readmit is an evolving R package that contains tools for working with and analyzing hospital readmissions data. Currently, it provides utilities for components of the Hospital Readmissions Reduction Program (HRRP), including program timeline functions, Hospital-Specific Report (HSR) helpers, and general importing tools for the Provider Data Catalog (PDC).
Installation
You can install readmit from CRAN:
# install.packages("pak")
install.packages("readmit")Or the development version from GitHub with:
# install.packages("pak")
pak::pak("centralstatz/readmit")Background
A readmission occurs when a patient is admitted to the hospital, again, after they were recently discharged (where 30 days is the typical time frame used between hospitalizations). First and foremost, it is an obvious burden to patients for multiple reasons (i.e., psychologically, financially, etc.).
Additionally, hospitals across the United States are penalized by the Centers for Medicare & Medicaid Services (CMS) on an annual basis in what is called the Hospital Readmissions Reduction Program (HRRP). In this program, up to 3% of Medicare reimbursement is witheld from hospitals for the duration of a fiscal year depending on the volume of excess readmissions in select patient populations during a preceding performance period. Readmissions also show up in other payer contracts, such as commericial insurers. Thus, it is a key area of focus for hospitals and part of the general measure of overall health of clinical and financial operations.
Typically, cross-functional teams are deployed within health systems to monitor and develop initiatives, interventions, and overall strategy to manage and prevent readmissions. This includes things like care coordination and patient outreach, as well as how to incorporate technology such as predictive analytics to identify high-risk patients and prevent readmissions before they occur.
The Problems
The issues in doing this flawlessly are multi-factorial, but we’ll list a few that we see relevant to (and motivators for) this package:
Reporting lineage: It is often difficult to have seamless line of sight and reconcile hospital-wide metrics (e.g., overall readmission rates, penalty amounts, etc.) down to individual patients and their associated impact. There are many reasons for this: some may be technical (e.g., reporting tools, data collection, systems/personnel constraints), but some are due to complexities of hospital operations: varying definitions of metrics (e.g., how do we define readmissions?), diverse patient populations (e.g., which patients should/should not be included in the rates?), differential impact on outcomes (e.g., only Medicare patients contribute to the penalty but the hospital cares about readmissions for all patients).
Payor Contracts/Reimbursement: Readmissions have different implications depending on whose paying for the service (among other things). It is difficult to disentangle and account for these nuances (especially in reporting/metrics) when developing readmission prevention strategies while optimizing financial health.
Oversaturation of research: Readmissions has a large body of research. As a result, hospitals are thrown all kinds of “evidence” about how they should prevent readmissions, but it’s difficult to confidently translate and distill that to a localized, optimized, actionable program for any one hospital, especially when it’s conflicting.
Over-reliance of risk tools: Especially in the hype of AI, machine learning (ML), etc., there are various vendor platforms and risk tools that purport to predict readmissions. The issue comes with how they are implemented into clinical workflows. These models may be good statistically, but must be implemented with intention and cross-functional teamwork for them to actually be useful. AI is not magic! Additionally, many out-of-the-box models focus on predicting readmission risk at the time of discharge. These may be good markers of baseline clinical risk but can quickly grow stale as the patient enters the post-discharge phase where the real drivers of readmissions occur.
Complexity of government programs: The HRRP has many moving parts and details that make it difficult to track what’s really going on. This includes the timing of the discharges that are actually counted in the program relative to when payment penalties are applied, the diagnosis codes and claims documentation used to identify patients to include, the statistical methodology behind the scenes that power program metrics, and the way all of that rolls up into a penalty percentage amount administered to the hospital, among other things. Each of these details have deep nuances that have tangible impact.
This package is meant to provide tools to help with components of these issues. In particular, the current state of the package focuses on the last item, making it easier to analyze information related to the HRRP. Over time, we hope this scope widens.
Examples
Here are a few ways to use the package.
Extracting key dates from the HRRP
An important piece of the HRRP is to understand the timelines and dates associated with the program. We provide built-in datasets to conveniently access these dates (see ?hrrp_keydates). For example, hrrp_performance_periods provides the date ranges for discharges that are included in each program year:
hrrp_performance_periods
#> ProgramYear StartDate EndDate
#> 1 2027 2023-07-01 2025-06-30
#> 2 2026 2021-07-01 2024-06-30
#> 3 2025 2020-07-01 2023-06-30
#> 4 2024 2019-07-01 2019-12-01
#> 5 2024 2020-07-01 2022-06-30
#> 6 2023 2018-07-01 2019-12-01
#> 7 2023 2020-07-01 2021-06-30
#> 8 2022 2017-07-01 2019-12-01
#> 9 2021 2016-07-01 2019-06-30
#> 10 2020 2015-07-01 2018-06-30
#> 11 2019 2014-07-01 2017-06-30And hrrp_snapshot_dates provides the date that CMS took the extract of claims data for each program year:
hrrp_snapshot_dates
#> ProgramYear SnapshotDate
#> 1 2027 2025-09-30
#> 2 2026 2024-10-22
#> 3 2025 2023-10-13
#> 4 2024 2022-09-30
#> 5 2023 2021-09-24
#> 6 2022 2020-09-25
#> 7 2021 2019-09-27
#> 8 2020 2018-09-28
#> 9 2019 2017-09-29Or, all of the individual hrrp_* datasets are pre-joined in hrrp_keydates:
hrrp_keydates
#> ProgramYear PerformanceStartDate PerformanceEndDate PaymentStartDate
#> 1 2027 2023-07-01 2025-06-30 2026-10-01
#> 2 2026 2021-07-01 2024-06-30 2025-10-01
#> 3 2025 2020-07-01 2023-06-30 2024-10-01
#> 4 2024 2019-07-01 2019-12-01 2023-10-01
#> 5 2024 2020-07-01 2022-06-30 2023-10-01
#> 6 2023 2018-07-01 2019-12-01 2022-10-01
#> 7 2023 2020-07-01 2021-06-30 2022-10-01
#> 8 2022 2017-07-01 2019-12-01 2021-10-01
#> 9 2021 2016-07-01 2019-06-30 2020-10-01
#> 10 2020 2015-07-01 2018-06-30 2019-10-01
#> 11 2019 2014-07-01 2017-06-30 2018-10-01
#> PaymentEndDate ReviewStartDate ReviewEndDate SnapshotDate AMI COPD HF PN
#> 1 2027-09-30 <NA> <NA> 2025-09-30 1 1 1 1
#> 2 2026-09-30 2025-08-12 2025-09-10 2024-10-22 1 1 1 1
#> 3 2025-09-30 2024-08-12 2024-09-10 2023-10-13 1 1 1 1
#> 4 2024-09-30 2023-08-08 2023-09-07 2022-09-30 1 1 1 1
#> 5 2024-09-30 2023-08-08 2023-09-07 2022-09-30 1 1 1 1
#> 6 2023-09-30 2022-08-08 2022-09-07 2021-09-24 1 1 1 0
#> 7 2023-09-30 2022-08-08 2022-09-07 2021-09-24 1 1 1 0
#> 8 2022-09-30 2021-08-09 2021-09-08 2020-09-25 1 1 1 1
#> 9 2021-09-30 2020-08-10 2020-09-09 2019-09-27 1 1 1 1
#> 10 2020-09-30 2019-08-09 2019-09-09 2018-09-28 1 1 1 1
#> 11 2019-09-30 2018-08-06 2018-09-05 2017-09-29 1 1 1 1
#> CABG HK
#> 1 1 1
#> 2 1 1
#> 3 1 1
#> 4 1 1
#> 5 1 1
#> 6 1 1
#> 7 1 1
#> 8 1 1
#> 9 1 1
#> 10 1 1
#> 11 1 1Finding relevant program dates
We can also use the hrrp_get_dates() function to extract relevant time periods for an inputted date. For example:
- “What is the performance period for payments my hospital is currently being penalized for?”
hrrp_get_dates(Sys.Date(), "performance", discharge = FALSE)
#> # A tibble: 1 × 3
#> ProgramYear StartDate EndDate
#> <dbl> <chr> <chr>
#> 1 2026 2021-07-01 2024-06-30- “What payment periods did a discharge from 1/1/2022 impact?”
hrrp_get_dates(as.Date("2022-01-01"), "payment", discharge = TRUE)
#> # A tibble: 3 × 3
#> ProgramYear StartDate EndDate
#> <int> <date> <date>
#> 1 2026 2025-10-01 2026-09-30
#> 2 2025 2024-10-01 2025-09-30
#> 3 2024 2023-10-01 2024-09-30We can see that not only are the discharges that impact today’s payment reductions multiple years old, but also individual discharges (and their associated readmissions) can impact the program result for three (3) years in a row.
Analyzing hospital reports
Note: CMS changed the format of Hospital-Specific Reports (HSRs) for FY2026 (see here). The current HSR functions support Excel-based formats through FY2025.
CMS sends out Hospital-Specific Reports (HSR) each program year detailing the calculations of the payment reduction for the upcoming fiscal year (hospitals are given a 1-month period to review and submit corrections, the dates of which can be accessed with hrrp_review_periods). These reports contain the penalty amount down to the individual, line-item discharges that were included in the program. The package functions prefixed like hsr_* are meant to be used with them. For example, we can use the hsr_discharges function to extract discharge-level data for the heart failure cohort included in the readmission denominator into a clean data frame:
# Mock report from QualityNet
my_hsr <- hsr_mock_reports("FY2025_HRRP_MockHSR.xlsx")
hsr_discharges(
file = my_hsr,
cohort = "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>, …We could also extract the risk factors for each patient used in the statistical models developed by CMS to estimate adjusted readmission risk:
hsr_discharges(
file = my_hsr,
cohort = "HF",
eligible_only = TRUE,
risk_factors = TRUE,
discharge_phi = FALSE
)
#> # 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>, …We could then choose to extract the actual model coefficients (weights) that get applied to the patient risk factors:
hsr_coefficients(
file = my_hsr,
cohort = "HF"
)
#> # A tibble: 40 × 2
#> Factor Value
#> <chr> <dbl>
#> 1 Years Over 65 (continuous) -0.00589
#> 2 Male -0.0359
#> 3 History of Coronary Artery Bypass Graft (CABG) Surgery 0.0199
#> 4 History of COVID-19 -0.00239
#> 5 Metastatic Cancer and Acute Leukemia 0.149
#> 6 Cancer 0.0126
#> 7 Diabetes Mellitus (DM) or DM Complications 0.0968
#> 8 Protein-Calorie Malnutrition 0.0856
#> 9 Other Significant Endocrine and Metabolic Disorders; Disorders of F… 0.163
#> 10 Liver or Biliary Disease 0.0865
#> # ℹ 30 more rowsThese tables can be joined together and each patient’s readmission risk that CMS used can be computed and analyzed. Or, we can use the hsr_readmission_risks() function to do this for us:
hsr_readmission_risks(
file = my_hsr,
cohort = "HF"
)
#> # A tibble: 25 × 3
#> `ID Number` Predicted Expected
#> <int> <dbl> <dbl>
#> 1 1 0.258 0.264
#> 2 2 0.186 0.192
#> 3 3 0.184 0.189
#> 4 4 0.188 0.193
#> 5 5 0.0857 0.0885
#> 6 6 0.133 0.138
#> 7 7 0.110 0.113
#> 8 8 0.183 0.188
#> 9 9 0.163 0.168
#> 10 10 0.179 0.184
#> # ℹ 15 more rowsAs you can see, there are many ways to use the information in these reports to gain insight into readmissions at your hospital. Further analysis strategies can be explored in the associated article.
Importing data from the Provider Data Catalog
CMS provides access to a large repository of datasets in the Provider Data Catalog (PDC), which includes, among many other datasets, readmission measures and HRRP program results for hospitals around the United States. The package functions prefixed like pdc_* are general functions to explore and import metadata/datasets straight from the website into clean datasets in R (see ?pdc_read). For example, we can use pdc_topics() to get the collection of topics seen here:
pdc_topics()
#> [1] "Dialysis facilities"
#> [2] "Doctors and clinicians"
#> [3] "Home health services"
#> [4] "Hospice care"
#> [5] "Hospitals"
#> [6] "Inpatient rehabilitation facilities"
#> [7] "Long-term care hospitals"
#> [8] "Nursing homes including rehab services"
#> [9] "Physician office visit costs"
#> [10] "Supplier directory"Then we can choose a topic (or topics) we want to find datasets for, and extract their metadata with pdc_datasets():
hospital_data <- pdc_datasets("Hospitals")
hospital_data
#> # A tibble: 69 × 7
#> datasetid topic title description issued modified downloadurl
#> <chr> <chr> <chr> <chr> <date> <date> <chr>
#> 1 axe7-s95e Hospitals Ambulatory… This file … 2025-10-01 2025-10-01 https://da…
#> 2 wue8-3vwe Hospitals Ambulatory… This file … 2025-10-01 2025-10-01 https://da…
#> 3 4jcv-atw7 Hospitals Ambulatory… A list of … 2025-10-01 2025-10-01 https://da…
#> 4 hbf-map Hospitals Birthing F… A list of … 2025-07-09 2025-10-14 https://da…
#> 5 muwa-iene Hospitals CMS Medica… This data … 2020-12-10 2025-10-14 https://da…
#> 6 ynj2-r877 Hospitals Complicati… Complicati… 2023-07-05 2025-10-20 https://da…
#> 7 qqw3-t4ie Hospitals Complicati… Complicati… 2020-12-10 2025-10-14 https://da…
#> 8 bs2r-24vh Hospitals Complicati… Complicati… 2020-12-10 2025-10-14 https://da…
#> 9 jfnd-nl7s Hospitals Complicati… Prospectiv… 2024-07-31 2025-10-14 https://da…
#> 10 z8ax-x9j1 Hospitals Complicati… Prospectiv… 2024-07-31 2025-10-14 https://da…
#> # ℹ 59 more rowsThis result contains information on all datasets included under the Hospitals topic. We can then explore this list to find a dataset we want to import. For example, we can search the titles of the datasets relevant to readmissions:
readmission_data <-
hospital_data |>
dplyr::filter(
stringr::str_detect(
title,
pattern = "(?i)readmission"
)
)
readmission_data
#> # A tibble: 1 × 7
#> datasetid topic title description issued modified downloadurl
#> <chr> <chr> <chr> <chr> <date> <date> <chr>
#> 1 9n3s-kdb3 Hospitals Hospital Re… In October… 2020-12-10 2025-01-08 https://da…Once we find the dataset we want, we can take note of the datasetid, and use the pdc_read() function to import it:
hrrp_data <- pdc_read(readmission_data$datasetid)
#> Rows: 18510 Columns: 12
#> ── Column specification ────────────────────────────────────────────────────────
#> Delimiter: ","
#> chr (11): Facility Name, Facility ID, State, Measure Name, Number of Dischar...
#> dbl (1): Footnote
#>
#> ℹ Use `spec()` to retrieve the full column specification for this data.
#> ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
hrrp_data
#> # A tibble: 18,510 × 12
#> `Facility Name` `Facility ID` State `Measure Name` `Number of Discharges`
#> <chr> <chr> <chr> <chr> <chr>
#> 1 SOUTHEAST HEALTH M… 010001 AL READM-30-AMI-… 296
#> 2 SOUTHEAST HEALTH M… 010001 AL READM-30-CABG… 151
#> 3 SOUTHEAST HEALTH M… 010001 AL READM-30-HF-H… 681
#> 4 SOUTHEAST HEALTH M… 010001 AL READM-30-HIP-… N/A
#> 5 SOUTHEAST HEALTH M… 010001 AL READM-30-PN-H… 490
#> 6 SOUTHEAST HEALTH M… 010001 AL READM-30-COPD… 130
#> 7 MARSHALL MEDICAL C… 010005 AL READM-30-CABG… N/A
#> 8 MARSHALL MEDICAL C… 010005 AL READM-30-HIP-… N/A
#> 9 MARSHALL MEDICAL C… 010005 AL READM-30-HF-H… 176
#> 10 MARSHALL MEDICAL C… 010005 AL READM-30-PN-H… 305
#> # ℹ 18,500 more rows
#> # ℹ 7 more variables: Footnote <dbl>, `Excess Readmission Ratio` <chr>,
#> # `Predicted Readmission Rate` <chr>, `Expected Readmission Rate` <chr>,
#> # `Number of Readmissions` <chr>, `Start Date` <chr>, `End Date` <chr>And then we can use this dataset for further analysis. For example:
“How many hospitals in this dataset are located in Wisconsin?”
hrrp_data |>
dplyr::filter(State == "WI") |>
with(data = _, dplyr::n_distinct(`Facility ID`))
#> [1] 65