Access information on prescription drugs provided to Medicare beneficiaries enrolled in Part D (Prescription Drug Coverage), by physicians and other health care providers; aggregated by provider, drug and geography.
The Medicare Part D Prescribers Datasets contain information on prescription drug events (PDEs) incurred by Medicare beneficiaries with a Part D prescription drug plan. The Part D Prescribers Datasets are organized by National Provider Identifier (NPI) and drug name and contains information on drug utilization (claim counts and day supply) and total drug costs.
Usage
prescribers(
year,
type,
npi = NULL,
first = NULL,
last = NULL,
organization = NULL,
credential = NULL,
gender = NULL,
entype = NULL,
city = NULL,
state = NULL,
zip = NULL,
fips = NULL,
ruca = NULL,
country = NULL,
specialty = NULL,
brand_name = NULL,
generic_name = NULL,
level = NULL,
opioid = NULL,
opioidLA = NULL,
antibiotic = NULL,
antipsychotic = NULL,
tidy = TRUE,
nest = TRUE,
na.rm = TRUE,
...
)
prescribers_(year = rx_years(), ...)
Arguments
- year
< integer > // required Year data was reported, in
YYYY
format. Runrx_years()
to return a vector of the years currently available.- type
<chr>
// required dataset to query,"Provider"
,"Drug"
,"Geography"
- npi
<int>
10-digit national provider identifier- first, last, organization
<chr>
Individual/Organizational prescriber's name- credential
<chr>
Individual prescriber's credentials- gender
<chr>
Individual prescriber's gender;"F"
(Female),"M"
(Male)- entype
<chr>
Prescriber entity type;"I"
(Individual),"O"
(Organization)- city
<chr>
City where prescriber is located- state
<chr>
State where prescriber is located- zip
<chr>
Prescriber’s zip code- fips
<chr>
Prescriber's state's FIPS code- ruca
<chr>
Prescriber’s RUCA code- country
<chr>
Country where prescriber is located- specialty
<chr>
Prescriber specialty code reported on the largest number of claims submitted- brand_name
<chr>
Brand name (trademarked name) of the drug filled, derived by linking the National Drug Codes (NDCs) from PDEs to a drug information database.- generic_name
<chr>
USAN generic name of the drug filled (short version); A term referring to the chemical ingredient of a drug rather than the trademarked brand name under which the drug is sold, derived by linking the National Drug Codes (NDCs) from PDEs to a drug information database.- level
<chr>
Geographic level by which the data will be aggregated:"State"
: Data is aggregated for each state"National"
: Data is aggregated across all states for a given HCPCS Code
- opioid
<lgl>
type = 'Geography',TRUE
returns Opioid drugs- opioidLA
<lgl>
type = 'Geography',TRUE
returns Long-acting Opioids- antibiotic
<lgl>
type = 'Geography',TRUE
returns antibiotics- antipsychotic
<lgl>
type = 'Geography',TRUE
returns antipsychotics- tidy
<lgl>
// default:TRUE
Tidy output- nest
<lgl>
// default:TRUE
Nest output- na.rm
<lgl>
// default:TRUE
Remove empty rows and columns- ...
Pass arguments to
prescribers()
.
By Provider
type ="Provider"
:
The Medicare Part D Prescribers by Provider dataset summarizes for each prescriber the total number of prescriptions that were dispensed, which include original prescriptions and any refills, and the total drug cost.
By Provider and Drug
type ="Drug"
:
The Medicare Part D Prescribers by Provider and Drug dataset contains the total number of prescription fills that were dispensed and the total drug cost paid organized by prescribing National Provider Identifier (NPI), drug brand name (if applicable) and drug generic name.
By Geography and Drug
type ="Geography"
:
For each drug, the Geography and Drug dataset includes the total number of prescriptions that were dispensed, which include original prescriptions and any refills, and the total drug cost.
The total drug cost includes the ingredient cost of the medication, dispensing fees, sales tax, and any applicable administration fees and is based on the amount paid by the Part D plan, Medicare beneficiary, government subsidies, and any other third-party payers.
Examples
if (FALSE) { # interactive()
prescribers(year = 2020,
type = 'Provider',
npi = 1003000423)
prescribers(year = 2019,
type = 'Drug',
npi = 1003000126)
prescribers(year = 2021,
type = 'Geography',
brand_name = 'Clotrimazole-Betamethasone')
prescribers(year = 2017,
type = 'Geography',
level = 'National',
brand_name = 'Paroxetine Hcl')
prescribers(year = 2017,
type = 'Geography',
opioid = TRUE)
# Use the years helper function to
# retrieve results for every year:
rx_years() |>
map(\(x) prescribers(year = x,
type = 'Provider',
npi = 1043477615)) |>
list_rbind()
# Parallelized version
prescribers_(type = 'Provider',
npi = 1043477615)
prescribers_(type = 'Drug',
npi = 1003000423)
prescribers_(type = 'Geography',
level = 'National',
generic_name = 'Mirabegron')
}