providers to make false assumptions or draw incorrect conclusions, resulting in unfavorable outcomes or an exacerbation of health disparities.

Data integrity also affects care continuity. Data of high integrity promotes safe care transitions, effective care coordination, and evidence-based decision-making. Additionally, the integrity of data is critical for ensuring regulatory compliance. Healthcare organizations must maintain accurate and reliable data to avoid lawsuits and penalties.

Finally, data integrity helps build trust and enhance reputation management. Data of high integrity is information everyone—including patients, providers, and others—can rely on to foster safe and effective care. 

Challenges With Maintaining Data Integrity in Healthcare

The busy and fragmented nature of health system and hospital operations, massive amounts of data captured in healthcare, and understaffed or technology-deficient departments all contribute to an environment in which data integrity can be difficult to sustain. Providers may document tests and diagnosis differently, creating data quality issues. Manual data entry may cause errors and delays, while data silos and an overly complex data infrastructure can undermine the organization’s ability to put data to practical use. Moreover, legacy systems are vulnerable to cyberattacks, which frequently compromise integrity.

System integration plays an important role in data integrity. For example, a provider may collect high-quality clinical data during an encounter but unintentionally compromise its integrity if the system doesn’t integrate with other software to provide a holistic view of the patient’s history and inform accurate decision-making.

This issue sometimes occurs when organizations implement a new electronic health record (EHR) while maintaining a legacy system that doesn’t integrate with the new EHR instead of archiving the data for easier, safer access. As such, preserving access to integrated archived data is critical for ensuring data integrity. 

How to Ensure Data Integrity in Healthcare

When considering how to ensure data integrity in your organization, you may implement various strategies before, during, and after data migration. For example, before migration, you could:

• Discontinue using legacy systems to store and manage data. Instead, use a data archiving solution that integrates with your EHR to support the transition.
• Establish a governance framework. Decide how your organization will collect, retain, use, access, and share data. This framework may include policies, procedures, standards, ownership, decision rights, roles and responsibilities, and accountability related to data management. You may want to think about including guiding principles as part of this framework, such as, “Individuals who create or acquire data are accountable for the quality of that data” or “Data is a strategic asset that has value and risk.”
• Train employees on ensuring data integrity. Provide training on data entry procedures to reduce errors. Also consider establishing and enforcing data entry policies and documentation best practices. Help employees understand differences in data quality vs. data integrity.
• Secure the data. Implement strict access controls, adopt comprehensive security measures (e.g., advanced encryption, firewalls, multi-factor authentication, and secure data transfer protocols), and perform frequent vulnerability scans. Establish a comprehensive security policy and provide regular employee training on data security best practices. Finally, partner with data archiving and migration vendors that can help you prioritize data security and comply with all data security standards.

Implement these strategies during data migration to ensure data integrity:

• Perform extensive testing. Test the system to ensure data transfers correctly without errors and omissions.
• Use secure data transfer protocols. Implement robust security measures to protect data from unauthorized access that can compromise data integrity during migration.
• Clean the data. Transform the data into a format compatible with the new system. Cleaning the data may require reorganizing folder structures, renaming files, extracting specific data points, and digitizing information.

After data migration is complete, implement the following strategies:

• Use third-party data to add context to existing datasets. Integrate data from different sources to gain a unified and consistent view of the information.
• Properly integrate and configure all analytics tools. Ensure your tools interpret and process data consistently.
• Perform ongoing data audits. Use various controls, processes, and technologies to maintain data integrity throughout the data lifecycle. Regular data audits can help you identify and rectify issues before they escalate.
• Maintain audit trails. Periodically review who alters data and why to ensure integrity.

Augment Data Quality and Integrity During Data Migration

Data integrity is critical during data migration. Organizations prioritizing data quality, integrity, and data protection in healthcare will reap the rewards of highly actionable information on which leaders can rely to make accurate decisions.

Olah™ is Verisma’s safe, secure, and fast enterprise archiving solution that helps promote data integrity during data migration. Contact us to see how Olah can help simplify data migration and easily integrates with your new software.

Like any complex process, medical data migration (i.e., the process of moving structured and unstructured data in healthcare from one system to another) becomes less daunting and more manageable when you break it down into smaller steps. A successful data migration strategy is one that incorporates data migration best practices in a way that everyone can understand and embrace.

Simplification is critical because data migrations often occur during times of major transitions—for example, when a healthcare organization implements a new electronic health record (EHR), merges with another health system, or acquires a medical practice. What you don’t want to do is embark on a data migration project without having a clear sense of what you’re trying to accomplish, why, and how you’ll do it. Without a strong healthcare data management plan, you may struggle to set expectations, meet short- and long-goals, and obtain the intended return on investment.
 

Exploring the Importance of Data Migration Strategy

There are several reasons why defining your data migration strategy is beneficial to your facility or organization. Consider the following advantages of a well-thought-out approach:

• Time and money savings
• The prevention of interruptions of critical business functions so your organization can stay focused on clinical data management and high-quality patient care
• Mitigation of compliance risks associated with migrating healthcare data
• The ability to more smoothly modernize your healthcare organization, providing a competitive advantage

While each hospital’s data migration strategy may vary depending on the specifics of the data and systems involved, there are several universal steps that apply.
 

Tailoring Your Approach: Essential Actions to Take

While no data migration is the same, they all should involve these steps:

1. Develop a detailed healthcare data management project plan. Take the time to think through (and document) why your data migration project is necessary, whether the project will include a full or partial migration, the estimated timeframe, key stakeholders and their responsibilities, budget, anticipated challenges and solutions, and tools that will be necessary to execute data migration best practices and automate processes for increased efficiency. This project plan will keep everyone united, accountable, and moving toward the common goal of successful data migration.
2. Assess and organize the data. In this step, you’ll take a deeper dive into what healthcare data you have and where it resides. You’ll need to review all structured and unstructured data in healthcare systems and go beyond electronic applications to investigate paper-based data sources as well. The goal? To gain a comprehensive understanding of the totality of your data management landscape.During this step, you’ll also ask questions to determine whether retaining any of your data is no longer necessary (potentially leading you to archive it or even destroy it), whether any of the data is of particularly high value (and should thus receive priority migration status), and whether the quality of the data is sufficient.

   Be sure to review all databases, files, and applications that hold healthcare data. If, at any point during the review process, you discover problems with data quality (e.g., missing or incomplete data), you may need to develop data quality standards or use data profiling or data normalization techniques before starting the migration process. Data profiling helps you assess issues with data duplication, inconsistency, and inaccuracy while data normalization ensures data entries are similar across all fields and records, making it easier to find, group, and analyze information.

3. Perform data conversion, if necessary. Healthcare data—particularly EHR data—comes in different formats. This means you may need to convert everything into a single, unified format suitable for the new system. A singular structure also makes it easier to work with the data moving forward as business goals and data use cases evolve. Data mapping shows the relationships of data elements in different systems and can help you identify precisely where to focus your conversion efforts.
4. Ensure compliance and security. Use data encryption and secure channels before, during, and after the data migration process to comply with HIPAA and any other state or local requirements. Also be sure to conduct a risk assessment as part of your data migration strategy to identify cybersecurity vulnerabilities and proactively address them for all data in transit and data at rest.
5. Choose the right tools. Given the volume and complexity of the structured and unstructured data in healthcare systems that your organization may choose to migrate or archive, you’ll need tools to automate certain processes. Automation helps reduce errors and keep your healthcare data management project on track. Any tools you leverage should be healthcare-specific and incorporate data migration best practices as well as the most modern data management techniques to ensure your success.
6. Validate data in the new system. Once you’ve migrated the data, you’ll need to ensure its accuracy. Why? Even if you don’t notice any obvious problems during migration, this doesn’t mean you can make widespread assumptions about the success of the project. Problems could lurk beneath the surface, and it’s important to recognize and address them immediately. Testing the data post-migration ensures it meets usability requirements and that the new system functions properly.
7. Prepare for downtime. Even despite your best intentions, downtime during data migration may occur. The good news? There are ways to minimize it, starting with adding safeguards into your data migration strategy. This includes scheduling data migration during off-peak hours, using modern data migration and data archiving solutions, and monitoring the process continuously to detect and respond to issues as they arise. Following the other data migration best practices outlined in this article can also mitigate downtime.

Plan Your Data Migration Strategy Today

Healthcare data migration may seem daunting at first, but having an effective data migration strategy can ease stress. With the right healthcare data management approach, your organization can quickly reap the rewards, including reduced costs, easier access to data, improved backup and disaster recovery, and greater storage capacity. Contact Verisma to learn more about Olah™, our safe, secure, and fast enterprise archiving solution.

Data privacy and security are critical as healthcare providers collect demographic, financial, and clinical information from patients. However, healthcare data security is a moving target with new threats emerging daily.

Last year, an astonishing 133 million records were exposed, stolen, or otherwise impermissibly disclosed. Nearly 80% of these data breaches were due to hacking incidents, and organizations are especially vulnerable when sharing or moving information between people or systems. Adhering to data security standards is important; however, organizations must take additional proactive steps to ensure data migration doesn’t leave them susceptible to breaches, regulatory fines, and exposure of sensitive information.

While it may be tempting to think of data migration as a technology-centric project, in reality, it’s more about business-critical risk mitigation on the journey toward modernizing clinical care. In other words, it’s about ensuring the right information flows successfully and securely from one system to another to improve care delivery and outcomes.
 

8 Data Security Standards for Protecting Patient Privacy

The following eight best practices can promote healthcare data security during a data migration project that requires archiving:
 

1. Perform a risk assessment before, during, and after data migration

Risk assessments help healthcare organizations identify and assess gaps that could compromise healthcare cybersecurity. Performing a risk assessment before data migration sets the stage for strong data privacy and security during the remainder of the project. However, issues can emerge at any time, which is why it’s important to perform an assessment during and after the project, as well.

Risk assessments should focus on technical, physical, and administrative safeguards to uncover vulnerabilities in systems, processes, and personnel. When performed correctly and frequently, the assessments help organizations prioritize security investments and develop remediation plans. Risk assessments should be comprehensive and include portable devices such as smartphones, tablets, and laptops that access and share patient information. Other connected devices include smart medical equipment and wearable monitoring devices. These technologies often have limited healthcare data security features and can be particularly vulnerable to hacking or malware during data migration.
 

2. Ensure strong encryption and data transfer protocols

Data encryption — a recommended healthcare cybersecurity measure under the Health Insurance Portability and Accountability Act (HIPAA)— converts plain text or data into a coded form that’s inaccessible to anyone without a decryption key. Without this key, unauthorized users can’t modify the data either, making it more difficult for hackers to exploit sensitive information.

Organizations can leverage encryption not only when data is stored ”at rest” in the electronic health record, a portable device, or other health information technology system, but also when it’s transmitted (e.g., during a data migration project or when sending data to a payer or external agency). Through data encryption, organizations demonstrate their commitment to data privacy and security, including during data migration projects.

While migrating data, it’s important to choose the correct encryption method (e.g., symmetric encryption, asymmetric encryption, and hashing algorithms) based on the type of information, the level of protection required, and the organization’s existing infrastructure. Ensuring appropriate key management is equally important, as is training staff members on encryption protocols and procedures.
 

3. Implement strict access controls and user authentication

Healthcare organizations must be cautious in granting access to data and applications as they migrate their data. Only individuals who need this access to perform their job duties should receive it.

In addition, organizations can assign permissions based on an employee’s position and responsibilities. Role-based access along with multi-factor authentication (i.e., requiring users to provide two or more verification factors to gain access) helps reduce data breaches and limit access to sensitive data before, during, and after data migration.
 

4. Use compliance monitoring logs

Compliance monitoring logs help organizations understand who accesses what information, when, and from where. Armed with this information, organizations can detect anomalies and unusual activity (e.g., large data transfers or access from unexpected locations) more easily, allowing leaders to quickly respond to potential security incidents and promote healthcare data security during the data migration process.
 

5. Create a backup recovery plan

Despite an organization’s best efforts, errors, omissions, and other challenges may occur during data migration projects. Creating a backup recovery plan ensures business continuity. This plan should address when and how organizations will access and maintain secure, offsite backups of sensitive data. The plan should also outline regular testing of backup and restore procedures.
 

6. Provide employee security training

When it comes to data privacy and security, employees are often an organization’s greatest asset as well as their biggest risk. While employees can take proactive steps to promote healthcare cybersecurity, they can also make mistakes that cause data breaches. Healthcare data security training helps everyone understand threats and vulnerabilities so they know what missteps to avoid.

Training should address how to identify and report potential healthcare data security threats, and how to maintain patient privacy and ensure compliance with HIPAA regulations. Training is not a “one and done” event. Rather, it’s an ongoing process that keeps employees up to date on the latest security protocols, data security standards, and vulnerabilities. Ongoing training also promotes a culture of security awareness and demonstrates the organization’s commitment to healthcare cybersecurity.
 

7. Perform vendor due diligence

Working with contract specialists presents unique opportunities to protect sensitive patient data. These third-party entities must be able to articulate their ability to comply with security and privacy standards, and healthcare organizations should carefully review each entity’s policies, procedures, and technical safeguards before formal engagement. This includes any vendors involved in the data migration project itself.
 

8. Protect mobile electronics

Implementing device encryption, remote wiping capabilities, and mobile device management solutions (i.e., solutions that enforce security policies, monitor device usage, and remotely lock or erase lost or stolen devices) can help reduce vulnerabilities during a data migration project.

Segmenting networks to isolate connected devices from other systems can also help promote healthcare data security. For example, if a healthcare device becomes infected with malware, the threat is contained within its segment, preventing lateral movement across other connected technologies.
 

Uphold Best-in-Class Data Privacy and Security Protocols

As healthcare organizations embark on important data migration projects, they need an archive vendor that holds itself accountable to the highest data privacy and security standards in the industry. Verisma’s enterprise archiving solution, Olah™, is SOC 2 Type II compliant, ensuring it protects customer and patient data. Contact Verisma to learn more about Olah.