For patient centered EBM, it's time to move Evidence Informed Synthesis past precise measurement of the pieces to practical assessments of the whole.
We can improve the quality and safety of the healthcare we deliver to patients.
That’s the premise behind quality improvement and patient safety programs, initiatives and interventions. The study of quality improvement interventions, called improvement science, is relatively new. Improvement science is just beginning to evaluate the performance of the tools and techniques. Many of the techniques such as Plan-Do-Study-Act (PDSA) cycle, Lean, and Six Sigma come from manufacturing and seek to improve efficiency. They seek to improve efficiency by doing things right.
While it is important to get better at delivering the right care,
it is essential to first know what is the right care to deliver.
The assumption behind many of the manufacturing improvement processes is that we just need to get better at delivering the right care. But all too often, we honestly don’t know what the right care is.
The right care should provide each person with care that is effective - that is care that improves their life compared to what would have happened had they not sought care.
What may surprise many people, even some working in health care is that many of the clinical actions we consider standard have not been shown to be more effective than other care options or even no care.
Evidence-based care seeks to fill this gap in knowing by:
First, using data to identify what we know
Then, develop and test solutions for what we don’t.
We identify what we know using data. We call the end result of the rigorous collection and critical analysis of all relevant data “evidence–based”. Good evidence–based analysis tells us how much confidence to have in a particular clinical action. Is there enough evidence to be reasonably sure that the clinical action works? Or perhaps there is just enough evidence to make a reasonable guess today that may change when we accumulate more and better data? Or is there simply not enough evidence to make any reasonable guess?
We use the science of evidence-based methods to help us determine which are the right clinical actions to deliver. We use manufacturing improvement processes to help us get better at doing the right things.
If your quality improvement toolbox only includes one or the other set of tools, you aren’t maximizing your effectiveness and efficiency. The incorporation of evidence-based methods can improve the quality of the care you deliver and encourage innovation while managing risk. Infusing your quality improvement innovations with evidence-based methods means you can begin to take more calculated risk. You can put evidence-based methods to work in your organization to identify the right things to do so that your manufacturing improvement tools can help you then standardize and systematize the best care.
This week's Powerball® mania seems a good time to talk about relative and absolute estimates of disease. This epi professor learned from her students years ago (especially from a bartender and professional gambler) that real world examples can be useful to explain epidemiology and biostatistic methods.
Do you have a lottery ticket for Saturday's draw? I'll admit to an occasional purchase when the payout is high, just for the entertainment value of having a ticket in hand in the company of friends as the numbers are called out. But only the one set of numbers. For after all, my absolute risk of winning the big payout, according to Powerball® is just 1 in 175,223,510 or 0.000000005707. If I buy 10 tickets, it's now just 10 in 175,223,510 or 0.00000005707, a barely detectable increase. But according to news reports, that doesn't stop people from buying 1,000 or more tickets to increase their chance of winning.
This lottery ticket example provides an easy example of how changing my relative odds or relative chance of winning - a ten fold increase by buying 10 instead of 1 ticket - doesn't really change my absolute chance of winning by any appreciable amount because the underlying chance of winning is such a rare event.
So why are epidemiologists like me so enamored with relative disease estimates and seemingly less enamored with absolute disease estimates?
These different measurements serve different purposes.
One of the big goals of epidemiology is to study patterns of disease and health and in so doing, to discover associations that may be causal relationships. So we do research like the studies of Sir Richard Doll that uncovered cigarette smoking as a cause of lung cancer, at a time when the suspected major cause was believed to be industrial pollution. Or like the research that uncovered occupational exposure to vinyl chloride as a cause of angiosarcoma of the liver. These kinds of studies compare the occurrence of disease in persons with exposure to those without. Through these relative comparisons, epidemiologists demonstrate 1)strength of association by comparing exposed to unexposed people and 2)dose response by measuring increasing occurrence of disease in persons with increasing levels of exposure. These represent two of HIll's causal criteria, with Hill's Causal Criteria being one of the logic frameworks for assessing causality. We'll talk more about Hill in context in a later blog. For now, we are simply explaining the relative importance of relative measures in an epidemiologist's armamentarium. Epidemiologists like to discover causal relationships, after all, Sir Richard Doll is famous, well, at least among us epidemiologists.
Of course, it's pretty obvious that having a ticket is causally related to winning. Your odds of winning with no ticket are zero. But there isn't a dose response; the person with the most tickets isn't guaranteed to win it.
So a causal path, or analytic framework for winning the lottery is having at least one ticket.
Absolute estimates provide an assessment of the frequency of a disease or condition in a population. Absolute estimates are an important tool for health policy and planning when determining where to place limited resources. Likewise, they should be used by people with limited resources when deciding how many tickets to buy or even whether to play the lottery.
Your doctor will balance information from relative and absolute estimates, which is important in direct patient care, particularly in shared decision making when there are care choices. We'll need to spend a whole blog on that topic. But our goal for now is to start with a clear picture of the difference between relative and absolute estimates. Got it?
TheEvidenceDoc May 2013
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