Data governance in healthcare plays a critical role in regulatory compliance. However, the consequences of poor data governance aren’t limited to compliance alone. The ability to quickly access accurate and consistent data not only streamlines business but can have serious consequences related to the health of customers and patients.
Data governance (DG) is an ongoing task organizations perform to manage data quality, use, security, and availability. It encompasses the people, processes, and technologies required to manage and protect data assets.
Data governance helps organizations protect data by preventing unapproved access. But it also helps organizations democratize data by making it readily available to approved users who need it. It then provides the framework to ensure it is used correctly.
Implementing a data governance program can be instrumental in assisting healthcare organizations effectively use their information resources.
The Importance of Data Governance in Healthcare
A robust data governance initiative identifies enterprise data resources and documents how, when, why, and by whom they can be accessed and used. The goal is to extract the maximum value from these assets and contribute to productivity gains throughout the organization.
Healthcare is an example of an industry where all parties must be speaking the same language regarding data resources. Misunderstandings cannot be tolerated when they may result in patients receiving inadequate care or an erroneous diagnosis.
The same is true for misunderstandings about data sensitivity, where neglect can lead to the exposure of confidential information. So it’s critical that all parties associated with the healthcare provider are using the same definitions for data elements throughout the organization.
A couple of illustrative examples help drive home the importance of working with a consistent data language. A misunderstanding based on different terminology used by the scheduling department and primary care staff can lead to wasted time while patients wait for critical tests to be performed.
Doctors who are not using the same data language as the attending nurses can have their instruction misinterpreted and risk patients missing vital medication.
Organizations that construct a detailed business glossary as part of their data governance initiative can limit such risks.
- Learn more: A Guide to Business Glossaries
IT departments in healthcare organizations need to ensure that protected health information (PHI) is handled in compliance with HIPAA security and privacy regulations.
Using data assets inconsistently can result in breaches when the necessary precautions are not taken with sensitive information. This puts patient data at risk and can lead to substantial financial penalties levied against the offending organization.
Mistakes caused by vague, outdated, incomplete, or even incorrect data definitions can impact any organization. However, the ramifications in the healthcare industry can put lives as well as sensitive personal information at risk. It’s an unacceptable situation that can be successfully addressed with data governance.
Implementing Healthcare Data Governance
Data governance cannot be implemented with a single, all-encompassing application. It requires a methodical approach that often demands a significant change in an organization’s culture regarding its data resources. For a data governance program to succeed requires the buy-in of everyone who interacts with enterprise data from the CEO on down.
Two important elements in a successful program are the data governance framework and policy. These items are necessary for governance to be implemented across a business.
Create a Data Governance Framework
A data governance framework is a logical structure for classifying, organizing, and communicating the complex activities that are involved in handling enterprise data resources. Its purpose is to document the following elements of data governance.
- Who is responsible for specific enterprise data resources so everyone understands their role in using data effectively and maintaining its quality.
- What information assets are in scope for governance. Consideration needs to be given to the type of data in question and any privacy or regulatory standards that need to be met.
- When audits should be scheduled to ensure proper handling of data resources. This includes reviewing security measures and data retention periods.
- Where data resources are located is an essential part of data governance. Without a full inventory of data assets, a governance program can never be successful.
- Why the organization is implementing data needs to be defined and disseminated to all stakeholders.
The answer to these questions, codified in the data governance framework, will provide the structure required for governance to succeed.
Create a Data Governance Policy
A data governance policy also documents an organization’s guidelines for effectively managing and using its data assets. The policy defines how data is accessed, processed, and secured. Roles and responsibilities for implementing data governance are also policy components.
The data governance policy is primarily concerned with the people and process elements of the governance framework. It involves collaboration with stakeholders from across the company and needs to be reviewed regularly to reflect changes in the environment.
Healthcare Data Governance Tools
Businesses in the healthcare industry have sensitive data resources that demand the benefits afforded by data governance. IDERA’s ER/Studio suite of data modeling applications provides the tools for building a solid data governance foundation.
Business Architect maps the relationships between the people, processes, and data that are necessary first steps when developing data governance.
ER/Studio Data Architect enables the construction of enterprise data models, locates and documents enterprise data resources, and creates an organizational data catalog to support governance activities.
ER/Studio Enterprise Edition is a collaborative tool for sharing data models across an organization. Its shared repository facilitates data consistency and building the common data language required for data governance.
Healthcare organizations interested in developing a data governance program should look into how ER/Studio can help them reach their goals. With a firm foundation built on reliable data models, data governance stands a good chance of successfully addressing the concerns of the healthcare industry.