Healthcare Data governance is the process of managing data and data assets from the moment it’s gotten, through its entire life. It’s monitoring data through its entire lifecycle to ensure that it meets all the standards that have been set. These data standards are those set by both governing bodies and by the organization itself. Data is important in the healthcare industry and that’s why data is governance is important. If there’s one industry that cannot afford the luxury of mistakes its the healthcare industry and ensuring this starts from proper data governance. In a healthcare organization, there can be different people in charge of data governance. Healthcare professionals can be in charge or the healthcare organization can pick a specific team to be in charge of everything that has to do with the data. Mostly this will be the health information manager. In the healthcare industry, health information management professionals are often responsible for developing and overseeing data governance principles that improve the consistency, validity, and usefulness of data assets while optimizing electronic health records EHR to eliminate unnecessary or duplicate steps for end-users and eradicate problematic workarounds.

The ultimate end goal of this (as is with most things in healthcare) is to help healthcare professionals provide better care to patients in more convenient ways. It is also the goal to increase the efficiency of all healthcare workers and foster collaborations and trust among these workers. The goal of these activities is to improve the efficiency of healthcare workers, foster an environment of accountability, and create a standardized, interoperable pool of big data that can be used for organizational improvements and higher quality clinical decision-making.

Healthcare governance is very important and that’s a given. So healthcare organizations that are concerned with proper governance adopt best practices. These best practices (which are proven and tested_ serve as a guide to healthcare organizations, health practitioners and data governance workers for navigating the data and its governance in the healthcare industry.

 

Data Governance Best Practices

 

The best practices are listed below and the first three will be discussed in detail.

 

  1. Data Quality
  2. Data Access
  3. Balanced and Lean Governance
  4. Data Literacy
  5. Data Content
  6. Analytical Prioritization
  7. Master Data Management

 

Data Quality

 

Data quality has to do with data that is of the highest quality. And for data to be of the highest quality, it has to be complete, valid, and timely. The completeness, validity and timeliness of data are all equally important and these three variables can be used to calculate data quality. Completeness of Data times Validity of Data times Timeliness of Data equals data quality. Healthcare governance is not complete without the strict monitoring of data quality. In fact, it is one of the main focuses of healthcare governance. The healthcare governance committee has to ensure data quality at all times and also monitor all three variables.  A Data Governance Committee must be set in place to quickly react to low-quality issues and enforce changes required in source data systems and workflows.

 

Data Access

 

If data cannot be accessed then of what use is it to have the data? Data should be accessible to all who want to use it. And in a healthcare organization, this includes doctors, nurses, clinicians, and even the administrative staff who constantly need to access patient files and patient history.

The healthcare data governance committee should ensure that there is easy access to data and that there is also much transparency. In the most effective organizations, the healthcare data governance and information security committees are combined and this streamlines decision-making processes cuts out bottlenecks and fosters collaboration between different teams and different departments.

 

Balanced and Lean Governance

 

Speaking of streamlining processes an cutting out middle-men bottlenecks in the data governance process.

The Data Governance Committee should practice a cultural philosophy that believes in governing data to the least extent necessary to achieve the greatest common good. This means they should design a system that’s hands-off and that is easy to use and navigate by those who need that data for everyday use.

 The Data Governance Committee should also enlist front-line employees as Data Stewards who are knowledgeable about the collection of data in the source transaction systems such as the EMR, cost accounting, scheduling, registration, and materials management systems. Data Stewards are invaluable to the mission of the Data Governance Committee. CIOs who function horizontally, across business lines, at the application and data content layers of the information technology stack (as opposed to those who operate primarily at the infrastructure layers) are a natural fit for facilitating and leading the Data Governance Committee.

Conclusion

 

This is where a new type of data model is needed one that goes beyond the bare bones and creates guidelines with milestones that could and should be met in order to progress onto the next level. Each level had specific objectives that cannot be skipped because one level builds on to and improves standards and structure of the previous.

Where will these data models lead? What will become possible as we get better and better and collecting, analyzing and redistributing information? Only time will tell.