diff --git a/R/add_activity_after_death_flag.R b/R/add_activity_after_death_flag.R
index fdede9001..a45e4296a 100644
--- a/R/add_activity_after_death_flag.R
+++ b/R/add_activity_after_death_flag.R
@@ -125,6 +125,7 @@ add_activity_after_death_flag <- function(
 #'
 #'
 # Read data------------------------------------------------
+
 process_combined_deaths_lookup <- function(update = latest_update(),
                                            write_to_disk = TRUE, ...) {
   dir_folder <- "/conf/hscdiip/SLF_Extracts/Deaths"
diff --git a/R/fill_ch_names.R b/R/fill_ch_names.R
index cd8d18677..b6aa85bb7 100644
--- a/R/fill_ch_names.R
+++ b/R/fill_ch_names.R
@@ -213,7 +213,7 @@ fill_ch_names <- function(ch_data,
       "match_mean2",
       # "open_interval",
       "ch_admission_date",
-      "qtr_start",
+      "period_start_date",
       "ch_date_registered",
       "latest_close_date",
       "ch_active",
@@ -305,7 +305,6 @@ fill_ch_names <- function(ch_data,
     "unique_identifier",
     "matching_quality_indicator",
     "sending_location",
-    "latest_sc_id",
     "chi",
     "ch_name",
     "ch_postcode",
@@ -320,9 +319,6 @@ fill_ch_names <- function(ch_data,
     "ch_admission_date",
     "ch_discharge_date",
     "age",
-    "record_date",
-    "qtr_start",
-    "latest_flag",
     "gender",
     "dob",
     "postcode",
@@ -763,7 +759,6 @@ fill_ch_names <- function(ch_data,
   ## produce output ----
   col_output <- c(
     "sending_location",
-    "latest_sc_id",
     "chi",
     "ch_name",
     "ch_postcode",
@@ -778,9 +773,6 @@ fill_ch_names <- function(ch_data,
     "ch_admission_date",
     "ch_discharge_date",
     "age",
-    "record_date",
-    "qtr_start",
-    "latest_flag",
     "gender",
     "dob",
     "postcode",
diff --git a/R/process_extract_care_home.R b/R/process_extract_care_home.R
index 54789eac9..db7997061 100644
--- a/R/process_extract_care_home.R
+++ b/R/process_extract_care_home.R
@@ -39,11 +39,11 @@ process_extract_care_home <- function(
       is_date_in_fyyear(year, .data$record_keydate1, .data$record_keydate2)
     ) %>%
     # remove any episodes where the latest submission was before the current year
+    # this is what stops cases being in future files
     dplyr::filter(
       substr(.data$sc_latest_submission, 1L, 4L) >= convert_fyyear_to_year(year)
     )
 
-
   # Data Cleaning ---------------------------------------
   source_ch_clean <- ch_data %>%
     # create variables
diff --git a/R/process_lookup_sc_demographics.R b/R/process_lookup_sc_demographics.R
index d6e24c87f..1b29c414c 100644
--- a/R/process_lookup_sc_demographics.R
+++ b/R/process_lookup_sc_demographics.R
@@ -143,8 +143,8 @@ process_lookup_sc_demographics <- function(
     dplyr::ungroup()
 
   # check to make sure all cases of chi are still there
-  dplyr::n_distinct(sc_demog_lookup$chi) # 524810
-  dplyr::n_distinct(sc_demog_lookup$social_care_id) # 636404
+  dplyr::n_distinct(sc_demog_lookup$chi) # 525,834
+  dplyr::n_distinct(sc_demog_lookup$social_care_id) # 637,422
 
   sc_demog_lookup <- sc_demog_lookup %>%
     slfhelper::get_anon_chi()
diff --git a/R/process_sc_all_care_home.R b/R/process_sc_all_care_home.R
index 7b87d68f0..8492268f7 100644
--- a/R/process_sc_all_care_home.R
+++ b/R/process_sc_all_care_home.R
@@ -19,38 +19,38 @@
 #' @family process extracts
 #'
 #' @export
-#'
 process_sc_all_care_home <- function(
     data,
     sc_demog_lookup = read_file(get_sc_demog_lookup_path()) %>% slfhelper::get_chi(),
     refined_death = read_file(get_combined_slf_deaths_lookup_path()) %>% slfhelper::get_chi(),
-    ch_name_lookup_path = read_file(get_slf_ch_name_lookup_path()),
-    spd_path = read_file(get_spd_path()),
+    ch_name_lookup_path = get_slf_ch_name_lookup_path(),
+    spd_path = get_spd_path(),
     write_to_disk = TRUE) {
   ## Data Cleaning-----------------------------------------------------
+
   ch_clean <- data %>%
     dplyr::mutate(
-      record_date = end_fy_quarter(.data[["period"]]),
-      qtr_start = start_fy_quarter(.data[["period"]]),
-      # Set missing admission date to start of the submitted quarter
+      # Set missing admission date to start of the submitted quarter (n = 2)
       ch_admission_date = dplyr::if_else(
         is.na(.data[["ch_admission_date"]]),
-        .data[["qtr_start"]],
+        .data[["period_start_date"]],
         .data[["ch_admission_date"]]
       ),
-      # TODO check if we should set the dis date to the end of the period?
-      # If the dis date is before admission, remove the dis date
+      # If the dis date is before admission, remove the dis date (n = 5)
       ch_discharge_date = dplyr::if_else(
         .data[["ch_admission_date"]] > .data[["ch_discharge_date"]],
         lubridate::NA_Date_,
         .data[["ch_discharge_date"]]
       )
     ) %>%
-    dplyr::left_join(sc_demog_lookup,
+    dplyr::full_join(sc_demog_lookup, # this is the correct join.
       by = c("sending_location", "social_care_id")
     ) %>%
-    replace_sc_id_with_latest()
+    replace_sc_id_with_latest() %>%
+    dplyr::select(-latest_flag, -latest_sc_id)
+
 
+  # cleaning and matching care home names
   name_postcode_clean <- fill_ch_names(
     ch_data = ch_clean,
     ch_name_lookup_path = ch_name_lookup_path,
@@ -58,23 +58,27 @@ process_sc_all_care_home <- function(
   )
 
   fixed_ch_provider <- name_postcode_clean %>%
+    dplyr::select(-ch_name_validated, -open_interval, -latest_close_date, -ch_name_old, -ch_postcode_old) %>%
     dplyr::mutate(
-      ch_provider = dplyr::if_else(is.na(.data[["ch_provider"]]), 6L, .data[["ch_provider"]])
+      ch_provider = dplyr::if_else(is.na(.data[["ch_provider"]]), 6L, .data[["ch_provider"]]) # (n = 2)
     ) %>%
     # sort data
     dplyr::arrange(
-      "sending_location",
-      "social_care_id",
-      "ch_admission_date",
-      "period"
+      .data[["sending_location"]],
+      .data[["social_care_id"]],
+      .data[["period"]],
+      .data[["ch_admission_date"]]
     ) %>%
     dplyr::group_by(
       .data[["sending_location"]],
       .data[["social_care_id"]]
     ) %>%
+    # work out the min and max ch provider in an episode
     dplyr::mutate(
       min_ch_provider = min(.data[["ch_provider"]]),
       max_ch_provider = max(.data[["ch_provider"]]),
+      # if care home provider is different across cases, set to "6".
+      # tidy up ch_provider using 6 when disagreeing values
       ch_provider = dplyr::if_else(
         .data[["min_ch_provider"]] != .data[["max_ch_provider"]],
         6L,
@@ -85,12 +89,9 @@ process_sc_all_care_home <- function(
       -"min_ch_provider",
       -"max_ch_provider"
     ) %>%
-    # tidy up ch_provider using 6 when disagreeing values
-    tidyr::fill(.data[["ch_provider"]], .direction = "downup") %>%
     dplyr::ungroup()
 
 
-
   fixed_nursing_provision <- fixed_ch_provider %>%
     dplyr::group_by(
       .data[["sending_location"]],
@@ -98,7 +99,7 @@ process_sc_all_care_home <- function(
       .data[["ch_admission_date"]]
     ) %>%
     # fill in nursing care provision when missing
-    # but present in the following entry
+    # but present in the following entry (n = 0)
     dplyr::mutate(
       nursing_care_provision = dplyr::na_if(.data[["nursing_care_provision"]], 9L)
     ) %>%
@@ -106,26 +107,44 @@ process_sc_all_care_home <- function(
 
 
   ready_to_merge <- fixed_nursing_provision %>%
-    # remove any duplicate records before merging for speed and simplicity
-    dplyr::distinct() %>%
+    # remove any duplicate records before merging
+    dplyr::distinct() %>% # (n = 3)
+    # sort data
+    dplyr::arrange(
+      .data[["sending_location"]],
+      .data[["social_care_id"]],
+      .data[["ch_admission_date"]],
+      .data[["period"]]
+    ) %>%
+    dplyr::group_by(
+      .data[["sending_location"]],
+      .data[["social_care_id"]],
+      .data[["ch_admission_date"]]
+    ) %>%
     # counter for split episodes
-    dplyr::mutate(
-      split_episode = tidyr::replace_na(
-        .data[["nursing_care_provision"]] != dplyr::lag(
-          .data[["nursing_care_provision"]]
-        ),
-        TRUE
-      ),
-      split_episode_counter = cumsum(.data[["split_episode"]])
+    # a split episode is an episode where the admission date is the same but the nursing provider has changed.
+    # We want to keep the nursing provision changes when we merge cases that have the same admission date
+    dplyr::mutate(previous_nursing_care_provision = dplyr::lag(.data[["nursing_care_provision"]])) %>%
+    # create a T/F flag for if nursing provision was the same as previous record with same admission date
+    dplyr::mutate(split_episode = tidyr::replace_na(.data[["previous_nursing_care_provision"]] != nursing_care_provision, TRUE)) %>%
+    dplyr::group_by(
+      .data[["social_care_id"]],
+      .data[["sending_location"]],
+      .data[["split_episode"]]
     ) %>%
-    dplyr::ungroup()
+    # create a count of each time the nursing provision changes between records with the same admission date
+    dplyr::mutate(split_episode_counter = ifelse(split_episode == TRUE, dplyr::row_number(), NA)) %>%
+    dplyr::group_by(
+      .data[["social_care_id"]],
+      .data[["sending_location"]]
+    ) %>%
+    # fill split episode counter. This will create a new id number for each different nursing provision within an episode
+    tidyr::fill(split_episode_counter, .direction = c("down")) %>%
+    dplyr::select(-previous_nursing_care_provision, -split_episode)
+
 
-  # Merge records to a single row per episode
-  # where admission is the same
+  # Merge records to a single row per episode where admission is the same
   ch_episode <- ready_to_merge %>%
-    # when nursing_care_provision is different on
-    # records within the episode, split the episode
-    # at this point.
     dplyr::group_by(
       .data[["chi"]],
       .data[["sending_location"]],
@@ -138,8 +157,8 @@ process_sc_all_care_home <- function(
       dplyr::desc(.data[["period"]]),
       dplyr::desc(.data[["ch_discharge_date"]]),
       dplyr::desc(.data[["ch_provider"]]),
-      dplyr::desc(.data[["record_date"]]),
-      dplyr::desc(.data[["qtr_start"]]),
+      dplyr::desc(.data[["period_end_date"]]),
+      dplyr::desc(.data[["period_start_date"]]),
       dplyr::desc(.data[["ch_name"]]),
       dplyr::desc(.data[["ch_postcode"]]),
       dplyr::desc(.data[["reason_for_admission"]]),
@@ -150,62 +169,39 @@ process_sc_all_care_home <- function(
     ) %>%
     dplyr::summarise(
       sc_latest_submission = dplyr::first(.data[["period"]]),
-      dplyr::across(
-        c(
-          "ch_discharge_date",
-          "ch_provider",
-          "record_date",
-          "qtr_start",
-          "ch_name",
-          "ch_postcode",
-          "reason_for_admission",
-          "type_of_admission"
-        ),
-        dplyr::first
-      ),
+      dplyr::across(c(
+        "ch_discharge_date",
+        "ch_provider",
+        "period_end_date",
+        "period_start_date",
+        "ch_name",
+        "ch_postcode",
+        "reason_for_admission",
+        "type_of_admission"
+      ), dplyr::first),
       dplyr::across(c("gender", "dob", "postcode"), dplyr::first)
     ) %>%
-    dplyr::ungroup() %>%
-    # Amend dates for split episodes
-    # Change the start and end date as appropriate when an episode is split,
-    # using the start / end date of the submission quarter
-    dplyr::group_by(
-      .data[["chi"]],
-      .data[["sending_location"]],
-      .data[["social_care_id"]],
-      .data[["ch_admission_date"]]
-    ) %>%
-    # counter for latest submission
-    # TODO check if this is the same as split_episode_counter?
-    dplyr::mutate(
-      latest_submission_counter = tidyr::replace_na(
-        .data[["sc_latest_submission"]] != dplyr::lag(
-          .data[["sc_latest_submission"]]
-        ),
-        TRUE
-      ),
-      sum_latest_submission = cumsum(.data[["latest_submission_counter"]])
-    ) %>%
+    # If the admission date is missing use the period start date
+    # otherwise use the start of the quarter
     dplyr::mutate(
-      # If it's the first episode(s) then keep the admission date(s),
-      # otherwise use the start of the quarter
-      ch_admission_date = dplyr::if_else(
-        .data[["sum_latest_submission"]] == min(.data[["sum_latest_submission"]]),
-        .data[["ch_admission_date"]],
-        .data[["qtr_start"]]
+      ch_admission_date = dplyr::if_else(is.na(.data[["ch_admission_date"]]),
+        .data[["period_start_date"]],
+        .data[["ch_admission_date"]]
       ),
       # If it's the last episode(s) then keep the discharge date(s), otherwise
       # use the end of the quarter
-      ch_discharge_date = dplyr::if_else(
-        .data[["sum_latest_submission"]] == max(.data[["sum_latest_submission"]]),
-        .data[["ch_discharge_date"]],
-        .data[["record_date"]]
+      ch_discharge_date = dplyr::if_else(is.na(.data[["ch_discharge_date"]]),
+        .data[["period_end_date"]],
+        .data[["ch_discharge_date"]]
       )
     ) %>%
-    dplyr::ungroup()
+    dplyr::ungroup() %>%
+    dplyr::select(-period_start_date, -split_episode_counter)
+
 
   # Compare to Deaths Data
   # match ch_episode data with deaths data
+  # TO DO should this be boxi nrs death dates instead of IT extract deaths?
   matched_deaths_data <- ch_episode %>%
     dplyr::left_join(refined_death,
       by = "chi"
@@ -228,7 +224,7 @@ process_sc_all_care_home <- function(
     dplyr::ungroup() %>%
     # remove any episodes where discharge is now before admission,
     # i.e. death was before admission
-    dplyr::filter(
+    dplyr::filter( # (n = 67)
       !tidyr::replace_na(
         .data[["ch_discharge_date"]] < .data[["ch_admission_date"]],
         FALSE
@@ -237,39 +233,65 @@ process_sc_all_care_home <- function(
 
   # Continuous Care Home Stays
   # Stay will be continuous as long as the admission date is the next day or
-  # earlier than the previous discharge date
-
-  ch_markers <- matched_deaths_data %>%
-    # ch_chi_cis
+  # earlier than the previous discharge date.
+  # creates a CIS  flag for CHI across all of scotland
+  # and a CIS for social care ID and sending location for just that LA
+  ch_chi_markers <- matched_deaths_data %>%
+    # uses the chi to flag continuous stays. Will flag cases even if in another LA
     dplyr::group_by(.data[["chi"]]) %>%
-    dplyr::mutate(
-      continuous_stay_chi = tidyr::replace_na(
-        .data[["ch_admission_date"]] <= dplyr::lag(
-          .data[["ch_discharge_date"]]
-        ) + lubridate::days(1L),
-        TRUE
-      ),
-      ch_chi_cis = cumsum(.data[["continuous_stay_chi"]])
+    # create variable for previous discharge date + 1 day
+    dplyr::mutate(previous_discharge_date_chi = dplyr::lag(.data[["ch_discharge_date"]]) + lubridate::days(1L)) %>%
+    # TRUE/FALSE flag for if admission date is before or equal to previous discharge date + 1 day
+    dplyr::mutate(continuous_stay_flag_chi = tidyr::replace_na(.data[["ch_admission_date"]] <= previous_discharge_date_chi, FALSE)) %>%
+    # different to code in above sections.
+    # we want to uniquely identify all cases where the flag is FALSE. and only the first case where the flag is TRUE
+    # to do this create a variable of the flag in the previous row
+    dplyr::mutate(previous_continuous_stay_flag_chi = tidyr::replace_na(dplyr::lag(.data[["continuous_stay_flag_chi"]]), FALSE)) %>%
+    dplyr::mutate(continuous_stay_chi = ifelse(continuous_stay_flag_chi == FALSE |
+      (continuous_stay_flag_chi == TRUE & previous_continuous_stay_flag_chi == FALSE), FALSE, TRUE)) %>%
+    dplyr::group_by(
+      .data[["chi"]],
+      .data[["continuous_stay_chi"]]
     ) %>%
-    dplyr::ungroup() %>%
-    # ch_sc_id_cis
-    # uses the social care id and sending location so can be used for
-    # episodes that are not attached to a CHI number
-    # This will restrict continuous stays to each Local Authority
-    dplyr::group_by(.data[["social_care_id"]], .data[["sending_location"]]) %>%
-    dplyr::mutate(
-      continuous_stay_sc = tidyr::replace_na(
-        .data[["ch_admission_date"]] <= dplyr::lag(
-          .data[["ch_discharge_date"]]
-        ) + lubridate::days(1L),
-        TRUE
-      ),
-      ch_sc_id_cis = cumsum(.data[["continuous_stay_sc"]])
+    # gives cases their unique CIS identifier
+    dplyr::mutate(ch_chi_cis = ifelse(continuous_stay_chi == FALSE, dplyr::row_number(), NA)) %>%
+    dplyr::group_by(
+      .data[["social_care_id"]],
+      .data[["sending_location"]]
     ) %>%
-    dplyr::ungroup()
+    # fills in CIS identifier for all cases
+    tidyr::fill(ch_chi_cis, .direction = c("down"))
+
+
+  # This is the same but uses the social care id and sending location so can be used for
+  # episodes that are not attached to a CHI number
+  # This will restrict continuous stays to each Local Authority
+  sc_ch_id_markers <- ch_chi_markers %>%
+    dplyr::group_by(.data[["social_care_id"]], .data[["sending_location"]]) %>%
+    # create variable for previous discharge date + 1 day
+    dplyr::mutate(previous_discharge_date_sc = dplyr::lag(.data[["ch_discharge_date"]]) + lubridate::days(1L)) %>%
+    # TRUE/FALSE flag for if admission date is before or equal to previous discharge date + 1 day
+    dplyr::mutate(continuous_stay_flag_sc = tidyr::replace_na(.data[["ch_admission_date"]] <= previous_discharge_date_sc, FALSE)) %>%
+    # we want to uniquely identify all cases where the flag is FALSE. and only the first case where the flag is TRUE
+    # to do this create a variable of the flag in the previous row
+    dplyr::mutate(previous_continuous_stay_flag_sc = tidyr::replace_na(dplyr::lag(.data[["continuous_stay_flag_sc"]]), FALSE)) %>%
+    dplyr::mutate(continuous_stay_sc = ifelse(continuous_stay_flag_sc == FALSE |
+      (continuous_stay_flag_sc == TRUE & previous_continuous_stay_flag_sc == FALSE), FALSE, TRUE)) %>%
+    dplyr::group_by(.data[["social_care_id"]], .data[["sending_location"]], .data[["continuous_stay_sc"]]) %>%
+    # gives cases their unique CIS identifier
+    dplyr::mutate(ch_sc_id_cis = ifelse(continuous_stay_sc == FALSE, dplyr::row_number(), NA)) %>%
+    dplyr::group_by(.data[["social_care_id"]], .data[["sending_location"]]) %>%
+    # fills in CIS identifier for all cases
+    tidyr::fill(ch_sc_id_cis, .direction = c("down")) %>%
+    dplyr::select(
+      -previous_discharge_date_chi, -continuous_stay_flag_chi, -previous_continuous_stay_flag_chi, -continuous_stay_chi,
+      -previous_discharge_date_sc, -continuous_stay_flag_sc, -previous_continuous_stay_flag_sc, -continuous_stay_sc,
+      -dis_after_death
+    )
+
 
-  # Do a recode on the old reason for admission
-  adm_reason_recoded <- ch_markers %>%
+  # Do a recode on the old reason for admission for respite stays.
+  adm_reason_recoded <- sc_ch_id_markers %>%
     dplyr::group_by(
       .data[["social_care_id"]],
       .data[["sending_location"]],
@@ -277,32 +299,32 @@ process_sc_all_care_home <- function(
     ) %>%
     dplyr::mutate(
       ch_ep_start = min(.data[["ch_admission_date"]]),
+      # Creates a vector for the earliest date out of the end of period and discharge date.
+      # And will then select what ever is the latest date out of those
       ch_ep_end = max(
         pmin(
-          .data[["record_date"]],
+          .data[["period_end_date"]],
           .data[["ch_discharge_date"]],
           na.rm = TRUE
         )
       )
     ) %>%
     dplyr::ungroup() %>%
+    # Flag respite stays.
     dplyr::mutate(
-      stay_los = lubridate::time_length(
-        lubridate::interval(.data[["ch_ep_start"]], .data[["ch_ep_end"]]),
-        "weeks"
-      ),
+      stay_los = lubridate::time_length(lubridate::interval(.data[["ch_ep_start"]], .data[["ch_ep_end"]]), "weeks"),
       stay_respite = .data[["stay_los"]] < 6.0,
-      type_of_admission = dplyr::if_else(
-        is.na(.data[["type_of_admission"]]),
-        dplyr::case_when(
-          .data[["reason_for_admission"]] == 1L ~ 1L,
+      type_of_admission = dplyr::if_else(is.na(.data[["type_of_admission"]]),
+        dplyr::case_when(.data[["reason_for_admission"]] == 1L ~ 1L,
           .data[["reason_for_admission"]] == 2L ~ 2L,
-          stay_respite ~ 1L,
+          stay_respite ~ 1L, # (n = 40573)
           .default = 3L
         ),
         .data[["type_of_admission"]]
       )
-    )
+    ) %>%
+    dplyr::select(-ch_ep_start, -ch_ep_end, -stay_los, -stay_respite)
+
 
   ch_data_final <- adm_reason_recoded %>%
     create_person_id() %>%
@@ -312,6 +334,15 @@ process_sc_all_care_home <- function(
       ch_adm_reason = "type_of_admission",
       ch_nursing = "nursing_care_provision"
     ) %>%
+    # recode the care home provider description
+    dplyr::mutate(ch_provider_description = dplyr::case_when( # from social care syntax
+      ch_provider == 1 ~ "LOCAL AUTHORITY/HSCP/NHS BOARD",
+      ch_provider == 2 ~ "PRIVATE",
+      ch_provider == 3 ~ "OTHER LOCAL AUTHORITY",
+      ch_provider == 4 ~ "THIRD SECTOR",
+      ch_provider == 5 ~ "NHS BOARD",
+      ch_provider == 6 ~ "OTHER"
+    )) %>%
     dplyr::select(
       "chi",
       "person_id",
@@ -327,6 +358,7 @@ process_sc_all_care_home <- function(
       "ch_chi_cis",
       "ch_sc_id_cis",
       "ch_provider",
+      "ch_provider_description",
       "ch_nursing",
       "ch_adm_reason",
       "sc_latest_submission"
diff --git a/R/read_sc_all_care_home.R b/R/read_sc_all_care_home.R
index 89ef7951b..b11879487 100644
--- a/R/read_sc_all_care_home.R
+++ b/R/read_sc_all_care_home.R
@@ -42,7 +42,7 @@ read_sc_all_care_home <- function(sc_dvprod_connection = phs_db_connection(dsn =
   }
 
   ch_data <- ch_data %>%
-    # Correct FY 2017
+    # Correct FY 2017 as data collection only started in 2017 Q4
     dplyr::mutate(period = dplyr::if_else(
       .data$period == "2017",
       "2017Q4",
diff --git a/man/process_sc_all_care_home.Rd b/man/process_sc_all_care_home.Rd
index 7d4234b25..f689c19f4 100644
--- a/man/process_sc_all_care_home.Rd
+++ b/man/process_sc_all_care_home.Rd
@@ -9,8 +9,8 @@ process_sc_all_care_home(
   sc_demog_lookup = read_file(get_sc_demog_lookup_path()) \%>\% slfhelper::get_chi(),
   refined_death = read_file(get_combined_slf_deaths_lookup_path()) \%>\%
     slfhelper::get_chi(),
-  ch_name_lookup_path = read_file(get_slf_ch_name_lookup_path()),
-  spd_path = read_file(get_spd_path()),
+  ch_name_lookup_path = get_slf_ch_name_lookup_path(),
+  spd_path = get_spd_path(),
   write_to_disk = TRUE
 )
 }