Thirty percent of the world’s data volume is generated by the healthcare industry. From the minute details of individual patient records to the large-scale data sets generated during clinical trials, healthcare organizations constantly generate new information. 

This data holds the keys to solving the healthcare industry’s biggest challenges. However, that’s easier said than done. Poor data quality and lack of interoperability can make data feel like more of a burden than an asset. A strong data governance program allows you to take control of your data and create a  foundation to use it to improve the quality of care, control costs, meet compliance requirements, and innovate. 

What Is Healthcare Data Governance?

Data governance is the collection of clearly defined roles, policies, procedures, and standards that ensure the effective and efficient use of data in enabling an organization to achieve its goals. Data—generated internally and externally—is constantly flowing through healthcare organizations. Without a disciplined, structured approach to managing it, it’s impossible to be confident in the accuracy of that data or access it when you need it. Data governance provides that structure and discipline. 

Data governance is related to, but not the same as data management. Your data governance strategy outlines who can take action, how they can act, the data they can interact with, the situations in which they can act, and the methods they use. Data management is the day-to-day work of implementing that strategy and effectively architecting and storing the data for use. 

The Data Lifecycle

According to AHIMA, healthcare data governance encompasses the people, processes, and systems used to manage data throughout its lifecycle. This lifecycle, specific to healthcare is: 

• Capture –  Data is recorded in various healthcare systems.
• Process – A  series of actions are taken to create a product and/or service.
• Use – Data is accessed, shared, and analyzed.
• Store – Data is maintained and stored.
• Dispose – Data is destroyed based on retention schedules. 

Healthcare data governance should be implemented across the entire organization, with the goal of creating a culture of data security, reliability, accessibility, and value. This moves beyond Technology, Reporting, and Data Science departments to all groups that contribute to data and leverage information. 

For example, physician and nurse champions groups, as well as financial leadership, generate and rely on massive amounts of data. This data often impacts other areas of the business throughout its lifecycle. If they aren’t engaged in data governance, they’ll unknowingly and unintentionally contribute to the mismanagement of data, only adding to the cycle of poor data practices. 

Why is Data Governance Essential for Healthcare?

Despite the fact that healthcare outpaces other industries in terms of data volume, many organizations struggle to use that data to improve patient care and make critical business decisions. Underlying this issue is a lack of trust in data, and an inability to leverage it for analytics. According to a recent survey of healthcare executives:

• 80%  say they can’t trust the quality of their data. 
• 51% say data interoperability and integration is the biggest barrier to achieving strategic data analytics priorities.
• Nearly half of respondents say data management challenges lead to long delays, difficulty scaling and democratizing data, and limited time to analyze the data.

These issues prevent healthcare organizations from leveraging their data to solve problems. Workarounds and manual processes are all too common, which only lead to lost productivity and errors. When healthcare organizations deal with inefficiencies and inaccuracies on a regular basis, improving patient care and meeting compliance requirements are exceedingly difficult. Implementing advanced IT processes that can improve competitiveness and drive innovation is practically out of the question.

Why is Data Governance in Healthcare So Difficult?

Despite the fact that healthcare leaders see the value of data, structural and historical issues prevent them from making the progress they should:

• Traditional, manual processes are deeply entrenched, and many organizations lack the bandwidth and capabilities to modernize their practices.
• There are insufficient standards to support data interoperability between systems and stakeholders.
• Healthcare technology adoption often lags behind other industries.
• Healthcare data is more complex and sensitive than that of other industries. It’s more difficult to implement automations and advanced analytics without increasing the risk of harm to patients or violating privacy.

The Benefits of Data Governance 

Implementing a data governance program empowers healthcare providers to take control of the vast amount of information they generate and make it trusted and actionable. It allows you to standardize data and make it accessible to the people who need it, when they need it. Everyone, from frontline workers to executives, can use data to make faster, more informed decisions. 

1. Improve data accuracy and reduce the likelihood of duplicated or incomplete records.
2. Improve efficiency of decision-making, care coordination, care delivery, and communication between providers, payers, etc.
3. Reduce errors in treatment.
4. Provide employees with more effective tools and information.
5. Use machine learning and artificial intelligence with confidence.
6. Improve compliance reporting.
7. Increase effectiveness and efficiency of data science staff and their insights.
8. Set the foundation for a scalable approach to data management.

Plus, most of the innovation in healthcare requires trusted data to succeed. As organizations implement value-based care and create products and services that solve complex healthcare challenges, many are looking to advanced analytics, machine learning, and artificial intelligence. These cutting-edge technologies must be built on a foundation of reliable, structured data.

How to Implement a Data Governance Framework

The first step to implementing a data governance framework is to develop your approach. There are a variety of paths to achieve data governance, but the most basic approaches center on three pillars:

1. Roles and Responsibilities – The individuals involved in data governance will be responsible for making the strategy a reality and taking accountability for adhering to policies, standards, and processes.
2. Processes – Processes define how the policies and standards will be implemented. They should include change management and training to promote the adoption of the data governance strategy. 
3. Policies and Standards – Guide how data is structured and used. Policies and standards enable organizations to use data in consolidated reporting and analytics platforms, and ensure that data is fit to be used in whichever capacity the organization needs it. 

Once your approach is set, you can use it to help build consensus among leadership. Data governance programs require a cultural shift and need company-wide buy-in to be successful. If the Technology and Data Analytics teams are the only departments engaged in data governance, then it will undoubtedly fall short. Implementing a data governance strategy may also require external expertise, which will require budget approval from leadership.

Define or Refine Your Data Governance Program with Kenway

At Kenway, we work with healthcare organizations to define and refine their data governance programs. We start by understanding your organization’s mission and vision, then creating a data governance program that aligns with it. We identify where data governance will have the most value and assess where you are along the data maturity curve. From there, we create your data governance roadmap.

Whether you have an existing data governance program, or want to start from the ground up, we can jump in to ensure that your next step is the right one. Contact us to begin the process! In the meantime, you can read more about our past experience with other healthcare organizations here.