Can Pay-for Performance Incentive Levels be Determined Using a Cost-Effectiveness Framework? - Stroke tPA?

 Wrong metric being used. 100% recovery is the correct metric. NOT REPERFUSION TIME OR DOOR-TO-NEEDLE TIME. Both of those are stuck in the tyranny of low expectations.  With 100% recovery as the metric only 12% of cases would be paid. THAT would quickly induce innovation and improvements. Nothing else is working to get survivors fully recovered.  In the business world 12% success would get you fired in no time. Yet nobody in stroke seems to get fired for this blatant incompetence. Should be pay for results NOT pay for performance. Just because you performed doesn't mean it was successful.

Can Pay-for Performance Incentive Levels be Determined Using a Cost-Effectiveness Framework? - Stroke tPA?

Ankur Pandya

,

Djøra I. Soeteman

,

Ajay Gupta

,

Hooman Kamel

,

Alvin I. Mushlin

,

Meredith B. Rosenthal

Originally published3 Jul 2020https://doi.org/10.1161/CIRCOUTCOMES.120.006492Circulation: Cardiovascular Quality and Outcomes. ;0

Abstract

Background

Healthcare payers in the United States are increasingly tying provider payments to quality and value using pay-for-performance policies. Cost-effectiveness analysis quantifies value in healthcare but is not currently used to design or prioritize pay-for-performance strategies or metrics. Acute ischemic stroke care provides a useful application to demonstrate how simulation modeling can be used to determine cost-effective levels of financial incentives used in pay-for-performance policies and associated challenges with this approach.

Methods and Results

Our framework requires a simulation model that can estimate quality-adjusted life years and costs resulting from improvements in a quality metric. A monetary level of incentives can then be back-calculated using the lifetime discounted quality-adjusted life year (which includes effectiveness of quality improvement) and cost (which includes incentive payments and cost offsets from quality improvements) outputs from the model. We applied this framework to an acute ischemic stroke microsimulation model to calculate the difference in population-level net monetary benefit (willingness-to-pay of $50 000 to $150 000/quality-adjusted life year) accrued under current Medicare policy (stroke payment not adjusted for performance) compared with various hypothetical pay-for-performance policies. Performance measurement was based on time-to-thrombolytic treatment with tPA (tissue-type plasminogen activator). Compared with current payment, equivalent population-level net monetary benefit was achieved in pay-for-performance policies with 10-minute door-to-needle time reductions (5057 more acute ischemic stroke cases/y in the 0–3-hour window) incentivized by increasing tPA payment by as much as 18% to 44% depending on willingness-to-pay for health.

Conclusions

Cost-effectiveness modeling can be used to determine the upper bound of financial incentives used in pay-for-performance policies, although currently, this approach is limited due to data requirements and modeling assumptions. For tPA payments in acute ischemic stroke, our model-based results suggest financial incentives leading to a 10-minute decrease in door-to-needle time should be implemented but not exceed 18% to 44% of current tPA payment. In general, the optimal level of financial incentives will depend on willingness-to-pay for health and other modeling assumptions around parameter uncertainty and the relationship between quality improvements and long-run quality-adjusted life expectancy and costs.

Footnotes

The Data Supplement is available with this article at https://www.ahajournals.org/doi/suppl/10.1161/CIRCOUTCOMES.120.006492.

Ankur Pandya, PhD, Department of Health Policy and Management, Harvard T.H. Chan School of Public Health, 718 Huntington Ave, 2nd Floor, Boston, MA 02115. Email anpandya@hsph.harvard.edu