3 healthcare quality secrets in this time of policy change

In 2016, our country is still conspicuously absent from the list of top twenty leaders when it comes to lowest infant mortality. Median life expectancy? Also nowhere near the top. So what is going on in the United States with our healthcare quality?

Previously, I discussed three facts about U.S. healthcare that won’t change with the inauguration. As America is thinking about healthcare policy in these early days of the Trump administration, consider these three secrets from healthcare quality insiders. Each relates to an opportunity for change in the field.

Staff who work in the system are usually shocked when they see its true performance.

{mosads}Even 16 years after the Institute of Medicine’s Crossing the Quality Chasm report, clinical staff in healthcare are very surprised when shown data about how a particular system of care is performing.

As a specialist in data driven quality improvement, I was taught early on that most professional service industries perform at an error rate of about one defect per every thousand opportunities at making a defect. In a high-stakes fields like aviation, for example, that error rate would produce about one plane crash every day at very busy airports.

As a surgeon, the defect rate in healthcare doesn’t feel that high on a day-to-day basis. There are many reasons why, and at least one is that not all defects are preventable — even though we start with the attitude that we can somehow do better and find a way.

Like me, my colleagues including nurses, physicians, and respiratory therapists also feel like things go very well day-to-day. We feel like we do a good job. No one shows up to work to be part of producing an error or problem, and many of us have spent long hours and years of education to be able to help patients. Unfortunately, how we feel about our system often doesn’t match its true performance.

Now, when I have the quality improvement hat on, I notice that prior to any improvement effort the healthcare error rate is somewhere in the ballpark of one defect in every thousand opportunities.

Staff, who feel they do a good job are usually shocked when the data are in. It may be because they believe a defect rate of one defect per thousand opportunities is good especially when we work late at night with critically ill patients in difficult situations, or it may be because that rate of one defect per 1000 opportunities is very difficult to feel day-to-day when we work.

It requires education to explain that the common rate of defect (1/1000) is nowhere near good enough in a high-stakes field like healthcare.

In any event, I agreed with my colleague who shared the secret with me for this article: staff who work in the system are routinely surprised when shown its true performance and that can be uncomfortable.

Getting data that are meaningful and accurate in healthcare is very difficult.

The colleague who shared this secret hit an important nail right on the head: it’s often very difficult to obtain meaningful data for healthcare quality improvement.

There are definitive reasons why: many centers do not have the ability to meaningfully collect good data. Others lack a clear, useful definition of what they are trying to measure.

For example, one project that repeats across health systems focuses on decreasing the time patients must spend in the emergency department. Often, existing data systems don’t capture the situation well for many reasons. One issue that’s prominent: when does the clock stop on how long the patient has been in the emergency department? Is it when the healthcare provider writes an order for the patient to be admitted to the hospital (or discharged)? Or does it stop when the patient physically leaves the emergency department?

Typically, the computer systems measure when the order is placed for the patient to leave the Emergency Department, but not when they actually go. Bad data means “garbage in, garbage out” to those trying to improve things.

This example highlights how my colleague’s secret is so common: getting data that have meaning and are accurate for decision-making is very difficult in healthcare. At many centers, it requires a person with a clipboard, paper, and watch to sample data directly from the system at the location where the process happens and according to a definition the quality group has created with everyone involved.

Healthcare lags behind other industries in quality improvement — and we know it.

My colleague who shared this secret started off with: “Well, this one is pretty well-known I think but maybe not everyone has heard about it.” She felt this item was so well-known in healthcare quality circles that it almost is no longer a secret.

I share it here because many outside of healthcare quality improvement staff may not yet have heard how healthcare lags other industries in quality improvement techniques.

Big data predictive analytics? Well,some forward thinking executives in healthcare believe it holds promise — that’s about as far as it goes. In a recent survey, although 81 percent believed predictive analytics held potential for healthcare, only 31 percent had integrated any portion of the process into their system. Nineteen percent replied they had no plans to do so.

“Yes”, added my colleague who shared this secret, “healthcare lags in quality improvement and we know it.”

Based on the secrets above, and the IOM’s Crossing The Quality Chasm, it’s easy to walk away with a sense of the many issues in healthcare quality. Is there hope for improvement?

Yes. In the last ten years I’ve noticed advanced quality tools being used in healthcare more and more.  Meaningful improvement projects are being performed at many institutions, and attention to the secrets above can only help improve the situation.

Now, at the beginning of Trump’s administration, let’s focus on how each of these important healthcare quality improvement secrets relates to the discussion of where to bring healthcare policy next.

David M. Kashmer is a trauma and acute care surgeon. He is a nationally known quality improvement expert who focuses on Lean & Six Sigma in Healthcare. Dr. Kashmer is the author of the recent Amazon bestseller Volume to Value: Proven Methods for Achieving High Quality in Healthcare, which focuses on the use of standard quality improvement tools in Healthcare. He writes on quality improvement in healthcare for TheHealthcareQualityBlog.com and Insights.TheSurgicalLab.com. Follow him on Twitter: @DavidKashmer.