ccbhc-measurements 2025.3.3

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

The goal of this package is to simplify the process for calculating CCBHC measurements for all CCBHCs. It does this by taking in all the relevant data for a measurement and outputing whether the data meets the measurements' criteria. Here's the DEP-REM-6 as an 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

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

At the moment we only have these Measurements, but we plan on releasing a new Measurement every two weeks.

Code Demonstration

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()s
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
• 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. Send us your recomendations, bugs, questions or feedback at [e-mail]

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