Hospital readmission risk can change after discharge as time passes and new information becomes available. A prediction made only at discharge cannot show how the remaining risk evolves during follow-up.
Readmission Risk Pool (RRP) is an analytical
platform for reassessing that risk at chosen times and retaining what
each assessment found. readmit is the R
package used to create and operate an organization’s RRP project.
For an episode still within follow-up and without a known readmission
or death, each assessment concerns the remaining probability of a
first hospital readmission strictly after the assessment time
and through 30 elapsed days after the original discharge. The
endpoint stays fixed; another assessment does not start a new 30-day
window. The estimate should use information available at that time. For
eligibility, readmit distinguishes when a readmission or
death occurred from when it became known, so later knowledge does not
rewrite an earlier assessment.
Repeated assessments retain accepted estimates, provider failures, and ineligible episodes in project history. This history shows what was assessed at each time rather than only the latest result.
An RRP project is an ordinary, organization-owned directory with
configuration, source-mapping and risk-provider scripts, and its own
Shiny application. readmit defines canonical inputs for
discharge episodes and terminal events, applies shared eligibility
rules, retains completed assessments, and derives three analytical
products: current remaining risk, recorded risk trajectories, and an
operational summary. The project application explores those
products.
This repository contains the RRP 1.0.0 release source.
The CRAN
readmit 0.0.1 package contains earlier, substantially
different functionality under an MIT license; it does not install RRP.
Until the RRP release is available on CRAN, install this repository
version from GitHub:
install.packages("remotes")
remotes::install_github("centralstatz/readmit")Choose a new project path whose parent directory already exists:
library(readmit)
project <- rrp_project_init(
path = "my-rrp-project",
project_id = "my-project"
)
rrp_project_validate(project)The initializer creates editable project guidance, configuration, branding, two authoring scripts, and a Shiny app. The scripts start as deliberate stubs. Validation checks that the structure and functions load; it does not run them or establish that assessments are ready.
Implement R/produce-canonical.R to map your source data
to the required discharge episodes and terminal events, and
R/calculate-risk.R to provide risk estimates for eligible
episodes. rrp_state_init(project) creates persistent
history and rrp_project_status(project) reports project
state; both can be used before those scripts are implemented. Once they
work, rrp_run(project, as_of_time) records an assessment at
a specified UTC time. Use rrp_inspect_history(project) to
examine retained results after state initialization, and
rrp_refresh_products(project) to derive products after at
least one completed run. Refresh does not run another assessment.
The application can open before products exist and will show empty
views. Install its runtime packages (shiny,
bslib, reactable, plotly, and
yaml), then launch it with
shiny::runApp(file.path(project, "app")). After a product
refresh, the app reads the generated products.
Your organization supplies source mapping and a risk provider,
evaluates whether its data and model are suitable for the intended use,
and decides how results inform clinical or operational decisions.
readmit supplies the analytical structure around those
inputs; it does not include a clinically validated prediction model or
establish that using RRP improves outcomes.
For function reference, use help(package = "readmit")
after installation. Each generated project’s README.md
explains the canonical contract, risk provider, and application in more
detail.