One target that caught my interest was for achieving a door-to-balloon time of less than 90 minutes in STEMI patients. As an interventional cardiologist who helped to organize the national D2B Alliance for Quality, I have spent some time thinking about door-to-balloon times. The hospital set the target of less than 90 minutes at 96%. I pointed out that this was unreasonable, but I was told that the number came from a payer’s pay-for-performance program and from the board.

If we are going to use statistics to determine pay and recognition, it is important to use the statistics correctly. A quality target is an estimate, and because of random effects that have nothing to do with quality, there is a confidence interval around that estimate that should be factored into the cutoff value. The random effects have different implications for the payer than they do for the payee. If a hospital gets close to the target but falls short of the cutoff, the payer receives effort for quality but pays nothing — not bad. For the payee, however, falling short of the cutoff means putting forth effort and receiving absolutely nothing for it — a demoralizing outcome to say the least. The randomness around the cutoff value is a bad, all-or-nothing bet for the payee but not for the payer. So, setting a reasonable target that accounts for random effects is an important consideration as you attempt to engage the people who actually do the work of achieving quality — and to keep them engaged.

Small numbers at individual hospitals can amplify these random effects. Of the STEMI alerts that are called, some will be for patients who have insignificant lesions or small vessels and who, therefore, do not undergo intervention. Some will have surgical disease. Others will be excluded from the statistics for a variety of reasons such as the need for intubation or a balloon pump. After all of those exclusions, the number of primary STEMI patients at a typical hospital may be around 40 per year. (Regionalization of STEMI care could increase that number, but that is topic for a different commentary.) So with 40 patients per year, if you have a door-to-balloon time of 91 minutes for 2 patients, you fail to reach a target of 96%. Enormous effort could go into creating a first-rate program, all for naught.

But these are really sick patients, you say. Why not set a high bar and intensify efforts? Why not 100%? Because achieving consistent 100% performance is impossible for any hospital. There is too much ambiguity in making the diagnosis. A patient can wander up to the triage desk of your emergency room complaining of back or abdominal pain, then sit for an hour before being brought back to the treatment area, and 30 minutes later, an ECG could show a STEMI. Or a patient with atypical symptoms could have an ECG that is ambiguous, due to left-ventricular hypertrophy or other artifacts. Despite the very best efforts of good caregivers, the diagnosis of some STEMI patients is occasionally delayed. But when we try to explain these difficulties to non-medical administrators, we are often accused of making excuses. Clinical ambiguity is not an excuse — it is an inescapable part of practicing medicine.

Setting an unreasonably high bar can have unintended side effects. One is to create a “hair trigger” for calling STEMI alerts. Hastily rushing patients with ambiguous clinical findings to the lab could be unsafe if the diagnosis is an aortic dissection, pulmonary embolus, or any number of other diagnoses that can sometimes mimic a STEMI. Excessive false-positive STEMI alerts also can erode the staff’s dedication and engagement, which are necessary to maintain a well-functioning system of care. Finally, there are opportunity costs when we apply excessive resources to chase after impossible statistical goals and neglect other areas of need for quality improvement.

Reliable care for STEMI patients remains an important goal. We should indeed set high standards. But we should not set unachievable goals that ignore the play of chance and clinical ambiguity. If we want to create new quality-improvement challenges, we can define new measures, or use composite measures, or all-or-nothing measures, or outcome measures. But ratcheting up the cutoffs to unachievable levels for individual targets is statistically unjustified and should be avoided.