“All-or-none” performance measures are a recently described way of viewing best practice, which can allow health systems to compare their ability to deliver care at the patient level to “perfect” care. This alternative method of measuring quality can also foster systems perspectives and increase the sensitivity for assessing improvements.7
For example, in the traditional item-by-item method of measuring performance, a practice with the following individual performance measures: pneumococcal immunization of 70%, LDL lowering to <100 of 53%, and blood pressure control to <130/80 of 55%—would have compliance higher than national benchmarks. Yet, when analyzed through an “all-or-none” viewpoint, individual patients in their practices may receive all 3 indicators, on average, only 20% of the time (.70
.55). This method of measurement “raises the bar” for quality improvement efforts and is being used by many large health care systems as an alternative method to measure improvements. In the 2004 National Healthcare Quality Report, item-by-item measurement shows the rates of performance of diabetes standards ranging from 56.5% for influenza vaccination to 93.8% for lipid profiling. However, all measures (five in this report) were recorded as met only 32.1% of the time.8
Major health systems using all-or-none bundling have reported only 2.4% to 12.8% of patients receiving 7 process measures for diabetes care.9
Medical groups reporting to the Minnesota Community Measurement project on a 5-component all-or-none diabetes bundle averaged 9.5% in 2006.10
In the current report, an integrated delivery system was able to make gains in several areas by traditional, single-measure criteria. In addition, all practices had success in improving their performance in a 9-component all-or-none bundle score. It was distressing to our physicians that their ‘bundle score’ was initially low. We believe that this response created an early momentum for practice change. This low initial score also made it clear that increased physician vigilance and hard work alone would not result in success and encouraged team-based approaches to care.
Most of the literature on changing physician behavior illustrates that multifaceted interventions (such as this one) will be more successful that single interventions.11
A combination of enabling tools, reminders, audit and feedback, and financial incentives were used in our program. As they were all implemented simultaneously, it is difficult to assess which had the most impact; this is a limitation of the current study. We also cannot rule out the contribution of confounding factors such as an increase in physician and patient awareness of diabetes measures, unrelated to the study interventions. Some measures, such as improved smoking, are mostly related to documentation than true improvements in getting smokers to quit. However, we assert that documenting smoking status is an important measure and one which we wanted to improve in the study.
We are aware that improvements in process measures do not necessarily translate into improved clinical outcomes. As illustrated by our results, it is much easier to make sure a patient with diabetes received a LDL order each year, than it is to ensure that the LDL is controlled to appropriate levels. One must extrapolate that improvements in certain process measures, such as immunization percentages, would lead to improvements in mortality and morbidity from respiratory illnesses—and the current work was not designed to assess such clinical outcomes.
Financial incentives often raise concerns about “gaming behavior”—either “cherry-picking” well patients or jettisoning high-risk patients to improve results. Because the number of patients with diabetes did increase during the period of study and we did not see an increase in the number of referrals to our Endocrinology department, it does not appear that patients were deselected. However, one would wonder if the increased awareness of diabetes measures encouraged physicians to diagnose more patients with diabetes earlier—when they could be easier controlled. We are unable to assess the impact of this possibility.
One disadvantage of all-or-none measurement is that all measures receive equal weighting despite the fact that their clinical benefits may differ greatly; for example, lowering blood pressure has stronger effects seen earlier than adding angiotensin converter inhibitors for microalbuminuria. An additional limitation of the present study was the inability to measure some very important performance criteria such as retinal exams and microfilament testing. It is difficult to capture this information in our EHR system, as such items do not appear as discrete data fields.
Critics of “all-or-none” approaches correctly point out that many elderly patients who have multiple diseases or with limited life expectancy cannot be expected to meet all of the bundle measures. For example, a glycosylated hemoglobin of <7.0 might subject such a patient to more risks than are justified by the limited benefit given the shorter period of time for treatment effectiveness. Durso suggests an algorithmic approach to evaluating which patients may benefit from specific recommendations, depending on comorbidities and estimated life expectancy.12
In addition, patient preferences, polypharmacy, and medication cost and coverage issues speak to the need to individualize care. For these reasons, it is difficult to know what a reasonable goal for optimal performance on the bundle is for our patient population. Future goals of our work include an analysis of patient level correlates with compliance to the quality measures. This may enable us to identify subgroups of the overall population that have the largest (or smallest) improvements over time. This information would allow for future fine-tuning of the intervention and appropriate allocation of resources with the goal of optimizing compliance.
We believe that EHR registries can create tools never before available in medical practice and can be used to galvanize physician-led teams to improve care. In addition, looking at quality data in “all-or-none” fashion may set higher standards for improvement. Medical groups should strive to further refine these methods and continue to work to apply them to improve the care of patients with diabetes and other chronic diseases.