Impact of Quality Care Measures on Patient Care
As discussed by Rodney Hayward, MD, and Sheldon Greenfield, MD, quality care measures may improve outcomes for health systems overall, but some investigators believe that overapplication of these measures may reduce healthcare quality for some groups of patients.
All parties agree that not all quality measures have equal value and that improvements in quality measures will continue to accrue over time. However, researchers have also documented concerns about overly simplistic measures of quality and unfavorable outcomes associated with overapplication of these measures.
Several challenges of use of quality care measures include the potential for lack of individualized treatment of patients and the attempt to create a single measure to apply to all patients. Some investigators complain that the “average patient” does not exist, and explain that treating patients with mild or early type 2 diabetes with the same goals as patients with later complications of diabetes may lead to ineffective use of resources.
The other side of the debate contends that quality care measures improve quality overall and provide some incentive that increases the overall quality of care to a higher level.
Treating individuals to an average glycated hemoglobin (A1C) of less than 7%, for instance, may not be appropriate or realistic for some patients. However, quality care measures provide a “floor,” or a general guideline, that clinicians may use in a majority of cases. These measures still allow providers to take into account concerns for individual cases.
One instance of a quality care measure that may lead to harm when overapplied is the A1C target of less than 7%. Although when initially proposed this standard was rejected 3 times by a panel of technical experts, the standard was eventually adopted. The Action to Control Cardiovascular Risk in Diabetes (ACCORD) randomized controlled trial utilized this A1C target; the results of ACCORD showed an increase in mortality with overly tight glucose control. By contrast, studies like the UK Prospective Diabetes Study (UKPDS) did not push for aggressive targets.
Other quality measures that may lack some of the nuances of individualized therapy include lipid control. With a low-density lipoprotein (LDL) goal of less than 100 mg/dL, many patients may receive more medication than they actually need. Over 80% of the benefit of statins comes from treatment with the lowest dose of medication, except in the highest risk groups. Again, this emphasizes the importance of individualized care.
These potential pitfalls do not mean that quality care measures are without merit; however, improvements are necessary. It is possible, for instance, to create a rating scale that takes into account patient factors and tailors an appropriate evidence-based A1C goal for each individual patient. Unfortunately, however, evidence can be difficult to find because subgroup analyses of studies based on individual patient factors seldom lead to meaningful results due to the fact that they are often underpowered.
Proponents of quality care measures contend that quality care measures simply provide general goals, but do not guide therapy. In fact, groups that generate nuanced treatment guidelines are separate from groups that create quality care measures. Quality care measures are applicable most of the time, but certainly not all of the time, and are not equivalent to clinical practice guidelines.
Demonstrating the value of quality care measures, a paper by Ali et al in a 2013 issue of The New England Journal of Medicine showed improvements in the percentage of patients receiving influenza vaccinations and blood pressure treatment for prevention of kidney problems associated with diabetes. Performance measures allow us to quantify these baseline improvements in care.
Negative effects of quality care measures generally occur at the level of individual facilities. For instance, physicians may stop treating patients who refuse to stop smoking or refuse to take medications. Removing these patients from the physician’s care improves the average health of all patients under that doctor’s care, thereby potentially improving quality measures.
In the future, less simplistic measures may help physicians evaluate patients and offer more tailored therapy, while allowing for exact measurement of how well individual patients are being treated.
The use of composite measures involves scoring several patient factors on a continuous scale. For instance, a composite measure of a proper target LDL level may take into account a patient’s baseline LDL, comorbidities, and baseline cardiac risk. The composite measure may then focus on the reduction from baseline cardiac risk for the individual patient, which is the outcome clinicians are trying to achieve—improved survival for patients. In other words, the composite measure approach supports treating the patient, not the level.
Quality measures have an important place, and will continue to have an important place, in guiding therapy. However, a more nuanced, individualized approach to developing quality measures may induce even greater benefits in the future.
The National Committee for Quality Assurance has already developed composite measures for diabetes care that correlate well with treatment guidelines. Ultimately, there is no side that does not support quality measures, but only a side that wants better, less simplistic quality measures that reflect clinical treatment guidelines and drive goals based on the risks of therapy and the benefits achieved by patients.