Abstract
In the last decade, healthcare systems have shifted their focus from increased volume of patients and procedures to improving patient outcomes and quality. While there are many societies and companies that have surrogate measures of excellence, these metrics are determined by those who do not directly participate and fully understand the best measurements of quality. In order to better assess quality and value, the Efficiency Quality Index (EQI) was created. The novel aspect of the EQI is the determination of metrics by the physicians who actually perform the procedures, in order to create an accurate and fair measurement of performance and outcomes. In this article, we describe how to create and implement the EQI, as well as outline its benefits.
Highlights
Over the past several years, there has been a movement toward quality over volume in the care of patients, with greater emphasis placed on delivering the highest quality of care as opposed to achieving the highest volume (1)
We have been using the Efficiency Quality Index (EQI) for the past several years at New York University (NYU), and the results are staggering when looking at national scoring systems
We should not allow outside arbitrators to be the determinants of quality, especially if they do not fully understand the best measurements of quality
Summary
Over the past several years, there has been a movement toward quality over volume in the care of patients, with greater emphasis placed on delivering the highest quality of care as opposed to achieving the highest volume (1). In response to the opaque quality-ranking-industrial-complex and long-time need for improved measurements (3), we have created and refined a more detailed system for our services and physicians to evaluate their performance: the Efficiency Quality Index (EQI). This novel metric engages staff because it is fair and transparent; allows the participants to ensure the data are correct and to decide what is the best metrics of quality in their specific field or operation; permits participants to remove data if others they are compared to agree; ensures an accurate attribution of complications and quality; and create evolving iterations and metrics as frequently as needed to best serve patients. The analytics team creates a database with relevant data— where data points do not exist, divisions themselves manually audit charts
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