BACKGROUND: Individual provider performance drives group metrics, and increasingly, individual providers are held accountable for these metrics. However, appropriate attribution can be challenging, particularly when multiple providers care for a single patient. OBJECTIVE: We sought to develop and operationalize individual provider scorecards that fairly attribute patient-level metrics, such as length of stay and patient satisfaction, to individual hospitalists involved in each patient's care. DESIGN: Using patients cared for by hospitalists from July 2010 through June 2014, we linked billing data across each hospitalization to assign "ownership" of patient care based on the type, timing, and number of charges associated with each hospitalization (referred to as "provider day weighted "). These metrics were presented to providers via a dashboard that was updated quarterly with their performance (relative to their peers). For the purposes of this article, we compared the method we used to the traditional method of attribution, in which an entire hospitalization is attributed to 1 provider, based on the attending of record as labeled in the administrative data. RESULTS: Provider performance in the 2 methods was concordant 56% to 75% of the time for top half versus bottom half performance (which would be expected to occur by chance 50% of the time). While provider percentile differences between the 2 methods were modest for most providers, there were some providers for whom the methods yielded dramatically different results for 1 or more metrics. CONCLUSION: We found potentially meaningful discrepancies in how well providers scored (relative to their peers) based on the method used for attribution. We demonstrate that it is possible to generate meaningful provider-level metrics from administrative data by using billing data even when multiple providers care for 1 patient over the course of a hospitalization.
BACKGROUND: Individual provider performance drives group metrics, and increasingly, individual providers are held accountable for these metrics. However, appropriate attribution can be challenging, particularly when multiple providers care for a single patient. OBJECTIVE: We sought to develop and operationalize individual provider scorecards that fairly attribute patient-level metrics, such as length of stay and patient satisfaction, to individual hospitalists involved in each patient's care. DESIGN: Using patients cared for by hospitalists from July 2010 through June 2014, we linked billing data across each hospitalization to assign "ownership" of patient care based on the type, timing, and number of charges associated with each hospitalization (referred to as "provider day weighted "). These metrics were presented to providers via a dashboard that was updated quarterly with their performance (relative to their peers). For the purposes of this article, we compared the method we used to the traditional method of attribution, in which an entire hospitalization is attributed to 1 provider, based on the attending of record as labeled in the administrative data. RESULTS: Provider performance in the 2 methods was concordant 56% to 75% of the time for top half versus bottom half performance (which would be expected to occur by chance 50% of the time). While provider percentile differences between the 2 methods were modest for most providers, there were some providers for whom the methods yielded dramatically different results for 1 or more metrics. CONCLUSION: We found potentially meaningful discrepancies in how well providers scored (relative to their peers) based on the method used for attribution. We demonstrate that it is possible to generate meaningful provider-level metrics from administrative data by using billing data even when multiple providers care for 1 patient over the course of a hospitalization.
Authors: Jason K Wang; Jason Hom; Santhosh Balasubramanian; Alejandro Schuler; Nigam H Shah; Mary K Goldstein; Michael T M Baiocchi; Jonathan H Chen Journal: J Biomed Inform Date: 2018-09-07 Impact factor: 6.317
Authors: Mark V Mai; Evan W Orenstein; John D Manning; Anthony A Luberti; Adam C Dziorny Journal: Appl Clin Inform Date: 2020-06-24 Impact factor: 2.342
Authors: Sajan Patel; Logan Pierce; Maggie Jones; Andrew Lai; Michelle Cai; Bradley A Sharpe; James D Harrison Journal: Jt Comm J Qual Patient Saf Date: 2021-10-28