External comparisons of hospital antimicrobial use (AU), risk-adjusted using encounter characteristics, may better inform antimicrobial stewardship program strategy. Barriers to encounter-level modeling include feasibility of data collection and defining optimal methods for selecting input variables for risk-adjustment purposes.
Fifty of 76 hospitals successfully shared validated datasets using local resources. MAE was lowest for modeling strategies with larger numbers of CCSR inputs. Agnostic and adjudicated strategies had highly correlated estimates and similar influential variables.
Expert adjudication required personnel effort and potentially introduced biases, yet did not produce results different from an agnostic approach. Risk-adjustment incorporating large encounter-level data and machine learning may prove feasible and meaningful in future hospital AU assessments.