What is Data Governance in Healthcare?
Data Governance is an essential function necessary to ensure data is managed appropriately and able to be used efficiently. Healthcare is one of the most data-intensive industries in the world, so as you can imagine, data governance is going to be of interest to healthcare organizations. So first let’s define what we’re talking about here. AHiMa defines data governance as: The overall administration, through clearly defined procedures and plans, that assures the availability, integrity, security, and usability of the structured and unstructured data available to an organization. (AHIMA, 2020) It’s a little wordy and hard to fathom.A more succinct definition comes from Robert S. Seiner, author of “Non Invasive Data Governance.” He defines it as: Formalizing behavior around the definitions, production, and usage of data to manage risk and improve quality and usability of selected data. (Robert S. Seiner, Non Invasive Data Governance, orig. pub. 2014) That’s a little better – short and to the point. Here’s another helpful analogy plucked from your high school physics class, specifically the second law of thermodynamics: Any given system will grow and become more disordered over time if no force is acted upon it. To me, this concept applies to every system that uses, stores, or manages data. Your data grows and becomes more disordered over time if you don’t adopt some form of data governance. Data governance can be defined as the force we enact upon data to prevent it from becoming more and more disordered as it grows. Data governance is how we ensure that data is orderly, meaningful, usable, findable, and secure. It ensures that users can access the data, understand what they are accessing, and trust what they use. The main goal of data governance is to allow us to answer questions about our data authoritatively and with confidence. Questions such as: What data do I have and what questions can I answer with it?- Where is it and how do I access it?
- How clean is it? Are there issues with it I need to be aware of?
- How much data do we have, and is it being stored efficiently?
- Where does it come from and how is it being sourced?
- What systems are using or changing my data and why?
- Who can access it and for what uses?
- Who decides its uses?
- How sensitive is it, and is it being secured according to its sensitivity level?
- Does it meet all regulatory and business requirements?
- Can I answer new and novel questions with it?
Why is Data Governance in Healthcare Important?
More and more over the last few years, healthcare and other organizations are coming to the realization that data is a strategic asset. Like any other organizational asset, such as people, capital, or inventory, data requires ongoing management and monitoring. Data governance provides a formal structure for data management so that healthcare organizations can extract clinical and business value. There are three main ways that data governance achieves this:- improving data quality
- reducing risks associated with data
- creating the ability to deliver faster and better insights
Data Governance in Healthcare: The Benefits
What happens once you’re improving data quality, reducing risks, and delivering better and faster insights due to data governance? For healthcare organizations, this means reduced operational costs associated with data analytics and increased financial performance because of it. Other benefits include better insights through higher data quality and consistency across the organization, lowered risks of regulatory non-compliance, and improved security based on stronger overall control and management of data. Along with these operational benefits, governed data could also drive improvements in patient care and the patient experience of care, based on an improved ability to analyze for trends, predict outcomes, and identify health problems earlier for intervention. Patient satisfaction with their quality of care is also likely to be reflected in these improvements. Governed, high-quality data also enables more targeted population health analytics based on traditional and also artificial intelligence/machine learning (AI/ML) algorithms. New applications for AI/ML insights in healthcare data are being developed, and these all depend upon high-quality data to generate solid results. Advanced solutions that are already improving patient care outcomes include drug interaction- or contraindication-checking algorithms, antibiotics resistance stewardship, medical image pattern recognition, and patient preventive monitoring based on wearables (Eg. heart-rate monitors and smartwatches). These are just some of the new and emerging technologies that require governed, reliable data as a foundation to build upon. And finding ways to solve these problems technologically frees up humans to spend more time caring for patients and improving their lives.Data Governance in Healthcare: The Challenges
Given the benefits, data governance has a strong appeal to healthcare organizations. But there are real challenges to implementing effective data governance for healthcare data, which explains why many healthcare organizations have struggled to achieve it. Some of the challenges specific to healthcare data include:- Large volumes of sensitive data
- Numerous and complex regulations
- Old or antiquated systems storing data in inconsistent formats or multiple locations
- Ad hoc customizations, preventing upgrades to newer application versions or the cloud
- Many data streams require time-consuming integration
- Multiple storage solutions that are hard to keep up with and administer
- Siloed stacks of both data and business information knowledge
Data Governance in Healthcare: 7 Best Practices To Deliver Data Governance
#1: Establish program priorities
- Identify the real needs and deepest pain points for the organization and tackle those first.
- Identify the biggest “bang for the buck” in improving your data situation then prioritize accordingly.
#2: Ensure accountability
- Identify the roles and responsibilities necessary for success in your organization.
- Secure and ensure ongoing executive support by educating and regularly measuring your success.
#3: Demonstrate value by defining key metrics
- Establish metrics for monitoring and demonstrating progress with the data governance program results.
- Set and measure progress on goals in each focus area, such as data quality, risk or cost reduction, catalog curation progress, and data/process issues corrected.
#4: Support collaboration
- Governance succeeds when everybody works together. Encourage Data Governance team members to collaborate, discuss challenges, and share best practices. Data Governance tools can help support this effort.
- All data users should have access to a single point of information about data at your organization, such as a metadata repository or data catalog.
#5: Implement a hierarchical strategy
- A hierarchical strategy is needed to determine how data is distributed and who can see what.
- Create the organizational structures – Chief Data Officer, Data Governance Team, data steward committees, working groups, advisory groups, and executive sponsorship – and ensure that accountability and decision-making are clearly delineated among players.
- Create the roles, and clearly define the responsibilities, taking your company culture into account as you create the data governance strategy.
#6: Integrate data governance into every department
- There are multiple ways to implement governance. However, ensuring that the business stakeholders maintain control and ownership of the data they create, use, and analyze is the best way to ensure success.
- Decentralized, or “federated” data governance, means that each business department, value stream, or domain takes responsibility for stewarding their data, while a central data office defines the strategy, goals, methods, and tools used for governance.
#7: Create risk milestones
- Identify the biggest risks throughout the organization, likely those associated with the sharing of your most sensitive data.
- Risk milestones allow you to highlight and manage the risks inherent whenever data is shared or moved, and avoid costly setbacks that can negatively impact patients or customers.
