Best Practices for Data Governance in Healthcare: All You Need to Know
Healthcare is slowly transitioning into an analytics-driven industry, which means that data is now one of the most valuable and challenging assets that companies in this sector possess. Healthcare companies have access to data from a multitude of sources, such as inpatient and ambulatory EHRs, laboratory information systems, pharmacy systems, and ERP systems. Hence, good […]READ MORE >>
Healthcare is slowly transitioning into an analytics-driven industry, which means that data is now one of the most valuable and challenging assets that companies in this sector possess. Healthcare companies have access to data from a multitude of sources, such as inpatient and ambulatory EHRs, laboratory information systems, pharmacy systems, and ERP systems. Hence, good data governance practices are highly essential for hospitals to ensure that all this data is well understood, trusted, accessible, and secure. Also, data governance prepares organizations in the healthcare industry for value-based care, which is another significant shift in healthcare and requires timely access to trusted data. Another critical factor that calls for the implementation of an appropriate system for governance of data is the data quality, which needs extensive clean-up of the different data sources. Here are some of the other reasons why data governance matters for healthcare organizations:
- Fosters and promotes organizational support for a successful healthcare analytics culture
- Establishes standards, creates a streamlined data feed, oversees documentation, and maintains the report request process
- Improves patient experience including quality and satisfaction
What are the barriers to data governance in healthcare?
Though data governance in an essential practice in today’s data-rich healthcare industry, there are several barriers to the successful implementation of good data governance program, they are:
- Organizational politics and “turf wars” over data ownership and reporting
- Multiple, isolated databases
- Lack of clarity in roles and responsibilities in the organization, which could result in a highly skilled analysts writing reports instead of analyzing data
- Absence of executive-level sponsorship and involvement
The best practices for successful healthcare data governance
To avoid the mistakes that can lead to an unsuccessful data governance program and build a stable data governance structure that would serve the players in the healthcare industry for years to come, Here are some of the best practices for companies to follow:
Make it about improvement, not an IT project: Though IT expertise is an essential factor, IT is a steward of the data and not the owner. The purpose of data governance projects is about getting the right data into the hands of end users to help them make the best decisions about patient care. Indeed, the responsibility for data ownership falls to clinical and operational decision-makers.
Secure commitment from senior management: The implementation of an advanced analytical infrastructure is a significant change management effort. In fact, it is a shared responsibility across the organization. Hence, the visible and active support of senior leaders and managers is a priority. Data governance programs should be staffed with the necessary technology, operational, and clinical expertise.
Aim for transparency: The data governance committee in healthcare organizations should not function as gatekeepers. Instead, they should transparently provide end-users with good quality data to that is usable.
Provide adequate resources: Data governance cannot be sidelined as a low priority or side job. Dedicated resources are critical to driving progress and maturity.
Data Quality: Overseeing and ensuring data quality is one of the most critical functions in the governance of data. Low-quality data has a negative impact on the accuracy or timeliness of the organization’s decision making. The Data Governance Committee must be quick in reacting to these issues and enforcing the changes required.