New Approaches to Defining and Measuring the Quality of Healthcare
Conference coverage of the 2012 meeting of the American Diabetes Association.
The widespread adoption of electronic health records is facilitating implementation of quality improvement measures across healthcare. As the US healthcare system becomes more quality focused, current methods of defining and measuring quality have come under scrutiny. Diabetes has been a major focus of early quality improvement initiatives. The speakers of a symposium titled “What Are the Ideal Quality Measures for Diabetes Care?” at the 72nd Scientific Sessions of the American Diabetes Association discussed the current state of quality measurement in diabetes.
“It is impossible to know how well we are doing without measuring something,” said Sandeep Vijan, MD, MS, associate professor of internal medicine, University of Michigan.“ There is little doubt that quality measurement is important and reasonably effective.” He explained that systems do better in those areas that are measured and reported, with financial incentives clearly driving behavior. “What you measure becomes a driver of what you do,” said Dr Vijan.
Quality measures may take the form of process measures of whether an action was performed (eg, glycated hemoglobin [A1C] measurement or eye examination), intermediate outcome measures (eg, A1C <7.0), or outcome measures (eg, hospital admissions for myocardial infarction). Defining quality is a significant challenge and prone to a number of problems.
Thus far, quality measures have generally been based on interpretations of trial evidence by clinical experts. This method poses a problem because the targets for continuous measures (eg, A1C and blood pressure) are based on mean clinical trial results. “If the mean A1C of a trial is 7.0%, then 50% of patients in the trial received less than optimal clinical care,” stated Dr Vijan. Furthermore, intermediate outcomes are not always tightly linked to patient-important outcomes.
Often, quality measures are falsely dichotomized. For example, if A1C was set as a therapeutic target, a measurement of 7.1% would be equally unacceptable as a measurement of 13%. Dr Vijan raised concern that this type of quality measure will drive treatment of patients with milder elevations—who are more likely to reach target—rather than those with severe elevations. Without considering each patient’s underlying risk profi le, he said, “These types of measures provide a potential incentive to overtreat and possibly harm patients.”
After reviewing some of the problems with existing quality measures, Dr Vijan spoke about new efforts to improve the science of quality measurement. According to him, quality measures should be based on rigorous interpretation of the evidence, incorporate reasonable exceptions, reward appropriate actions, consider how much benefit is gained, incorporate patient preference, and consider benefit and value. To date, patient involvement in defining and measuring quality has been very limited.
One potential improvement on the dichotomous quality measure is the linked action measure, also called a clinical action measure.1 Linked action measures give credit for appropriate actions. For example, for a patient who is above goal (A1C >8 or systolic blood pressure >140), the provider would get credit if new medication is added, existing dose is increased, or the therapeutic target is achieved within a certain time frame. This measure allows inclusion of reasonable exceptions. Linked action measures have already been developed, specified, and tested. Dr Vijan shared published and unpublished data demonstrating his institution’s successful use of electronic medical records and linked action measures: 85% of providers from the system of more than 1 million patients were taking appropriate action or maintained patients at therapeutic target.2
The next step, explained Dr Vijan, is to develop and test patient-selected, personalized measures of quality. To exemplify the importance of patient preference in measuring quality, he presented data from a cost-effectiveness analysis of intensive glucose control among patients 65 years or older that considered patient preference.3 Intensive glycemic control has been shown to be cost-effective among patients younger than 50 years. The results of this study suggest that among older patients, factoring in their patient preferences decreases quality-adjusted life-years of intensive glycemic control with insulin and makes treatment more harmful than beneficial. Factoring in patient preference regarding oral agents reduced cost-effectiveness. This type of study demonstrates how significantly patient experience can influence value.
Measuring Quality of Care for Patients With Multiple Morbidities
Cynthia Boyd, MD, MPH, associate professor of medicine, division of geriatric medicine and gerontology, Johns Hopkins University School of Medicine, presented “Measuring Quality of Care in Complex Patients.” Multimorbidity is common—43% of Medicare enrollees 65 years or older have 3 or more chronic conditions.4,5 Decisions for patients with multimorbidity are complex. Most clinical practice guidelines are developed from a single-disease perspective, making them difficult to apply to patients with complex medical histories.6 Dr Boyd discussed how clinicians need guidelines that help them navigate the treatment of patients living with multiple diseases or chronic conditions. Boyd explained that to best care for people with multimorbidities, clinicians need guidance on how to: maximize use of therapies likely to benefi t patients with multimorbidity, minimize use of therapies unlikely to benefit or likely to harm patients with multimorbidity, and incorporate patient preferences and values regarding burdens, risks, and benefits.
The National Quality Forum has engaged its Consensus Development Process (CDP) toward a measurement framework that will assess the efficiency of care (defined as quality and cost) provided to individuals with multiple chronic conditions (MCCs).7 The framework defines MCCs, identifies high-leverage measurement areas for the population with MCCs (to mitigate unintended consequences and measurement burden), presents a conceptual model that serves as an organizing structure for identifying and prioritizing quality measures, and offers guidance on methodological and practical measurement issues.
The framework is centered on patient and family goals and preferences for care and will allow for patient prioritization of disease-specific measures based on individual needs, preferences, and discussions with healthcare providers. Non—disease-specific measures that apply to all patients regardless of condition also factor into the model. Measurements fall under specific domains that are aligned with priorities of the US Department of Health and Human Services National Quality Strategy: person- and family-centered care, health and well-being, patient safety, effective communication and care, effective prevention and treatment, and affordable care.8
Patient-Centered Outcomes Research Institute
In recognition of the need to integrate patient preferences into the planning and execution of health research, the American Recovery and Reinvestment Act implemented the Patient-Centered Outcomes Research Institute (PCORI). According to its mission statement, PCORI “helps people make informed healthcare decisions—and improves healthcare delivery and outcomes—by producing and promoting high integrity, evidence-based information—that comes from research guided by patients, caregivers, and the broader health care community.”
In May 2012, PCORI issued its National Priorities and Research Agenda, which lists 5 National Priorities: assessment of options for prevention, diagnosis, and treatment; improvement of healthcare systems; research on communication and dissemination; reduction of disparities; and acceleration of PCOR and methodological research (
Figure
).9 That same month, PCORI issued its first 4 funding announcements.
Through its patient-driven research funding programs, PCORI aims to align research questions and methods with patient needs. According to Joe Selby, MD, MPH, of PCORI, who presented “Insights on Quality of Care From Institutional Decision Makers,” PCORI stipulates patient and stakeholder engagement from the earliest phases of study design through the final stages of research to gain better understanding of the choices patients face, ensure that all elements of study design are truly relevant, keep research on track, and avoid changes in course that undermine the utility of the study to patients. Results of PCORI-funded studies will be disseminated to provide patients and providers with supplementary information to improve decision making.
