Getting started:

To get started, navigate to our customer Portal and create an account. From the Menu panel, select MSCORE^® to access your license key. This key is required to activate the MSCORE^® Python package, and a copy of your license key will also be emailed to your registered email address.

Python Package Index (PyPI) Tutorial:

MSCORE^® is a Python package designed to streamline the CMS-HCC model risk score calculation process for health plans and providers participating in Medicare Advantage and value-based care. It provides a SAS^®-free alternative to produce HCCs and risk scores. It streamlines your data processing for enhanced accessibility and allows you to keep your data secure in-house, so you can focus on executing of your risk adjustment strategy.

Benefits

• Implement Anywhere: MSCORE^® unlocks multiple integration opportunities including real-time scoring, embedding in EHR or enterprise data warehouse, incorporating into cloud applications, and traditional batch scoring.

• SAS^®-Free: Create HCC and risk scores without SAS^® software. Reduce software license costs and avoid the headache of re-coding and maintaining software in another programming language.

• Enhanced Output: MSCORE^® provides the numeric relative factor value for each HCC assigned to a person allowing for a deeper understanding of how each factor contributes to an individual's overall risk score.

• Reliable: Validated on over 1 million enrollees. MSCORE^® is rigorously tested and updated with each HCC model release.

• CMS-HCC Model Support: Supports HCC models V22, V24, and V28, covering payment years 2021-2025 with upgrades for each release from CMS (Initial, Mid-Year, Final).

• Easy to Set Up & Maintain: Easily installed and upgraded using pip, Python's standard package manager.

• Platform Compatibility: Compatible with Mac, Windows and Linux operating systems.

• Optimized Performance: Efficient processing with fast runtimes, ensuring quick and accurate risk score calculations.

• Improved Data Security: Securely process personal health information within your organization's IT infrastructure.

Installation

                $ pip install mscore
              

Registration

Registration is required to use the mscore package. Please log in or register on this site to create a user account and receive a license key.

Trial License

After you've installed mscore , run the following command to activate your free 30-day trial. An internet connection is required to activate & run the package.

                $ mscore -a <license key>
            

Subscribe

To use MSCORE^® for internal business operations beyond the 30-day trial period, a pay-as-you-go subscription and payment method is needed. Please set up your payment method in our customer portal on the billing tab. To license MSCORE^® for commercial purposes other than internal business operations, please contact us directly for alternative licensing.

For more information on pricing, please see our product page. If you do not see a price you like, then please email us so we can discuss your requirements.

Academic users are eligible for a free license of our software. Please contact us to obtain your complimentary academic license.

Basic Use

Before you can run your scores, you will need to generate an authorization token object.

    
    import sourcedefender  # This package is required at the top
    from mscore import AuthorizeLicense, MScore
    
    auth = AuthorizeLicense(license_key).validate()
    

Requirements

• The mscore class requires 7 arguments in order to run with 1 optional argument.

    
    authorizer   # The authorization token obtained when AuthorizeLicense is run
    
    year         # Literal['2021', '2022', '2023', '2024', '2025']
                    # The year of the HCC model to apply
    
    version      # Literal['V21', 'V22', 'V24', 'V28']
                    # The model version
    
    model        # Literal['CMS-HCC']
                    # Model type
    
    person_data  # Pandas DataFrame, CSV, or Parquet file
    
    diag_data    # Pandas DataFrame, CSV, or Parquet file
    
    columns      # Literal[
                       "scores-only",
                       "scores-demography",
                       "scores-demography-hcc",
                       "scores-demography-hcc-cc",
                       "scores-hcc-interactions",
                       "all-fields"
                    ]
                    # Output format level
    
    

Optional

• dev (boolean) - Runs the application in 'Development' mode

• Person File
• Diagnosis File

    
    import sourcedefender
    import pandas as pd
    from mscore import AuthorizeLicense, MScore

    # Generate DataFrames if using our test files
    person_df = pd.read_csv('person.csv')
    diag_df = pd.read_csv('diag.csv')

    auth = AuthorizeLicense(license_key).validate()

    model = MScore(
        authorizer = auth,
        year = '2025',
        version = 'V28',
        model = 'CMS-HCC',
        person_data = person_df,
        diag_data = diag_df,
        columns = 'all-fields',
    )

    scores = model.score_mscore()
    risk_score = scores.risk_scores
    
    

The resulting 'scores' output is a data model object of pandas.DataFrames. You can access your risk scores or relative factors data sets by calling the respective model name from the grouped data model.

• scores.risk_scores
• scores.relative_factors

From here you can proceed to use the risk score or relative factor DataFrames for further downstream processing or utilize the Pandas built-in methods to save to a file type of your choosing.

Features

relative_factors(attribute)

MScore offers the ability to view the relative factors for each person run through MSCORE®. To access this data, you simply access the relative_factors attribute of the mscore output. The relative factors are the individual contributions (from the variables in the model) to the risk score. MSCORE®'s relative_factors output contains these values with 1 row per person scored, and each relative factor in the output as the columns.

To leverage this feature, you would do the following:

                    relative_factors = scores.relative_factors
                

get_disease_scores(method)

For users wanting to compute the disease contribution to a person's risk score, you can do this in MScore by using the get_disease_score method of mscore. After instantiating your mscore object, you can use get_disease_score and pass the mscore output as a parameter. This will create a new dataframe containing only the portion of the risk score that comes from disease factors. Alternatively, if you would like this data appended to your MScore risk score output, you can use join_scores parameter to do this. NOTE: Disease interactions with DISABLED variables are not included in the disease score.

                    disease_scores = model.get_disease_scores(scores)
                

Or using the join_scores parameter:

                    disease_scores = model.get_disease_scores(scores, join_scores=True)
                

Supported Data

The mscore class attributes, person_data and diag_data, will accept a Pandas.DataFrame object, CSV or Parquet filepath directly without the need to load your input data to DataFrame first. The resulting outputs will be a Pandas DataFrame object.

Supported Environments

MSCORE^® is designed to be universally compatible, ensuring seamless integration across various platforms.

• Operating Systems: Mac, Windows and Linux
• Python Versions: 3.10 or greater (both 32-bit and 64-bit architectures)

If your required environment is not listed, please contact us as support@riskadjustmentmodel.com for an alternate solution.

Input File Format:

We recommend downloading our synthesized Person and Diagnosis data so you can get started right away with testing out the application.

• The Diagnosis file Download
• The Person file Download

Diagnosis File

Upload file by providing the full file path name. A Diagnosis file is required for Model execution. Accepted input format is CSV. This field can be entered manually; however, we recommend using the browser button for streamlined navigation and to eliminate user input errors.

Required Diagnosis input file format:

Person File

Upload file by providing the full file path name. A Person file is required for Model execution. Accepted input format is CSV. This field can be entered manually; however, we recommend using the browser button for streamlined navigation and to eliminate user input errors.

Required Person input file format:

Output File Format:

The table below maps all of the potential columns available in the output file. Depending on your model version and column output selection, the amount and order of the columns may vary. You can find which columns to expect, for each model version selected, on the Model column in the table below.

For example, running the V24 model will produce a column named "D" on the output file. The V22 model does not use this column so it will not be present on the output file if selected.

Model HCCs:

Select a model version to display the HCCs or download a copy for your records.

CMS-HCC Version 22 (V22)

CMS-HCC Version 24 (V24)

CMS-HCC Version 28 (V28)

Support and Stay Connected:

We are experts in Risk Adjustment and we're here to help. Whether it's general questions, billing, or integration support, please contact us📧. We are here to make risk scoring easy.

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