move data calls to one file

data.py

@@ -0,0 +1,307 @@
+import streamlit as st
+import pandas as pd
+import util
+
+conn = util.connection(database="dev_lipsa")
+
+
+@st.cache_data
+def test_results():
+ query = """
+ select * from data_profiling.test_result
+ """
+ data = util.safe_to_pandas(conn, query)
+ return data
+
+
+@st.cache_data
+def use_case():
+ query = """
+ select * from data_profiling.use_case
+ """
+ data = util.safe_to_pandas(conn, query)
+ return data
+
+
+@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
+ year
+ , lag(year) over(order by year) as prior_year
+ , medical_paid_amount + pharmacy_paid_amount as current_period_total_paid
+ , lag(medical_paid_amount + pharmacy_paid_amount)
+ over(order by year) as prior_period_total_paid
+ , div0null(
+ medical_paid_amount + pharmacy_paid_amount
+ - lag(medical_paid_amount + pharmacy_paid_amount) over(order by year),
+ lag(medical_paid_amount + pharmacy_paid_amount) over(order by year)
+ ) as pct_change_total_paid
+ , medical_paid_amount as current_period_medical_paid
+ , lag(medical_paid_amount) over(order by year) as prior_period_medical_paid
+ , div0null(
+ medical_paid_amount - lag(medical_paid_amount) over(order by year),
+ lag(medical_paid_amount) over(order by year)
+ ) as pct_change_medical_paid
+ , pharmacy_paid_amount as current_period_pharmacy_paid
+ , lag(pharmacy_paid_amount) over(order by year) as prior_period_pharmacy_paid
+ , div0null(
+ pharmacy_paid_amount - lag(pharmacy_paid_amount) over(order by year),
+ lag(pharmacy_paid_amount) over(order by year)
+ ) as pct_change_pharmacy_paid
+ , member_month_count as current_period_member_months
+ , lag(member_month_count) over(order by year) as prior_period_member_months
+ , div0null(
+ member_month_count - lag(member_month_count) over(order by year),
+ lag(member_month_count) over(order by year)
+ ) as pct_change_member_months
+ 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_claim_type():
+ query = """
+ with spend_summary as (
+ select
+ year(claim_end_date)::text || '-' ||
+ lpad(month(claim_end_date)::text, 2, '0')
+ as year_month
+ , claim_type
+ , 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
+ ), 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 together
+ 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():
+ 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_data():
+ query = """SELECT PT.*, PB.MEMBER_COUNT, PHARMACY_SPEND FROM PMPM.PMPM_TRENDS PT
+ LEFT JOIN (SELECT CONCAT(LEFT(YEAR_MONTH, 4), '-', RIGHT(YEAR_MONTH, 2)) AS YEAR_MONTH,
+ COUNT(*) AS MEMBER_COUNT,
+ SUM(PHARMACY_PAID) AS PHARMACY_SPEND
+ FROM PMPM.PMPM_BUILDER
+ GROUP BY YEAR_MONTH) AS PB
+ ON PT.YEAR_MONTH = PB.YEAR_MONTH;"""
+
+ 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
+
+
+@st.cache_data
+def gender_data():
+ query = """SELECT GENDER, COUNT(*) AS COUNT FROM CORE.PATIENT GROUP BY 1;"""
+ data = util.safe_to_pandas(conn, query)
+ return data
+
+
+@st.cache_data
+def race_data():
+ query = """SELECT RACE, COUNT(*) AS COUNT FROM CORE.PATIENT GROUP BY 1;"""
+ data = util.safe_to_pandas(conn, query)
+ return data
+
+
+@st.cache_data
+def age_data():
+ query = """SELECT CASE
+ WHEN div0(current_date() - BIRTH_DATE, 365) < 49 THEN '34-48'
+ WHEN div0(current_date() - BIRTH_DATE, 365) >= 49 AND div0(current_date() - BIRTH_DATE, 365) < 65 THEN '49-64'
+ WHEN div0(current_date() - BIRTH_DATE, 365) >= 65 AND div0(current_date() - BIRTH_DATE, 365) < 79 THEN '65-78'
+ WHEN div0(current_date() - BIRTH_DATE, 365) >= 79 AND div0(current_date() - BIRTH_DATE, 365) < 99 THEN '79-98'
+ WHEN div0(current_date() - BIRTH_DATE, 365) >= 99 THEN '99+' END
+ AS AGE_GROUP,
+ COUNT(*) AS COUNT
+ FROM CORE.PATIENT
+ GROUP BY 1
+ 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
+
+
+@st.cache_data
+def condition_data():
+ query = """SELECT
+ CONCAT(date_part(year, FIRST_DIAGNOSIS_DATE), '-', lpad(date_part(month, FIRST_DIAGNOSIS_DATE), 2, 0)) AS DIAGNOSIS_YEAR_MONTH,
+ CONDITION,
+ COUNT(*) AS CONDITION_CASES,
+ AVG(LAST_DIAGNOSIS_DATE + 1 - FIRST_DIAGNOSIS_DATE) AS DIAGNOSIS_DURATION
+ FROM CHRONIC_CONDITIONS.TUVA_CHRONIC_CONDITIONS_LONG
+ GROUP BY 1,2
+ ORDER BY 3 DESC;"""
+ data = util.safe_to_pandas(conn, query)
+ data["diagnosis_year"] = pd.to_datetime(
+ data["diagnosis_year_month"]
+ ).dt.year.astype(str)
+ return data