Best Practices for Healthcare Data Governance

The healthcare sector has taken the technology route a few years ago, and hence become an industry driven by analytics. Data has become its most important asset, and managing this data effectively to ensure security, accuracy, reliability, and understandability is crucial. Data governance is taking a strong foothold in healthcare owing the sheer amount of information that is created, stored, integrated, and shared across the industry on a regular basis. In this article, we are going to briefly talk about data governance in healthcare, and then delve into some of the best practices for the same.

Now let’s take a look at why we need data governance and the objectives that it aims to achieve.

The thing about data is that ensuring quality throughout is difficult often because of the large volume, various contributors, discrepancy in communication, etc. Identifying data quality issues before they are spotted by a higher authority, like an executive, and then corrected is what good data governance does. When these issues are pointed out and subsequently corrected, it becomes a matter of negligence, and is expensive to correct. Data governance practices put various techniques and methods on the table to identify these problems at the outset.

Once the issues regarding data quality are identified, resolving them is equally important. Good data governance builds workflows that are properly documented, approved, and have a set of defined rules. This takes the blame wrongly appointed on certain departments, and ensures that equal responsibility is taken by all those involved.

Data governance creates and maintains standards and ensures proper business rules and definitions. It streamlines processes such as data feeds, workflows, establishing roles and responsibilities, maintaining documentation, and bringing an overall consistency to standards of data management.

Data governance ensures that data and its processes are streamlined. For instance, it makes sure that users receive the right data that’s meant for them. It also provides data in a timely fashion to enable proper decision making. A good data governance system helps to better automate data, thereby making it readily available and accessible to users.

When it comes to data governance, the roles and responsibilities are divided into 4 different types, which are –

Execs who hold positions that are director level or above. They are typically fully accountable for one or multiple types of data.

Supporting the data owner, this person is a subject matter expert (SME). This person is responsible for preparing data definitions and making rules, ensuring data quality, and determining solutions among other functions.

This person is responsible for identifying data quality issues using various tools, and enforcing fixes to the program code, after approval from the data owner.

This is the person responsible for keeping a track of the data quality issues once they are found. He logs them, assigns them to a technical data steward, and then keeps a track of the resolution progress as well as final outcome.

Now that we know about the basics of data governance, let’s head to the best practices.

It is crucial to have an updated system of provider information as well as patient information in healthcare organization databases. Many providers offer their services to multiple patients and healthcare institutions at a given time, which means that their data is stored as such. Information such as name, address, Medicare and Medicaid numbers, etc. is important to be able to link various profiles to the same provider. The same goes for patients. It is pertinent to note that since the same patient may not have the same information stored in his/her name at every healthcare institution they visited, a consolidated format of data linking back to them is important. If a key piece of information, like an allergy, is missing in one of the avenues of information, it can become a costly mistake.