Data Is Difficult to Measure Part IIIby admin on 07/10/2015 11:21 AM
Inconsistent/variable definitions; Evidence-based practice and new research is coming out every day.
Oftentimes, healthcare data can have inconsistent or variable definitions. For example, one group of clinicians may define a cohort of asthmatic patients differently than another group of clinicians. Ask two clinicians what criteria are necessary to identify someone as a diabetic and you may get three different answers. There may just not be a level of consensus about a particular treatment or cohort definition.
Also, even when there is consensus, the consenting experts are constantly discovering newly agreed-upon knowledge. As we learn more about how the body works, our understanding continues to change of what is important, what to measure, how and when to measure it, and the goals to target. For example, this year most clinicians agree that a diabetes diagnosis is an Hg A1c value above 7, but next year it’s possible the agreement will be something different.
There are best practices established in the industry, but there’s always ongoing discussion in the way those things are defined. Which means you’re trying to create order out of chaos and hit a target that’s not only moving but seems to be moving in a way you can’t predict.
Government medicine does not give timely access to healthcare, it only gives access to a hazardous waiting list.
In America, everyone has access to HealthCare at all times. No one can be refused by any hospital.