All medical professionals are familiar with the jargon: quality metrics, P4P, low-cost/high-value care, PQRS, and meaningful use. We see these terms in multiple emails, staff and clinical meetings, and organization-wide initiatives. These measures and guidelines will significantly impact the type and quality of care we provide to our patients and how we are paid for it. This movement is not necessarily detrimental and could lead to improved health among our patient populations if implemented reasonably. However, amongst all the initiatives and loaded terminology, we should consider another critical term: the perverse incentive. A perverse incentive is an incentive that has an unintended and undesirable result that is contrary to the intentions of its designers. This concept was well illustrated during the British rule of India in the late 1880s. The British government, concerned about the number of venomous cobras in the city and region of Delhi, offered a bounty for each dead cobra brought to the authorities. The initiative was quite successful in the beginning as the locals killed many cobras. However, some saw this as an opportunity to breed cobras, kill them, and then present them to the British government for a bounty. The Brits eventually became wise to this endeavor and scrapped the program. In response, these snake farmers released their cobras into the wild, creating a more significant cobra problem than the one that existed previously. The moral of the story is that we can make an existing problem worse through the noblest of intentions. We should keep that in mind when we make organizational decisions in pursuit of “quality.” As a primary care physician, I find it difficult to reconcile the quality metric standards and my ability to provide patient-centered care through shared decision-making with the individual patient. Couple that with the fact that chronic disease management guidelines have a way of changing at least every 3 to 5 years, and it’s easy to understand that quality metrics and P4P can directly conflict with providing quality patient care. Suppose you tell a provider that their compensation will be tied to meeting population-level metrics and goals. This incentive can directly conflict with delivering personalized patient care. Population medicine and generalized metrics and guidelines are vital because they provide a framework in which our situational judgment can be performed. However, patient populations are not a grouping of identical game pieces that you can put on a checkerboard and assume they all have the same function, desires, care goals, and risk factors. David Hahn provided a fascinating perspective on the conflict between quality metrics and individualized patient care in his 2017 article in the Annals of Family Medicine. He argued that the “benefits of [blending quality measures with P4P] include use of medical evidence and population-based thinking. Limitations include use of disease-oriented instead of patient-oriented measures, and arbitrary benchmarks lacking actionable information.” He mentions numerous examples of misguided population-level benchmarks being used to provide poor individual care. For instance, it took four years from the publishing of the ACCORD Trial for quality metrics surrounding A1c goals to change. We now know aggressive lowering of every diabetic patient to an A1c of more than seven increases mortality. In turn, physicians were faced with adhering to “expert-guided” quality measures or practicing evidence-based medicine. Also, a very current quality guideline all primary care providers face is the hypertensive goal of less than 140/90. Setting aside that the AAFP loosened its guidelines regarding BP goals in patients older than 60 with no diabetes or vascular disease, we are still accountable for the 140/90 benchmark in all our patient populations. Hahn explains that this benchmark will inevitably lead to gaming. For example, a provider could see a hypertensive patient in January with a BP of 139/89 and not schedule his follow-up visit until the following year. The provider would meet quality goals but provide substandard care in the process. An organization should think long and hard before implementing guidelines that can lead to these actions. How can we stop this ever-growing cascade of metric goals and focus on what matters? A 2012 BMJ article voiced that we must analyze the return on measurement involved with quality measures. Every attempt to measure and construct a quality metric comes at a cost. We must ensure the standards consider the inherent worth of collecting and reporting data to the measure’s impact on patient-centered outcomes. I also propose we consider the psychological burden that excessive quality metrics place on providers. Many of our contemporaries have retired or transitioned to nonclinical jobs due to burnout from these demands. Quality metrics and P4P are not inherently negative, but if their implementation interferes with proper patient care and shared decision-making, they need to be reevaluated. In addition, it would be beneficial to us as providers to be able to document exceptions to the measures if they pose an inherent risk to our patients and not be penalized for providing mindful, patient-centered care. Source
