{"identifier":"870ab832-d99c-4b78-8e42-eb91c41bb474","data":{"title":"Medicare PAC Utilization - LTCH by Geography and Provider Data Dictionary 2014 - 2019","fields":[{"name":"YEAR","label":"Year","description":"The year of data"},{"name":"YEAR_TYPE","label":"Year Type","description":"Identifies if the data is for calendar year (CY) or fiscal year (FY)."},{"name":"SMRY_CTGRY","label":"Summary Category","description":"Identifies if the data is at the national, state, or provider level."},{"name":"SRVC_CTGRY","label":"Service Category","description":"Identifies the PAC setting: HH, hospice, SNF, IRF, or LTCH."},{"name":"PRVDR_ID","label":"Provider ID","description":"The 6-digit CCN for the provider."},{"name":"PRVDR_NAME","label":"Provider Name","description":"The provider name, as reported in the Provider of Services file."},{"name":"PRVDR_CITY","label":"Provider City","description":"The city where the provider is located, as reported in the Provider of Services file."},{"name":"STATE","label":"Provider State","description":"The state where the provider is located, as reported in Provider of Services file. The fifty U.S. states, the District of Columbia, and Puerto Rico are reported by their postal abbreviation. Note for national level, no state is reported."},{"name":"PRVDR_ZIP","label":"Provider ZIP","description":"The provider\u2019s ZIP code, as reported in the Provider of Services file. Note, for state and national levels, ZIP code is not reported."},{"name":"BENE_DSTNCT_CNT","label":"Distinct Beneficiaries","description":"Number of unique Medicare beneficiaries with at least one paid claim in the calendar or fiscal year."},{"name":"TOT_EPSD_STAY_CNT","label":"Episode or Stay Count","description":"For home health, this is the total count of 60-day episodes in the CY. For hospice, SNF, IRF, and LTCH this is the total count of stays provided in the fiscal year."},{"name":"TOT_SRVC_DAYS","label":"Days of Service","description":"Total count of covered days delivered by a provider in the calendar or fiscal year."},{"name":"TOT_CHRG_AMT","label":"Total Charge Amount","description":"Total charges submitted by the provider."},{"name":"TOT_ALOWD_AMT","label":"Total Allowed Amount","description":"Total of the Medicare allowed amount; this figure is the sum of the amount Medicare pays, the deductible and coinsurance amounts that the beneficiary is responsible for paying, and any amounts that a third party is responsible for paying. This applies only to the SNF, IRF, and LTCH settings."},{"name":"TOT_MDCR_PYMT_AMT","label":"Total Medicare Payment Amount","description":"Total amount that Medicare paid after deductible and coinsurance amounts have been deducted."},{"name":"TOT_MDCR_STDZD_PYMT_AMT","label":"Total Medicare Standard Payment Amount","description":"Total amount that Medicare paid adjusted for geographic differences in payment rates."},{"name":"TOT_OUTLIER_PYMT_AMT","label":"Total Outlier Payment Amount","description":"Total amount that Medicare paid for episodes or stays that were extraordinarily costly to the provider. This applies only to the home health, IRF, and LTCH settings."},{"name":"BENE_DUAL_PCT","label":"Percent (%) Dual Beneficiaries","description":"Percent of Medicare beneficiaries qualified to receive Medicare and Medicaid benefits, can also be referred to as dual eligibility. Beneficiaries are classified as a dual if in any month in the given calendar year they were receiving full or partial Medicaid benefits"},{"name":"BENE_RRL_PCT","label":"Percent (%) Medicare Beneficiaries in a Rural ZIP","description":"Percent of Medicare beneficiaries who receive their Medicare correspondence in a rural area based on having a Core-based Statistical Area (CBSA) designation equal to Non-CBSA."},{"name":"BENE_AVG_AGE","label":"Beneficiary Average Age","description":"The arithmetic mean age of beneficiaries. Beneficiary age is calculated at the end of the calendar or at the time of validated death."},{"name":"BENE_MALE_PCT","label":"Percent (%) of Beneficiaries who are Male","description":"Percent of beneficiaries who are male."},{"name":"BENE_FEML_PCT","label":"Percent (%) of Beneficiaries who are  Female","description":"Percent of beneficiaries who are female."},{"name":"BENE_RACE_WHT_PCT","label":"Percent (%) of Beneficiaries who are White","description":"Percent of beneficiaries who are non-Hispanic white."},{"name":"BENE_RACE_BLACK_PCT","label":"Percent (%) of Beneficiaries who are Black","description":"Percent of beneficiaries who are a non-Hispanic black or African American."},{"name":"BENE_RACE_API_PCT","label":"Percent (%) of Beneficiaries who are Asian Pacific Islander","description":"Percent of beneficiaries are Asian Pacific Islander."},{"name":"BENE_RACE_HSPNC_PCT","label":"Percent (%) of Beneficiaries who are Hispanic","description":"Percent of beneficiaries who are Hispanic."},{"name":"BENE_RACE_NATIND_PCT","label":"Percent (%) of Beneficiaries who are American Indian or Alaska Native","description":"Percent of beneficiaries who are American Indian or Alaska Native."},{"name":"BENE_RACE_UNK_PCT","label":"Percent (%) of Beneficiaries who are Race Unknown","description":"Percent of beneficiaries with an unknown race."},{"name":"BENE_RACE_OTHR_PCT","label":"Percent (%) of Beneficiaries who are Other","description":"Percent of beneficiaries with are an other race."},{"name":"BENE_AVG_RISK_SCRE","label":"Average Risk Score","description":"The arithmetic mean Hierarchical Condition Category (HCC) risk score of beneficiaries."},{"name":"BENE_CC_BH_ADHD_OTHCD_V1_PCT","label":"Percent (%) Of Beneficiaries Identified With ADHD and other Conduct Disorders","description":"Percent of beneficiaries meeting the CCW chronic condition algorithm for ADHD and other conduct disorders. Please note that this condition was not updated as part of the 2020 technical expert panel (TEP) to refine and enhance chronic conditions algorithms, hence the V1 component of the variable name. Chronic condition metrics are only available for file years 2017 forward. For more details on this condition, see https:\/\/www2.ccwdata.org\/web\/guest\/condition-categories-other."},{"name":"BENE_CC_BH_ALCOHOL_DRUG_V1_PCT","label":"Percent (%) Of Beneficiaries Identified With Alcohol and Drug Use Disorders","description":"Percent of beneficiaries meeting the CCW chronic condition algorithms for both alcohol use disorder and\/or drug use disorder. Please note that this condition was not updated as part of the 2020 technical expert panel (TEP) to refine and enhance chronic conditions algorithms, hence the V1 component of the variable name. Chronic condition metrics are only available for file years 2017 forward. For more details on this condition, see https:\/\/www2.ccwdata.org\/web\/guest\/condition-categories-other."},{"name":"BENE_CC_BH_ALZ_NONALZDEM_V2_PCT","label":"Percent (%) Of Beneficiaries Identified With Alzheimer\u0027s Disease And Non-Alzheimer\u0027s Dementia","description":"Percent of beneficiaries meeting the CCW chronic condition algorithms for both Alzheimer\u0027s disease and\/or non-Alzheimer\u0027s dementia. In 2020, CMS contracted an technical expert panel (TEP) to refine and enhance the chronic condition algorithms, including the algorithms for Alzheimer\u0027s disease and non-Alzheimer\u0027s dementia. The V2 portion of the variable name indicates that this variable was updated as part of the TEP. Chronic condition metrics are only available for file years 2017 forward. For more details on this condition, see https:\/\/www2.ccwdata.org\/web\/guest\/condition-categories-chronic."},{"name":"BENE_CC_BH_ANXIETY_V1_PCT","label":"Percent (%) Of Beneficiaries Identified With Anxiety Disorders","description":"Percent of beneficiaries meeting the CCW chronic condition algorithm for anxiety disorders. Please note that this condition was not updated as part of the 2020 technical expert panel (TEP) to refine and enhance chronic conditions algorithms, hence the V1 component of the variable name. Chronic condition metrics are only available for file years 2017 forward. For more details on this condition, see https:\/\/www2.ccwdata.org\/web\/guest\/condition-categories-other."},{"name":"BENE_CC_BH_BIPOLAR_V1_PCT","label":"Percent (%) Of Beneficiaries Identified With Bipolar Disorder","description":"Percent of beneficiaries meeting the CCW chronic condition algorithm for bipolar disorders. Please note that this condition was not updated as part of the 2020 technical expert panel (TEP) to refine and enhance chronic conditions algorithms, hence the V1 component of the variable name. Chronic condition metrics are only available for file years 2017 forward. For more details on this condition, see https:\/\/www2.ccwdata.org\/web\/guest\/condition-categories-other."},{"name":"BENE_CC_BH_DEPRESS_V1_PCT","label":"Percent (%) Of Beneficiaries Identified With Major Depressive Affective Disorder","description":"Percent of beneficiaries meeting the CCW chronic condition algorithm for bipolar disorders. Please note that this condition was not updated as part of the 2020 technical expert panel (TEP) to refine and enhance chronic conditions algorithms, hence the V1 component of the variable name. Chronic condition metrics are only available for file years 2017 forward. For more details on this condition, see https:\/\/www2.ccwdata.org\/web\/guest\/condition-categories-other."},{"name":"BENE_CC_BH_MOOD_V2_PCT","label":"Percent (%) Of Beneficiaries Identified With Depression, Bipolar Or Other Depressive Mood Disorders","description":"Percent of beneficiaries meeting the CCW chronic condition algorithm for depression and other depressive mood disorders. In 2020, CMS contracted an technical expert panel (TEP) to refine and enhance the chronic condition algorithms, including the algorithm for depressive and other depressive mood disorders. The V2 portion of the variable name indicates that this variable was updated as part of the TEP. Chronic condition metrics are only available for file years 2017 forward. For more details on this condition, see https:\/\/www2.ccwdata.org\/web\/guest\/condition-categories-chronic."},{"name":"BENE_CC_BH_PD_V1_PCT","label":"Percent (%) Of Beneficiaries Identified With Personality Disorders","description":"Percent of beneficiaries meeting the CCW chronic condition algorithm for personality disorders. Please note that this condition was not updated as part of the 2020 technical expert panel (TEP) to refine and enhance chronic conditions algorithms, hence the V1 component of the variable name. Chronic condition metrics are only available for file years 2017 forward. For more details on this condition, see https:\/\/www2.ccwdata.org\/web\/guest\/condition-categories-other."},{"name":"BENE_CC_BH_PTSD_V1_PCT","label":"Percent (%) Of Beneficiaries Identified With Post - Traumatic Stress Disorder","description":"Percent of beneficiaries meeting the CCW chronic condition algorithm for post-traumatic stress disorders. Please note that this condition was not updated as part of the 2020 technical expert panel (TEP) to refine and enhance chronic conditions algorithms, hence the V1 component of the variable name. Chronic condition metrics are only available for file years 2017 forward. For more details on this condition, see https:\/\/www2.ccwdata.org\/web\/guest\/condition-categories-other."},{"name":"BENE_CC_BH_SCHIZO_OTHPSY_V1_PCT","label":"Percent (%) Of Beneficiaries Identified With Schizophrenia and Other Psychotic Disorders","description":"Percent of beneficiaries meeting the CCW chronic condition algorithm for schizophrenia and other psychotic disorders. Please note that this condition was not updated as part of the 2020 technical expert panel (TEP) to refine and enhance chronic conditions algorithms, hence the V1 component of the variable name. Chronic condition metrics are only available for file years 2017 forward. For more details on this condition, see https:\/\/www2.ccwdata.org\/web\/guest\/condition-categories-other."},{"name":"BENE_CC_BH_TOBACCO_V1_PCT","label":"Percent (%) Of Beneficiaries Identified With Tobacco Use Disorders","description":"Percent of beneficiaries meeting the CCW chronic condition algorithm for tobacco use disorders. Please note that this condition was not updated as part of the 2020 technical expert panel (TEP) to refine and enhance chronic conditions algorithms, hence the V1 component of the variable name. Chronic condition metrics are only available for file years 2017 forward. For more details on this condition, see https:\/\/www2.ccwdata.org\/web\/guest\/condition-categories-other."},{"name":"BENE_CC_PH_AFIB_V2_PCT","label":"Percent (%) Of Beneficiaries Identified With Atrial Fibrillation And Flutter","description":"Percent of beneficiaries meeting the CCW chronic condition algorithm for atrial fibrillation\/flutter. In 2020, CMS contracted an technical expert panel (TEP) to refine and enhance the chronic condition algorithms, including the algorithm for atrial fibrillation\/flutter. The V2 portion of the variable name indicates that this variable was updated as part of the TEP. For more details on this condition, see https:\/\/www2.ccwdata.org\/web\/guest\/condition-categories-chronic."},{"name":"BENE_CC_PH_ARTHRITIS_V2_PCT","label":"Percent (%) Of Beneficiaries Identified With Rheumatoid Arthritis \/ Osteoarthritis","description":"Percent of beneficiaries meeting the CCW chronic condition algorithm for rheumatoid arthritis\/osteoarthritis. In 2020, CMS contracted an technical expert panel (TEP) to refine and enhance the chronic condition algorithms, including the algorithm for rheumatoid arthritis\/osteoarthritis. The V2 portion of the variable name indicates that this variable was updated as part of the TEP. Chronic condition metrics are only available for file years 2017 forward. For more details on this condition, see https:\/\/www2.ccwdata.org\/web\/guest\/condition-categories-chronic."},{"name":"BENE_CC_PH_ASTHMA_V2_PCT","label":"Percent (%) Of Beneficiaries Identified With Asthma","description":"Percent of beneficiaries meeting the CCW chronic condition algorithms for asthma. In 2020, CMS contracted an technical expert panel (TEP) to refine and enhance the chronic condition algorithms, including the algorithm for asthma. The V2 portion of the variable name indicates that this variable was updated as part of the TEP. Chronic condition metrics are only available for file years 2017 forward. For more details on this condition, see https:\/\/www2.ccwdata.org\/web\/guest\/condition-categories-chronic."},{"name":"BENE_CC_PH_CANCER6_V2_PCT","label":"Percent (%) Of Beneficiaries Identified With Combined Cancer Flag For 6 Cancer Indicators","description":"Percent of beneficiaries meeting the CCW chronic condition algorithms for six cancer types; includes breast cancer, colorectal cancer, endometrial cancer, lung cancer, prostate cancer and urological cancer. In 2020, CMS contracted an technical expert panel (TEP) to refine and enhance the chronic condition algorithms. The V2 portion of the variable name indicates that this variable was updated as part of the TEP. Chronic condition metrics are only available for file years 2017 forward. For more details on this condition, see https:\/\/www2.ccwdata.org\/web\/guest\/condition-categories-chronic."},{"name":"BENE_CC_PH_CKD_V2_PCT","label":"Percent (%) Of Beneficiaries Identified With Chronic Kidney Disease","description":"Percent of beneficiaries meeting the CCW chronic condition algorithms for chronic kidney disease. In 2020, CMS contracted an technical expert panel (TEP) to refine and enhance the chronic condition algorithms, including the algorithm for chronic kidney disease. The V2 portion of the variable name indicates that this variable was updated as part of the TEP. Chronic condition metrics are only available for file years 2017 forward. For more details on this condition, see https:\/\/www2.ccwdata.org\/web\/guest\/condition-categories-chronic."},{"name":"BENE_CC_PH_COPD_V2_PCT","label":"Percent (%) Of Beneficiaries Identified With Chronic Obstructive Pulmonary Disease","description":"Percent of beneficiaries meeting the CCW chronic condition algorithm for COPD. In 2020, CMS contracted an technical expert panel (TEP) to refine and enhance the chronic condition algorithms, including the algorithm for COPD. The V2 portion of the variable name indicates that this variable was updated as part of the TEP. Chronic condition metrics are only available for file years 2017 forward. For more details on this condition, see https:\/\/www2.ccwdata.org\/web\/guest\/condition-categories-chronic."},{"name":"BENE_CC_PH_DIABETES_V2_PCT","label":"Percent (%) Of Beneficiaries Identified With Diabetes","description":"Percent of beneficiaries meeting the CCW chronic condition algorithm for diabetes. In 2020, CMS contracted an technical expert panel (TEP) to refine and enhance the chronic condition algorithms, including the algorithm for diabetes. The V2 portion of the variable name indicates that this variable was updated as part of the TEP. Chronic condition metrics are only available for file years 2017 forward. For more details on this condition, see https:\/\/www2.ccwdata.org\/web\/guest\/condition-categories-chronic."},{"name":"BENE_CC_PH_HF_NONIHD_V2_PCT","label":"Percent (%) Of Beneficiaries Identified With Heart Failure And Non-Ischemic Heart Disease","description":"Percent of beneficiaries meeting the CCW chronic condition algorithm for heart failure\/non-ischemic heart disease. In 2020, CMS contracted an technical expert panel (TEP) to refine and enhance the chronic condition algorithms, including the algorithm for heart failure\/non-ischemic heart disease. The V2 portion of the variable name indicates that this variable was updated as part of the TEP. Chronic condition metrics are only available for file years 2017 forward. For more details on this condition, see https:\/\/www2.ccwdata.org\/web\/guest\/condition-categories-chronic."},{"name":"BENE_CC_PH_HYPERLIPIDEMIA_V2_PCT","label":"Percent (%) Of Beneficiaries Identified With Hyperlipidemia","description":"Percent of beneficiaries meeting the CCW chronic condition algorithm for hyperlipidemia. In 2020, CMS contracted an technical expert panel (TEP) to refine and enhance the chronic condition algorithms, including the algorithm for hyperlipidemia. The V2 portion of the variable name indicates that this variable was updated as part of the TEP. Chronic condition metrics are only available for file years 2017 forward. For more details on this condition, see https:\/\/www2.ccwdata.org\/web\/guest\/condition-categories-chronic."},{"name":"BENE_CC_PH_HYPERTENSION_V2_PCT","label":"Percent (%) Of Beneficiaries Identified With Hypertension","description":"Percent of beneficiaries meeting the CCW chronic condition algorithm for hypertension. In 2020, CMS contracted an technical expert panel (TEP) to refine and enhance the chronic condition algorithms, including the algorithm for hypertension. The V2 portion of the variable name indicates that this variable was updated as part of the TEP. Chronic condition metrics are only available for file years 2017 forward. For more details on this condition, see https:\/\/www2.ccwdata.org\/web\/guest\/condition-categories-chronic."},{"name":"BENE_CC_PH_ISCHEMICHEART_V2_PCT","label":"Percent (%) Of Beneficiaries Identified With Ischemic Heart Disease","description":"Percent of beneficiaries meeting the CCW chronic condition algorithm for ischemic heart disease. In 2020, CMS contracted an technical expert panel (TEP) to refine and enhance the chronic condition algorithms, including the algorithm for ischemic heart disease. The V2 portion of the variable name indicates that this variable was updated as part of the TEP. Chronic condition metrics are only available for file years 2017 forward. For more details on this condition, see https:\/\/www2.ccwdata.org\/web\/guest\/condition-categories-chronic."},{"name":"BENE_CC_PH_OSTEOPOROSIS_V2_PCT","label":"Percent (%) Of Beneficiaries Identified With Osteoporosis With Or Without Pathological Fracture","description":"Percent of beneficiaries meeting the CCW chronic condition algorithm for osteoporosis. In 2020, CMS contracted an technical expert panel (TEP) to refine and enhance the chronic condition algorithms, including the algorithm for osteoporosis. The V2 portion of the variable name indicates that this variable was updated as part of the TEP. Chronic condition metrics are only available for file years 2017 forward. For more details on this condition, see https:\/\/www2.ccwdata.org\/web\/guest\/condition-categories-chronic."},{"name":"BENE_CC_PH_PARKINSON_V2_PCT","label":"Percent (%) Of Beneficiaries Identified With Parkinson\u0027s Disease And Secondary Parkinsonism To","description":"Percent of beneficiaries meeting the CCW chronic condition algorithm for Parkinson\u0027s disease\/secondary parkinsonism. In 2020, CMS contracted an technical expert panel (TEP) to refine and enhance the chronic condition algorithms, including the algorithm for Parkinson\u0027s disease\/secondary parkinsonism. The V2 portion of the variable name indicates that this variable was updated as part of the TEP. Chronic condition metrics are only available for file years 2017 forward. For more details on this condition, see https:\/\/www2.ccwdata.org\/web\/guest\/condition-categories-chronic."},{"name":"BENE_CC_PH_STROKE_TIA_V2_PCT","label":"Percent (%) Of Beneficiaries Identified With Stroke \/ Transient Ischemic Attack","description":"Percent of beneficiaries meeting the CCW chronic condition algorithm for stroke\/transient ischemic attack. In 2020, CMS contracted an technical expert panel (TEP) to refine and enhance the chronic condition algorithms, including the algorithm for stroke\/transient ischemic attack. The V2 portion of the variable name indicates that this variable was updated as part of the TEP. Chronic condition metrics are only available for file years 2017 forward. For more details on this condition, see https:\/\/www2.ccwdata.org\/web\/guest\/condition-categories-chronic."},{"name":"PRMRY_DX_INFCTN_PCT","label":"Percent (%) of Episodes with a Primary Diagnosis of Infectious and Parasitic Diseases","description":"Percent of episodes with a primary diagnosis of Infections or Parasitic Disease. This includes diagnoses within an ICD-10 code range of A00-B99 or ICD-9 code range of 001-039."},{"name":"PRMRY_DX_NEOBLD_PCT","label":"Percent (%) of Episodes with a Primary Diagnosis of Neoplasms \u0026 Diseases of the Blood and Blood forming organs","description":"Percent of Episodes with a primary diagnosis of Neoplasms \u0026amp; Diseases of the Blood and Blood forming organs or certain disorders involving the immune mechanism. This includes diagnoses within an ICD-10 code range of C00-D89 (ICD-10) or an ICD-9 code range of 140-239, 280-289."},{"name":"PRMRY_DX_ENDONUTRMET_PCT","label":"Percent (%) of Episodes with a Primary Diagnosis of Endocrine, Nutritional and Metabolic Diseases","description":"Percent of episodes with a primary diagnosis of Endocrine, Nutritional or Metabolic diseases. This includes diagnoses within an ICD-10 code range of E00-E89 (ICD-10) or an ICD-9 code range of 240-279."},{"name":"PRMRY_DX_MNTBEHNEUDIS_PCT","label":"Percent (%) of Episodes with a Primary Diagnosis of Mental, Behavioral and Neurodevelopmental disorders","description":"Percent of episodes with a primary diagnosis of Mental, Behavioral, or Neurodevelopmental Disorders. This includes diagnoses within an ICD-10 code range of F01-F99 or ICD-9 code range of 290-319."},{"name":"PRMRY_DX_NERVSYSTM_PCT","label":"Percent (%) of Episodes with a Primary Diagnosis of Diseases of the Nervous System","description":"Percent of episodes with a primary diagnosis of Diseases of the Nervous System. This includes diagnoses within an ICD-10 code range of G00-G99 or ICD-9 code range of 320-389."},{"name":"PRMRY_DX_ENTSYS_PCT","label":"Percent (%) of Episodes with a Primary Diagnosis of Diseases of the Eye and Adnexa \u0026 Disease of the Ear and Mastoid process","description":"Percent of Episodes with a primary diagnosis of Diseases of the Eye and Adnexa \u0026amp; Disease of the Ear and Mastoid process. This includes diagnoses within an ICD-10 code range of H00-H95 or an ICD-9 range of 360-379."},{"name":"PRMRY_DX_CIRCSYSTM_PCT","label":"Percent (%) of Episodes with a Primary Diagnosis of Diseases of the Circulatory System","description":"Percent of Episodes with a primary diagnosis of Diseases of the Circulatory System. This includes diagnoses within an ICD-10 code range of I00-I99 or an ICD-9 code range of 390-459."},{"name":"PRMRY_DX_RSPSYSTM_PCT","label":"Percent (%) of Episodes with a Primary Diagnosis of Diseases of the Respiratory System","description":"Percent of episodes with a primary diagnosis of Diseases of the Respiratory System. This includes diagnoses within an ICD-10 code range of J00-J99 or an ICD-9 code range of 460-519."},{"name":"PRMRY_DX_DIGSYSTM_PCT","label":"Percent (%) of Episodes with a Primary Diagnosis of Diseases of the Digestive System","description":"Percent of episodes with a primary diagnosis of Diseases of the Digestive System. This includes diagnoses within an ICD-10 code range of K00-K95 or an ICD-9 code range of 520-579."},{"name":"PRMRY_DX_SKNMUSSYSTM_PCT","label":"Percent (%) of Episodes with a Primary Diagnosis of Diseases of the Skin and Subcutaneous Tissue \u0026 Diseases of the Musculoskeletal System and Connective Tissue","description":"Percent of Episodes with a primary diagnosis of Diseases of the Skin and Subcutaneous Tissue \u0026amp; Diseases of the Musculoskeletal System and Connective Tissue This includes diagnoses within an ICD-10 code range of L00-M99 or an ICD-9 code range of 680-709, 710-739."},{"name":"PRMRY_DX_GUSYSTM_PCT","label":"Percent (%) of Episodes with a Primary Diagnosis of Diseases of the Genitourinary System","description":"Percent of episodes with a primary diagnosis of Diseases of the Genitourinary System. This includes diagnoses within an ICD-10 code range of N00-N99 or an ICD-9 code range of 580-629."},{"name":"PRMRY_DX_PRGPERICONG_PCT","label":"Percent (%) of Episodes with a Primary Diagnosis of Pregnancy, Childbirth and the Puerperium, certain conditions originating in the perinatal Period, \u0026 Congenital malformations, Deformations and Chromosomal Abnormalities","description":"Percent of Episodes with a primary diagnosis of Pregnancy, Childbirth and the Puerperium, certain conditions originating in the perinatal Period, \u0026amp; Congenital malformations, Deformations and Chromosomal Abnormalities This includes diagnoses within an ICD-10 code range of O00-Q99 or an ICD-9 code range of 630-679, 740-759, or 760-779."},{"name":"PRMRY_DX_SXILLDEF_PCT","label":"Percent (%) of Episodes with a Primary Diagnosis of Symptoms, Signs, and Ill-defined Conditions","description":"Percent of episodes with a primary diagnosis of Symptoms, Signs and Abnormal Clinical and Laboratory findings, not elsewhere classified. This includes diagnoses within an ICD-10 code range of R00-R99 or an ICD-9 code range of 780-799."},{"name":"PRMRY_DX_INJPOIS_PCT","label":"Percent (%) of Episodes with a Primary Diagnosis of Injury, Poisoning and certain other consequences of external causes \u0026 external causes of Morbidity","description":"Percent of episodes with a primary diagnosis of Injury, Poisoning and certain other consequences of external causes \u0026amp; external causes of Morbidity. This includes diagnoses within an ICD-10 code range of S00-T88 or V00-Y99 or an ICD-9 code range of 800-999."},{"name":"PRMRY_DX_HLTHSRV_PCT","label":"Percent (%) of Episodes with a Primary Diagnosis of Factors influencing health status and contact with health services \u0026 codes for special purposes","description":"Percent of episodes with a primary diagnosis of Factors influencing health status and contact with health services \u0026amp; codes for special purposes. This includes diagnoses within an ICD-10 code range of Z00-Z99 or U00-U85 or an ICD-9 code range of E000-E999."}]}}