Risk Adjustment & Clinical Algorithms¶

medicaid-utils includes Python implementations of several published clinical algorithms for risk adjustment, procedure classification, and quality measurement.

Elixhauser Comorbidity Index¶

Flags 31 comorbidity groups from diagnosis codes (Elixhauser et al., 1998; implementation splits hypertension into uncomplicated/complicated, extending the original 30 categories).

from medicaid_utils.adapted_algorithms.py_elixhauser.elixhauser_comorbidity import score

# Requires LST_DIAG_CD column — see "MAX vs TAF" guide for construction
df_ip = score(ip.df, lst_diag_col_name="LST_DIAG_CD", cms_format="MAX")

CDPS-Rx Risk Adjustment¶

Pharmacy-based risk adjustment using the Chronic Illness and Disability Payment System (Kronick et al., 2000, UC San Diego).

from medicaid_utils.adapted_algorithms.py_cdpsmrx import cdps_rx_risk_adjustment

df_risk = cdps_rx_risk_adjustment.cdps_rx_risk_adjust(
    df, lst_diag_col_name="LST_DIAG_CD", lst_ndc_col_name="LST_NDC"
)

BETOS Procedure Classification¶

Assigns Berenson-Eggers Type of Service (BETOS) categories to procedure codes.

from medicaid_utils.adapted_algorithms.py_betos import betos_proc_codes

df_ot = betos_proc_codes.assign_betos_cat(ot.df, year=2012)

ED Prevention Quality Indicators¶

Flags potentially preventable emergency department visits (Davies et al., 2017).

from medicaid_utils.adapted_algorithms.py_ed_pqi.ed_pqi import get_ed_pqis

df_ed = get_ed_pqis(df_ip, df_ot, df_ps, df_ed, restrict_months=False)

Inpatient PQI¶

AHRQ Prevention Quality Indicators for inpatient admissions.

from medicaid_utils.adapted_algorithms.py_ip_pqi.prevention_quality_indicators import pqirecode

df_adult, df_children = pqirecode(ip.df)

NYU/Billings ED Classification¶

Classifies ED visits by severity and preventability (Billings, Parikh, Mijanovich, 2000).

from medicaid_utils.adapted_algorithms.py_nyu_billings.billings_ed import get_nyu_ed_proba

pdf_nyu = get_nyu_ed_proba(df_ed, date_col="srvc_bgn_date", index_col="MSIS_ID", cms_format="MAX")

Pediatric Medical Complexity Algorithm (PMCA)¶

Classifies pediatric patients by medical complexity (Simon et al., 2014, Seattle Children’s).

from medicaid_utils.adapted_algorithms.py_pmca.pmca import pmca_chronic_conditions

df_pmca = pmca_chronic_conditions(df, diag_cd_lst_col="LST_DIAG_CD_RAW")

Low-Value Care¶

Identifies low-value care services (Charlesworth et al., JAMA Intern Med, 2016).

from medicaid_utils.adapted_algorithms.py_low_value_care.low_value_care import construct_low_value_care_measures

pdf_lvc = construct_low_value_care_measures(
    state="AL", year=2012, lst_bene_msis_filter=[], index_col="BENE_MSIS",
    max_data_root="/data/max", out_folder="/output"
)

Algorithm Summary¶

Algorithm	Reference	Module
Elixhauser Comorbidity Index	Elixhauser et al., 1998	`py_elixhauser`
CDPS-Rx Risk Adjustment	Kronick et al., 2000, UC San Diego	`py_cdpsmrx`
BETOS Classification	CMS Berenson-Eggers Type of Service	`py_betos`
ED PQI	Davies et al., 2017	`py_ed_pqi`
IP PQI	AHRQ Prevention Quality Indicators	`py_ip_pqi`
NYU/Billings ED Algorithm	Billings, Parikh, Mijanovich, 2000	`py_nyu_billings`
PMCA	Simon et al., 2014, Seattle Children’s	`py_pmca`
Low-Value Care	Charlesworth et al., JAMA Intern Med, 2016	`py_low_value_care`