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import streamlit as st
import pandas as pd
import random
import plotly.graph_objects as go
import plotly.express as px

def health_game():
    # Define the states and conditions of interest
    states = ["Minnesota", "Florida", "California"]
    top_n = 10

    # Define the list dictionary of top 10 health conditions descending by cost, with emojis, treatment recommendation and potential savings
    health_conditions = [
        {"condition": "๐Ÿ’” Heart disease", "emoji": "๐Ÿ’—", "spending": 214.3, "treatment": "Regular checkups with a cardiologist", "savings": "$1000"},
        {"condition": "๐Ÿค• Trauma-related disorders", "emoji": "๐Ÿš‘", "spending": 198.6, "treatment": "Counseling and physical therapy", "savings": "$500"},
        {"condition": "๐Ÿฆ€ Cancer", "emoji": "๐ŸŽ—๏ธ", "spending": 171.0, "treatment": "Early detection and treatment", "savings": "$2000"},
        {"condition": "๐Ÿง  Mental disorders", "emoji": "๐Ÿง˜", "spending": 150.8, "treatment": "Therapy and medication", "savings": "$1500"},
        {"condition": "๐Ÿฆด Osteoarthritis and joint disorders", "emoji": "๐Ÿฅ", "spending": 142.4, "treatment": "Low-impact exercise and physical therapy", "savings": "$800"},
        {"condition": "๐Ÿ’‰ Diabetes", "emoji": "๐Ÿฉธ", "spending": 107.4, "treatment": "Regular checkups and medication", "savings": "$1200"},
        {"condition": "๐Ÿ˜ฎโ€๐Ÿ’จ Chronic obstructive pulmonary disease and asthma", "emoji": "๐Ÿ˜ฎโ€๐Ÿ’จ", "spending": 91.0, "treatment": "Inhalers and breathing exercises", "savings": "$600"},
        {"condition": "๐Ÿฉบ Hypertension", "emoji": "๐Ÿ’‰", "spending": 83.9, "treatment": "Lifestyle changes and medication", "savings": "$900"},
        {"condition": "๐Ÿ”ฌ Hyperlipidemia", "emoji": "๐Ÿ”ฌ", "spending": 83.9, "treatment": "Lifestyle changes and medication", "savings": "$700"},
        {"condition": "๐Ÿฆด Back problems", "emoji": "๐Ÿง", "spending": 67.0, "treatment": "Physical therapy and exercise", "savings": "$400"}
    ]

    # Create a DataFrame from the list dictionary
    df_top_conditions = pd.DataFrame(health_conditions)

    # Calculate the total spending
    total_spending = round(df_top_conditions["spending"].sum(), 1)

    # Define the roll function
    def roll():
        rolls = [random.randint(1, 10) for _ in range(1000)]
        frequencies = [rolls.count(i) for i in range(1, 11)]
        return frequencies

    # Define the sunburst chart
    fig_sunburst = go.Figure(go.Sunburst(
        labels=df_top_conditions["emoji"] + " " + df_top_conditions["condition"],
        parents=[""] * top_n,
        values=df_top_conditions["spending"],
        maxdepth=2
    ))

    # Customize the layout of the sunburst chart
   
    #fig_sunburst.update_layout(title=f"Top {top_n} Health Conditions in {', '.join(states)} by Spending (Total: ${total_spending}B)")
    fig_sunburst.update_layout(title=f"Top {top_n} Health Conditions by Spending (Total: ${total_spending}B)")

    # Display the sunburst chart and variants per condition in the Streamlit app
    st.plotly_chart(fig_sunburst)
    condition_idx = st.selectbox("Select your current health condition", df_top_conditions.index)
    row = df_top_conditions.loc[condition_idx]

    st.write(f"Based on the severity of your {row['condition']}, we recommend {row['treatment']} for early treatment. This could save you up to {row['savings']} in healthcare costs.")

    frequencies = roll()
    fig_bar = px.bar(x=[f"Variant {i}" for i in range(1, 11)], y=frequencies[:10], labels={'x': 'Variant', 'y': 'Frequency'})
    fig_bar.update_layout(title=f"Variants of {row['condition']} ({row['emoji']})")
    st.plotly_chart(fig_bar)


health_game()

import streamlit as st

health_conditions = [
    {
        "name": "Diabetes",
        "patient_population": "๐Ÿ‘ฅ Patients with diabetes",
        "icd10_code_range": "๐Ÿ“ˆ E08 - E13",
        "gap_identification": "๐Ÿ” Identifying patients with HbA1c levels > 9%, patients who have not received eye exams, and patients who have not received nephropathy screening.",
        "gap_closure_process": "๐Ÿ› ๏ธ Providing timely and appropriate interventions, such as regular HbA1c testing, eye exams, and nephropathy screening."
    },
    {
        "name": "Cardiovascular Disease",
        "patient_population": "๐Ÿ‘ฅ Patients with cardiovascular disease",
        "icd10_code_range": "๐Ÿ“ˆ I20 - I25",
        "gap_identification": "๐Ÿ” Identifying patients who have not received appropriate medication therapy, patients who have not received lipid profile screening, and patients who have not received blood pressure control.",
        "gap_closure_process": "๐Ÿ› ๏ธ Providing timely and appropriate interventions, such as medication therapy, lipid profile screening, and blood pressure control."
    },
    {
        "name": "Asthma",
        "patient_population": "๐Ÿ‘ฅ Patients with asthma",
        "icd10_code_range": "๐Ÿ“ˆ J45",
        "gap_identification": "๐Ÿ” Identifying patients who have not received appropriate medication therapy, patients who have not received asthma action plans, and patients who have not received follow-up care after hospitalization.",
        "gap_closure_process": "๐Ÿ› ๏ธ Providing timely and appropriate interventions, such as medication therapy, asthma action plans, and follow-up care after hospitalization."
    },
    {
        "name": "Behavioral Health",
        "patient_population": "๐Ÿ‘ฅ Patients with behavioral health conditions",
        "icd10_code_range": "๐Ÿ“ˆ F00 - F99",
        "gap_identification": "๐Ÿ” Identifying patients who have not received appropriate medication therapy, patients who have not received appropriate counseling, and patients who have not received follow-up care after hospitalization.",
        "gap_closure_process": "๐Ÿ› ๏ธ Providing timely and appropriate interventions, such as medication therapy, counseling, and follow-up care after hospitalization."
    },
    {
        "name": "Cancer Screening",
        "patient_population": "๐Ÿ‘ฅ Patients eligible for cancer screening",
        "icd10_code_range": "๐Ÿ“ˆ C00 - D48",
        "gap_identification": "๐Ÿ” Identifying patients who have not received appropriate cancer screening tests, such as mammograms, colonoscopies, and cervical cancer screening.",
        "gap_closure_process": "๐Ÿ› ๏ธ Providing timely and appropriate cancer screening tests."
    },
    {
        "name": "Immunizations",
        "patient_population": "๐Ÿ‘ฅ Patients eligible for immunizations",
        "icd10_code_range": "๐Ÿ“ˆ Z23",
        "gap_identification": "๐Ÿ” Identifying patients who have not received appropriate immunizations, such as flu shots, pneumococcal vaccines, and HPV vaccines.",
        "gap_closure_process": "๐Ÿ› ๏ธ Providing timely and appropriate immunizations."
    },
    {
        "name": "Maternity Care",
        "patient_population": "๐Ÿ‘ฅ Pregnant patients",
        "icd10_code_range": "๐Ÿ“ˆ O00 - O99",
        "gap_identification": "๐Ÿ” Identifying patients who have not received appropriate prenatal care, patients who have not received postpartum care, and patients who have not received appropriate screenings.",
        "gap_closure_process": "๐Ÿ› ๏ธ Providing timely and appropriate prenatal care, postpartum care, and screenings."
    },
    {
        "name": "Osteoporosis",
        "patient_population": "๐Ÿ‘ฅ Patients with osteoporosis",
        "icd10_code_range": "๐Ÿ“ˆ M80 - M82",
        "gap_identification": "๐Ÿ” Identifying patients who have not received appropriate medication therapy, patients who have not received bone density testing, and patients who have not received appropriate follow-up care.",
        "gap_closure_process": "๐Ÿ› ๏ธ Providing timely and appropriate interventions, such as medication therapy, bone density testing, and follow-up care."
    },
    {
        "name": "Chronic Kidney Disease",
        "patient_population": "๐Ÿ‘ฅ Patients with chronic kidney disease",
        "icd10_code_range": "๐Ÿ“ˆ N18",
        "gap_identification": "๐Ÿ” Identifying patients who have not received appropriate medication therapy, patients who have not received appropriate monitoring, and patients who have not received appropriate follow-up care.",
        "gap_closure_process": "๐Ÿ› ๏ธ Providing timely and appropriate interventions, such as medication therapy, monitoring, and follow-up care."
    },
    {
        "name": "Depression",
        "patient_population": "๐Ÿ‘ฅ Patients with depression",
        "icd10_code_range": "๐Ÿ“ˆ F32 - F33",
        "gap_identification": "๐Ÿ” Identifying patients who have not received appropriate medication therapy, patients who have not received appropriate counseling, and patients who have not received appropriate follow-up care.",
        "gap_closure_process": "๐Ÿ› ๏ธ Providing timely and appropriate interventions, such as medication therapy, counseling, and follow-up care."
    },
    {
        "name": "Attention Deficit Hyperactivity Disorder (ADHD)",
        "patient_population": "๐Ÿ‘ฅ Patients with ADHD",
        "icd10_code_range": "๐Ÿ“ˆ F90",
        "gap_identification": "๐Ÿ” Identifying patients who have not received appropriate medication therapy, patients who have not received appropriate counseling, and patients who have not received appropriate follow-up care.",
        "gap_closure_process": "๐Ÿ› ๏ธ Providing timely and appropriate interventions, such as medication therapy, counseling, and follow-up care."
    }
]

st.title("HEDIS Gap Closure Process for Top 10 Health Conditions")

for condition in health_conditions:
    st.header(f"{condition['name']}")
    st.markdown(f"{condition['patient_population']}")
    st.markdown(f"{condition['icd10_code_range']}")
    st.markdown(f"{condition['gap_identification']}")
    st.markdown(f"{condition['gap_closure_process']}")

st.markdown("""
# References on gap closure and reporting for value-based care:

These resources provide information and guidance on how to close gaps in care, improve quality scores, and report on quality measures to receive compensation in value-based care. They also discuss the use of health information technology and pay for performance programs to improve quality reporting and outcomes.

1. "Closing Gaps in Care: How to Improve Quality Scores and Boost Reimbursement" by Becker's Hospital Review - https://www.beckershospitalreview.com/close-care-documentation-gaps-expert-tips-for-better-quality-reporting.html
2. "Closing Gaps in Care with Advanced Interoperability Capabilities" by HealthPayerIntelligence - https://healthpayerintelligence.com/news/closing-gaps-in-care-with-advanced-interoperability-capabilities
3. https://www.healthit.gov/topic/federal-incentive-programs/MACRA/MIPS/quality-measures-reporting
4. https://www.ama-assn.org/practice-management/medicare-medicaid/quality-payment-program-qpp-specifics#:~:text=The%20QPP%20was%20created%20by,Incentive%20Payment%20System%20(MIPS).
5. https://qpp.cms.gov/about/qpp-overview
""")


st.markdown("""
# Top 20 HEDIS Gap Closure Evidence

Below is a list of the top 20 HEDIS gap closure evidence, along with the corresponding clinical codes that best represent the evidence submitted to show the gap is closed.

1. Childhood Immunization: % of children immunized by age two ๐Ÿง’๐Ÿ’‰ | Clinical Code: ICD10 Z28.2
2. Breast Cancer Screening: % of women with mammogram in past 2 yrs ๐Ÿฉบ๐ŸŽ€ | Clinical Code: CPT 77067
3. Colorectal Cancer Screening: % of adults screened for colorectal cancer ๐Ÿฉบ๐Ÿ’ฉ | Clinical Code: CPT 82274
4. Comprehensive Diabetes Care: % of diabetic patients who had all recommended tests ๐Ÿฉบ๐Ÿฉน | Clinical Code: LOINC 4548-4
5. Controlling High Blood Pressure: % of patients with controlled blood pressure ๐Ÿฉบ๐Ÿ’Š | Clinical Code: ICD10 I10
6. Medication Management for Asthma: % of asthma patients with proper meds ๐Ÿ’Š๐ŸŒฌ๏ธ | Clinical Code: SNOMED 195967001
7. Follow-up After Mental Illness Hospitalization: % of patients with follow-up care ๐Ÿฉบ๐Ÿฅ | Clinical Code: HCPCS G0181
8. Prenatal & Postpartum Care: % of pregnant women with proper care ๐Ÿคฐ๐Ÿฉบ | Clinical Code: ICD10 Z34
9. Comprehensive Eye Exam: % of diabetic patients with eye exam ๐Ÿฉบ๐Ÿ‘€ | Clinical Code: CPT 92014
10. Childhood Weight Assessment: % of children with BMI assessment ๐Ÿง’๐Ÿ“ | Clinical Code: ICD10 Z00.121
11. Chlamydia Screening in Women: % of sexually active women screened ๐Ÿฉบ๐Ÿ‘ฉ | Clinical Code: CPT 87491
12. Avoidance of Antibiotic Treatment for Acute Bronchitis: % of patients without antibiotics ๐Ÿฉบ๐Ÿ’Š | Clinical Code: ICD10 J20.9
13. Osteoporosis Management in Women: % of women with bone density test ๐Ÿฉบ๐Ÿ’ช | Clinical Code: CPT 77080
14. Use of High-Risk Medications in the Elderly: % of elderly with safe meds ๐Ÿ’Š๐Ÿ‘ด๐Ÿ‘ต | Clinical Code: HCPCS G9612
15. Diabetes Screening for Schizophrenia or Bipolar Disorder: % of patients with mental illness screened ๐Ÿง ๐Ÿฉบ | Clinical Code: SNOMED 169609005
16. All-Cause Readmissions: % of patients readmitted within 30 days ๐Ÿฉบ๐Ÿฅ | Clinical Code: ICD10 Z51.5
17. Antidepressant Medication Management: % of depressed patients with proper meds & follow-up ๐Ÿฉบ๐Ÿง  | Clinical Code: CPT 96127
18. Follow-up Care for Children Prescribed ADHD Medication: % of children with follow-up care ๐Ÿฉบ๐Ÿง’ | Clinical Code: ICD10 F90
19. Imaging Studies for Low Back Pain: % of patients without imaging studies ๐Ÿฉบ๐Ÿ“Š | Clinical Code: ICD10 M54.5
20. Spirometry Testing for COPD: % of COPD patients with spirometry testing ๐Ÿฉบ๐Ÿซ | Clinical Code: CPT 94010

""")

st.markdown("""

ICD10 Code Categories for ICD10 Diagnosis used in ADT, Health Service Cases, and Claims :

๐Ÿ’‰ A, B: Infectious and parasitic diseases
๐Ÿงฌ C, D0-D4: Neoplasms (tumors)
๐Ÿ’‰ D5-D9: Blood disorders and immune system diseases
๐Ÿฝ๏ธ E: Endocrine, nutritional, and metabolic diseases
๐Ÿง  F: Mental, behavioral, and neurodevelopmental disorders
๐Ÿง  G: Diseases of the nervous system
๐Ÿ‘๏ธ H0-H5: Eye and adnexa (accessory organs) diseases
๐Ÿ‘‚ H6-H9: Ear and mastoid process diseases
โค๏ธ I: Circulatory system diseases
๐Ÿ’จ J: Respiratory system diseases
๐Ÿฝ๏ธ K: Digestive system diseases
๐Ÿฆต L: Skin and subcutaneous tissue diseases
๐Ÿ’ช M: Musculoskeletal and connective tissue diseases
๐Ÿ† N: Genitourinary system diseases
๐Ÿ‘ถ O: Pregnancy, childbirth, and the puerperium (postpartum) issues
๐Ÿ‘ถ P: Perinatal (before and after birth) conditions
๐Ÿ‘ถ Q: Congenital (present at birth) malformations and deformations
๐Ÿค’ R: Symptoms, signs, and abnormal clinical and laboratory findings
๐Ÿค• S, T: Injury, poisoning, and other consequences of external causes
๐Ÿฅ U: Special-purpose codes
๐Ÿ” V, W, Y: External causes of morbidity and mortality
๐Ÿงฌ Z: Factors influencing health status and contact with health services

""")