app.py

#Prompt: Create a short Streamlit python application which summarizes  health assessments, reporting processes, standards bodies, and lists top ten questions and followup questions along with terminology relationships to ICD10 diagnosis, CPT and HCPCS services, OMS Omaha System types and codes, SNOMED CT US codes, and LOINC codes along with LOINC Panels and Forms codes for standardized assessments.  Use three assessments: PHQ9, GADD7, Social Determinants of Health as the three assessments. 

import streamlit as st
import pandas as pd

st.set_page_config(layout="wide")

st.title("Assessments Clinical Terminology FHIR PHQ GAD SDOH")

st.write("Select an assessment:")

assessment = st.selectbox("", ["PHQ9", "GAD7", "Social Determinants of Health"])

st.write(f"Selected assessment: {assessment}")

data = {
    "Assessment": [assessment],
    "Top 10 Questions": [""],
    "Follow-up Questions": [""],
    "ICD10 Diagnosis": [""],
    "CPT Services": [""],
    "HCPCS Services": [""],
    "OMS Omaha System Types and Codes": [""],
    "SNOMED CT US Codes": [""],
    "LOINC Codes": [""],
    "LOINC Panels and Forms Codes": [""],
}

# Sample data
if assessment == "PHQ9":
    data["Top 10 Questions"] = ["Q1, Q2, Q3, Q4, Q5, Q6, Q7, Q8, Q9"]
    data["Follow-up Questions"] = ["FQ1, FQ2, FQ3"]
    data["ICD10 Diagnosis"] = ["F32.0, F32.1, F32.2, F32.3, F32.4, F32.5"]
    data["CPT Services"] = ["96127, 96130"]
    data["HCPCS Services"] = ["G0444"]
    data["OMS Omaha System Types and Codes"] = ["1110, 1112"]
    data["SNOMED CT US Codes"] = ["67678005, 19335004"]
    data["LOINC Codes"] = ["72104-3, 72100-1"]
    data["LOINC Panels and Forms Codes"] = ["LP29608-4"]
elif assessment == "GAD7":
    data["Top 10 Questions"] = ["Q1, Q2, Q3, Q4, Q5, Q6, Q7"]
    data["Follow-up Questions"] = ["FQ1, FQ2"]
    data["ICD10 Diagnosis"] = ["F41.1"]
    data["CPT Services"] = ["96127"]
    data["HCPCS Services"] = ["G0444"]
    data["OMS Omaha System Types and Codes"] = ["1110, 1112"]
    data["SNOMED CT US Codes"] = ["19602008, 713675009"]
    data["LOINC Codes"] = ["71206-5"]
    data["LOINC Panels and Forms Codes"] = ["LP29608-4"]
elif assessment == "Social Determinants of Health":
    data["Top 10 Questions"] = ["Q1, Q2, Q3, Q4, Q5, Q6, Q7, Q8, Q9, Q10"]
    data["Follow-up Questions"] = ["FQ1, FQ2, FQ3, FQ4"]
    data["ICD10 Diagnosis"] = ["Z55-Z65"]
    data["CPT Services"] = ["99421, 99422, 99423"]
    data["HCPCS Services"] = ["T1015"]
    data["OMS Omaha System Types and Codes"] = ["1110, 1112"]
    data["SNOMED CT US Codes"] = ["718555008, 722109000"]
    data["LOINC Codes"] = ["93029-2, 93030-0"]
    data["LOINC Panels and Forms Codes"] = ["LP29609-2"]
df = pd.DataFrame(data)
st.write(df)
st.write(
f"Here is a summary of the selected assessment ({assessment}) with the top 10 questions, "
"follow-up questions, and their relationships to various codes and standards."
)

# List the questions in the GADD 7 and describe in three sentence markdown outline how the questions are used and what ICD10, LOINC, and SNOMED codes might be used for each if answered yes.
st.markdown("""
# GAD-7

The GAD-7 (Generalized Anxiety Disorder-7) is a self-report questionnaire for screening and assessing the severity of anxiety symptoms. It consists of 7 questions, each focusing on a specific symptom of generalized anxiety disorder experienced over the past two weeks:

Feeling nervous, anxious, or on edge
Not being able to stop or control worrying
Worrying too much about different things
Trouble relaxing
Being so restless that it's hard to sit still
Becoming easily annoyed or irritable
Feeling afraid as if something awful might happen
The GAD-7 is used to help identify individuals with generalized anxiety disorder and monitor the severity of their symptoms. For each question answered with a significant presence of the symptom, relevant ICD-10, LOINC, and SNOMED codes might be assigned as follows:

ICD-10: A positive GAD-7 result could be assigned a diagnosis code for Generalized Anxiety Disorder, which is F41.1.
LOINC: The LOINC code for the GAD-7 assessment is 71206-5, which represents the overall score of the questionnaire.
SNOMED: SNOMED CT codes for anxiety symptoms could be used to represent each specific symptom identified by the GAD-7, such as 197480006 for excessive worrying, 399166001 for difficulty relaxing, or 228147008 for fear of losing control.
Keep in mind that the codes provided are for illustration purposes and may not be exhaustive. Always consult authoritative sources for accurate and up-to-date coding information.

""")

st.markdown("""
# PHQ-9

The PHQ-9 (Patient Health Questionnaire-9) is a self-administered questionnaire used to screen and assess the severity of depression symptoms. It consists of 9 questions, each focusing on a specific symptom of depression experienced over the past two weeks:

Little interest or pleasure in doing things
Feeling down, depressed, or hopeless
Trouble falling or staying asleep, or sleeping too much
Feeling tired or having little energy
Poor appetite or overeating
Feeling bad about yourself or that you are a failure or have let yourself or your family down
Trouble concentrating on things, such as reading the newspaper or watching television
Moving or speaking so slowly that other people could have noticed, or the opposite - being so fidgety or restless that you have been moving around a lot more than usual
Thoughts that you would be better off dead or of hurting yourself in some way
The PHQ-9 is used to help identify individuals with depression and monitor the severity of their symptoms. For each question answered with a significant presence of the symptom, relevant ICD-10, LOINC, and SNOMED codes might be assigned as follows:

ICD-10: A positive PHQ-9 result could be assigned a diagnosis code for Major Depressive Disorder, such as F32.0 (mild), F32.1 (moderate), or F32.2 (severe).
LOINC: The LOINC codes for the PHQ-9 assessment are 72100-1 (questionnaire) and 72104-3 (overall score).
SNOMED: SNOMED CT codes for depression symptoms could be used to represent each specific symptom identified by the PHQ-9, such as 248234008 for anhedonia, 267060006 for excessive sleep, or 247441003 for feelings of worthlessness.
Keep in mind that the codes provided are for illustration purposes and may not be exhaustive. Always consult authoritative sources for accurate and up-to-date coding information.

""")

st.markdown("""
# Social Determinants of Health - SDoH

The Social Determinants of Health (SDOH) assessment is not a standardized questionnaire like the PHQ-9 or GAD-7. Instead, it refers to a broad range of factors that influence an individual's health and well-being. These factors can include various aspects of an individual's life, such as socioeconomic status, education, neighborhood and physical environment, employment, social support networks, and access to healthcare.

Since there isn't a single standardized questionnaire for SDOH, questions may vary depending on the specific assessment tool or framework used. However, some common themes in SDOH assessments include:

Financial stability (e.g., income, employment, financial support)
Education (e.g., highest level of education, access to quality education)
Neighborhood and built environment (e.g., housing, transportation, safety)
Social and community context (e.g., social support, community engagement, discrimination)
Health and healthcare access (e.g., access to primary care, health insurance coverage)
The questions in an SDOH assessment are used to identify social, economic, and environmental factors that may impact an individual's health outcomes. When specific issues are identified, relevant ICD-10, LOINC, and SNOMED codes might be assigned as follows:

ICD-10: SDOH-related codes in the ICD-10 system fall under the categories Z55-Z65, which cover issues like problems related to education and literacy, employment, housing, and social environment.
LOINC: Some LOINC codes related to SDOH assessments include 93029-2 (Social determinants of health domain) and 93030-0 (Social determinants of health measure), which capture specific domains and measures of SDOH.
SNOMED: SNOMED CT codes for SDOH-related issues could be used to represent specific factors identified in the assessment, such as 160684000 for unemployment, 275145007 for inadequate housing, or 425181000 for educational attainment.
Keep in mind that the codes provided are for illustration purposes and may not be exhaustive. Always consult authoritative sources for accurate and up-to-date coding information.

# Summary of ICD10 codes that begin with Z in the range of Z55-Z65 for SDoH:

Z55: Problems related to education and literacy*
Z56: Problems related to employment and unemployment*
Z57: Occupational exposure to risk factors
Z58: Problems related to physical environment*
Z59: Problems related to housing and economic circumstances*
Z60: Problems related to social environment*
Z61: Problems related to negative life events in childhood
Z62: Problems related to upbringing
Z63: Other problems related to primary support group, including family circumstances*
Z64: Problems related to certain psychosocial circumstances*
Z65: Problems related to other psychosocial circumstances*


# eCQM (Electronic Clinical Quality Measures) codes and types associated with social determinants of health:
CMS2v9 (Type: Process) - Preventive Care and Screening: Screening for Clinical Depression and Follow-Up Plan
CMS125v8 (Type: Process) - Breast Cancer Screening
CMS122v8 (Type: Process) - Diabetes: Hemoglobin A1c Poor Control
CMS149v8 (Type: Process) - Dementia: Cognitive Assessment
CMS156v8 (Type: Process) - Use of High-Risk Medications in the Elderly
CMS90v9 (Type: Process) - Functional Status Assessment for Complex Chronic Conditions
CMS155v8 (Type: Process) - Falls: Screening for Future Fall Risk
CMS146v8 (Type: Process) - Appropriate Testing for Children with Pharyngitis
CMS153v8 (Type: Process) - Chlamydia Screening for Women
CMS347v1 (Type: Process) - Statin Therapy for the Prevention and Treatment of Cardiovascular Disease
It's worth noting that while some of these eCQMs may not seem explicitly related to social determinants of health, they are important for addressing health disparities and inequities that are often rooted in social determinants. For example, CMS122v8 measures the percentage of patients with diabetes whose hemoglobin A1c level is poorly controlled, which can be affected by factors like access to healthy food, medication affordability, and transportation to medical appointments.


""")

st.markdown("""
# Mediapipe - Cross Platform Real Time AI for All Platforms

(Google Mediapipe)[https://github.com/google/mediapipe] is an AI/ML framework which allows you to run (GPU)[https://developers.google.com/mediapipe/framework/getting_started/gpu_support] aware AI programs in nearly any platform.

This short list of examples shows the power of Mediapipe:
1. (Face Mesh - Real Time Facial Mesh Deformation)[https://codepen.io/mediapipe/full/KKgVaPJ]
2. (Face Detection)[https://codepen.io/mediapipe/full/dyOzvZM]
3. (3D Hand Vertex and Joint detection)[https://codepen.io/mediapipe/full/dyOzvZM]
4. (Hand Body and Face)[https://codepen.io/mediapipe/full/LYRRYEw]
5. (3D Object Detection and Orientation)[https://codepen.io/mediapipe/full/BaWvzdY]
6. (Face and Body Pose Detection)[https://codepen.io/mediapipe/full/jOMbvxw]
7. (Selfie Segmentation)[https://codepen.io/mediapipe/full/wvJyQpq]

Developers: https://developers.google.com/mediapipe/framework/getting_started/gpu_support

""")