Quality Payment Program — quality • provider

The Quality Payment Program (QPP) Experience dataset provides participation and performance information in the Merit-based Incentive Payment System (MIPS) during each performance year. They cover eligibility and participation, performance categories, and final score and payment adjustments.

Usage

quality(
  year = NULL,
  npi = NULL,
  state = NULL,
  size = NULL,
  specialty = NULL,
  years = NULL,
  patients = NULL,
  services = NULL,
  charges = NULL,
  final_score = NULL,
  adjustment = NULL,
  count = FALSE
)

metrics(year = NULL)

Arguments

year

<int> A vector of years; for quality(), 2013-2024; for metrics() 2018-2025

npi

<int> National Provider Identifier. Multiple rows for the same NPI indicate that an individual clinician has reassigned billing rights to multiple TINs and was identified as a MIPS eligible clinician under multiple TIN/NPI combinations.

state

<chr> The practice state of the TIN associated with the clinician.

size

<int> Number of clinicians associated with the TIN through Medicare Part B claims for the performance year.

specialty

<chr> Derived from the specialty codes in Medicare Part B claims.

years

<int> Number of years since NPI's first approved enrollment date across all enrollments in PECOS.

patients

<int> Number of Medicare patients who received covered professional services during MIPS eligibility determination period.

services

<int> Number of covered professional services provided to Medicare Part B patients with a service date during MIPS eligibility determination period.

charges

<int> Allowed charges under the PFS on Medicare Part B claims with a service date during MIPS eligibility determination period.

final_score

<int> The MIPS final score attributed to the clinician (identified by TIN/NPI combination).

adjustment

<dbl> Determined by comparing the final_score to performance thresholds and scaling to ensure budget neutrality.

The Maximum negative adjustment is -9%. (final_score = 0 - 18.75)
A negative adjustment is between -9% and0%. (final_score = 18.76 - 74.99)
A neutral adjustment is 0%. (final_score = 75)
A positive adjustment is greater than 0%. (final_score = 75.01 - 100)

count

<lgl> Return the total row count

Value

A tibble containing the search results.

Details

The dataset provides additional details at the TIN/NPI level on what was published in the previous performance year. You can sort the data by variables like clinician type, practice size, scores, and payment adjustments.

Examples

quality(year = c(2021, 2024), state = "GA", count = TRUE)
#> ✔ quality returned 41,788 results

quality(npi = c(1003026055, 1316939655))
#> ✔ quality returned 10 results
#> ✔ Retrieving 6 pages
#> # A tibble: 10 × 25
#>     year        npi state  size specialty        years patients services charges
#>    <int>      <int> <chr> <int> <chr>            <int>    <int>    <int>   <int>
#>  1  2017 1003026055 FL      189 Endocrinology        8    13189       NA  5.84e6
#>  2  2017 1003026055 NC      191 Endocrinology        8    14784       NA  8.90e6
#>  3  2018 1003026055 FL      135 Endocrinology        8    12317        0  5.02e6
#>  4  2019 1003026055 FL      150 Endocrinology        9    12415    52009  5.62e6
#>  5  2020 1003026055 FL      151 Endocrinology       10    12917    53599  5.46e6
#>  6  2020 1003026055 FL        7 Endocrinology       10     1244     8160  7.19e5
#>  7  2021 1003026055 FL        9 Endocrinology       11     1181     7068  6.98e5
#>  8  2020 1316939655 NY      295 Missing             16    22242   101308  9.12e6
#>  9  2021 1316939655 NY      455 Physician Assis…    17    23586   116187  1.09e7
#> 10  2022 1316939655 NY      352 Physician Assis…    18    23244   110514  1.05e7
#> # ℹ 16 more variables: final_score <dbl>, adjustment <dbl>, pi_score <int>,
#> #   qa_score <dbl>, complex_bonus <dbl>, participation <chr>, qi_score <dbl>,
#> #   ia_score <int>, cost_score <dbl>, indicators <chr>, cred <chr>,
#> #   dual_ratio <dbl>, small_bonus <int>, reporting <chr>, mvp <chr>,
#> #   ci_score <dbl>

metrics()
#> # A tibble: 32 × 4
#>     year category   metric               mean
#>    <int> <chr>      <chr>               <dbl>
#>  1  2018 Group      Dual Eligible Ratio 0.230
#>  2  2019 Group      Dual Eligible Ratio 0.216
#>  3  2020 Group      Dual Eligible Ratio 0.210
#>  4  2021 Group      Dual Eligible Ratio 0.208
#>  5  2022 Group      Dual Eligible Ratio 0.211
#>  6  2023 Group      Dual Eligible Ratio 0.206
#>  7  2024 Group      Dual Eligible Ratio 0.202
#>  8  2025 Group      Dual Eligible Ratio 0.202
#>  9  2018 Individual Dual Eligible Ratio 0.288
#> 10  2019 Individual Dual Eligible Ratio 0.269
#> # ℹ 22 more rows