The pprof software package provides a variety of
risk-adjusted models for provider profiling, efficiently handling
large-scale provider data. It includes standardized measure
calculations, hypothesis testing, and visualization tools for evaluating
the performance of healthcare providers and identifying significant
deviations from expected standards.
Provider profiling involves assessing and comparing the performance
of healthcare providers by evaluating specific metrics that reflect
quality of care, efficiency, and patient outcomes. To achieve this, it
is essential to fit statistical models and design appropriate measures.
We developed the pprof package that facilitates fitting a
variety of risk-adjusted models, each of which includes tools for
calculating standardized measures, conducting statistical inference, and
visualizing results, thereby offering a comprehensive tool for provider
profiling.
This package addresses key limitations in existing R functions for
provider profiling, which often suffer from computational inefficiency
when applied to large-scale provider data. For the logistic fixed effect
model, the serial blockwise inversion Newton (SerBIN) algorithm is
implemented, which leverages the block structure of the information
matrix. For linear fixed effect models, a profile-based method is used.
These, along with parallel computing capabilities, improve computational
speed significantly. pprof handles diverse outcomes
(e.g. binary and continuous) and offers both direct and indirect
standardization. pprof provides a comprehensive and user-friendly tool
for provider profiling, enabling users to fit risk-adjusted models,
calculate standardized measures, perform hypothesis tests, and visualize
results.
Note: The package is still in the early stages of development, so please don’t hesitate to report any problems you may experience.
You can install ‘pprof’ via CRAN or github:
require("devtools")
require("remotes")
remotes::install_github("UM-KevinHe/pprof", ref = "main")If you encounter any problems or bugs, please contact us at: xhliuu@umich.edu, lfluo@umich.edu, kevinhe@umich.edu.
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