Quality is a hot topic in healthcare. Changing reimbursement, improved patient experience focus, and many other emerging trends have lead to "healthcare quality" as a Google search term with 300+ million results.
When a healthcare team looks to improve a process, it often is faced with choosing among several alternatives. So what, then, do good solutions look like in healthcare quality improvement? Here are three useful signs that the solution you've chosen will be a home run for quality:
- The solution uses "poka-yoke": Poka-yoke is the concept that the solution makes it easier to do the right thing. It's rooted in the concept of designing something that is error-proof. Does your proposed solution create paperwork, friction for the providers involved, or wasted time? If yes, consider hunting for an alternative. Watch out for solutions that make it more difficult to achieve the success you're looking for.
- Impacts the bulk of the system you're studying: This hallmark means you need to know your system with data. Does the candidate solution you're considering only impact a few members of the histogram you're looking at? Or will it impact the system as a whole? Sometimes it's worthwhile to focus on the tails of the distribution (especially if they're above the upper spec limit, beneath the lower spec limit, or otherwise prohibitive) and yet choosing a solution that will really decrease variation in the entire system is often a good move.
- The solution is implementable: Sure, you could chose a solution that no one has ever been able to achieve, or one that costs a million dollars, or requires you to lift 400lbs over your head every day...but I mean...should you?
These three highlights go a long way to letting you sort the valuable PI solutions from the ones that are, well, not so good. Consider checking the next solution you devise to improve quality against these important factors.
Have some other key factors for the list? Let me know.
In its attempt to solve a problem, the hospital chose bad solutions that, in some cases, actually made patients sicker. Bad solutions often have a certain look about them: they’re solutions that are difficult to implement, are expensive, are otherwise prohibitive, take multiple steps to get done, don’t work or just generally make things worse. What do good solutions look like? Above all, a good solution is implementable. A good system makes it easy to do the right thing and hard to make a mistake. A good system is error-proof because the playing field is tilted toward making it easier to do the right thing. In designing the system, the questions are always “What’s easy for the physician or healthcare provider?” and “What’s the right thing for the patient?” and “What’s doable?”