Today, data is seemingly everywhere. Whether it’s a computer used for work, a watch that tracks our fitness, or a smartphone used for everything, it’s easy to assume that data quantity is synonymous with data quality.
Of course, the opposite is often true. Indeed, when it comes to major industries with many moving parts – like healthcare – there’s such a thing as too much information, or too much of a good thing.
But that doesn’t have to be a cause for concern. When it comes to health IT, there are valuable lessons to be learned from sectors that also manage large amounts of information for high-risk decision making. We’re exploring those insights in our latest eBook, Lessons on data quality: What health IT can learn from 5 peer industries. Keep reading below for an excerpt on what national intelligence can teach our industry.
Data quality challenge
National security agencies are adept at gathering intelligence. However, in the wake of 9/11, and again after the Iraq War, these organizations needed a better way to communicate confidence levels in their intelligence data.
How the challenge was addressed
The intelligence sector underwent a massive overhaul and developed a framework for communicating relative certainty in the data they gathered. In 2015, Intelligence Community Directive 2031 was updated, requiring all intelligence agency reports to implement and exhibit specific analytic standards. This framework emphasizes assessing the credibility of information and its sources; acknowledging any uncertainties about the insights gleaned from data; and incorporating an analysis of possible alternative options before making a high-stakes decision.
What healthcare can learn from the fix
The value of incorporating formal decision science strategies into data-based decision-making. Just like physicians, those making tough choices in the national intelligence community rarely have all the information they need. But difficult decisions must be made despite the uncertainty. The key difference? In healthcare, the art of decision science is not a routine part of training.
By contrast, in the absence of precise, high-quality data the national intelligence community has not only embraced the teaching of decision science, but also has adopted a framework of principles to help reduce the risk and uncertainty faced when making decisions based on data that is, quite often, less than perfect.
Healthcare can learn from this model by incorporating decision strategy into clinical training as early as possible – even in medical school. However, exposing providers to this sort of framework and education at any point in their career can help them integrate a more formal, strategic, and standardized approach to assessing data quality during the decision-making process – a win for patients and providers alike.