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#' Hospital acquired infections
#'
#' A dataset containing the number of hospital acquired bacteremia, Clostridium
#' difficile infections, and urinary tract infections in six hospitals in the
#' Capital Region of Denmark 2015-2016.
#'
#' @format A data frame with 432 rows and 5 variables: \itemize{ \item
#' {hospital} Abbreviated hospital name. \item{infection} Type of infection.
#' BAC: Bacteremia, CDI: Clostridium difficile infection. UTI: Urinary tract
#' infection. \item{month} First day of month. \item {n} Number of cases.
#' \item{days} Number of risk days. A risk day is a patient day without
#' infection. }
#' @source www.esundhed.dk (Capital Region of Denmark).
"hospital_infections"
#' Coronary artery bypass graft operations
#'
#' A dataset with data on individual coronary artery bypass graft operations.
#'
#' @format A data frame with 2205 rows and 6 variables: \itemize{ \item
#' {data} Date of operation. \item{age} Patient age in years.
#' \item{gender} Patient gender. \item{los} Length og stay in
#' days. \item{death} TRUE if patient died within 30 days after
#' surgery. \item{readmission} TRUE if patient were readmitted for any
#' reason within 30 days after surgery. }
#' @source Omitted for privacy concerns.
"cabg"
#' Clostridium difficile infections
#'
#' A dataset with data on hospital acquired Clostridium difficile infections
#' (CDI) before and after an intervention to reduce the risk of CDI.
#'
#' @format A data frame with 36 rows and 5 variables: \itemize{ \item {month}
#' Month of observation. \item{n} Number of hospital acquired CDI. \item{days}
#' Number of risk days. A risk day is a patient day without CDI. \item{period}
#' Factor indicating the period 'pre' or 'post' intervention. \item{notes}
#' Annotations. }
#' @source www.esundhed.dk (Amager Hvidovre Hospital).
"cdi"
#' NHS accidents
#'
#' The number of attendances to major accident and emergency hospital
#' departments in the NHS that were seen within 4 hours of arrival over twenty
#' weeks.
#'
#' @format A data frame with 20 rows and 3 variables: \itemize{ \item i Week
#' number. \item r Attendances seen within 4 hours. \item n Total number of
#' attendances. }
#' @source Mohammed MA, et al. Quality and Safety in Health Care
#' 2013;22:362–368. \doi{10.1136/bmjqs-2012-001373}.
"nhs_accidents"
#' Patient harm indentified using the Global Trigger Tool
#'
#' A dataset with data on adverse events during hospitalisation found by the
#' Global Trigger Tool.
#'
#' @format A data frame with 340 rows and 11 variables: \itemize{ \item
#' admission_id Admission ID. \item admission_dte Date of admission. \item
#' discharge_dte Date of discharge. \item month Month of discharge. \item days
#' Duration of hospital stay in days. \item harms Number of adverse events.
#' \item {E-I} Type of adverse event by severity category. E-F: Temporary
#' harm; G-H: Permanent harm; I: Fatal harm. }
#' @source Omitted for privacy concerns.
#' @references
#' \url{https://www.ihi.org/resources/white-papers/ihi-global-trigger-tool-measuring-adverse-events}
#'
"gtt"
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