Managing Risk
Making beer decisions in Surgical Workflows
Getinge Surgical Workflows
Volume 1 • Issue 2 • June 2018
Do outcomes depend on income?
Why the pursuit of best practices isn't always enough.
Recently, Bloomberg.com posted an article that asserted "the
successor to income inequality is longevity inequality." The
report cited sobering statistics from a 2016 study in the Journal
of the American Medical Association that revealed the richest
1% of American women by income live more than 10 years
longer than the poorest 1%. For men, the gap between the
richest and poorest Americans is almost 15 years.
While the "earn more, live longer" correlation between life
expectancy and household income is not a particularly
surprising finding, the degree of disparity points to the challenge
that hospitals face in their quest to improve patient outcomes.
The JAMA study is a disquieting reminder for healthcare
institutions that no maer how much time, talent and treasure
is directed to sustain and improve the quality of life for their
patients, some outcomes are beyond their ability to control.
It has been well documented that low socioeconomic status is
associated with chronic stress, obesity, cardiovascular disease,
low self-esteem, poor nutrition, smoking and a host of other
risk factors.
For many rural and inner-city hospitals that serve an economically
disadvantaged population, outcome measures such as mortality
rates and readmissions typically used to calculate overall
hospital quality – further underscore the uphill bale these
hospitals face to receive full credit for a job well done.
The Agency for Healthcare Research and Quality (AHRQ), the
lead Federal agency charged with improving the safety and
quality of America's healthcare system, acknowledges this
issue on its website:
Outcome measures may seem to represent the "gold standard"
in measuring quality, but an outcome is the result of numerous
factors, many beyond providers' control. Risk-adjustment
methods – mathematical models that correct for differing
characteristics within a population, such as patient health
status – can help account for these factors. However, the science
of risk adjustment is still evolving. Experts acknowledge that
beer risk-adjustment methods are needed to minimize the
reporting of misleading or even inaccurate information about
health care quality.
While it's imperative to refine these risk-adjustment methods
to level the hospital performance evaluation playing field, it's
even more urgent for the healthcare industry, government, and
private sector to address the root causes of longevity inequality
to improve the baseline health of all people before they have
the inevitable need to seek care.
