Healthcare providers and health plans are increasingly turning to business intelligence and the data warehouse to unleash the power data. This is a relief since poor data quality increases uncertainty and risk with an ultimate impact on care outcomes, efficiency, regulatory compliance, and financial effectiveness. I’ve said it time and time again (and have heard it from others many more): healthy data will lead to lower healthcare costs. But what does it mean to have good data quality and how does one obtain it?
An organization with good data quality has data that is consistent, timely, and easily accessible. It is integrated from data sources across the organization to provide a “single version of the truth”. In order to maintain this data quality, healthcare organizations need a good data governance program. The term “data governance” sounds deceptively IT-oriented, and, unfortunately, a search engine query on the topic will turn up a litany of scary IT graphics. However, data governance has little to do with IT at all. Data governance actually streamlines the planning and processes surrounding data and how it is used. Don’t get fooled into thinking that this makes data governance simple. In fact, data governance gets quite complicated in the healthcare industry. In a previous post we spoke of data integration being the key theme in healthcare technology. While decreasing the barriers to data is a good thing, it can increase the struggle over where data sits, who can access health data and, ultimately, how that data is governed.
Pulling the Team Together to Govern Patient Data
In healthcare we need to remove data silos in order to connect patients to doctors, health plans, and researchers. We also need to determine the circumstances that precipitate the sharing of certain types of data. Eventually, trust is built as data sharing policies are grown and maintained. This is exactly what data governance is defined to do. Without good data governance we continue to spend scarce healthcare IT resources building additional data siloes, which ultimately drive up the cost of healthcare as a result.
But given the challenges described above, how can organizations achieve and maintain data quality? Here are some key data governance tips:
- Create a cross-functional, multi-level data warehouse governance structure. Many organizations make the mistake of creating a governance board consisting solely of IT folks. This creates serious adoption issues. Creating a cross-functional team of stakeholders is vital. Pick “ambassadors” from across the organization or partnership that work directly in the business environment these policies will govern. Incorporating these ambassadors from various places within the organization will also help with the change management hiccups that will occur along the way. It is also important to incorporate C-level sponsorship via executive membership to the board. Though the CIO should certainly be involved, having the CIO as the only executive sponsor is not recommended.
- Establish a Data Governance Roadmap: Stay away from complicated IT diagrams and concentrate on simple language that establishes the overall strategy for data sharing. The teams needs to define responsibilities, identify data sources, identify responsible parties, and find the policies that need updating. Establish priorities and timelines, facilitate discussions, and challenge ideas. These discussions will also provide the CIO with a solid understanding of the needed skills and budgetary needs to maintain strong data governance into the future.
- Do not make these decisions in a vaccuum. Ultimately, this team needs to have a clear understanding of the business processes, information processes, and data needs of the organization. Proactively seek outside information. This not only helps evade group think, but can aid in increased adoption of policies and processes as well. All of this information is then used to develop and document common data definitions and business rules that provides ease of use for non-technical users and supports sophisticated analytical processes.
- Follow the announcement of data governance policies with a workshop series. Creating workshops for those impacted by these data governance policies is a good opportunity for training and continuing the objectives of good data governance into the future. These workshops will fuel adoption and momentum across the organization as well.
