Extract discharge-level data from a Hospital-Specific Report (HSR)
Source:R/hsr_discharges.R
hsr_discharges.RdParses out the discharge-level data for a specific program cohort that contributed to penalty program in the reporting fiscal year (FY).
Note: CMS changed the format of Hospital-Specific Reports (HSRs) for FY2026 (see here). The current HSR functions support formats through FY2025.
Usage
hsr_discharges(
file,
cohort,
discharge_phi = TRUE,
risk_factors = FALSE,
eligible_only = FALSE
)Arguments
- file
File path to a report
- cohort
Cohort to extract the discharges for. One of
c("AMI", "COPD", "HF", "PN", "CABG", "HK")- discharge_phi
Should discharge PHI be included? Defaults to
TRUE(see details).- risk_factors
Should readmission risk factors be included? Defaults to
FALSE(see details).- eligible_only
Should only eligible discharges be included? Defaults to
FALSE(see details).
Details
The first set of columns in the discharge level data (typically through column R) contain the protected health information (PHI) associated with the discharges, such as medical record identifiers, admission/discharge/readmission dates, index diagnoses, etc. which can be used to identify the specific patients contributing (and not contributing) to the CMS penalty calculation for the cohort.
The risk factors contain the discharge-level clinical information used for individual risk adjustment by
CMS to estimate individual level readmission rates. These can be useful to explore to understand risk factor
distributions and prevalence, especially in combination with hsr_coefficients() which indicates the
risk factors most heavily-weighted in the readmission risk calculation.
The HSR contains discharges that were not necessarily included/eligible to be counted in the
Hospital Readmissions Reduction Program (HRRP).
Setting eligible_only = TRUE will filter the returned result to only those that are eligible, and thus should match the denominator
displayed in hsr_cohort_summary().
Examples
# Access a report
my_report <- hsr_mock_reports("FY2025_HRRP_MockHSR.xlsx")
# All discharges
hsr_discharges(my_report, "HF")
#> # A tibble: 30 × 17
#> `ID Number` MBI `Medical Record Number` `Beneficiary DOB`
#> <int> <chr> <chr> <chr>
#> 1 1 9AA9AA9AA99 99999A 99/99/9999
#> 2 2 9AA9AA9AA99 99999A 99/99/9999
#> 3 3 9AA9AA9AA99 99999A 99/99/9999
#> 4 4 9AA9AA9AA99 99999A 99/99/9999
#> 5 5 9AA9AA9AA99 99999A 99/99/9999
#> 6 6 9AA9AA9AA99 99999A 99/99/9999
#> 7 7 9AA9AA9AA99 99999A 99/99/9999
#> 8 8 9AA9AA9AA99 99999A 99/99/9999
#> 9 9 9AA9AA9AA99 99999A 99/99/9999
#> 10 10 9AA9AA9AA99 99999A 99/99/9999
#> # ℹ 20 more rows
#> # ℹ 13 more variables: `Admission Date of Index Stay` <chr>,
#> # `Discharge Date of Index Stay` <chr>,
#> # `Cohort Inclusion/Exclusion Indicator` <chr>, `Index Stay (Yes/No)` <chr>,
#> # `Principal Discharge Diagnosis of Index Stay` <chr>,
#> # `Discharge Destination` <chr>,
#> # `Unplanned Readmission within 30 Days (Yes/No) [a]` <chr>, …
# Discharges eligible for HRRP
hsr_discharges(my_report, "HF", eligible_only = TRUE)
#> # A tibble: 25 × 17
#> `ID Number` MBI `Medical Record Number` `Beneficiary DOB`
#> <int> <chr> <chr> <chr>
#> 1 1 9AA9AA9AA99 99999A 99/99/9999
#> 2 2 9AA9AA9AA99 99999A 99/99/9999
#> 3 3 9AA9AA9AA99 99999A 99/99/9999
#> 4 4 9AA9AA9AA99 99999A 99/99/9999
#> 5 5 9AA9AA9AA99 99999A 99/99/9999
#> 6 6 9AA9AA9AA99 99999A 99/99/9999
#> 7 7 9AA9AA9AA99 99999A 99/99/9999
#> 8 8 9AA9AA9AA99 99999A 99/99/9999
#> 9 9 9AA9AA9AA99 99999A 99/99/9999
#> 10 10 9AA9AA9AA99 99999A 99/99/9999
#> # ℹ 15 more rows
#> # ℹ 13 more variables: `Admission Date of Index Stay` <chr>,
#> # `Discharge Date of Index Stay` <chr>,
#> # `Cohort Inclusion/Exclusion Indicator` <chr>, `Index Stay (Yes/No)` <chr>,
#> # `Principal Discharge Diagnosis of Index Stay` <chr>,
#> # `Discharge Destination` <chr>,
#> # `Unplanned Readmission within 30 Days (Yes/No) [a]` <chr>, …
# Only show risk factors for eligible discharges
hsr_discharges(
file = my_report,
cohort = "HF",
discharge_phi = FALSE,
risk_factors = TRUE,
eligible_only = TRUE
)
#> # A tibble: 25 × 39
#> `ID Number` `Years Over 65 (continuous)` Male History of Coronary Artery B…¹
#> <int> <dbl> <dbl> <dbl>
#> 1 1 8 1 0
#> 2 2 25 1 1
#> 3 3 9 0 0
#> 4 4 9 0 0
#> 5 5 30 0 0
#> 6 6 13 0 0
#> 7 7 12 1 1
#> 8 8 7 1 1
#> 9 9 25 1 0
#> 10 10 22 0 0
#> # ℹ 15 more rows
#> # ℹ abbreviated name: ¹`History of Coronary Artery Bypass Graft (CABG) Surgery`
#> # ℹ 35 more variables: `History of COVID-19` <dbl>,
#> # `Metastatic Cancer and Acute Leukemia` <dbl>, Cancer <dbl>,
#> # `Diabetes Mellitus (DM) or DM Complications` <dbl>,
#> # `Protein-Calorie Malnutrition` <dbl>,
#> # `Other Significant Endocrine and Metabolic Disorders; Disorders of Fluid/Electrolyte/Acid-base Balance` <dbl>, …
# Row count matches denominator for HF
hsr_cohort_summary(my_report)
#> # A tibble: 6 × 10
#> `Measure [a]` `Number of Eligible Discharges [b]` Number of Readmissions Amo…¹
#> <chr> <dbl> <dbl>
#> 1 AMI 2 0
#> 2 COPD 18 3
#> 3 HF 25 2
#> 4 Pneumonia 32 5
#> 5 CABG NA NA
#> 6 THA/TKA 45 0
#> # ℹ abbreviated name: ¹`Number of Readmissions Among Eligible Discharges [c]`
#> # ℹ 7 more variables: `Predicted Readmission Rate [d]` <dbl>,
#> # `Expected Readmission Rate [e]` <dbl>,
#> # `Excess Readmission Ratio (ERR) [f]` <dbl>,
#> # `Peer Group Median ERR [g]` <dbl>, `Penalty Indicator (Yes/No) [h]` <chr>,
#> # `Ratio of DRG Payments Per Measure to Total Payments [i]` <dbl>,
#> # `National Observed Readmission Rate [j]` <dbl>