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Introduction
Data governance in healthcare comes with arguably the highest stakes of any industry. For starters, healthcare organizations are constantly face-to-face with a near-incomprehensible (and ever-increasing) amount of highly regulated personal data.
The impact that healthcare data usage has on people’s lives is at the heart of why data governance in healthcare is so crucial. In healthcare, data governance focuses on managing the accuracy, quality, and integrity of data. When healthcare organizations do this well, it can lead to better clinical decision-making, improved patient outcomes, and medical error prevention.
Despite this, many healthcare organizations face an uphill battle. Healthcare organizations need a strong data governance framework to ensure that they are collecting, processing, and sharing data in compliance with regulations like the Health Insurance Portability and Accountability Act of 1996 (HIPAA) in the U.S. and the General Data Protection Regulation (GDPR) in the EU.
How can a healthcare provider improve their data governance strategy, especially when small changes have a ripple effect? Data lineage can help.
With data lineage, you'll establish a strong data governance strategy that gives your team full control of your healthcare data pipeline once and for all.
Understanding Data Governance in Healthcare
While the need for a strong data governance framework is undeniable in any highly regulated industry, the healthcare industry is unique because it is collecting and processing massive amounts of personal data to make informed decisions about patient care. And one broken or incomplete piece of data can not only create noncompliance and audit issues, but can hurt real people.
For example:
- Healthcare providers regularly rely on medical records to diagnose and establish treatment plans for patients. If those medical records contain inaccuracies that lead to a mis-diagnosis, medication error, or delayed care, the consequences can be serious – even fatal.
- Inaccuracies can lead to additional delays or coverage complications with insurance companies.
- Healthcare organizations must also comply with data privacy regulations like HIPAA and GDPR. Noncompliance with these laws is costly and can damage your reputation -- not to mention it poses a danger to patients and practitioners alike when their data is breached.
Conversely, if you have confidence in the accuracy and consistency of your data, you can minimize the risk of adverse health outcomes instead of failing to prevent them (or worse, causing them).
You can take this even further, leveraging predictive analytics to identify trends, patterns, and potential future health risks in your patients.
Of note: most electronic health records (EHR) systems offer predictive analytics capabilities, but the accuracy of those analytics is limited by the accuracy of the data used.
This makes having a full picture of your data environment and a clear chain of custody imperative. Finding leaks and pressure points depends on having a strong data governance strategy, of which data lineage is a critical component.
The Difference Between Data Lineage and Data Governance in Healthcare
Data lineage and data governance are both crucial for healthcare data management. Data governance focuses broadly on managing the quality, accuracy, and integrity of healthcare data.
Alternatively, data lineage provides an ongoing and continuously updated map of all your data flows and dependencies, which can help you achieve data governance in healthcare.
While data lineage and data governance are not the same, you need data lineage to achieve successful data governance. With detailed information on data flows, sources, and transformations, data lineage equips you with the information you need to identify and resolve data quality issues, ensure regulatory compliance, and make informed patient care decisions based on reliable data.
Data Lineage |
Data Governance |
| Focuses on tracing the flow of a healthcare organization’s data from its origin to its destination. | Focuses on managing the quality, accuracy, and integrity of data in a healthcare organization. |
| Helps healthcare organizations identify and resolve data inconsistencies, reduce errors, and enhance data accuracy. | Helps healthcare organizations manage risk and protect sensitive patient data. |
| Supports effective data governance by providing insight into healthcare data flows, sources, and transformations. | Supports healthcare data management and informed decision-making based on accurate data insights. |
Challenges in Data Governance for Healthcare and How Data Lineage Can Help
Data governance can help healthcare organizations maximize the accuracy and security of your data assets. At the same time, implementing a data governance framework poses some challenges, such as data quality issues, data silos, and security and privacy concerns.
1. Data Quality Issues
Positive business decisions and outcomes are reliant on trustworthy, high-quality data. But, data quality issues plague healthcare facilities, despite best efforts of business leaders, because of the sheer number of people inputting data and the high-pressure situations in which they are often entering the data.
A study from the Journal of American Medical Association (JAMA) revealed that one-fifth of patients with access to ambulatory care notes found errors in their records. Among those patients, 21% identified the errors as critical, with diagnoses errors, medication data errors, and incomplete or inaccurate EHR data conversions being common. These errors are a matter of life-and-death, and they occur every day.
To prevent these errors, it's critical to map out your data flows and use root-cause analysis to flag issues with data quality, thereby reducing the impact on patients.
2. Data Silos
In the healthcare industry, which is generating an estimated 30% of the world’s total data, patient data is often unstructured and scattered across disparate systems. The result? An incomplete picture of patient health and multiple sources of truth, which prevents you from achieving benefits of data visibility such as informed patient care. These scattered data sources also create issues when it comes to staying in compliance and conducting audits.
The solution to this is the ability to visualize patient data from different sources in one place. That’s exactly what enterprise-wide data lineage does. Data lineage reaches into every corner of your data environment to create a comprehensive map of all your data flows and dependencies, resolving data silos for good.
But, not all data lineage solutions can visualize data from different silos. Some platforms only allow you to see data stored in their specific catalog. A catalog-agnostic solution will help you address this issue.
3. Security Concerns & Chain of Custody
Healthcare organizations are in a unique position in that they both depend on cross-departmental information sharing to facilitate patient care, and are bound by strict regulations to do so securely.
As part of both HIPAA and GDPR compliance, healthcare organizations need to provide auditors with details around chain of custody – who accessed patient records, and from where and when did they access them? For data stored in an EHR system that’s accessible across several devices within a medical facility, establishing a chain of custody is laborious and time-intensive – especially when so many records are part of a literal paper trail that have been manually entered or scanned in.
Data lineage significantly reduces the amount of effort needed to establish a chain of custody in their information systems in healthcare. With a map of your data flows, you can trace your data’s journey backwards to see where and when it was changed in your systems, which is necessary to share with auditors.
We're 90% Faster
"Our ETL teams can identify the impacts of planned ETL process changes 90% faster than before."
Robert D
BI Team Leader at GEMU
Manta Flow scored a 9/10
"After evaluating the top IBM/Information Governance Catalog Data Lineage solutions in the market, Manta Flow scored a 9/10. The closest competitor was a 6/10."
Jonathan G.
VP of Cigna at Healthspring
90% Increase in Analyzing Source System Changes
"Effort for analyzing impact of a source system change has decreased by at least 90%, from hours to minutes (or seconds)."
Michael L.
BI Manager at Schumacher ClinicalConclusion and Next Steps
With the emergence of EHR systems, the proliferation of healthcare data, and an increasingly complex regulatory landscape, the complexity of the modern healthcare industry is undeniable.
To keep up, healthcare companies today need data governance. With a strong data governance framework, you can make sure that the data you’re collecting, processing, and using is accurate, consistent, and dependable. Without it, you run the risk of making a poorly informed decision about a patient’s care based on erroneous data or inaccurate predictive insights – a decision that can have a serious or even fatal outcome on a patient’s life.
Data governance is also integral when it comes to complying with healthcare data privacy regulations like HIPAA and GDPR. Any healthcare organization processing protected data needs to have a data governance strategy in place to stay compliant with these regulations, and be prepared to face any new regulations that emerge.
While challenges like data quality issues, data silos, security concerns, and proving chain of custody can stand in the way of data governance, there is a solution: automated data lineage. Using automated data lineage, your organization can break down common data governance barriers, achieve better patient care, enhance regulatory compliance, increase data security and privacy, and improve operational efficiency and reduce costs.
Ready to see for yourself how you can leverage Manta’s automated data lineage solution for data governance in healthcare? Book a demo with one of our experts today!
