ccbhc-measurements 2025.5.22

An easy way to calculate CCBHC measurements.

CCBHC Measurements

Table of Contents

• Purpose
• Currently Supported Measurements
• Code Demonstration
• De-panda-cies
• License
• Installation
• Contributors
• Contributions and Discussions

Purpose

This package is designed to simplify CCBHC reporting by automating the measurement process. It accepts all relevant clinical —such as PHQ-9s, AUDITs, and SDOH screenings— and insurance data and returns a labeled DataFrame indicating whether each patient meets the criteria for the chosen CCBHC measurements. The package is designed to be comprehensive in that you can give it ALL your historical data and it will output EVERY patient labeled in EVERY Measurement Year for a given Measurement. Take a look at this DEP-REM-6 example:

Click here for the more detailed DEP-REM-6 example pdf

1. Data Processing

Our code is simple:

1. Import the Measurement you want to calculate.
2. Give it the required data (downloaded excel reports from your EHR, query data from SQL, or whatever)
3. Run get_all_submeasures(). Under the hood, get_all_submeasures() determines which data meets the Measurments criteria and which does not.
4. Export your data to your preferred tool for analysis. If you want to keep it in pandas, you already have it. If you want to use Excel, or Power BI, you can export it there as well!

2. Dashboard Display

We've also created a base Power BI file as a foundation for a dashboard. Check out the dashboard folder here.

Currently Supported Measurements

• DEP REM 6 - Depression Remission at Six Months
  • DEP-REM-6 Detailed Data Input and Output Diagram
  • DEP-REM-6 README
• ASC - Preventive Care and Screening: Unhealthy Alcohol Use: Screening & Brief Counseling
  • ASC Detailed Data Input and Output Diagram
  • ASC README
• SDOH - Social Determinants of Health
  • SDOH Detailed Data Input and Output Diagram
  • SDOH README
• CDF-AD - Clinical Depression & Follow up
  • Coming soon!
• CDF-CH - Clinical Depression & Follow up
  • Coming soon!

The definition for these Measurements can be found at https://www.samhsa.gov/sites/default/files/ccbhc-quality-measures-technical-specifications-manual.pdf

IMPORTANT We will NOT be implementing the I-SERV Measurements as it just calculates the average number of days between the initial evaluation or first clinical service and the date of the call across all patients with little exclusions. If the data is structured correctly, this calculation can be performed easily as needed.

Moving forward, our focus will shift from the SAMHSA-required measurements to those mandated by New York State.

Code Demonstration

This is a basic demonstration for the Dep-Rem-6 Measurement, see here for more in depth demonstrations

import pandas as pd
import ccbhc_measurements as ccbhc

- Step 1: Load in the data into Pandas Dataframes

all_inclusive_excel_file = r"../file/path/to/Dep Rem Data.xlsx"
phq_data = pd.read_xlsx(all_inclusive_excel_file, sheet_name = "phq9")
diagnosis_data = pd.read_xlsx(all_inclusive_excel_file, sheet_name = "diagnosis")
demographic_data = pd.read_xlsx(all_inclusive_excel_file, sheet_name = "demographic")
insurance_data = pd.read_xlsx(all_inclusive_excel_file, sheet_name = "insurance")

- Step 2: Ensure that the dataframes follows the correct schema

Dep Rem 6 Required Data Input and Output Diagram

phq_data = phq_data[["patient_id","patient_DOB","encounter_id","encounter_datetime","total_score"]].copy()
diagnosis_data = diagnosis_data[["patient_id","encounter_datetime","diagnosis"]].copy()
demographic_data = demographic_data[["patient_id","race","ethnicity"]].copy()
insurance_data = insurance_data[["patient_id","insurance","start_datetime","end_datetime"]].copy()

- Step 3: Calculate Dep-Rem

submeasure_data = [phq_data,diagnosis_data,demographic_data,insurance_data]
measure = ccbhc.Dep_Rem(submeasure_data)
results = measure.get_all_submeasures()
for name, data in results.items():
    data.to_excel(name+".xlsx", index=False)

Example Data Output:

patient_id	patient_measurement_year_id	encounter_id	age	medicaid	numerator	numerator_reason
1	1-2024	1	18+	FALSE	TRUE	Has Remission
2	1-2025	3	18+	FALSE	FALSE	Remission Period not Reached
3	3-2024	4	18+	FALSE	FALSE	No PHQ-9 Follow Up
4	4-2024	5	18+	FALSE	FALSE	No Remission

- Step 4: Create a Dashboard in Power BI (Optional)

We've created an example dashboard in Power BI for easy implementation but feel free to use the analysis tool of your choice. Feel free to download it from the dashboard folder.

De-panda-cies

• pandas - Powerful data structures for data analysis, time series, and statistics

License

CC BY-NC-SA 4.0

Installation

Binary installers for the latest released version are available at the Python Package Index (PyPI)

# PyPI
pip install ccbhc_measurements

Contributors

• Alex Gursky - Data Engineer
• Max Friedman - Data Engineer
• Yisroel Len - Director of Data Analytics & CCBHC Project Evaluator

Contributions and Discussions

Feel free to add and create you own Measurements. All Measurements should follow this uml and you can use this guide to show you how to do it!

Send us your recomendations, bugs, questions or feedback at [e-mail]

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