diff --git a/pages/02_financial_summary.py b/pages/02_financial_summary.py
index 021c367..24b4dd5 100644
--- a/pages/02_financial_summary.py
+++ b/pages/02_financial_summary.py
@@ -80,11 +80,23 @@ def pmpm_by_claim_type():
             group by 1, 2
             having sum(paid_amount) > 0
             order by 1, 2 desc
+        ), pharmacy_summary as (
+           select
+               year(dispensing_date)::text || '-' ||
+                 lpad(month(dispensing_date)::text, 2, '0')
+                 as year_month
+               , 'pharmacy' as claim_type
+               , sum(paid_amount) as paid_amount_sum
+           from core.pharmacy_claim
+           group by 1
+        ), together as (
+           select * from spend_summary union all
+           select * from pharmacy_summary
         )
         select
            *
            , paid_amount_sum / member_month_count as paid_amount_pmpm
-        from spend_summary
+        from together
         join pmpm._int_member_month_count using(year_month)
     """
     data = util.safe_to_pandas(conn, query)
@@ -143,8 +155,8 @@ def pmpm_by_service_category_1_2():
     return data
 
 
-year_month_values = year_months()
-year_values = sorted(list(set([x[:4] for x in year_month_values['year_month']])))
+year_month_values = sorted(list(set(year_months()["year_month"])))
+year_values = sorted(list(set([x[:4] for x in year_month_values])))
 
 ## --------------------------------- ##
 ## Header
@@ -190,18 +202,18 @@ def pmpm_by_service_category_1_2():
     [["paid_amount_sum", "member_month_count"]].sum()\
     .assign(paid_amount_pmpm = lambda x: x["paid_amount_sum"] / x["member_month_count"])
 
-st.table(pmpm_claim_type_data)
-
-# plost.bar_chart(
-#     data=pmpm_claim_type_data
-#     bar='',
-#     value=,
-#     stack='normalize',
-#     direction='horizontal',
-#     legend='top',
-#     height=200
-# )
-
+st.markdown("## Claim Type")
+st.markdown("""
+Explore the per member per month costs across different claim types to gain insights into healthcare expenditure patterns. Inpatient spend will tend to be much higher than professional spend. Dig deeper to find out what is hidden in these costs.
+""")
+plost.bar_chart(
+    data=pmpm_claim_type_data,
+    bar='claim_type',
+    value='paid_amount_pmpm',
+    direction='horizontal',
+    legend='top',
+    height=200
+)
 
 
 ## --------------------------------- ##
@@ -235,7 +247,7 @@ def pmpm_by_service_category_1_2():
 ## Service Category 2
 ## --------------------------------- ##
 service_cat_options = service_1_data["service_category_1"].drop_duplicates().tolist()
-col1, col2 = st.columns(2)
+col1, col2, col3 = st.columns(3)
 with col1:
     st.markdown("""
     Use the following dropdown to get more detail on the service category that interested you.
@@ -246,10 +258,20 @@ def pmpm_by_service_category_1_2():
         options=service_cat_options,
         label_visibility='collapsed',
     )
+with col3:
+    selected_year_month = st.selectbox(
+        label="Select a Year Month",
+        options=["All Time"] + year_month_values,
+        label_visibility='collapsed',
+    )
 
 service_2_data = pmpm_by_service_category_1_2()
 service_2_data = service_2_data.loc[
     service_2_data["year_month"].str[:4].isin(selected_range)
+    & (
+        (service_2_data["year_month"] == selected_year_month) |
+        (selected_year_month == "All Time")
+    )
     & service_2_data["service_category_1"].isin([selected_service_cat])
 ].drop("service_category_1", axis=1).reset_index(drop=True)
 service_2_data = service_2_data\
@@ -257,11 +279,12 @@ def pmpm_by_service_category_1_2():
     [["paid_amount_sum", "member_month_count"]].sum()\
     .assign(paid_amount_pmpm = lambda x: x["paid_amount_sum"] / x["member_month_count"])
 
-
 service_2_chart = alt.Chart(service_2_data).mark_bar().encode(
     x="paid_amount_pmpm",
-    y=alt.Y("service_category_2", sort="-x"),
-    tooltip=["service_category_2", "paid_amount_pmpm"]
+    y=alt.Y("service_category_2", sort="-x", axis=alt.Axis(labelLimit=300)),
+    tooltip=["service_category_2", "paid_amount_pmpm"],
+).properties(
+    height=300
 )
 
 st.altair_chart(service_2_chart, use_container_width=True)
@@ -271,5 +294,8 @@ def pmpm_by_service_category_1_2():
 ## Cost Variables
 ## --------------------------------- ##
 st.markdown("## Cost Variable Quality Summary")
+st.markdown("""
+Explore common descriptive statistics to gain a comprehensive understanding of the quality and distribution of a particular claim cost variable.
+""")
 cost_summary_data = cost_summary()
 st.dataframe(cost_summary_data, use_container_width=True)
diff --git a/pages/03_chronic_conditions.py b/pages/03_chronic_conditions.py
new file mode 100644
index 0000000..92c0eb9
--- /dev/null
+++ b/pages/03_chronic_conditions.py
@@ -0,0 +1,97 @@
+import streamlit as st
+import pandas as pd
+import altair as alt
+import plost
+import util
+import components
+
+conn = util.connection(database="dev_lipsa")
+
+@st.cache_data
+def year_months():
+    query = """
+        select distinct
+           year(claim_end_date)::text || '-' ||
+             lpad(month(claim_end_date)::text, 2, '0')
+             as year_month
+           , sum(paid_amount)
+        from core.medical_claim
+        group by 1
+        having sum(paid_amount) > 10
+        order by 1
+    """
+    data = util.safe_to_pandas(conn, query)
+    return data
+
+
+@st.cache_data
+def pmpm_by_chronic_condition():
+    query = """
+        with conditions as (
+            select distinct
+                year(condition_date)::text || '-' || lpad(month(condition_date)::text, 2, '0') as year_month
+                , claim_id
+                , patient_id
+                , code
+                , condition
+                , condition_family
+            from core.condition
+            inner join chronic_conditions._value_set_tuva_chronic_conditions_hierarchy vs on condition.code = vs.icd_10_cm_code
+            where code_type = 'icd-10-cm'
+        )
+        , medical_spend as (
+            select
+                year(claim_start_date)::text || '-' || lpad(month(claim_start_date)::text, 2, '0') as year_month
+                , claim_id
+                , patient_id
+                , sum(paid_amount) as medical_paid_amount
+            from core.medical_claim
+            group by 1, 2, 3
+        ), merged as (
+            select
+                year_month
+                , condition_family
+                , sum(medical_paid_amount) as medical_paid_amount_sum
+            from conditions
+            join medical_spend using(patient_id, claim_id, year_month)
+            group by 1, 2
+        )
+        select
+            *
+        from merged
+        join pmpm._int_member_month_count using(year_month)
+        order by 2, 1
+    """
+    data = util.safe_to_pandas(conn, query)
+    return data
+
+
+## --------------------------------- ##
+## Chronic Condition
+## --------------------------------- ##
+st.markdown("## Chronic Condition")
+st.markdown("""
+Certain conditions will be more expensive in your population. Here we see that the top driver of spend
+is `Cardiovascular disease`. The second highest driver is `Metabolic Disease`.
+""")
+
+year_month_values = year_months()
+year_values = sorted(list(set([x[:4] for x in year_month_values['year_month']])))
+selected_range = components.year_slider(year_values)
+
+
+chronic_condition_data = pmpm_by_chronic_condition()
+chronic_condition_data = chronic_condition_data.loc[
+    chronic_condition_data["year_month"].str[:4].isin(selected_range)
+]
+chronic_condition_data = chronic_condition_data\
+    .groupby("condition_family", as_index=False)\
+    [["medical_paid_amount_sum", "member_month_count"]].sum()\
+    .assign(medical_paid_amount_pmpm = lambda x: x["medical_paid_amount_sum"] / x["member_month_count"])\
+    .round()\
+    .sort_values("medical_paid_amount_pmpm", ascending=False)
+
+st.dataframe(
+    chronic_condition_data[["condition_family", "medical_paid_amount_pmpm"]],
+    use_container_width=True
+)
diff --git a/pages/financial.py b/pages/financial.py
deleted file mode 100644
index 17ea339..0000000
--- a/pages/financial.py
+++ /dev/null
@@ -1,291 +0,0 @@
-import streamlit as st
-import pandas as pd
-import altair as alt
-import plost
-import util
-
-conn = util.connection(database="dev_lipsa")
-
-@st.cache_data
-def cost_summary():
-    query = """
-        select *
-        from dbt_lipsa.cost_summary
-        order by 1, 2, 3
-    """
-    data = util.safe_to_pandas(conn, query)
-    return data
-
-
-@st.cache_data
-def year_months():
-    query = """
-        select distinct
-           year(claim_end_date)::text || '-' ||
-             lpad(month(claim_end_date)::text, 2, '0')
-             as year_month
-           , sum(paid_amount)
-        from core.medical_claim
-        group by 1
-        having sum(paid_amount) > 10
-        order by 1
-    """
-    data = util.safe_to_pandas(conn, query)
-    return data
-
-
-@st.cache_data
-def summary_stats():
-    query = """
-        with medical as (
-           select distinct
-               year(claim_end_date)::text year
-               , sum(paid_amount) as medical_paid_amount
-           from core.medical_claim
-           group by 1
-        )
-        , pharmacy as (
-           select
-               year(dispensing_date)::text year
-               , sum(paid_amount) as pharmacy_paid_amount
-           from core.pharmacy_claim
-           group by 1
-        ), elig as (
-           select
-               substr(year_month, 0, 4) as year
-               , sum(member_month_count) as member_month_count
-           from pmpm._int_member_month_count
-           group by 1
-        )
-        select *
-        from medical
-        join pharmacy using(year)
-        join elig using(year)
-    """
-    data = util.safe_to_pandas(conn, query)
-    return data
-
-
-@st.cache_data
-def pmpm_by_service_category_1():
-    query = """
-        with spend_summary as (
-            select
-               year(claim_end_date)::text || '-' ||
-                 lpad(month(claim_end_date)::text, 2, '0')
-                 as year_month
-               , service_category_1
-               , sum(paid_amount) as paid_amount_sum
-            from core.medical_claim
-            group by 1, 2
-            having sum(paid_amount) > 0
-            order by 1, 2 desc
-        )
-        select
-           *
-           , paid_amount_sum / member_month_count as paid_amount_pmpm
-        from spend_summary
-        join pmpm._int_member_month_count using(year_month)
-    """
-    data = util.safe_to_pandas(conn, query)
-    return data
-
-
-@st.cache_data
-def pmpm_by_service_category_1_2():
-    query = """
-        with spend_summary as (
-            select
-               year(claim_end_date)::text || '-' ||
-                 lpad(month(claim_end_date)::text, 2, '0')
-                 as year_month
-               , service_category_1
-               , service_category_2
-               , sum(paid_amount) as paid_amount_sum
-            from core.medical_claim
-            group by 1, 2, 3
-            having sum(paid_amount) > 0
-            order by 1, 2, 3 desc
-        )
-        select
-           *
-           , paid_amount_sum / member_month_count as paid_amount_pmpm
-        from spend_summary
-        join pmpm._int_member_month_count using(year_month)
-    """
-    data = util.safe_to_pandas(conn, query)
-    return data
-
-
-@st.cache_data
-def pmpm_by_chronic_condition():
-    query = """
-        with conditions as (
-            select distinct
-                year(condition_date)::text || '-' || lpad(month(condition_date)::text, 2, '0') as year_month
-                , claim_id
-                , patient_id
-                , code
-                , condition
-                , condition_family
-            from core.condition
-            inner join chronic_conditions._value_set_tuva_chronic_conditions_hierarchy vs on condition.code = vs.icd_10_cm_code
-            where code_type = 'icd-10-cm'
-        )
-        , medical_spend as (
-            select
-                year(claim_start_date)::text || '-' || lpad(month(claim_start_date)::text, 2, '0') as year_month
-                , claim_id
-                , patient_id
-                , sum(paid_amount) as medical_paid_amount
-            from core.medical_claim
-            group by 1, 2, 3
-        ), merged as (
-            select
-                year_month
-                , condition_family
-                , sum(medical_paid_amount) as medical_paid_amount_sum
-            from conditions
-            join medical_spend using(patient_id, claim_id, year_month)
-            group by 1, 2
-        )
-        select
-            *
-        from merged
-        join pmpm._int_member_month_count using(year_month)
-        order by 2, 1
-    """
-    data = util.safe_to_pandas(conn, query)
-    return data
-
-
-
-year_month_values = year_months()
-year_values = sorted(list(set([x[:4] for x in year_month_values['year_month']])))
-
-## --------------------------------- ##
-## Header
-## --------------------------------- ##
-st.markdown("# Financial Overview")
-st.markdown(f"""
-These financial summary charts offer a concise and comprehensive snapshot of your organization's financial performance, providing key metrics and insights at a glance.
-
-The top three metrics you need to know about your data at all times are medical paid amount, pharmacy
-paid amount and member months.
-""")
-st.markdown(f"## Spend Summary in {year_values[-1]}")
-summary_stats_data = summary_stats()
-summary_stats_data = summary_stats_data.loc[summary_stats_data["year"] == year_values[-1]]
-col1, col2, col3 = st.columns(3)
-col1.metric("Medical Spend", util.human_format(summary_stats_data["medical_paid_amount"].iloc[0]))
-col2.metric("Pharmacy Spend", util.human_format(summary_stats_data["pharmacy_paid_amount"].iloc[0]))
-col3.metric("Member Months", util.human_format(summary_stats_data["member_month_count"].iloc[0]))
-
-st.divider()
-st.markdown("""
-Use the following time slider to cut the following charts by the year range of your interest.
-""")
-
-start_year, end_year = st.select_slider(
-    label="Select a range of years",
-    options=year_values,
-    value=(year_values[0], year_values[-1]),
-    label_visibility='collapsed'
-)
-selected_range = year_values[year_values.index(start_year): year_values.index(end_year)+1]
-
-
-
-## --------------------------------- ##
-## Service Category 1
-## --------------------------------- ##
-st.markdown("## Service Category")
-st.markdown("""
-Analyzing medical claims by service category allows healthcare insurers to identify patterns, trends, and cost drivers in the service type being performed for the patient.
-
-Here we see that, **outpatient and inpatient spend** have the highest amount of variation over time. Overall spend
-seems to **spike in 2018**, driven by a large increase in outpatient spend 1 month and inpatient
-spend the next.
-""")
-service_1_data = pmpm_by_service_category_1()
-service_1_data = service_1_data.loc[
-    service_1_data["year_month"].str[:4].isin(selected_range)
-]
-
-service_1_chart = alt.Chart(service_1_data).mark_bar().encode(
-    x="year_month",
-    y=alt.Y("paid_amount_pmpm"),
-    color="service_category_1:N",
-    tooltip=["service_category_1", "paid_amount_pmpm"]
-).configure_legend(
-  orient='bottom'
-)
-
-st.altair_chart(service_1_chart, use_container_width=True)
-
-## --------------------------------- ##
-## Service Category 2
-## --------------------------------- ##
-service_cat_options = service_1_data["service_category_1"].drop_duplicates().tolist()
-col1, col2 = st.columns(2)
-with col1:
-    st.markdown("""
-    Use the following dropdown to get more detail on the service category that interested you.
-    """)
-with col2:
-    selected_service_cat = st.selectbox(
-        label="Select a Service Category",
-        options=service_cat_options,
-        label_visibility='collapsed',
-    )
-
-service_2_data = pmpm_by_service_category_1_2()
-service_2_data = service_2_data.loc[
-    service_2_data["year_month"].str[:4].isin(selected_range)
-    & service_2_data["service_category_1"].isin([selected_service_cat])
-].drop("service_category_1", axis=1).reset_index(drop=True)
-service_2_data = service_2_data\
-    .groupby("service_category_2", as_index=False)\
-    [["paid_amount_sum", "member_month_count"]].sum()\
-    .assign(paid_amount_pmpm = lambda x: x["paid_amount_sum"] / x["member_month_count"])
-
-
-service_2_chart = alt.Chart(service_2_data).mark_bar().encode(
-    x="paid_amount_pmpm",
-    y=alt.Y("service_category_2", sort="-x"),
-    tooltip=["service_category_2", "paid_amount_pmpm"]
-)
-
-st.altair_chart(service_2_chart, use_container_width=True)
-
-## --------------------------------- ##
-## Chronic Condition
-## --------------------------------- ##
-st.markdown("## Chronic Condition")
-st.markdown("""
-Certain conditions will be more expensive in your population. Here we see that the top driver of spend
-is `Cardiovascular disease`. The second highest driver is `Metabolic Disease`.
-""")
-chronic_condition_data = pmpm_by_chronic_condition()
-chronic_condition_data = chronic_condition_data.loc[
-    chronic_condition_data["year_month"].str[:4].isin(selected_range)
-]
-chronic_condition_data = chronic_condition_data\
-    .groupby("condition_family", as_index=False)\
-    [["medical_paid_amount_sum", "member_month_count"]].sum()\
-    .assign(medical_paid_amount_pmpm = lambda x: x["medical_paid_amount_sum"] / x["member_month_count"])\
-    .round()\
-    .sort_values("medical_paid_amount_pmpm", ascending=False)
-
-st.dataframe(
-    chronic_condition_data[["condition_family", "medical_paid_amount_pmpm"]],
-    use_container_width=True
-)
-
-
-## --------------------------------- ##
-## Cost Variables
-## --------------------------------- ##
-st.markdown("## Cost Variable Quality Summary")
-cost_summary_data = cost_summary()
-st.dataframe(cost_summary_data, use_container_width=True)