Research now demonstrates that sole reliance on measuring and reporting simple processes is unlikely to have a substantial impact on patient outcomes, and improvement in process measures can no longer be taken as evidence that quality of care has improved (24). Performance measures based on control of risk factors such as A1C, blood pressure, and LDL cholesterol are appealing because these risk factors predict clinical outcomes, but this approach presents measurement complexities and challenges. Control of these risk factors is influenced not only by provider actions, but also by factors such as patient behaviors, comorbidity, and concerns about medication safety and cost. Current performance measures identify thresholds for A1C, blood pressure, and LDL cholesterol control and usually dichotomized performance measures based on these threshold levels. The use of thresholds is easily understood and simple to report, but selection of an appropriate threshold is difficult, especially in the light of recent clinical trial results and subsequent guideline recommendations to individualize clinical goals for A1C and blood pressure (25–29).

Dichotomous threshold-based measures suggest that all patients above the threshold need additional pharmacologic or lifestyle intervention. Setting high threshold goals (such as A1C <9%, or systolic blood pressure [sBP] <160 mmHg) reduces poor quality care and can be appropriately applied to all patients eligible for the measure. However, in most care systems, only a small fraction of patients will fail to meet such a high threshold. As threshold goals are lowered, an increasing proportion of patients require additional treatment to reach the more stringent threshold goals. However, the marginal benefits of increased treatment diminish as patients approach the goal, while the likelihood of treatment-related side effects and costs of treatment typically increase.

If the risks associated with more intensive treatment are substantive, then setting low thresholds for accountability measures (such as A1C <7% or sBP <130 mmHg) may actually do more harm than good for many patients—clearly an undesirable situation (30). Lack of benefit or unintended harm is possible, especially for those above the accountability threshold but already on high dose therapy, those with terminal illness or limited life expectancy, and those susceptible to serious side effects of aggressive therapy such as hypoglycemia or hypotension (31–34). In the past, some guidelines have adopted blood pressure or A1C goals more stringent than those validated in clinical trials (25,35). While low blood pressure or A1C levels may benefit some subsets of patients, incorporating low threshold goals in accountability measures is problematic (36). Finally, aiming for stringent targets in every patient ignores patient preferences (37).

Since 2008, many diabetes clinical guidelines recommend individualization of A1C and blood pressure goals. In response, some quality measures now include a complex set of exemptions and exclusions that may remain challenging to implement even when EHRs data are available. Alternative approaches discussed below are to increase the accountability threshold to a value that is appropriated for nearly all patients, to move from goal-based to risk-based measures, or to implement new “clinical action” measures, which are more tightly linked to outcomes than some current measures.

Composite performance scores have been widely adopted and may improve the reliability of performance measurement and ranking compared with single measures (38–40). However, various approaches to combining indicators (averaging by indicator, averaging by patient, or simply measuring all indicators across all patients) may yield somewhat different rankings (41). Composite measures convey less granular clinical information and should be supplemented by providing individual measure data to the physicians. Current composite scores typically weight each indicator equally, so that simple process measures contribute as much to the score as having risk factors in control. This problem can be remedied by weighting the components of a composite measure based on clinical importance.

One variant of the composite score is the “all-or-none” score, which is the proportion of patients for whom all of a set of process indicators are met. It has been suggested that the all-or-nothing approach is the best way to drive toward excellence (42). However, because the score reduces a set of indicators to a single dichotomous score for each patient, all-or-none measures discard a large amount of information. Consequently they lack sensitivity for distinguishing between plans or physicians and tend to have poor reliability (41). All-or-none measures may be more useful for evaluating a multistep process (e.g., diagnosing and treating pneumonia), in which each step is necessary to achieve a successful outcome. They have less to offer in assessing or improving the parallel and often independent processes of diabetes care, especially since not all care components are of equal importance to individual patients.

One possible refinement of dichotomous intermediate outcome measures is the clinical action measure. Clinical action measures are of two types: 1) those that combine a threshold measure for an intermediate outcome with a process of care for those above the threshold, and 2) those that suggest a high-benefit evidence-based clinical action in certain clinical circumstances (55,56). Examples of these measures include prescribing moderate dose statins to patients with diabetes over age 40 years, or prescribing an ACE-inhibitor or angiotensin receptor blocker to patients with albuminuria. Clinical action measures could take exclusions into account by removing patients for whom care may be contraindicated or not beneficial from the denominator (e.g., women of childbearing potential, patients with end-stage renal disease). By focusing on the clinical treatment (e.g., statins) rather than only a threshold intermediate outcome value (e.g., LDL cholesterol <100 mg/dL), these measures are less likely to motivate treatment with nonevidence-based treatments (e.g., ezetimibe) in order to reach a clinical threshold (14,57).

For example, clinical action measures may credit the clinician for appropriate care if 1) the threshold is met (e.g., blood pressure below the measure threshold), or 2) the provider takes an appropriate clinical action (e.g., starting or increasing the dose of an appropriate medication) for a patient above threshold, or 3) the risk factor returns to below the threshold within a given time frame without changes in therapy, or 4) the patient has a contraindication to further therapy intensification (e.g., a very low diastolic blood pressure) or is already on high-dose therapy despite an elevated risk factor level.

Clinical action measures have several strengths. They direct attention to patients most likely to benefit from added therapy, and they point directly to the appropriate treatment rather than just the risk factor level. Thus, they help providers do the “right” thing for the right patient. They also give credit when the appropriate clinical action is to not intensify medications, thereby diminishing the potential for unintended consequences related to overtreatment. Finally, they take known variation in measurement into account by giving credit for values that return to target within a specified time period. Because many clinical action measures require access to detailed clinical data, they depend on evolved electronic data systems (56,58,59).

Some have expressed concern that threshold-based performance measures could focus clinician attention inordinately on patients currently just above the target and away from those who are further from the target and may benefit more (60). Others are concerned that performance thresholds could also increase health disparities because vulnerable patients are often further from control, although one recent study allays this concern (61). In general, current use of threshold measures may discard important information compared with considering the full distribution of values in physician or health plan populations (62).

If an A1C threshold measure for “good care” is set at 7%, a provider could get full credit for moving a patient from 7.1 to 6.9%, but no credit for improving control in another patient from 8.8 to 7.1%, despite the fact that the latter patient's risk has been reduced much more than the former's (63,64). These concerns and others may be somewhat allayed by giving “partial credit” to clinicians or systems for treatment efforts, even when a patient does not reach the target.

Credit is assigned based on predicted clinical benefit gained by moving patients from prior poor control to a more favorable clinical level. This requires specifying a threshold for poor control (e.g., A1C >8%) above which no credit is given, and a threshold for good control (e.g., A1C <7%) at which point full credit is given. Some experts have suggested that benefits be quantified using quality-adjusted life-years saved (65). Other methods to assign partial credit have also been proposed and deserve careful consideration (66).

The use of risk-based prediction models can extend the concept of risk and benefit in performance measurement by considering each patient's calculated risk for an adverse outcome and defining the benefit a patient is likely to obtain from a specific clinical action based on the UK Propective Diabetes Study (UKPDS), QRISK, Framingham, or other risk engines (67–69). Depending on known evidence, the selected risk engine integrates age, comorbidity, other risk factors (e.g., smoking), and current treatments to predict the patient's risk for a poor outcome. This approach facilitates a patient-specific performance measurement across the continuum of benefit and risk. Such performance measures might assess 1) whether patients above a certain threshold of high risk (where benefit of therapy would clearly outweigh potential harms of treatment) received the therapy in question; 2) whether those below a certain threshold of low risk (where benefit is lower than potential harms) did not receive the therapy; and 3) whether those in between the two thresholds had a documented discussion of risk and benefit of the therapy and engaged in shared or informed decision making (70). Before this approach is ready for prime time, more work needs to be done to assure that the risk and benefit estimates provided by the risk engines are accurate and based on evidence from intervention trials whenever possible. At present, some risk engines overestimate benefits by relying too heavily on epidemiological rather than clinical trial evidence.

While the suggestions outlined above are likely to maximize appropriate care and minimize unintended consequences of performance measures (60,71–76), an additional fruitful area lies in constructing and testing direct measures of potential overtreatment, inappropriate treatment, or harm (74,77). Creating and reporting such measures could serve to counter any pressure to intensify therapy inappropriately in the name of performance improvement. Such measures might identify suboptimal practices such as further intensification of therapy for patients with low diastolic blood pressure and moderate sBP levels (e.g., <140/65 mmHg); use of glyburide among the elderly or those with impaired renal function; falls or episodes of hypoglycemia severe enough to require emergency care or hospitalization in patients on complex glucose-lowering regimens or insulin; or on high doses of blood pressure medications. Patient-reported data regarding symptoms and treatment burden may enhance our future ability to quantify overtreatment or potential harm.

Currently, diabetes quality measures focus on the treatment of those with diagnosed diabetes. The Diabetes Prevention Program (DPP) demonstrated that either intensive lifestyle change leading to 7% weight loss or use of metformin substantially reduced the incidence of type 2 diabetes in a diverse U.S. population with impaired glucose tolerance (78). Both interventions were cost-effective from the perspective of the health system and society as delivered in the DPP (79), and similar weight loss and exercise outcomes have been achieved when the DPP lifestyle intervention is implemented in a much less costly form in community settings (80). New performance measures could be designed to assess 1) appropriate implementation of diagnostic tests to identify those at high risk of diabetes; 2) appropriate referral to lifestyle programs and/or metformin therapy; and 3) relative quality and efficacy of lifestyle programs designed to achieve weight loss. Such measures would not only foster the implementation of proven interventions of tremendous public health significance, but would be important models of measuring community-based public health initiatives designed to address weight management, healthy eating, and physical activity.

An estimated 20–50% of patients with chronic disease do not take their medications as prescribed (81). Poor medication adherence contributes to poor diabetes control, disability, unnecessary hospitalization, and death (82,83). A meta-analysis of 63 studies with over 19,000 participants reported that higher adherence rate decreases the risk for poor treatment outcome by 26% (84). Measures of patient adherence are impeded by 1) lack of agreement on the best methods to measure medication adherence, 2) paucity of integrated data systems that include both prescription and medication dispensing data, and 3) a sparse body of research on interventions to improve medication adherence (85). As information systems evolve and more effective interventions to improve adherence are identified, quality measures related to medication adherence may catalyze new efforts to improve adherence and patient health outcomes.

Patients with diabetes generate medical care costs that are on average two to three times higher than age- and gender-matched patients without diabetes. Cardiovascular complications remain the principal driver of high diabetes care costs; medication costs are also rising more rapidly than overall inflation (86–88). Diabetes and other medical expenditures vary greatly across care systems in relation to the benefits achieved, and a substantial portion of expenditures does not appear to provide any net benefit to the patient (89). These services are usually labeled as wasteful, inappropriate, or inefficient.

NCQA has recently developed diabetes relative resource use measures at the plan level and is testing them at the group practice level. These measures are designed to look at resource use in diabetes care and, when combined with quality measures, can provide an overview of efficiency (high resource use–low quality vs. low resource use–high quality). Barriers to expanding such measures include the need for large sample sizes, the difficulty of accurately quantifying expenditures, and the need for accurate risk adjustment. A measure of total expenditures per patient is now available within the Medicare program and is based on administrative claims data. The information can be further categorized as hospital inpatient, outpatient, or pharmacy-related (medications). Expenditures can be compared with a set of outcome-related quality measures as an initial step toward trying to define the value (benefit per unit of expenditure) of care in diabetes.

Over the short term, we need more analysis and understanding of which elements of resource use have positive or negative correlations with measures of quality and outcomes of care. Currently most diabetes performance measures only assess whether tests or examinations are being underused. Development of measures that look at overuse of tests, examinations, procedures, or technology may be useful in evaluating and maximizing efficiency of care. Measures that encourage the use of generic medications, when available, may also conserve resources. Care provided by various subspecialties for patients with advanced complications of diabetes may be variably efficient or inefficient. With further refinement of both quality and cost-related measures, diabetes could become the poster child for efficient and effective health care.

As with many chronic diseases, diabetes is marked by disparities in both treatment and outcomes. Such disparities are primarily based on socioeconomic status (SES), race, and ethnicity, but also exist by sex and age. Because patients of lower SES often have more barriers to self care and worse control of risk factors, clinicians who provide care to many such patients may have lower quality-of-care scores publicly reported, or lose income or incentives related to unadjusted measures of clinical performance. Currently, the HEDIS data are grouped by Medicare, Medicaid, and commercial insurance. In the future, quality measures could be adjusted in more sophisticated ways to account for variation in patient SES, health literacy, or other factors related to disparities in care. Possible methods include geo-coding, case-mix adjustment, or use of other metrics for SES. On the other hand, “overadjusting” for race/ethnicity and SES could mask real differences in quality of care provided to different groups; such disparities can only be corrected if they are identified.