I want to dispel a common myth: the myth that there is not enough data or enough of the right data to accomplish meaningful clinical improvement. The fact is we have enough data to reach the ultimate pursuit: reducing clinical variation and improving quality at the same time.
The key is having the right amount of the right data. This means having data that is transparent, comes with the ability to drill down substantially and includes comparable benchmarks.
A single source of truth
It’s no secret that doctors love data. We love it because it informs which devices we use, our treatment methodologies and how we can improve patient outcomes. And, while the move to value-based care has been a challenge for physicians, I believe the data that is being generated as a result offers an incredible opportunity to advance the quality of care as well as lower costs.
A great example of this is the information captured in episodes of care.
Have you ever tried to drive change in your organization? If you have, then chances are that you must have encountered some resistance. Perhaps colleagues told you ‘we’ve always done it this way’ and were reluctant to consider your perspective or ideas.
A key component of the Patient Safety and Quality Improvement Act of 2005 was the collection of data related to patient safety events for accountability and learning. Fast forward to 2017 and what we often see are organizations placing all quality and safety data into their Patient Safety Evaluation System (PSES), reporting it to the Patient Safety Organization (PSO) and calling it Patient Safety Work Product (PSWP).
If only it were that simple.
One of the reasons I find health care to be such an exciting field to work in is the vast amount of data it produces and the fact that there’s always a unique set of challenges with each data set. And with each challenge, there’s also an opportunity to engineer process improvements that can save money and maybe even lives.
No two words struck more fear into the hearts of radiologists in 2016 than “artificial” and “intelligence,” unless you added “deep learning” to the mix. Some radiologists have expressed concern that big imaging data could continue feeding the deep-learning beast to the point where computing power becomes robust enough to replace the radiologist entirely.
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