Stranded Patient (Patients flagged as having a greater than 7 day LOS) Model

Source: R/stranded_patient_model.R

This model is to be used as a machine learning classification model, for supervised learning. The binary outcome is stranded vs not stranded patients.

data(stranded_data)

Format

Tibble with nine columns (1 x outcome and 8 predictors)

stranded.label

Outcome variable - whether the patient is stranded or not

age

Patient age on admission

care.home.referral

Whether than have been referred from a care home

medicallysafe

Medically safe for discharge - means the patient is assessed as safe, but has not been discharged yet

hcop

Indicates whether they have been triaged from a Health Care for Older People specialty

mental_health_care

Flag to indicate whether they need mental health support and care

periods_of_previous_care

Count of the number of previous spells of care

admit_date

Date they were admitted to hospital

frailty_index

An initial index assessment to say if the patient is frail or not. This is needed for alignment of service provision.

Source

Synthetically generated by Gary Hutson, Mar-2021.

Examples

library(dplyr)

data(stranded_data)

stranded_data |>
  glimpse()
#> Rows: 768
#> Columns: 9
#> $ stranded.label           <chr> "Not Stranded", "Not Stranded", "Not Stranded…
#> $ age                      <int> 50, 31, 32, 69, 33, 75, 26, 64, 53, 63, 30, 7…
#> $ care.home.referral       <int> 0, 1, 0, 1, 0, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, …
#> $ medicallysafe            <int> 0, 0, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 1, 0, 1, …
#> $ hcop                     <int> 0, 1, 0, 0, 1, 0, 1, 1, 0, 0, 0, 1, 0, 0, 1, …
#> $ mental_health_care       <int> 0, 0, 1, 1, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, …
#> $ periods_of_previous_care <int> 1, 1, 1, 1, 1, 1, 1, 1, 5, 1, 1, 1, 1, 1, 4, …
#> $ admit_date               <chr> "29/12/2020", "11/12/2020", "19/01/2021", "07…
#> $ frailty_index            <chr> "No index item", "No index item", "No index i…