The Quality Chasm: Diabetes Mellitus

Over the past several decades there has been much interest in refining the definition of quality of health care, moving the definition beyond an individual’s perception of quality. A pivotal article by Avedis Donabedian in 1988 introduced the concept that quality of care can and should be measurable and that there will be multiple dimensions to identifying high-quality care depending on the perspective from which one is looking. A provider may view quality in one way, a patient in another, and a payer in yet another. Furthermore, Donabedian introduced the idea that quality may be related not only to individuals delivering care but also to the systems in which care is delivered. Quality is likely influenced by structural attributes of the system or the processes of care used within the systems to deliver care. Thus, quality becomes much broader than a health care provider’s performance; it involves defining and measuring the structures and processes used by these providers and the systems delivering care. Shortly after this pivotal article, the U.S. Institute of Medicine (IOM) put forth a definition of quality of health care that encompassed Donabedian’s conceptual model by stating that quality can be defined by how well health care itself aims at increasing the health outcomes for individuals and populations based on some set of agreed-on standards of care.

With this broader definition of quality, the IOM examined the quality of health care in the United States; the culmination of this examination was the publication of two pivotal reports in 2000 and 2001 entitled “To Err Is Human: Building a Safer Health System” and “Crossing the Quality Chasm: A New Health System for the 21st Century.” Coupled together, these reports raised significant concerns regarding the state of quality of care delivered by the U.S. health care system. In the second publication, the IOM cited an overwhelming amount of research illustrating the serious variation in quality of care across the nation across multiple settings (hospital, emergency, and ambulatory) and types of care delivered (acute, chronic, and emergent). More specifically, the Quality Chasm report outlined how the U.S. health care system struggled with an overwhelming amount of overuse and underuse of services and errors in health care practice.

After publication of these IOM reports, McGlynn and colleagues published results from an observational study corroborating the IOM observations, which identified that across 12 metropolitan areas involving 490 quality metrics for 30 acute and chronic conditions and preventative services, adults receiving care in the United States were only 54.9% likely to receive all of the evidence-based, recommended care. Further analysis revealed that the variation occurred across all studied settings (hospital, emergency room, and outpatient) and types of care (acute, chronic, and preventative), with no single disease or specific clinical setting driving the low proportional use of guideline-recommended care.

The Quality Chasm report concluded, using Donabedian’s quality framework of structure and process as related to outcome, that the United States health care system’s struggle with achieving its expected or desired health outcomes is due to four key issues: increasing complexity of science and technology; increased prevalence of chronic conditions; poorly organized delivery systems; and constraints on exploiting the revolution in information technology. In addition, the IOM put forth six specific aims for improvement to obtain comprehensive, consistent health care delivery. The first aim is to optimize safety, focusing on avoiding injury or harm to patients in the context of health care delivery, including diagnostic evaluations, treatments, and the settings in which these are delivered. The second aim is to improve effectiveness, providing health care interventions based on evidence-based knowledge when possible. The third aim is to evolve toward more patient-centered health care delivery, offering care that is respectful of and responsive to individual patient preferences, needs, and values throughout the breadth of clinical decisions. The fourth aim is to improve timeliness of medical evaluation and treatment, reducing delays that are potentially harmful. The fifth aim is to improve efficiency, focusing on eliminating unnecessary processes to reduce cost, time, or use of resources. The last aim is to increase the equitable delivery of health care, providing care across populations with minimization of disparities based on individual patient characteristics, such as race and ethnicity, sex and other personal characteristics.

Assessing the Quality of Care for Diabetes

After publication of the IOM reports on health care quality, significant efforts to systematically improve the quality of health care delivery in the United States were undertaken. Given the increase in diabetes prevalence and the staggering associated health care costs (see also Chapter 1 ), the quality of diabetes care has been a key area of focus to reduce the morbidity and mortality resulting from diabetic macrovascular and microvascular disease complications. To best characterize the quality of care for patients with diabetes, evidence-based metrics of assessment are needed. These metrics should be accurate, reliable, and valid with regard to associations with important patient-experienced clinical outcomes to determine the present level of quality of health care delivery and to prospectively analyze progress. Furthermore, with valid and widely accepted measurements, there can also be accountability regarding achievement of objectives by patient, provider, or health care system.

For diabetes, there are several different measurements endorsed by various professional organizations and societies for determining the quality of health care delivery ( Fig. 31-1 ). These measurements include traditional process measurements (e.g., measuring hemoglobin A1c [HbA1c]) and control process measurements (e.g., controlling HbA1c), sometimes called intermediate outcomes. It is important to note that none of the measures have been validated as surrogates for clinical outcomes such as retinopathy, end-stage renal disease, or cardiovascular disease, although some have been shown to be associated with potentially lowering the risk of development of these clinical outcomes.