The amount of healthcare data has increased exponentially over the past few years. As a result, many of those who work in health IT are now acutely aware of the importance of clinical informatics to their overall operations. However, using this vast amount of information effectively isn’t always an easy task.
There are a number of obstacles to the efficient use of healthcare information, such as data silos, incomplete information, and interoperability issues. Additionally, data is increasingly moving into cloud architectures in order to decrease the computing costs of generating insights. This, in turn, creates bigger silos that require more sophisticated, scalable data quality solutions.
Unfortunately, the overarching task of ensuring quality data is a challenge that doesn’t necessarily have a clear solution. But that doesn’t mean that common sense strategies can’t make a big difference in an organization’s overall information gathering and use.
A helping hand for clinical informatics
A data quality manager – someone who helps to break down data silos, ensure adherence to organizational standards, and unlock insights – can help institutions make significant strides toward meeting their data quality goals. And hiring an individual who understands the principles behind data quality strategy, along with the adjacent business implications, can help institutions detect and remedy situations where data does not meet their quality standards.
In addition, an effective data quality manager focuses on improving both inputs and outputs that are important for research, clinical, and business initiatives by understanding the existing state of an institution’s data and then taking steps toward improvement. This helps unlock valuable insights that can help address new use cases and inform prospective business decisions.
Downstream impact
The effects of data that is managed correctly can be dramatic. For example, the analysis of data trends about mask utilization based on service line can help project future supply needs and, ultimately, costs. But without a good data governance strategy, it’s easy for those numbers to be incorrect – leading to over- or under-purchasing and impacting both staff safety and an organization’s bottom line.
That’s where solutions – like IMO Precision Normalize – that can help data quality managers standardize large amounts of data from disparate sources to a common, clinical terminology come into play. This technology can help data quality managers feel confident that when they receive requests for data analysis, a consistently managed terminology solution is working behind the scenes to help keep scale and uniformity in information. And when clinical informaticists don’t have to spend precious time cleaning up dirty data, data quality managers can allocate their team’s time to more important tasks – like gleaning important insights from high-quality information.
