diff --git a/R/prescribers.R b/R/prescribers.R
index eda7343..b93b004 100644
--- a/R/prescribers.R
+++ b/R/prescribers.R
@@ -93,47 +93,74 @@
 #' @name prescribers
 NULL
 
-#' @param year < *integer* > // **required** Year data was reported, in `YYYY`
-#' format. Run [rx_years()] to return a vector of the years currently available.
-#' @param type < *character* > // **required** dataset to query, `"Provider"`,
-#' `"Drug"`, `"Geography"`
-#' @param npi < *integer* > 10-digit national provider identifier
-#' @param first,last,organization < *character* > Individual/Organizational
-#' prescriber's name
-#' @param credential < *character* > Individual prescriber's credentials
-#' @param gender < *character* > Individual prescriber's gender; `"F"` (Female),
-#' `"M"` (Male)
-#' @param entype < *character* > Prescriber entity type; `"I"` (Individual),
-#' `"O"` (Organization)
-#' @param city < *character* > City where prescriber is located
-#' @param state < *character* > State where prescriber is located
-#' @param fips < *character* > Prescriber's state's FIPS code
-#' @param zip < *character* > Prescriber’s zip code
-#' @param ruca < *character* > Prescriber’s RUCA code
-#' @param country < *character* > Country where prescriber is located
-#' @param specialty < *character* > Prescriber specialty code reported on the
-#' largest number of claims submitted
-#' @param brand_name < *character* > Brand name (trademarked name) of the drug
+#' @param year `<int>` // **required** Year data was reported, in `YYYY` format.
+#'   Run [rx_years()] to return a vector of the years currently available.
+#'
+#' @param type `<chr>` // **required** dataset to query, `"Provider"`, `"Drug"`,
+#'   `"Geography"`
+#'
+#' @param npi `<int>` 10-digit national provider identifier
+#'
+#' @param first,last,organization `<chr>` Individual/Organizational prescriber's
+#'   name
+#'
+#' @param credential `<chr>` Individual prescriber's credentials
+#'
+#' @param gender `<chr>` Individual prescriber's gender; `"F"` (Female), `"M"`
+#'   (Male)
+#'
+#' @param entype `<chr>` Prescriber entity type; `"I"` (Individual), `"O"`
+#'   (Organization)
+#'
+#' @param city `<chr>` City where prescriber is located
+#'
+#' @param state `<chr>` State where prescriber is located
+#'
+#' @param fips `<chr>` Prescriber's state's FIPS code
+#'
+#' @param zip `<chr>` Prescriber’s zip code
+#'
+#' @param ruca `<chr>` Prescriber’s RUCA code
+#'
+#' @param country `<chr>` Country where prescriber is located
+#'
+#' @param specialty `<chr>` Prescriber specialty code reported on the largest
+#'   number of claims submitted
+#'
+#' @param 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.
-#' @param generic_name < *character* > USAN generic name of the drug filled (short
+#'
+#' @param 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.
-#' @param level < *character* > 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
-#' @param opioid < *boolean* > _type = 'Geography'_, `TRUE` returns Opioid drugs
-#' @param opioidLA < *boolean* > _type = 'Geography'_, `TRUE` returns Long-acting Opioids
-#' @param antibiotic < *boolean* > _type = 'Geography'_, `TRUE` returns antibiotics
-#' @param antipsychotic < *boolean* > _type = 'Geography'_, `TRUE` returns antipsychotics
-#' @param tidy < *boolean* > // __default:__ `TRUE` Tidy output
-#' @param nest < *boolean* > // __default:__ `TRUE` Nest output
-#' @param na.rm < *boolean* > // __default:__ `TRUE` Remove empty rows and columns
-#' @param ... For future use.
+#'
+#' @param 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
+#'
+#' @param opioid `<lgl>` _type = 'Geography'_, `TRUE` returns Opioid drugs
+#'
+#' @param opioidLA `<lgl>` _type = 'Geography'_, `TRUE` returns Long-acting Opioids
+#'
+#' @param antibiotic `<lgl>` _type = 'Geography'_, `TRUE` returns antibiotics
+#'
+#' @param antipsychotic `<lgl>` _type = 'Geography'_, `TRUE` returns antipsychotics
+#'
+#' @param tidy `<lgl>` // __default:__ `TRUE` Tidy output
+#'
+#' @param nest `<lgl>` // __default:__ `TRUE` Nest output
+#'
+#' @param na.rm `<lgl>` // __default:__ `TRUE` Remove empty rows and columns
+#'
+#' @param ... Empty dots.
+#'
 #' @rdname prescribers
+#'
 #' @autoglobal
+#'
 #' @export
 prescribers <- function(year,
                         type,
@@ -165,7 +192,7 @@ prescribers <- function(year,
 
   rlang::check_required(year)
   year <- as.character(year)
-  year <- rlang::arg_match(year, as.character(rx_years()))
+  year <- rlang::arg_match0(year, as.character(rx_years()))
 
   npi   <- npi %nn% validate_npi(npi)
   zip   <- zip %nn% as.character(zip)
@@ -173,10 +200,10 @@ prescribers <- function(year,
   ruca  <- ruca %nn% as.character(ruca)
 
   rlang::check_required(type)
-  type <- rlang::arg_match(type, c('Provider', 'Drug', 'Geography'))
+  type <- rlang::arg_match0(type, c('Provider', 'Drug', 'Geography'))
 
   if (type == 'Provider') {
-    param_npi <- 'PRSCRBR_NPI'
+    param_npi <- 'Prscrbr_NPI'
     param_state <- 'Prscrbr_State_Abrvtn'
     param_fips <- 'Prscrbr_State_FIPS'
     brand_name <- NULL
@@ -213,7 +240,7 @@ prescribers <- function(year,
     zip <- NULL
     ruca <- NULL
     country <- NULL
-    level <- level %nn% rlang::arg_match(level, c('National', 'State'))
+    level <- level %nn% rlang::arg_match0(level, c('National', 'State'))
     if (!is.null(state) && (state %in% state.abb)) state <- abb2full(state)
     opioid <- opioid %nn% tf_2_yn(opioid)
     opioidLA <- opioidLA %nn% tf_2_yn(opioidLA)
@@ -244,17 +271,25 @@ prescribers <- function(year,
     'Antbtc_Drug_Flag',      antibiotic,
     'Antpsyct_Drug_Flag',    antipsychotic)
 
-  yr <- switch(type,
-               'Provider'  = api_years('rxp'),
-               'Drug'      = api_years('rxd'),
-               'Geography' = api_years('rxg'))
+  yr <- switch(
+    type,
+    'Provider'  = api_years('rxp'),
+    'Drug'      = api_years('rxd'),
+    'Geography' = api_years('rxg'))
 
-  id <- dplyr::filter(yr, year == {{ year }}) |> dplyr::pull(distro)
+  id <- dplyr::filter(
+    yr,
+    year == {{ year }}) |>
+    dplyr::pull(distro)
 
-  url <- paste0("https://data.cms.gov/data-api/v1/dataset/",
-                id, "/data.json?", encode_param(args))
+  url <- paste0(
+    "https://data.cms.gov/data-api/v1/dataset/",
+    id,
+    "/data.json?",
+    encode_param(args))
 
-  response <- httr2::request(url) |> httr2::req_perform()
+  response <- httr2::request(url) |>
+    httr2::req_perform()
 
   if (vctrs::vec_is_empty(response$body)) {
 
@@ -284,7 +319,9 @@ prescribers <- function(year,
     return(invisible(NULL))
   }
 
-  results <- httr2::resp_body_json(response, simplifyVector = TRUE)
+  results <- httr2::resp_body_json(
+    response,
+    simplifyVector = TRUE)
 
   if (!tidy) results <- df2chr(results)
 
@@ -292,13 +329,13 @@ prescribers <- function(year,
 
     results$year <- year
 
-    results <- switch(type,
-                      'Provider'  = tidyup_provider.rx(results, nest = nest),
-                      'Drug'      = tidyup_drug.rx(results, nest = nest),
-                      'Geography' = tidyup_geography.rx(results))
+    results <- switch(
+      type,
+      'Provider'  = tidyup_provider.rx(results, nest = nest),
+      'Drug'      = tidyup_drug.rx(results, nest = nest),
+      'Geography' = tidyup_geography.rx(results))
 
     if (na.rm) results <- narm(results)
-
   }
   return(results)
 }
@@ -323,10 +360,12 @@ tidyup_geography.rx <- function(results) {
     dplyr::mutate(state = fct_stname(state),
                   level = fct_level(level))
 
-  results <- dplyr::mutate(results,
-                           dplyr::across(
-                             dplyr::contains('suppress_'),
-                             suppress_flag))
+  results <- dplyr::mutate(
+    results,
+    dplyr::across(
+      dplyr::contains('suppress_'),
+      suppress_flag)
+    )
 
   return(results)
 }
@@ -344,25 +383,32 @@ tidyup_drug.rx <- function(results, nest = TRUE) {
            dbl  = c('tot_fills',
                     'tot_cost')) |>
     dplyr::mutate(level = 'Provider',
-                  source = fct_src(source), # nolint
+                  # source = fct_src(source), # nolint
                   state = fct_stabb(state),
                   level = fct_level(level))
 
-  results <- dplyr::mutate(results,
-                           dplyr::across(
-                             dplyr::contains('suppress_'),
-                             suppress_flag))
+  results <- dplyr::mutate(
+    results,
+    dplyr::across(
+      dplyr::contains('suppress_'),
+      suppress_flag)
+  )
 
   if (nest) {
     results <- results |>
-      tidyr::nest(gte_65 = dplyr::any_of(c(
-        'tot_claims_ge65',
-        'tot_fills_ge65',
-        'tot_cost_ge65',
-        'tot_supply_ge65',
-        'tot_benes_ge65',
-        'suppress_ge65',
-        'suppress_bene_ge65')))
+      tidyr::nest(
+        gte_65 = dplyr::any_of(
+          c(
+            'tot_claims_ge65',
+            'tot_fills_ge65',
+            'tot_cost_ge65',
+            'tot_supply_ge65',
+            'tot_benes_ge65',
+            'suppress_ge65',
+            'suppress_bene_ge65'
+            )
+          )
+        )
   }
   return(results)
 }
@@ -399,21 +445,25 @@ tidyup_provider.rx <- function(results, nest = TRUE) {
            cred = 'credential',
            zip  = 'zip') |>
     combine(address, c('prscrbr_st1', 'prscrbr_st2')) |>
-    dplyr::mutate(source = fct_src(source),              # nolint
-                  entity_type = fct_ent(entity_type),
+    dplyr::mutate(entity_type = fct_ent(entity_type),
+                  # source = fct_src(source),              # nolint
                   gender = fct_gen(gender),
                   state = fct_stabb(state)) |>
     dplyr::mutate(bene_race_nonwht = tot_benes - bene_race_wht,
                   .after = bene_race_wht)
 
-  results <- dplyr::mutate(results,
-                           dplyr::across(
-                             dplyr::contains('suppress_'),
-                             suppress_flag))
+  results <- dplyr::mutate(
+    results,
+    dplyr::across(
+      dplyr::contains('suppress_'),
+      suppress_flag)
+  )
 
   if (nest) {
     results <- results |>
-      tidyr::nest(detailed = dplyr::any_of(c(
+      tidyr::nest(
+        detailed = dplyr::any_of(
+          c(
         'tot_claims_brand',
         'tot_cost_brand',
         'tot_claims_generic',
@@ -445,8 +495,13 @@ tidyup_provider.rx <- function(results, nest = TRUE) {
         'suppress_mapd',
         'suppress_lis',
         'suppress_nlis',
-        'suppress_pdp'))) |>
-      tidyr::nest(demographics = dplyr::any_of(c(
+        'suppress_pdp'
+        )
+      )
+    ) |>
+      tidyr::nest(
+        demographics = dplyr::any_of(
+          c(
         'bene_age_avg',
         'bene_age_lt65',
         'bene_age_65_74',
@@ -460,8 +515,13 @@ tidyup_provider.rx <- function(results, nest = TRUE) {
         'bene_race_nat',
         'bene_race_oth',
         'bene_dual',
-        'bene_ndual'))) |>
-      tidyr::nest(gte_65 = dplyr::any_of(c(
+        'bene_ndual'
+        )
+      )
+    ) |>
+      tidyr::nest(
+        gte_65 = dplyr::any_of(
+          c(
         'tot_claims_ge65',
         'tot_fills_ge65',
         'tot_cost_ge65',
@@ -469,7 +529,10 @@ tidyup_provider.rx <- function(results, nest = TRUE) {
         'tot_benes_ge65',
         'tot_claims_antipsych_ge65',
         'tot_cost_antipsych_ge65',
-        'tot_benes_antipsych_ge65')))
+        'tot_benes_antipsych_ge65'
+        )
+      )
+    )
   }
   return(results)
 }
@@ -483,9 +546,11 @@ tidyup_provider.rx <- function(results, nest = TRUE) {
 #' @export
 prescribers_ <- function(year = rx_years(),
                          ...) {
-  furrr::future_map_dfr(year, prescribers, ...,
-                        .options = furrr::furrr_options(seed = NULL))
-
+  furrr::future_map_dfr(
+    year,
+    prescribers,
+    ...,
+    .options = furrr::furrr_options(seed = NULL))
 }
 
 #' Convert specialty source to unordered labelled factor
@@ -493,10 +558,11 @@ prescribers_ <- function(year = rx_years(),
 #' @autoglobal
 #' @noRd
 fct_src <- function(x) {
-  factor(x,
-         levels = c("S", "T"),
-         labels = c("Medicare Specialty Code",
-                    "Taxonomy Code Classification"))
+  factor(
+    x,
+    levels = c("S", "T"),
+    labels = c("Medicare Specialty Code",
+               "Taxonomy Code Classification"))
 }
 
 #' @param df data frame
@@ -507,7 +573,7 @@ cols_rx <- function(df, type) {
 
   if (type == 'Provider') {
     cols <- c('year',
-              'npi' = 'PRSCRBR_NPI',
+              'npi' = 'Prscrbr_NPI',
               'entity_type' = 'Prscrbr_Ent_Cd',
               'first' = 'Prscrbr_First_Name',
               'middle' = 'Prscrbr_MI',
@@ -515,7 +581,7 @@ cols_rx <- function(df, type) {
               'gender' = 'Prscrbr_Gndr',
               'credential' = 'Prscrbr_Crdntls',
               'specialty' = 'Prscrbr_Type',
-              'source' = 'Prscrbr_Type_src',
+              'source' = 'Prscrbr_Type_Src',
               'Prscrbr_St1',
               'Prscrbr_St2',
               'city' = 'Prscrbr_City',
diff --git a/README.Rmd b/README.Rmd
index b435860..b7b6a90 100644
--- a/README.Rmd
+++ b/README.Rmd
@@ -141,9 +141,9 @@ outpatient(year  = 2021,
 ### `prescribers()`
 
 ```{r}
-prescribers(year = 2021, 
+prescribers(year = 2019, 
             type = 'Provider', 
-            npi = 1003000423) |> 
+            npi = 1003000126) |> 
   glimpse()
 ```
 
diff --git a/README.md b/README.md
index 1d0d3dc..bc35ce9 100644
--- a/README.md
+++ b/README.md
@@ -357,13 +357,41 @@ outpatient(year  = 2021,
 ### `prescribers()`
 
 ``` r
-prescribers(year = 2021, 
+prescribers(year = 2019, 
             type = 'Provider', 
-            npi = 1003000423) |> 
+            npi = 1003000126) |> 
   glimpse()
 ```
 
-    #> Error in readBin(body, character()): R character strings are limited to 2^31-1 bytes
+    #> Rows: 1
+    #> Columns: 27
+    #> $ year           <int> 2019
+    #> $ npi            <chr> "1003000126"
+    #> $ entity_type    <fct> Individual
+    #> $ first          <chr> "Ardalan"
+    #> $ last           <chr> "Enkeshafi"
+    #> $ gender         <fct> Male
+    #> $ credential     <chr> "MD"
+    #> $ specialty      <chr> "Internal Medicine"
+    #> $ source         <chr> "Claim-Specialty"
+    #> $ address        <chr> "900 Seton Dr"
+    #> $ city           <chr> "Cumberland"
+    #> $ state          <ord> MD
+    #> $ zip            <chr> "21502"
+    #> $ fips           <chr> "24"
+    #> $ ruca           <chr> "1"
+    #> $ country        <chr> "US"
+    #> $ tot_claims     <int> 589
+    #> $ tot_fills      <dbl> 681.7333
+    #> $ tot_cost       <dbl> 28902.12
+    #> $ tot_supply     <int> 15955
+    #> $ tot_benes      <int> 214
+    #> $ rx_rate_opioid <dbl> 5.093379
+    #> $ bene_race_blk  <int> 73
+    #> $ hcc_risk_avg   <dbl> 2.708114
+    #> $ detailed       <list> [<tbl_df[1 x 32]>]
+    #> $ demographics   <list> [<tbl_df[1 x 14]>]
+    #> $ gte_65         <list> [<tbl_df[1 x 8]>]
 
 ``` r
 prescribers(year = 2019, 
@@ -374,7 +402,7 @@ prescribers(year = 2019,
 ```
 
     #> Rows: 1
-    #> Columns: 17
+    #> Columns: 18
     #> $ year         <int> 2019
     #> $ npi          <chr> "1003000126"
     #> $ last         <chr> "Enkeshafi"
@@ -383,6 +411,7 @@ prescribers(year = 2019,
     #> $ state        <ord> MD
     #> $ fips         <chr> "24"
     #> $ specialty    <chr> "Internal Medicine"
+    #> $ source       <chr> "Claim-Specialty"
     #> $ brand_name   <chr> "Atorvastatin Calcium"
     #> $ generic_name <chr> "Atorvastatin Calcium"
     #> $ tot_claims   <int> 41
diff --git a/man/prescribers.Rd b/man/prescribers.Rd
index 2c28b1a..afb3199 100644
--- a/man/prescribers.Rd
+++ b/man/prescribers.Rd
@@ -41,66 +41,65 @@ prescribers_(year = rx_years(), ...)
 \item{year}{< \emph{integer} > // \strong{required} Year data was reported, in \code{YYYY}
 format. Run \code{\link[=rx_years]{rx_years()}} to return a vector of the years currently available.}
 
-\item{type}{< \emph{character} > // \strong{required} dataset to query, \code{"Provider"},
-\code{"Drug"}, \code{"Geography"}}
+\item{type}{\verb{<chr>} // \strong{required} dataset to query, \code{"Provider"}, \code{"Drug"},
+\code{"Geography"}}
 
-\item{npi}{< \emph{integer} > 10-digit national provider identifier}
+\item{npi}{\verb{<int>} 10-digit national provider identifier}
 
-\item{first, last, organization}{< \emph{character} > Individual/Organizational
-prescriber's name}
+\item{first, last, organization}{\verb{<chr>} Individual/Organizational prescriber's
+name}
 
-\item{credential}{< \emph{character} > Individual prescriber's credentials}
+\item{credential}{\verb{<chr>} Individual prescriber's credentials}
 
-\item{gender}{< \emph{character} > Individual prescriber's gender; \code{"F"} (Female),
-\code{"M"} (Male)}
+\item{gender}{\verb{<chr>} Individual prescriber's gender; \code{"F"} (Female), \code{"M"}
+(Male)}
 
-\item{entype}{< \emph{character} > Prescriber entity type; \code{"I"} (Individual),
-\code{"O"} (Organization)}
+\item{entype}{\verb{<chr>} Prescriber entity type; \code{"I"} (Individual), \code{"O"}
+(Organization)}
 
-\item{city}{< \emph{character} > City where prescriber is located}
+\item{city}{\verb{<chr>} City where prescriber is located}
 
-\item{state}{< \emph{character} > State where prescriber is located}
+\item{state}{\verb{<chr>} State where prescriber is located}
 
-\item{zip}{< \emph{character} > Prescriber’s zip code}
+\item{zip}{\verb{<chr>} Prescriber’s zip code}
 
-\item{fips}{< \emph{character} > Prescriber's state's FIPS code}
+\item{fips}{\verb{<chr>} Prescriber's state's FIPS code}
 
-\item{ruca}{< \emph{character} > Prescriber’s RUCA code}
+\item{ruca}{\verb{<chr>} Prescriber’s RUCA code}
 
-\item{country}{< \emph{character} > Country where prescriber is located}
+\item{country}{\verb{<chr>} Country where prescriber is located}
 
-\item{specialty}{< \emph{character} > Prescriber specialty code reported on the
-largest number of claims submitted}
+\item{specialty}{\verb{<chr>} Prescriber specialty code reported on the largest
+number of claims submitted}
 
-\item{brand_name}{< \emph{character} > Brand name (trademarked name) of the drug
+\item{brand_name}{\verb{<chr>} Brand name (trademarked name) of the drug
 filled, derived by linking the National Drug Codes (NDCs) from PDEs to a
 drug information database.}
 
-\item{generic_name}{< \emph{character} > USAN generic name of the drug filled (short
+\item{generic_name}{\verb{<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.}
 
-\item{level}{< \emph{character} > Geographic level by which the data will be
-aggregated:
+\item{level}{\verb{<chr>} Geographic level by which the data will be aggregated:
 \itemize{
 \item \code{"State"}: Data is aggregated for each state
 \item \code{"National"}: Data is aggregated across all states for a given HCPCS Code
 }}
 
-\item{opioid}{< \emph{boolean} > \emph{type = 'Geography'}, \code{TRUE} returns Opioid drugs}
+\item{opioid}{\verb{<lgl>} \emph{type = 'Geography'}, \code{TRUE} returns Opioid drugs}
 
-\item{opioidLA}{< \emph{boolean} > \emph{type = 'Geography'}, \code{TRUE} returns Long-acting Opioids}
+\item{opioidLA}{\verb{<lgl>} \emph{type = 'Geography'}, \code{TRUE} returns Long-acting Opioids}
 
-\item{antibiotic}{< \emph{boolean} > \emph{type = 'Geography'}, \code{TRUE} returns antibiotics}
+\item{antibiotic}{\verb{<lgl>} \emph{type = 'Geography'}, \code{TRUE} returns antibiotics}
 
-\item{antipsychotic}{< \emph{boolean} > \emph{type = 'Geography'}, \code{TRUE} returns antipsychotics}
+\item{antipsychotic}{\verb{<lgl>} \emph{type = 'Geography'}, \code{TRUE} returns antipsychotics}
 
-\item{tidy}{< \emph{boolean} > // \strong{default:} \code{TRUE} Tidy output}
+\item{tidy}{\verb{<lgl>} // \strong{default:} \code{TRUE} Tidy output}
 
-\item{nest}{< \emph{boolean} > // \strong{default:} \code{TRUE} Nest output}
+\item{nest}{\verb{<lgl>} // \strong{default:} \code{TRUE} Nest output}
 
-\item{na.rm}{< \emph{boolean} > // \strong{default:} \code{TRUE} Remove empty rows and columns}
+\item{na.rm}{\verb{<lgl>} // \strong{default:} \code{TRUE} Remove empty rows and columns}
 
 \item{...}{Pass arguments to \code{\link[=prescribers]{prescribers()}}.}
 }
diff --git a/pkgdown/_pkgdown.yml b/pkgdown/_pkgdown.yml
index 74181a9..24306e9 100644
--- a/pkgdown/_pkgdown.yml
+++ b/pkgdown/_pkgdown.yml
@@ -4,7 +4,7 @@ template:
   bootstrap: 5
   bootswatch: simplex
   bslib:
-    font_scale: 1.1
+    font_scale: 1
     base_font: {google: "IBM Plex Sans"}
     heading_font: {google: "Kanit"}
     code_font: {google: "Fira Code"}
@@ -107,7 +107,6 @@ reference:
     Access a provider's statistical data.
   contents:
   - beneficiaries
-  - conditions
   - open_payments
   - open_payments_
   - quality_eligibility