Barbara Morrison’s Quality Improvement Success Story titled “Increasing Attendance at Scheduled Appointments for Group Classes at a Diabetes Education Center” (1), which is published in this issue of Clinical Diabetes, raises several important issues.

First, although other investigators have studied no-show rates for primary care visits (and have investigated various tactics to lower those rates), Morison’s article reminds us that not all literature-based solutions are generalizable to every care setting. The staff of the diabetes center at which this quality improvement (QI) project took place recognized that 1) they had a no-show rate, 2) they care for an ethnically diverse community, and 3) the development of tactics to address their no-show rate should be locally derived. To that end, they decided to undertake a systematic exploration of their no-show rates.

Why is this impressive and important? In QI, for interventions to have the highest likelihood of success, practices should attempt to implement tactics that fit their own setting characteristics. Although many tactics can be taken from the literature, these often need to be tweaked or altered in some way to fit the unique characteristics of each practice setting. These characteristics are the structures, processes, and resources that exist in the practice itself, as well as the characteristics of the patient population it serves.

The next step in any QI approach is to have an understanding of the problem before implementing solutions. The approach Morrison and her colleagues undertook to study the problem was simple and reminds us that data collection for QI need not be complex. Data collection simply needs to be good enough for practitioners to understand the performance of the current system. These data are then used as the basis for experiments (i.e., changes to the system) that aim to improve performance.

In this case, the investigators found that transportation was a key barrier to individuals showing up for group diabetes education classes. However, an intervention that included facilitating free transportation to the clinic had no impact on no-show rates. So, what happened? Perhaps the investigators did not have the right explanatory model for their no-show rate.

What is clear is that these investigators now have an opportunity to take the next step in QI, which is to ask “Why?” When we use tools such as the “5 Whys” (an established QI approach to root cause analysis using five open-ended questions), we eventually get to the root causes of a problem rather than its symptoms. When root causes are addressed, sustained improvement becomes more likely. In this case, the next step would be for patients who were no-shows to be interviewed in a respectful, nonjudgmental way to uncover the reasons they did not attend classes so the right tactics to reduce no-shows can be designed.

Questions remain. Do patients trust their providers? Is managing diabetes important to them? Do they have underlying depression and feelings of despair? Or, are there other issues not yet discovered? What these investigators find through this analysis will allow the practitioners to grow personally and professionally and allow the patients and community to derive the greatest benefit from the available diabetes education services. Patient-centered care starts with asking the patients.

Duality of Interest

D.S. is a consultant to Lilly and Novo Nordisk. No other potential conflicts of interest relevant to this article were reported.

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