18. 02. 28
posted by: Super User
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It won’t be over-reaching to say that the single most important function of Healthcare Data Governance is overseeing and ensuring the quality of healthcare data. Low quality data has a negative impact on accuracy and timeliness of a healthcare organization’s decision making. And in healthcare, the slightest delay could mean the loss of life. With proper healthcare data governance, the Data Governance Committee should be capable of quickly reacting to data quality issues and enforcing the changes required in source data systems and workflows that are necessary for raising data quality.

 

Data governance can, therefore, be defined as the practice of managing data assets throughout their lifecycle to ensure that they meet organizational quality and integrity standards.  Data governance is geared towards making sure that users can trust their data.  Simply defined, Data Quality is equal to the Completeness of Data x Validity of Data x Timeliness of Data. The Data Governance Committee must make each of these variables in the data quality equation a leadership priority which is especially important when making patient care decisions.

 

As part of a comprehensive data governance program, users are held accountable for creating high-quality data and using that data in a secure, ethical, authorized manner. In the healthcare industry, health information management professionals are often responsible for developing and overseeing data governance principles that improve the consistency, reliability, and usability of data assets while optimizing EHR interfaces to eliminate unnecessary or duplicate steps for end-users and eradicate problematic workarounds.


Every Healthcare organization should have a healthcare data governance committee and this data governance committee should plot a multi-year strategy for data acquisition and data provisioning, seeking to constantly expand the data ecosystem that is available for analysis in the business of healthcare delivery and health management. For example, activity-based-costing data, genetic and familial data, bedside devices data, and patient reported observations and outcomes data are all critically important to the evolution of analytics in the industry. Building and acquiring the systems to collect this data is the first step in the analytic journey and can take as long as five years to complete.

When dealing with lots of data more commonly known as big data. When it comes to governing or managing big data, mastering the art of prioritization becomes a necessary skill.The Healthcare Data Governance Committee should play a major role in developing a strategic analytic plan and then play an active role in ensuring the requirements of that plan are implemented. Inevitably, there will be more demand for analytic services than there are resources available to meet that demand. The Data Governance Committee cannot resolve every priority, but it can balance top-down corporate priorities with bottom-up requests from the clinical and business units by advocating a resource allocation of 60/40 between centralized and decentralized analytic resources—that is, 60% of the organization’s analytic resources should be dedicated to top-down, centrally managed priorities, while 40% of the resources should be distributed to support the tactical requirements of departments, business units, clinical service lines, and research.

Another way to approach the challenges of healthcare data governance is through the consolidation of IT systems. IT system consolidation is a popular way to address many of the data management challenges within an organization. Implementing these consolidated solutions involves reconciling all of an organization’s master data. The advantages of this are that in addition to its relative comprehensiveness, you get another benefit of this approach: when data management is handled at the level of these transactional systems, master data is reconciled at the time of the transaction. For example, a patient is matched at the moment that she or he is registered in the system rather than upstream or downstream. It, however, is not flawless.

 


A disadvantage is that it complex and expensive. These systems are not cheap, and the change over consumes significant resources. It is also important to realize that, while these initiatives solve master data challenges within an organization, when there is a desire to integrate outside data with mastered organizational data, there may be a need for more MDM between the data sources.