diff --git a/README.md b/README.md
index daba89c..f1e104a 100644
--- a/README.md
+++ b/README.md
@@ -21,10 +21,10 @@ pip install "avoidable_admissions @ git+https://github.com/LTHTR-DST/hdruk_avoid
 
 Additional installation options are described in the [documentation](https://lthtr-dst.github.io/hdruk_avoidable_admissions/).
 
-Replace `<release-name>` with the latest release version e.g. `v0.2.1-alpha`.
+Replace `<release-name>` with the latest release version e.g. `v0.3.0`.
 List of releases can be found here - <https://github.com/LTHTR-DST/hdruk_avoidable_admissions/releases>.
 
-Omit `<release-name>` to install the latest code in the repo.
+Omit `@<release-name>` to install the latest code in the repo.
 
 ## Quickstart
 
diff --git a/avoidable_admissions/__init__.py b/avoidable_admissions/__init__.py
index 3a3c3f4..a554b00 100644
--- a/avoidable_admissions/__init__.py
+++ b/avoidable_admissions/__init__.py
@@ -2,4 +2,4 @@
 
 __all__ = ["data", "features", "models", "utils", "visualization"]
 
-__version__ = "0.2.2"
+__version__ = "0.3.0"
diff --git a/avoidable_admissions/data/validate.py b/avoidable_admissions/data/validate.py
index 2ccc384..bfc029e 100644
--- a/avoidable_admissions/data/validate.py
+++ b/avoidable_admissions/data/validate.py
@@ -1,6 +1,5 @@
 import warnings
-from contextlib import nullcontext
-from datetime import date, datetime
+from datetime import datetime
 from typing import Tuple
 
 import numpy as np
@@ -23,7 +22,7 @@ class AdmittedCareEpisodeSchema(pa.SchemaModel):
     visit_id: Series[str] = pa.Field(nullable=False, unique=True, coerce=True)
 
     # Ensure this has been pseudonymised appropriately.
-    patient_id: Series[str] = pa.Field(nullable=False)
+    patient_id: Series[str] = pa.Field(nullable=False, coerce=True)
 
     gender: Series[str] = pa.Field(
         description=nhsdd.gender["url"],
@@ -221,7 +220,7 @@ class EmergencyCareEpisodeSchema(pa.SchemaModel):
 
     visit_id: Series[str] = pa.Field(nullable=False, unique=True, coerce=True)
 
-    patient_id: Series[str] = pa.Field(nullable=False)
+    patient_id: Series[str] = pa.Field(nullable=False, coerce=True)
 
     gender: Series[str] = pa.Field(
         description=nhsdd.gender["url"],
@@ -394,7 +393,7 @@ class Config:
                 )
             ],
         ),
-        "edtreat_[0-9]{2}": pa.Column(
+        "edtreat_[0-9]{2}$": pa.Column(
             description="https://www.datadictionary.nhs.uk/data_elements/emergency_care_procedure__snomed_ct_.html",
             dtype=np.int64,
             nullable=True,
@@ -430,6 +429,26 @@ class Config:
             nullable=True,
             checks=[pa.Check.isin(set(feature_maps.ethnos.values()))],
         ),
+        "edarrival_dayofweek": pa.Column(
+            str,
+            nullable=True,
+            checks=[
+                pa.Check.isin(
+                    [
+                        "Monday",
+                        "Tuesday",
+                        "Wednesday",
+                        "Thursday",
+                        "Friday",
+                        "Saturday",
+                        "Sunday",
+                    ]
+                )
+            ],
+        ),
+        "edarrival_hourofday": pa.Column(
+            int, nullable=True, checks=[pa.Check.ge(0), pa.Check.le(23)]
+        ),
         "accommodationstatus_cat": pa.Column(
             str,
             nullable=True,
@@ -539,23 +558,14 @@ def validate_dataframe(
         schema (pa.DataFrameSchema): Pandera schema to validate against
         kwargs: The following keyword arguments are currently supported
         start_date (datetime): Study start date (inclusive)
-        end_date (datetime): Study end date (inclusive)
-
+        end_date (datetime): Study end date (excluded)
+        ignore_cols (list): Columns to ignore during validation checks.
+        update_cols (dict[str:dict]): Dictionary of column:properties to update schema.
 
     Returns:
         _Good_ and _Bad_ dataframes. See example below.
 
-    ## Customising Study Dates
-
-    As a default, the study dates specified in the initial protocol will be used (`admidate>=datetime(2021,11,1) and admidate<datetime(2022,11,1)`).
-    However, these can be altered by providing these as keyword arguments as shown in example below.
-
-    This validation rule will be applied to `admidate` in the Acute Admissions dataset and to `edarrivaldatetime` in the emergency care dataset as below.
-
-    `admidate>=start_date` and `admidate<end_date`. The `<` for end_date allows `admidate=31-10-2022 23:59:00` to pass validation.
-
-
-    ## Validation Example:
+    ## Validation example
 
     ``` python
     from avoidable_admissions.data.validate import (
@@ -566,10 +576,6 @@ def validate_dataframe(
 
     df = pd.read_csv('path/to/data.csv')
     good, bad = validate_dataframe(df, AdmittedCareEpisodeSchema)
-
-    # To use an alternative study period, specify the start_date and end_date parameters
-    good, bad = validate_dataframe(df, AdmittedCareEpisodeSchema, start_date=datetime(2019,1,1), end_date=datetime(2019,12,31))
-
     ```
 
     If `df` had rows that fail validation, the function will print an output similar to below.
@@ -590,8 +596,73 @@ def validate_dataframe(
 
     Fix data quality iteratively to ensure there are no errors.
 
-    If you find a bug in the validation code, and correct data fails validation, please raise a
-    [GitHub issue](https://github.com/LTHTR-DST/hdruk_avoidable_admissions/issues).
+    If you find a bug in the validation code, and correct data fails validation,
+    please raise a [GitHub issue](https://github.com/LTHTR-DST/hdruk_avoidable_admissions/issues).
+
+    ## Customising validation
+
+    ### Customise study dates
+
+    As a default, the study dates specified in the initial protocol will be used
+    (`admidate>=datetime(2021,11,1) and admidate<datetime(2022,11,1)`).
+    However, these can be altered by providing these as keyword arguments.
+
+    The following rule is applied to `admidate` in the Acute Admissions dataset
+    and to `edarrivaldatetime` in the emergency care dataset.
+
+    `admidate>=start_date` and `admidate<end_date`
+
+    The `<end_date` allows `31-10-2022 23:59:00` to pass validation when
+    `end_date` is set to `datetime(2022,11,1)`.
+
+    ### Ignore selected columns
+
+    Passing a list of column names to`ignore_cols` as a keyword argument will
+    apply the following properties, effectively turning off validation.
+
+    ```python
+    {
+        'dtype': None,
+        'checks': [],
+        'nullable': False,
+        'unique': False,
+        'coerce': False,
+        'required': True
+    }
+    ```
+
+    ### Update validation rules
+
+    Passing a dictionary of {column_name: property_dict} allows fine-grained control.
+    For example, to update remove checks on `edchiefcomplaint` but preserve
+    other validation rules, pass the following to `update_cols`.
+
+    ### Custom validation example
+
+    The example below applies the following custom rules:
+
+    - Change study start and end dates
+    - Ignore validation on `eddiag_NN` columns.
+        This requires both *_01* and *_[0-9]{2}$* regex suffixes to be set.
+        Note the _$_ at the end of regex.
+    - Don't perform checks on `edchiefcomplaint` but retain other rules e.g dtype
+    - Dont' check data type for `accommodationstatus` but retain other rules
+
+
+    ```python
+    good, bad = validate_dataframe(
+        df,
+        schema,
+        start_date=datetime(2021, 10, 1),
+        end_date=datetime(2022, 11, 1),
+        ignore_cols=["eddiag_01", "eddiag_[0-9]{2}$"],
+        update_cols={
+            "edchiefcomplaint": {"checks": []},
+            "accommodationstatus": {"dtype": None},
+        }
+    )
+    ```
+
 
     """
 
@@ -610,16 +681,36 @@ def validate_dataframe(
     ]
 
     if schema.name.startswith("AdmittedCare"):
-        schema = schema.update_column(
-            column_name="admidate",
-            checks=date_checks,
-        )
-
+        cohort_date_col = "admidate"
     elif schema.name.startswith("EmergencyCare"):
+        cohort_date_col = "edarrivaldatetime"
 
-        schema = schema.update_column(
-            column_name="edarrivaldatetime", checks=date_checks
-        )
+    updated_column_props = {}
+
+    updated_column_props[cohort_date_col] = {"checks": date_checks}
+
+    # New feature - allow user to ignore checks on some columns
+    ignore_cols = kwargs.get("ignore_cols", [])
+
+    update_cols = kwargs.get("update_cols", {})
+
+    blank_props = {
+        "dtype": None,
+        "checks": [],
+        "nullable": False,
+        "unique": False,
+        "coerce": False,
+        "required": True,
+    }
+
+    for col in ignore_cols:
+        updated_column_props[col] = blank_props
+
+    updated_column_props.update(update_cols)
+
+    # If a column in ignore_cols is not present in schema, this will raise
+    # a SchemaInitError with name of column causing the error.
+    schema = schema.update_columns(updated_column_props)
 
     try:
         # Capture all errors
diff --git a/avoidable_admissions/features/emergency_care_features.py b/avoidable_admissions/features/emergency_care_features.py
index 25b87dc..6323786 100644
--- a/avoidable_admissions/features/emergency_care_features.py
+++ b/avoidable_admissions/features/emergency_care_features.py
@@ -4,54 +4,82 @@
 from avoidable_admissions.features import feature_maps
 
 
+def replace_values(
+    data: pd.Series, replacements: dict, other: str = "ERROR:Unmapped - Not In Refset"
+) -> pd.Series:
+    # if value is in replacements, keep the value, else use `other` for all others
+    # then use replacements to assign the other categories
+
+    data_cat = (
+        data.where(data.isin(replacements), other).replace(replacements).astype(str)
+    )
+
+    return data_cat
+
+
 def _age(df: pd.DataFrame) -> pd.DataFrame:
 
     age_labels = feature_maps.age_labels
     age_bins = feature_maps.age_bins
     df["activage_cat"] = pd.cut(df.activage, bins=age_bins, labels=age_labels)
+    df["activage_cat"] = df["activage_cat"].astype(str)
 
     return df
 
 
 def _gender(df: pd.DataFrame) -> pd.DataFrame:
 
-    df["gender_cat"] = df.gender.astype(str).replace(feature_maps.gender)
+    df["gender_cat"] = replace_values(df.gender.astype(str), feature_maps.gender)
 
     return df
 
 
 def _ethnos(df: pd.DataFrame) -> pd.DataFrame:
 
-    df["ethnos_cat"] = df.ethnos.replace(feature_maps.ethnos)
+    df["ethnos_cat"] = replace_values(df.ethnos, feature_maps.ethnos)
 
     return df
 
 
 def _accommodationstatus(df: pd.DataFrame) -> pd.DataFrame:
 
-    df["accommodationstatus_cat"] = df.accommodationstatus.replace(
-        feature_maps.accommodationstatus
+    df["accommodationstatus_cat"] = replace_values(
+        df.accommodationstatus, feature_maps.accommodationstatus
     )
+
+    return df
+
+
+def _edarrivaldatetime(df: pd.DataFrame) -> pd.DataFrame:
+
+    df.edarrivaldatetime = pd.to_datetime(df.edarrivaldatetime)
+    df["edarrival_dayofweek"] = df.edarrivaldatetime.dt.strftime("%A")
+    df["edarrival_hourofday"] = df.edarrivaldatetime.dt.hour
+
     return df
 
 
 def _edarivalemode(df: pd.DataFrame) -> pd.DataFrame:
 
-    df["edarrivalmode_cat"] = df.edarrivalmode.replace(feature_maps.edarrivalmode)
+    df["edarrivalmode_cat"] = replace_values(
+        df.edarrivalmode, feature_maps.edarrivalmode
+    )
 
     return df
 
 
 def _edattendsource(df: pd.DataFrame) -> pd.DataFrame:
 
-    df["edattendsource_cat"] = df.edattendsource.replace(feature_maps.edattendsource)
+    df["edattendsource_cat"] = replace_values(
+        df.edattendsource, feature_maps.edattendsource
+    )
 
     return df
 
 
 def _edacuity(df: pd.DataFrame) -> pd.DataFrame:
 
-    df["edacuity_cat"] = df.edacuity.replace(feature_maps.edacuity)
+    df["edacuity_cat"] = replace_values(df.edacuity, feature_maps.edacuity)
 
     return df
 
@@ -63,11 +91,7 @@ def _edinvest(df: pd.DataFrame) -> pd.DataFrame:
 
     for col in cols:
 
-        # if value is in replacements, keep the value, else use 'Urgent' for all others
-        # then use replacements to assign the other categories
-        df[col + "_cat"] = (
-            df[col].where(df[col].isin(replacements), "Urgent").replace(replacements)
-        )
+        df[col + "_cat"] = replace_values(df[col], replacements, "Urgent")
 
     return df
 
@@ -78,24 +102,17 @@ def _edtreat(df: pd.DataFrame) -> pd.DataFrame:
     replacements = feature_maps.edtreat
     for col in cols:
 
-        # if value is in replacements, keep the value, else use 'Urgent' for all others
-        # then use replacements to assign the other categories
-        df[col + "_cat"] = (
-            df[col].where(df[col].isin(replacements), "Urgent").replace(replacements)
-        )
+        df[col + "_cat"] = replace_values(df[col], replacements, "Urgent")
 
     return df
 
 
 def _eddiag_seasonal(df: pd.DataFrame) -> pd.DataFrame:
     # Only use first diagnosis recorded (eddiag_01) to record seasonal diagnosis
-    replacements = feature_maps.eddiag_seasonal
 
-    # if value is in replacements, keep the value, else use 'nan' for all others
-    # then use replacements to assign the other categories
-    df["eddiag_seasonal_cat"] = df.eddiag_01.where(
-        df.eddiag_01.isin(replacements), np.nan
-    ).replace(replacements)
+    df["eddiag_seasonal_cat"] = replace_values(
+        df.eddiag_01, feature_maps.eddiag_seasonal
+    )
 
     return df
 
@@ -103,31 +120,26 @@ def _eddiag_seasonal(df: pd.DataFrame) -> pd.DataFrame:
 def _edattenddispatch(df: pd.DataFrame) -> pd.DataFrame:
     # Discharge Destination
 
-    df["edattenddispatch_cat"] = df.edattenddispatch.replace(
-        feature_maps.edattenddispatch
+    df["edattenddispatch_cat"] = replace_values(
+        df.edattenddispatch, feature_maps.edattenddispatch
     )
+
     return df
 
 
 def _edrefservice(df: pd.DataFrame) -> pd.DataFrame:
 
-    replacements = feature_maps.edrefservice
-
-    df["edrefservice_cat"] = df.edrefservice.where(
-        df.edrefservice.isin(replacements), "Other"
-    ).replace(replacements)
+    df["edrefservice_cat"] = replace_values(
+        df.edrefservice, feature_maps.edrefservice, "Other"
+    )
 
     return df
 
 
 def _eddiagqual(df: pd.DataFrame) -> pd.DataFrame:
     # Only applicable to eddiag_01
-    # This deviates from the spec by assigning nan to values not in the spec
 
-    replacements = feature_maps.eddiagqual
-    df["eddiagqual_01_cat"] = df.eddiagqual_01.where(
-        df.eddiagqual_01.isin(replacements), np.nan
-    ).replace(replacements)
+    df["eddiagqual_01_cat"] = replace_values(df.eddiagqual_01, feature_maps.eddiagqual)
 
     return df
 
@@ -137,17 +149,14 @@ def _acsc_code(df: pd.DataFrame) -> pd.DataFrame:
     # TODO: This section needs manual review of a good sample size to ensure it works
 
     acsc_mapping = feature_maps.load_ed_acsc_mapping()
-    df["eddiag_01_acsc"] = df.eddiag_01.replace(acsc_mapping)
-    df.eddiag_01_acsc = df.eddiag_01_acsc.where(
-        df.eddiag_01_acsc.isin(set(acsc_mapping.values())), "-"
-    )
+    df["eddiag_01_acsc"] = replace_values(df.eddiag_01, acsc_mapping)
 
     return df
 
 
 def _disstatus(df: pd.DataFrame) -> pd.DataFrame:
 
-    df["disstatus_cat"] = df.disstatus.replace(feature_maps.disstatus)
+    df["disstatus_cat"] = replace_values(df.disstatus, feature_maps.disstatus)
 
     return df
 
@@ -161,6 +170,7 @@ def build_all(df: pd.DataFrame) -> pd.DataFrame:
         .pipe(_disstatus)
         .pipe(_edacuity)
         .pipe(_edarivalemode)
+        .pipe(_edarrivaldatetime)
         .pipe(_edattenddispatch)
         .pipe(_edattendsource)
         .pipe(_eddiag_seasonal)
diff --git a/avoidable_admissions/features/feature_maps.py b/avoidable_admissions/features/feature_maps.py
index c28229b..33a2fa9 100644
--- a/avoidable_admissions/features/feature_maps.py
+++ b/avoidable_admissions/features/feature_maps.py
@@ -4,6 +4,11 @@
 import numpy as np
 import pandas as pd
 
+from avoidable_admissions.data import nhsdd_snomed
+
+# to prevent accidentally removing nhsdd_snomed import which is used in eval
+_ = nhsdd_snomed.__doc__
+
 age_labels = [
     "18-19",
     "20 - 24",
@@ -24,7 +29,6 @@
 
 age_bins = age_bins = [17, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 130]
 
-
 gender = {
     "1": "Male",
     "2": "Female",
@@ -55,243 +59,6 @@
     "99": "Not known",
 }
 
-accomodationstatus = {
-    0: np.nan,
-    1064831000000106: "Unknown",
-    1064841000000102: "Unknown",
-    1066881000000100: "Unknown",
-    160734000: "Yes",
-    224221006: "No",
-    224225002: "No",
-    224231004: "No",
-    32911000: "No",
-    394923006: "No",
-    414418009: "No",
-}
-
-edarrivalmode = {
-    0: np.nan,
-    1047991000000102: "Sheffield-ToDo",
-    1048001000000106: "Sheffield-ToDo",
-    1048021000000102: "Sheffield-ToDo",
-    1048031000000100: "Sheffield-ToDo",
-    1048041000000109: "Sheffield-ToDo",
-    1048051000000107: "Sheffield-ToDo",
-    1048061000000105: "Walk-In",
-    1048071000000103: "Walk-In",
-    1048081000000101: "Sheffield-ToDo",
-}
-
-edattendsource = {
-    0: np.nan,
-    1052681000000105: "Community",
-    1065391000000104: "Personal",
-    1065401000000101: "Community",
-    1065991000000100: "Community",
-    1066001000000101: "Community",
-    1066011000000104: "Community",
-    1066021000000105: "Emergency Services",
-    1066031000000107: "Emergency Services",
-    1066041000000103: "Emergency Services",
-    1066051000000100: "Emergency Services",
-    1066061000000102: "Emergency Services",
-    1066431000000102: "Hospital",
-    1066441000000106: "Hospital",
-    1077191000000103: "Community",
-    1077201000000101: "Community",
-    1077211000000104: "Community",
-    1077761000000105: "Community",
-    1079521000000104: "Hospital",
-    1082331000000106: "Primary Care",  # OOH
-    166941000000106: "Primary Care",
-    183877003: "Private Referral",  # from refset
-    185363009: "Community",
-    185366001: "Community",
-    185368000: "Community",
-    185369008: "Community",
-    198261000000104: "Emergency Services",
-    276491000: "Primary Care",
-    315261000000101: "Personal",
-    507291000000100: "Personal",
-    835091000000109: "Hospital",
-    835101000000101: "Hospital",
-    877171000000103: "Community",
-    879591000000102: "Primary Care",
-    889801000000100: "Emergency Services",
-}
-
-edacuity = {
-    0: np.nan,
-    1064891000000107: "1 - Immediate care level emergency care",
-    1064901000000108: "3 - Urgent level emergency care",
-    1064911000000105: "2 - Very urgent level emergency care",
-    1077241000000103: "4 - Standard level emergency care",
-    1077251000000100: "5 - Low acuity level emergency care",
-}
-
-edinvest = {
-    0: np.nan,
-    1088291000000101: np.nan,
-    167252002: "Non-urgent",
-    27171005: "Non-urgent",
-    53115007: "Non-urgent",
-    67900009: "Non-urgent",
-}
-
-edtreat = {
-    0: np.nan,
-    183964008: np.nan,
-    266712008: "Non-urgent",
-    413334001: "Non-urgent",
-    81733005: "Non-urgent",
-}
-
-eddiag_seasonal = {
-    0: np.nan,
-    12295008: "Chronic disease exacerbation",
-    1325161000000102: "Respiratory infection",
-    1325171000000109: "Respiratory infection",
-    1325181000000106: "Respiratory infection",
-    13645005: "Chronic disease exacerbation",
-    195951007: "Chronic disease exacerbation",
-    195967001: "Chronic disease exacerbation",
-    205237003: "Respiratory infection",
-    233604007: "Respiratory infection",
-    278516003: "Respiratory infection",
-    36971009: "Respiratory infection",
-    50417007: "Respiratory infection",
-    54150009: "Respiratory infection",
-    6142004: "Respiratory infection",
-    62994001: "Respiratory infection",
-    80384002: "Respiratory infection",
-    90176007: "Respiratory infection",
-}
-
-eddiagqual = {
-    415684004: "Suspected",
-    410605003: "Confirmed",
-}
-
-edattenddispatch = {
-    0: np.nan,
-    1066331000000109: "Ambulatory / Short Stay",
-    1066341000000100: "Ambulatory / Short Stay",
-    1066351000000102: "Ambulatory / Short Stay",
-    1066361000000104: "Admitted",
-    1066371000000106: "Admitted",
-    1066381000000108: "Admitted",
-    1066391000000105: "Admitted",
-    1066401000000108: "Admitted",
-    183919006: "Transfer",
-    19712007: "Transfer",
-    305398007: "Died",
-    306689006: "Discharged",
-    306691003: "Discharged",
-    306694006: "Discharged",
-    306705005: "Discharged",
-    306706006: "Admitted",
-    50861005: "Discharged",
-}
-
-edrefservice = {
-    0: np.nan,
-    1064851000000104: "Medical",
-    183516009: "Medical",
-    183518005: "Medical",
-    183519002: "Medical",
-    183521007: "Medical",
-    183522000: "Medical",
-    183523005: "Medical",
-    183524004: "Psychiatric",
-    183542009: "Surgical",
-    183543004: "Surgical",
-    183544005: "Surgical",
-    183545006: "Surgical",
-    183546007: "Surgical",
-    183548008: "ObGyn",
-    183549000: "ObGyn",
-    183561008: "Local Medical",
-    202291000000107: "Psychiatric",
-    247541000000106: "Community / OPD",
-    276490004: "Local Medical",
-    306107006: "Critical Care",
-    306111000: "Medical",
-    306114008: "Medical",
-    306118006: "Medical",
-    306123006: "Medical",
-    306124000: "Medical",
-    306125004: "Medical",
-    306127007: "Medical",
-    306129005: "Community / OPD",
-    306136006: "Psychiatric",
-    306138007: "Psychiatric",
-    306140002: "Medical",
-    306148009: "Medical",
-    306152009: "Local Medical",
-    306182003: "Surgical",
-    306184002: "Surgical",
-    306198005: "Surgical",
-    306200004: "Surgical",
-    306201000: "Surgical",
-    306237005: "Medical",
-    306285006: "Medical",
-    306802002: "Medical",
-    306934005: "Surgical",
-    307374004: "Medical",
-    307375003: "Community / OPD",
-    307376002: "Community / OPD",
-    307380007: "Community / OPD",
-    327121000000104: "Surgical",
-    353961000000104: "Community / OPD",
-    380241000000107: "Psychiatric",
-    382271000000102: "Critical Care",
-    384711009: "Surgical",
-    384712002: "Surgical",
-    38670004: "Community / OPD",
-    413127007: "Psychiatric",
-    415263003: "Community / OPD",
-    4266003: "Community / OPD",
-    516511000000107: "Community / OPD",
-    61801003: "Community / OPD",
-    770411000000102: "Local Medical",
-    770677000: "Critical Care",
-    78429003: "Community / OPD",
-    785621000000108: "Community / OPD",
-    785681000000109: "Community / OPD",
-    785701000000106: "Community / OPD",
-    785721000000102: "Community / OPD",
-    785761000000105: "Community / OPD",
-    785781000000101: "Community / OPD",
-    811391000000104: "Community / OPD",
-    818861000000107: "Community / OPD",
-    823961000000102: "Community / OPD",
-    894171000000100: "Community / OPD",
-    898791000000105: "Medical",
-    975951000000109: "Critical Care",
-}
-
-disstatus = {
-    0: np.nan,
-    1066301000000103: "Non-urgent",
-    1066311000000101: "Non-urgent",
-    1066321000000107: "Non-urgent",
-    1077021000000100: "Non-urgent",
-    1077031000000103: "Urgent",
-    1077041000000107: "Urgent",
-    1077051000000105: "Urgent",
-    1077061000000108: "Urgent",
-    1077071000000101: "Urgent",
-    1077081000000104: "Urgent",
-    1077091000000102: "Urgent",
-    1077101000000105: "Urgent",
-    1077781000000101: "Urgent",
-    1324201000000109: "Urgent",
-    182992009: "Non-urgent",
-    63238001: "Died",
-    75004002: "Died",
-}
-
-
 admisorc = {
     "19": "Residence",
     "29": "Residence",
@@ -389,6 +156,356 @@
 }
 
 
+def generate_map(name: str, feature_r: dict) -> dict:
+
+    # First generate a reverse map as snomed_code:category
+
+    feature = {
+        snomed_code: category
+        for category, snomed_list in feature_r.items()
+        for snomed_code in snomed_list
+    }
+
+    # Get the members of the refset from nhsdd_snomed
+    # This file has been automatically generated from the Ontology Server
+    refset_members = eval(f"nhsdd_snomed.{name}['members']")
+
+    # Create a set of all snomed codes in feature
+    feature_members = feature.keys()
+
+    # Unmapped codes are the codes in the refset that are not in feature
+    # For each code in refset that is not in feature, set to 'unmapped'
+
+    for i in refset_members:
+        if i not in feature_members:
+            feature[i] = "ERROR:Unmapped - In Refset"
+
+    # For codes that appear in the mapping but not in the refset
+    # append '|Not-In-Refset' tp existing value
+
+    for k, v in feature.items():
+        if k not in refset_members:
+            feature[k] = "ERROR:Not In Refset|" + v
+
+    feature[0] = "ERROR:Missing Data"
+
+    # Add in a placeholder for codes that are neither in the featuremap nor in refset
+    # These are for unforeseen values that may appear in the source data
+
+    feature[1] = "ERROR:Unmapped - Not In Refset"
+
+    return feature
+
+
+##############################################################################
+# accommodationstatus
+##############################################################################
+accommodationstatus_r = {
+    "Unknown": [1064831000000106, 1064841000000102, 1066881000000100],
+    "Yes": [160734000],
+    "No": [224221006, 224225002, 224231004, 32911000, 394923006, 414418009],
+}
+
+accommodationstatus = generate_map("accommodationstatus", accommodationstatus_r)
+
+##############################################################################
+# edarrivalmode
+##############################################################################
+edarrivalmode_r = {
+    "Walk-In": [1048061000000105, 1048071000000103],
+}
+
+edarrivalmode = generate_map("edarrivalmode", edarrivalmode_r)
+
+##############################################################################
+# edattendsource
+##############################################################################
+edattendsource_r = {
+    "Community": [
+        1052681000000105,
+        1065401000000101,
+        1065991000000100,
+        1066001000000101,
+        1066011000000104,
+        1077191000000103,
+        1077201000000101,
+        1077211000000104,
+        1077761000000105,
+        185363009,
+        185366001,
+        185368000,
+        185369008,
+        877171000000103,
+    ],
+    "Personal": [1065391000000104, 315261000000101, 507291000000100],
+    "Emergency Services": [
+        1066021000000105,
+        1066031000000107,
+        1066041000000103,
+        1066051000000100,
+        1066061000000102,
+        198261000000104,
+        889801000000100,
+    ],
+    "Hospital": [
+        1066431000000102,
+        1066441000000106,
+        1079521000000104,
+        835091000000109,
+        835101000000101,
+    ],
+    "Primary Care": [1082331000000106, 166941000000106, 276491000, 879591000000102],
+    "Private Referral": [183877003],
+}
+
+edattendsource = generate_map("edattendsource", edattendsource_r)
+
+##############################################################################
+# edacuity
+##############################################################################
+edacuity = {
+    0: "ERROR:Missing Data",
+    1064891000000107: "1 - Immediate care level emergency care",
+    1064901000000108: "3 - Urgent level emergency care",
+    1064911000000105: "2 - Very urgent level emergency care",
+    1077241000000103: "4 - Standard level emergency care",
+    1077251000000100: "5 - Low acuity level emergency care",
+}
+
+##############################################################################
+# edinvest
+##############################################################################
+
+edinvest_r = {
+    "Non-urgent": [167252002, 27171005, 53115007, 67900009],
+    "Urgent": [
+        104686004,
+        105000003,
+        113091000,
+        16254007,
+        16310003,
+        164729009,
+        165320004,
+        167036008,
+        16830007,
+        168338000,
+        168537006,
+        179929004,
+        252167001,
+        252316009,
+        252375001,
+        26604007,
+        26958001,
+        269874008,
+        270982000,
+        271232007,
+        282096008,
+        29303009,
+        29893006,
+        30088009,
+        3116009,
+        35650009,
+        363255004,
+        392010000,
+        397798009,
+        401294003,
+        40701008,
+        416838001,
+        43396009,
+        55235003,
+        56027003,
+        60170009,
+        61911006,
+        62847008,
+        68793005,
+        70648006,
+        74500006,
+        77477000,
+        86944008,
+        89659001,
+    ],
+    "No-investigation": [1088291000000101],
+}
+
+edinvest = generate_map("edinvest", edinvest_r)
+
+##############################################################################
+# edtreat
+##############################################################################
+edtreat_r = {
+    "Non-urgent": [
+        183964008,
+        266712008,
+        413334001,
+        81733005,
+    ]
+}
+
+edtreat = generate_map("edtreat", edtreat_r)
+
+##############################################################################
+# eddiag_seasonal
+##############################################################################
+eddiag_seasonal_r = {
+    "Chronic disease exacerbation": [12295008, 13645005, 195951007, 195967001],
+    "Respiratory infection": [
+        1325161000000102,
+        1325171000000109,
+        1325181000000106,
+        205237003,
+        233604007,
+        278516003,
+        36971009,
+        50417007,
+        54150009,
+        6142004,
+        62994001,
+        80384002,
+        90176007,
+    ],
+}
+
+eddiag_seasonal = generate_map("eddiag", eddiag_seasonal_r)
+
+##############################################################################
+# eddiagqual
+##############################################################################
+eddiagqual = {
+    415684004: "Suspected",
+    410605003: "Confirmed",
+}
+
+##############################################################################
+# edattenddispatch
+##############################################################################
+edattenddispatch_r = {
+    "Ambulatory / Short Stay": [1066331000000109, 1066341000000100, 1066351000000102],
+    "Admitted": [
+        1066361000000104,
+        1066371000000106,
+        1066381000000108,
+        1066391000000105,
+        1066401000000108,
+        306706006,
+    ],
+    "Transfer": [183919006, 19712007],
+    "Died": [305398007],
+    "Discharged": [306689006, 306691003, 306694006, 306705005, 50861005],
+}
+
+edattenddispatch = generate_map("edattenddispatch", edattenddispatch_r)
+
+##############################################################################
+# edrefservice
+##############################################################################
+edrefservice_r = {
+    "Medical": [
+        1064851000000104,
+        183516009,
+        183518005,
+        183519002,
+        183521007,
+        183522000,
+        183523005,
+        306111000,
+        306114008,
+        306118006,
+        306123006,
+        306124000,
+        306125004,
+        306127007,
+        306140002,
+        306148009,
+        306237005,
+        306285006,
+        306802002,
+        307374004,
+        898791000000105,
+    ],
+    "Psychiatric": [
+        183524004,
+        202291000000107,
+        306136006,
+        306138007,
+        380241000000107,
+        413127007,
+    ],
+    "Surgical": [
+        183542009,
+        183543004,
+        183544005,
+        183545006,
+        183546007,
+        306182003,
+        306184002,
+        306198005,
+        306200004,
+        306201000,
+        306934005,
+        327121000000104,
+        384711009,
+        384712002,
+    ],
+    "ObGyn": [183548008, 183549000],
+    "Local Medical": [183561008, 276490004, 306152009, 770411000000102],
+    "Community / OPD": [
+        247541000000106,
+        306129005,
+        307375003,
+        307376002,
+        307380007,
+        353961000000104,
+        38670004,
+        415263003,
+        4266003,
+        516511000000107,
+        61801003,
+        78429003,
+        785621000000108,
+        785681000000109,
+        785701000000106,
+        785721000000102,
+        785761000000105,
+        785781000000101,
+        811391000000104,
+        818861000000107,
+        823961000000102,
+        894171000000100,
+    ],
+    "Critical Care": [306107006, 382271000000102, 770677000, 975951000000109],
+}
+
+edrefservice = generate_map("edrefservice", edrefservice_r)
+
+##############################################################################
+# disstatus
+##############################################################################
+disstatus_r = {
+    "Non-urgent": [
+        1066301000000103,
+        1066311000000101,
+        1066321000000107,
+        1077021000000100,
+        182992009,
+    ],
+    "Urgent": [
+        1077031000000103,
+        1077041000000107,
+        1077051000000105,
+        1077061000000108,
+        1077071000000101,
+        1077081000000104,
+        1077091000000102,
+        1077101000000105,
+        1077781000000101,
+        1324201000000109,
+    ],
+    "Died": [63238001, 75004002],
+}
+
+disstatus = generate_map("disstatus", disstatus_r)
+
+
 @lru_cache(maxsize=1)
 def load_apc_acsc_mapping() -> Dict[str, str]:
     """Download ICD10 to Ambulatory Care Sensitive Conditions mapping from Sheffield Google Docs
@@ -425,4 +542,35 @@ def load_ed_acsc_mapping() -> Dict[str, str]:
     acsc.columns = acsc.columns.str.lower().str.replace("[^a-z0-9]+", "_", regex=True)
     acsc_mapping = acsc.set_index("snomed_code").aec_clinical_conditions.to_dict()
 
+    # Set ERROR codes to allow validation to pass after feature engineering
+    # TODO: Tidy this up
+
+    # Get the members of the refset from nhsdd_snomed
+    # This file has been automatically generated from the Ontology Server
+    refset_members = nhsdd_snomed.eddiag["members"]
+
+    # Create a set of all snomed codes in feature
+    feature_members = acsc_mapping.keys()
+
+    # Unmapped codes are the codes in the refset that are not in feature
+    # For each code in refset that is not in feature, set to 'unmapped'
+
+    for i in refset_members:
+        if i not in feature_members:
+            acsc_mapping[i] = "ERROR:Unmapped - In Refset"
+
+    # For codes that appear in the mapping but not in the refset
+    # append '|Not-In-Refset' tp existing value
+
+    for k, v in acsc_mapping.items():
+        if k not in refset_members:
+            acsc_mapping[k] = "ERROR:Mapped - Not In Refset|" + v
+
+    acsc_mapping[0] = "ERROR:Missing Data"
+
+    # Add in a placeholder for codes that are neither in the featuremap nor in refset
+    # These are for unforeseen values that may appear in the source data
+
+    acsc_mapping[1] = "ERROR:Unmapped - Not In Refset"
+
     return acsc_mapping
diff --git a/docs/features.md b/docs/features.md
index a1b4e22..1dce737 100644
--- a/docs/features.md
+++ b/docs/features.md
@@ -10,6 +10,23 @@ The functions described below generate these features automatically in preparati
 
 Ensure that data has undergone preprocessing and has passed the first validation step as described in the [analysis pipeline][data-analysis-pipeline] before using these functions.
 
+## Error codes
+
+A pragmatic approach has been used in dealing with missing data, unmapped codes and codes not in refsets.
+Please read section on [missing values][missing-values] in the [Data Validation][data-validation] chapter as well.
+
+During feature engineering, especially in the Emergency Care dataset that has several columns with SNOMED codes, the following rules are applied to assign the appropriate categories.
+
+| Source Data   | Mapping   | Refset    | Category                              | Who fixes             |
+|:-------------:|:---------:|:---------:|---------------------------------------|-----------------------|
+| Yes           | Yes       | Yes       | Assign to `Category`                  |                       |
+| Yes           | No        | Yes       | `ERROR:Unmapped - In Refset`          | Lead site to advise   |
+| Yes           | Yes       | No        | `ERROR:Not In Refset|{Category}`   | Lead site to fix      |
+| No            | x         | x         | `ERROR:Missing Data`                  | Local site if feasible|
+| Yes           | No        | No        | `ERROR:Unmapped - Not In Refset`      | Local site to fix     |
+
+Please see the source code for [`feature_maps.py`](https://github.com/LTHTR-DST/hdruk_avoidable_admissions/blob/dev/avoidable_admissions/features/feature_maps.py) and raise a [GitHub issue](https://github.com/LTHTR-DST/hdruk_avoidable_admissions/issues) for any questions or bugs.
+
 ::: avoidable_admissions.features.build_features
     handler: python
     options:
diff --git a/docs/index.md b/docs/index.md
index 7918499..e444ea4 100644
--- a/docs/index.md
+++ b/docs/index.md
@@ -33,8 +33,8 @@ To install with optional dependecies for _contributing to development and docume
 pip install "avoidable_admissions[dev] @ git+https://github.com/LTHTR-DST/hdruk_avoidable_admissions.git@<release-name>"
 ```
 
-Replace `<release-name>` with the latest release version e.g. `v.0.2.1-alpha`. List of releases can be found here - <https://github.com/LTHTR-DST/hdruk_avoidable_admissions/releases>.
+Replace `<release-name>` with the latest release version e.g. `v.0.3.0`. List of releases can be found here - <https://github.com/LTHTR-DST/hdruk_avoidable_admissions/releases>.
 
-Omit `<release-name>` to install the latest code in the repo.
+Omit `@<release-name>` to install the latest code in the repo.
 
 See <https://lthtr-dst.github.io/hdruk_avoidable_admissions/admitted_care_pipeline_example> for a complete example.
diff --git a/docs/validation.md b/docs/validation.md
index 5d531e4..c402836 100644
--- a/docs/validation.md
+++ b/docs/validation.md
@@ -37,7 +37,7 @@ df['admidate'] = df['admidate'].dt.date
 df['accommodationstatus'] = df['accommodationstatus'].fillna(0)
 ```
 
-## Mising Values
+## Missing Values
 
 _To be finalised after further discussion and testing._
 
@@ -48,4 +48,4 @@ For the purposes of this project, several pragmatic choices have been made regar
 1. Where a definition exists for how missing values should be coded, for instance in the NHS data model, use this.
 2. For SNOMED codes, which are always integers, use 0 (zero) to replace all missing values. This avoids validation errors caused by `NaN` values that are treated as `float` dtype by Pandas.
 3. For strings, use `"-"` (without the quotes) for missing values.
-4. During feature engineering, if a code has not been assigned a category in the specification, the value `"Other"` is assigned.
+4. During [feature engineering][feature-engineering], custom error values are assigned to codes that are missing from either the refsets or mapping.