Why pay for performance may be incompatible with quality improvement.

In a linked article (doi:10.1136/bmj.e5047), Glasziou and colleagues highlight the tenuous nature of the evidence that financial reward systems work in healthcare settings. They propose that before pay for performance schemes are implemented the potential benefits and harms should be assessed. Such schemes, which aim to improve the quality and efficiency of healthcare by the use of financial incentives to encourage desirable behaviours, have been adopted as a key strategy by the NHS in the United Kingdom, Medicare in the United States, andmany private insurers. The schemes are based on a basic tenet of economics and psychology: that people respond to rewards. Beyond the simple criticism that pay for performance can’t operate on an extended time frame and that years may elapse between treatment and outcome, the concept of pay for performance in healthcare rests on flawed assumptions about medicine, measurement, and motivation. Performance based pay may increase output for straightforward manual tasks. However, a growing body of evidence from behavioral economics and social psychology indicates that rewards can undermine motivation and worsen performance on complex cognitive tasks, especially when motivation is high to begin with. One questionable assumption underlying pay for performance is that measurements of doctors’ performance reflect their overall performance and not—for example—their patients’ characteristics or their ability to “game” the system. Health outcomes such as death or disability are the most easily measured and unambiguous indicators of overall performance, but they require risk adjustment. Hospital mortality provides the best case scenario for risk adjustment—outcomes are frequent, unambiguous, and likely to reflect performance; time horizons are short; and experts have spent years analysing the rich trove of hospital data. Yet, four widely used algorithms yield divergent rankings of inpatient mortality. Risk adjustment is devilishly difficult, partly because key inputs—clinical diagnoses—aren’t solely patient characteristics but also reflect the aggressiveness of coding and diagnostic investigations. 4 Seeking out and documenting unimportant comorbidities and diagnoses (such as occult prostate cancer in older people) exaggerate the severity of illness and artefactually raise risk adjusted quality scores. Hence, excessive testing inflates quality scores without improving quality. Similarly, intensive coding—that is, embellishing diagnoses to maximise payment under per case or risk adjusted capitation schemes—also makes patients seem sicker on paper, and hence boosts risk adjusted quality scores. Under USMedicare’s DRG (diagnosis related groups) hospital payment system, recoding a diagnosis as “aspiration pneumonia with acute on chronic systolic heart failure” rather than simply “pneumonia with chronic heart failure” triples the payment and increases the risk score. Such “upcoding” is endemic among private health maintenance organisations that contract with Medicare for risk adjusted capitation payments, as well as among hospitals.One Maryland rehabilitation hospital reportedly urged doctors to document “protein malnutrition” in patients’ charts, and this enabled the hospital to bill for 287 cases of “kwashiorkor” in 2007 (up from 0 in 2004).Yet pay for performance programmes assume that quality indicators accurately reflect global quality and won’t be distorted by payment incentives. Process based indicators, although easier to calculate than risk adjusted outcomes, are poor proxies for quality of care. Even seemingly clear cut measures have hidden complexity. As examples, total hospital readmission rates correlate poorly with avoidable readmission rates, and starting treatment for patients with pneumonia within four hours of arrival correlates with quality, yet Medicare’s incentives for hospitals to do so resulted in the administration of antibiotics to almost any patient in the emergency department with a cough. Patients’ social characteristics also confound process based measures; even excellent doctors who care for disadvantaged or difficult patients may look bad on current pay for performance metrics. Among respected doctors at a flagship Harvard teaching hospital, those who cared for more non-English speaking, poor,