The emerging importance of data analytics in healthcare has the potential to transform how healthcare organizations function, improving patient treatment and bolstering the quality of care while simultaneously lowering administrative costs. However, data analytics can only help a healthcare organization if the integrity and value of the underlying data are correctly managed. This can be a monumental task as the amount of data under management grows. For example, Kaiser Permanente is estimated to manage approximately 26 to 44 petabytes of data in electronic health records alone. To put that in perspective, that's the equivalent of all of the information in the Library of Congress times 4,400.
This is where the importance of proper data governance enters the picture. By putting best practices regarding data governance into place, healthcare organizations can help ensure that data is collected and utilized effectively and efficiently. With that in mind, here are five key data governance best practices that healthcare organizations can utilize today.
Create Effective Data Oversight
Authorizing specific individuals within your organization to oversee and implement data governance procedures will be significantly more effective than attempting to do so piecemeal. However, it is important that your data governance committee achieves the correct balance between governing data and allowing growth and innovation. To this end, draft the charter of your data governance committee to have a broad framework but relatively limited application; you can expand their oversight functionality in increments as needed. Base this oversight in the principles of lean but effective governance.
Accountability is one of the most important features of data governance. Empowering individuals to be responsible for the data that they enter and process is key for ensuring accuracy and completeness. Moreover, no tool can be substituted for human accountability. To this end, your healthcare organization should establish clear guidelines regarding the responsibility of users who enter, manage and consume data. Assuming that employees will voluntarily take ownership of the data that they interact with can lead to errors that are difficult to trace and correct.
Data is only valuable to an organization if its quality is high. Low-quality data can negatively affect the accuracy of analysis, leading to delay and issues that can ripple throughout your organization. When allocating responsibility, your data governance committee must also emphasize the importance of quality data above all else. As a good rule of thumb, the quality of a data set is equal to its completeness times its validity times the timeliness of the data. Procedures need to be implemented that ensure that the data collected is whole, accurate and timely.
Increase Data Literacy and Access
Ultimately, when crafting any data governance procedures, the ability of the end users to access and process the data must be kept in mind. After all, the entire goal of data governance is to increase the quality of the data used in other departments' data analytics functions. To this end, establishing clear access protocols to allow all users permission to retrieve the information that they need is critical, as is helping those throughout your healthcare organization understand how to interpret and use your critical and valuable data.
Create Procedures to Monitor Data Changes
The nature of all large data sets is that they will change over time, and the data analytics procedures that rely on this data can only remain accurate if they respond to these changes. For this reason, your data governance committee must implement procedures to establish traceability and monitor changes to your data over time. Creating tools that can record when data is added, removed or altered is key to maintaining the integrity of your database, and being able to refer to a particular record for any point in time is critical for reconciliation purposes.