The Importance of Data Governance

Is your Healthcare Data Being Governed?

In today's rapidly evolving healthcare landscape, effective data management is crucial for delivering high-quality patient care, ensuring regulatory compliance, and improving operational efficiency. While working with Meditech hospitals, we recognize that robust healthcare data governance and a well-defined reporting structure are essential components that must work together.

If your data team continues to do what we call "custom one-off" reporting and is not working to ensure your organization can securely and effectively manage, analyze and utilize your data, then you should establish data governance practices now. Custom reporting is the process of generating tailored reports designed to meet a specific need, but gone are the days for this type of reporting. At Acmeware, we continue to strive for what is best for our healthcare organization partners and have found that Microsoft Power BI (and the Fabric platform) are becoming foundational tools in this process, providing comprehensive solutions to address the challenges faced by healthcare.

What is Healthcare Data Governance?

Healthcare data governance refers to the overall management of the availability, usability, integrity, and security of the data employed in an organization. It should involve creating policies and procedures that ensure data are accurate, accessible, consistently used across your organization and are protected. This governance framework encompasses data standards, data integrity, data literacy, data quality management, and compliance with regulatory requirements.

Why is Healthcare Data Governance Important for Hospitals?

Ensuring Data Quality and Integrity: Data quality and integrity are critical for clinical decision-making. Inaccurate or incomplete data can lead to diagnostic errors, inappropriate treatments, and adverse patient outcomes. This leads to better clinical outcomes and enhances the trust healthcare providers place in their EHR systems.
Compliance and Regulatory Requirements: Healthcare organizations must comply with various regulations such as HIPAA (Health Insurance Portability and Accountability Act) and other local and federal laws concerning patient data privacy and security. A robust data governance framework helps Meditech hospitals maintain compliance by establishing clear protocols for data handling, access control, and breach response.
Operational Efficiency: Efficient data management streamlines hospital operations by reducing redundancy and minimizing errors. This efficiency extends to various aspects of healthcare delivery, including patient admissions, discharge processes, billing, and reporting. Effective data governance ensures that data flows seamlessly across departments, improving overall operational efficiency.
Enhanced Patient Care: High-quality, well-governed data supports clinical decision-making and patient care. With reliable data, healthcare providers can track patient histories, monitor treatment plans, and make informed decisions quickly. This not only improves individual patient outcomes but also contributes to broader public health goals.
Strategic Decision-Making: Data-driven decision-making is pivotal for hospital management. Through accurate data analytics, hospitals can identify trends, assess performance metrics, and allocate resources more effectively. This strategic use of data supports everything from financial planning to patient care strategies, ensuring the hospital remains competitive and responsive to changing healthcare demands.

Who is responsible?

Governance adoption is the biggest challenge we continually talk about with our healthcare organization partners. Well-governed data is only helpful if the organization as a whole embraces it. How often has your IT report writer completed a custom report , sent it to the end user or department manager only to find out six months later that they are not using it. When you ask why you find out multiple answers: they don't have time to review it to make sure it is right; they didn't know how to get to it because there are reports in many areas of their applications; they wanted more detail behind the summary numbers; it didn't work as they expected; they didn't know how the data was being calculated behind the scenes; they got busy with other work; it's easier for them to keep on manually using spreadsheets to enter data from multiple data sources; change is hard; the list goes on and on. These types of behavior create data silos in your organization which will lead to duplicate or inconsistent data once everything is unified.

So WHO is responsible for governing your data? Really, the answer is everyone in your organization, but knowing your role and responsibilities are key. Defining who will manage the data architecture, data integrity and validation, data visualization, data security, all the way down to the decision makers viewing and understanding the data presented to them.

The American Health Information Management Association (AHIMA) does a phenomenal job of outlining important data governance roles. It is imperative that your organization identifies who your key employees, departments and vendors are that will make up your data governance team.

How does healthcare data governance work?

Data sponsor: Data governance starts with a data champion within leadership. The sponsor represents the system and advocates for its usage across the organization.
Define your data sources: Identify operational data so that it can be used and referenced across all departments consistently. Define your source of truth for all data models. This means no more "one off" custom reports. Outside of data sources, there should be careful consideration around defining the data itself. For example, how does your organization define length of stay or surgical blocks? Including these data definitions in your single source of truth allows for consistent use of these types of measured calculations.
Strategic goals and planning: Data governance serves as a critical framework that ensures data is managed in a way that directly supports the strategic goals and planning of an organization. Identifying key performance indicators (KPIs) and metrics that are critical for achieving objectives like improving patient care, increasing operation efficiency, or complying with regulatory requirements. Another consideration is the integration and sharing of data across different systems, which is critical for strategic initiatives like population health management or value based care.
Tools and technology: Select the data warehouse solution that will house your many data sources. More and more healthcare organizations are using Microsoft Fabric and Power BI to manage their data warehouse. Ameware's Empower solution uses Power BI with certified semantic models which is a visual indicator to your data team and end users writing reports that it meets your organizational certification criteria.
Data governance team: Define your roles and responsibilities for who is responsible for managing the data from start to finish.

This is all easier said than done. I recently discussed this topic with a friend of mine who happens to be a Quality Director at a local hospital. She indicated how they struggle to keep up with the various tools, state and federal regulatory monitoring, reporting, along with internal KPI monitoring required for accreditation services. What I heard was: "it is overwhelming, the data is everywhere." She then told me that they pull data manually from all their systems into a spreadsheet. They are making decisions off this data. I have had this same conversation with many people over the years and with today's available technology there is no reason to do this. We can do better, so let's work together to improve our clinical and business analytics for our community. Many of you have already started looking at your data governance structure within your organization. By facilitating interoperability, enhancing transparency, and supporting real-time insights, Microsoft Fabric and Power BI are crucial for the digital transformation and continuous improvement of healthcare services. The key here is to work together to empower everyone in your organization, regardless of their technical experience, to work with data comfortably and make informed decisions.