fix merge conflicts

components.py

@@ -7,34 +7,38 @@
 
 def financial_bans(summary_stats_data):
  # TODO: Add Year filter
+ """Takes dataframe of financial summary data at the year level and displays BANs
+ for med spend, pharm spend, member months and average pmpm. Can handle dataframe with
+ multiple years or a single year. Dataframe should be pre-filtered to the time frame desired.
+ """
+ year_values = sorted(list(set(summary_stats_data["year"])))
+ summary_stats_data = summary_stats_data.copy(deep=True)
+ summary_stats_data = summary_stats_data.loc[
+ summary_stats_data["year"].isin(year_values)
+ ]
+ if len(year_values) == 1:
+ year_string = year_values[0]
+ else:
+ year_string = "{} - {}".format(year_values[0], year_values[-1])
  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 pmpm.
- """
+ paid amount and pmpm."""
  )
- st.markdown(f"## Spend Summary in {year_values[-1]}")
+ st.markdown(f"### Spend Summary in {year_string}")
 
- summary_stats_data = summary_stats_data.copy(deep=True)
- 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]),
- )
+ med_spend = summary_stats_data["medical_paid_amount"].sum()
+ pharm_spend = summary_stats_data["pharmacy_paid_amount"].sum()
+ member_mon_count = summary_stats_data["member_month_count"].sum()
+ avg_pmpm = med_spend / member_mon_count
+ col1, col2, col3, col4 = st.columns(4)
+ col1.metric("Medical Spend", util.human_format(med_spend))
+ col2.metric("Pharmacy Spend", util.human_format(pharm_spend))
+ col3.metric("Member Months", util.human_format(member_mon_count))
+ col4.metric("Average PMPM", util.human_format(avg_pmpm))
 
 
 def year_slider(year_values):

main_page.py

@@ -5,6 +5,7 @@
 from dotenv import load_dotenv
 import os
 import util
+import components as comp
 
 load_dotenv()
 
@@ -13,7 +14,39 @@
 
 
 @st.cache_data
-def summary_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_data():
  query = """SELECT PT.*, PB.MEMBER_COUNT, PHARMACY_SPEND FROM TUVA_PROJECT_DEMO.PMPM.PMPM_TRENDS PT
  LEFT JOIN (SELECT CONCAT(LEFT(YEAR_MONTH, 4), '-', RIGHT(YEAR_MONTH, 2)) AS YEAR_MONTH,
  COUNT(*) AS MEMBER_COUNT,
@@ -24,6 +57,9 @@ def summary_data():
 
  data = util.safe_to_pandas(conn, query)
  data["year_month"] = pd.to_datetime(data["year_month"], format="%Y-%m").dt.date
+ data["year"] = pd.to_datetime(data["year_month"], format="%Y-%m").dt.year
+ data["pharmacy_spend"] = data["pharmacy_spend"].astype(float)
+
  return data
 
 
@@ -58,47 +94,41 @@ def age_data():
  return data
 
 
-data = summary_data()
+cost_data = summary_stats()
+data = pmpm_data()
 demo_gender = gender_data()
 demo_race = race_data()
 demo_age = age_data()
 
 st.markdown("# Summary of Claims")
-start_date, end_date = st.select_slider(
+start_year, end_year = st.select_slider(
  "Select date range for claims summary",
- options=data["year_month"].sort_values(),
- value=(data["year_month"].min(), data["year_month"].max()),
+ options=sorted(list(set(data["year"]))),
+ value=(data["year"].min(), data["year"].max()),
 )
-filtered_data = data.loc[
- (data["year_month"] >= start_date) & (data["year_month"] <= end_date), :
+filtered_cost_data = cost_data.loc[
+ (cost_data["year"] >= str(start_year)) & (cost_data["year"] <= str(end_year)), :
+]
+filtered_pmpm_data = data.loc[
+ (data["year"] >= start_year) & (data["year"] <= end_year), :
 ]
 
+
 st.markdown("### High Level Summary")
 st.markdown(
  """At a glance, see the total medical spend and PMPM for the chosen time period. As well as a trend
 graph for other important financial metrics"""
 )
 st.sidebar.markdown("# Claims Summary")
-
-# Summary Metrics
-total_spend = filtered_data["medical_spend"].sum()
-total_member_months = filtered_data["member_month_count"].sum()
-avg_pmpm = total_spend / total_member_months
-total_pharm_spend = filtered_data["pharmacy_spend"].sum()
-
-col1, col2, col3, col4 = st.columns([1, 1, 1, 1])
-col1.metric("Total Medical Spend", "${}".format(util.human_format(total_spend)))
-col2.metric("Total Member Months", util.human_format(total_member_months))
-col3.metric("Average PMPM", "${}".format(util.human_format(avg_pmpm)))
-col4.metric("Total Pharmacy Spend", "${}".format(util.human_format(total_pharm_spend)))
+comp.financial_bans(filtered_cost_data)
 
 st.divider()
 y_axis = st.selectbox(
- "Select Metric for Trend Line", [x for x in data.columns if x != "year_month"]
+ "Select Metric for Trend Line", [x for x in data.columns if "year" not in x]
 )
 
 if y_axis:
- st.line_chart(filtered_data, x="year_month", y=y_axis)
+ st.line_chart(filtered_pmpm_data, x="year_month", y=y_axis)
 
 # Patient Demographic Section
 st.divider()
@@ -128,6 +158,8 @@ def age_data():
  legend=None,
  use_container_width=True,
  title="Counts by Age Group",
+ direction="horizontal",
+ height=300,
  )
 
 plost.bar_chart(