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Pediatric All-Condition Readmission Measure

CBE ID
2393
Project
Cost and Efficiency
Endorsed
New or Maintenance
Maintenance
Is Under Review
No
E&M Cycle Comments

Prior to the Cost and Efficiency endorsement meeting, the measure developer withdrew this measure due to data access concerns. This measure was last endorsed in 2016 and used data from 2008 for endorsement. After the measure was submitted to the Fall 2023 cycle, Battelle drew attention to the lack of more recent data, which limits the committee's ability to assess whether a performance gap remains, whether scientific acceptability (i.e., reliability and validity) of the measure is still established, and whether improvement on this measure has occurred due to its use. The developer withdrew the measure from the Fall 2023 cycle prior to the endorsement meeting and endorsement was removed at the conclusion of the Fall 2023 cycle.

Measure Description

This measure calculates case-mix-adjusted readmission rates, defined as the percentage of admissions followed by 1 or more readmissions within 30 days, for patients less than 18 years old. The measure covers patients discharged from general acute care hospitals, including children’s hospitals.

  • Measure Type
    Outcome
    Composite Measure
    No
    Electronic Clinical Quality Measure (eCQM)
    No
    Level Of Analysis
    Facility
    Care Setting
    Hospital: Inpatient
    Measure Rationale

    Not applicable.

    Measure Webpage
    https://www.childrenshospital.org/research/centers/center-excellence-pediatric-…
    MAT output not attached
    Attached
    Data dictionary not attached
    Yes
    Numerator

    The numerator consists of hospitalizations at general acute care hospitals for patients less than 18 years old that are followed by 1 or more readmissions to general acute care hospitals within 30 days. Readmissions are excluded from the numerator if the readmission was for a planned procedure or for chemotherapy. 

    The measure outcome is a readmission rate, defined as the percentage of index admissions with 1 or more readmissions within 30 days. The readmission rate, unadjusted for case-mix, is calculated as follows:

    number of index admissions with 1 or more readmissions within 30 days/
    total number of index admissions

    Numerator Details

    A readmission is operationalized as the first unplanned admission to any acute care hospital within 30 days of discharge from a prior hospitalization at an acute care hospital. This prior admission, which serves as the reference point for enumerating 30-day readmissions, is the index admission. Additional admissions within 30 days from discharge from an index admission are not counted as index admissions. An admission more than 30 days from discharge from an index admission is counted as a new index admission.

     

    We chose 30 days as the follow-up period during which to evaluate readmissions for multiple reasons. Readmissions within 30 days seem likely to reflect the quality of care provided both in the hospital and following discharge, which is consistent with the measure's intended purpose of assessing quality not just for a hospital but also for its wider health system. A follow-up period of 30 days is consistent with many readmission measures already in use, including the CMS readmission measures for adults. In addition, when we used a time-to-event curve to evaluate the proportion of readmissions within 1 year that occur within timeframes from 1 day up to 365 days, we observed a smooth curve with no obvious break to suggest an alternative follow-up period.

     

    Readmissions are excluded if they are for a planned procedure or for chemotherapy. Readmissions for planned procedures and for chemotherapy are part of a patient’s intended course of care and thus unlikely to be related to health system quality. This measure therefore focuses on unplanned readmissions because they are more likely to be related to a defect in quality of care during the index admission or during the interval between the index admission and readmission. In adult and pediatric medicine, most planned readmissions are for planned procedures or chemotherapy; therefore, these exclusions are intended to capture the majority of planned readmissions.

     

    We identify planned procedures using an algorithm based on primary procedure codes. Expert pediatric clinicians in 15 different procedure-oriented specialties reviewed procedures typically performed by their specialty. The reviewers indicated which procedures (1) are usually planned (defined as planned in more than 80% of cases) and (2) could require hospitalization. Admissions for which the principal International Classification of Diseases, Tenth Revision, Procedure Coding System (ICD-10-PCS) procedure code for a planned procedure coded was 1 of these procedures are excluded from readmissions. ICD-10-CM diagnosis codes and ICD-10 Procedure Coding System (PCS) codes will be referred to as ICD-10 diagnosis and ICD-10 procedure codes, respectively.

     

    EXCLUSIONS FROM THE NUMERATOR (READMISSIONS): 

    • Hospitalizations with a principal ICD-10 code for a planned procedure (i.e., planned = 1). 
    • Hospitalizations with a principal ICD-10 diagnosis or procedure code for chemotherapy (i.e., chemo = 1). 

     

    These exclusions are applied without deleting the records from the dataset as these hospitalizations may still meet criteria for index admissions, detailed in Section S.10.

     

    Variable definitions and ICD-10 codes for identifying readmissions for planned procedures and for chemotherapy are provided in the Data Dictionary.

     

    If a planned readmission occurs within 30 days of an index admission, it does not count as a readmission against the index admission, and no subsequent admissions occurring within 30 days of discharge from the index admission count as readmissions against the index admission. After 30 days from discharge from the index admission, a new index admission can be counted.

    Denominator

    Hospitalizations at general acute care hospitals for patients less than 18 years old.

    Denominator Details

    All index hospitalizations are included in the denominator unless excluded based on one of the criteria in Sections S.10 and S.11 below.

    Denominator Exclusions

    EXCLUSIONS FROM THE NUMERATOR (READMISSIONS) AND DENOMINATOR (INDEX HOSPITALIZATIONS)

     

    We exclude certain hospitalizations from the measure entirely (i.e., from the numerator and denominator) based on clinical criteria or for issues of data completeness or quality that could prevent assessment of eligibility for the measure cohort or compromise the accuracy of readmission rates. Hospitalizations are excluded from the measure if they meet any of the following criteria: 

     

    (1) The hospitalization was at a specialty or non-acute care hospital.

    Rationale: The focus of the measure is admissions to hospitals that provide general pediatric acute care. Records for admissions to specialty and non-acute care hospitals are therefore omitted from the dataset. Because hospital type cannot be determined for records with missing data in the hospital type variable, these records are also removed from the dataset.

     

    (2) Records for the hospitalization contain incomplete data for variables needed to assess eligibility for the measure or calculate readmission rates, including hospital type, patient identifier, admission date, discharge date, disposition status, date of birth, principal ICD-10 diagnosis codes, or gender.

    Rationale: Complete and valid information for the variables listed above is needed to define the measure cohort and calculate case-mix-adjusted readmission rates. Identifying readmissions within 30 days requires information on dates of admission and end-of-service dates and the ability to link unique patient identifiers across inpatient claims records. Hospital identifiers are needed to determine the hospital at which index admissions occurred. The disposition status is needed to determine whether a patient was discharged or experienced some other outcome (e.g., was transferred to another acute care hospital, left against medical advice, died). Establishing a patient’s eligibility for membership in the pediatric cohort and performing case-mix adjustment requires an accurate date of birth and end-of-service date. Because gender is 1 of the variables used for case-mix adjustment, episodes of care with missing or inconsistent gender cannot be evaluated in the measure.

     

    (3) Records for the hospitalization contain data of questionable quality for calculating readmission rates, including 

    1. Inconsistent date of birth across records for a patient. 
    2. Discharge date prior to admission date. 
    3. Admission or discharge date prior to date of birth. 
    4. Admission date after a disposition status of death during a prior hospitalization.

     

    Rationale: Complete and valid information for the variables listed above is needed to define the measure cohort and calculate case-mix-adjusted readmission rates. Identifying readmissions within 30 days requires information on dates of admission and end-of-service. A valid disposition status is needed to determine whether a patient was discharged or experienced some other outcome (e.g., was transferred to another acute care hospital, left against medical advice, died). Establishing a patient’s eligibility for membership in the pediatric cohort and performing case-mix adjustment requires an accurate date of birth and end-of-service date. 

     

    (4) Codes other than ICD-10 codes are used for the primary procedure.

    Rationale: ICD-10 procedure codes are necessary for applying clinical exclusions. 

     

    (5) The patient was older than 18 years, 29 days at the time of admission.

    Rationale: This age exclusion limits the population to pediatric patients and prevents inclusion of records that overlap with adult readmission measures. Age eligibility for inclusion in the measure is based on age at the time of discharge from the index admission. Because the focus of the measure is pediatric patients, a patient’s hospitalization is ineligible for inclusion in the measure as an index admission if the patient was 18 years old or greater at the time of discharge. Because the subsequent observation period for readmissions is 30 days, a patient´s hospitalization is ineligible for inclusion in the measure as a readmission if the patient was older than 18 years, 29 days at the start of the readmission. 

     

    (6) The hospitalization was for obstetric care, including labor and delivery.

    Rationale: Hospitalizations for obstetric conditions are excluded because care related to pregnancy does not generally fall within the purview of pediatric providers. 

     

    (7) The principal ICD-10 diagnosis code was for a mental health condition.

    Rationale: Hospitalizations for mental health conditions are excluded because patients admitted for psychiatric treatment are typically cared for in separate psychiatric centers that are not comparable to short-term acute care hospitals. We describe this analysis in detail in Section 2b.3 of the Measure Testing Submission Form. 

     

    (8) The hospitalization was for birth of a healthy newborn.

    Rationale: Hospitalizations for birth of healthy newborns are excluded because these hospitalizations, unlike all others, are not for evaluation and management of disease.

     

    EXCLUSIONS FROM THE DENOMINATOR ONLY (INDEX HOSPITALIZATIONS ONLY)

     

    We also apply further exclusions to the denominator only (i.e., these hospitalizations are excluded from index hospitalizations but could still meet criteria for readmissions). Hospitalizations are excluded from the denominator only if they meet any of the following criteria: 

     

    (9) The patient was 18 years old or older at the time of discharge.

    Rationale: Age eligibility for inclusion in the measure is based on age at the time of discharge from the index admission. Because the measure covers pediatric patients, a patient´s hospitalization is ineligible for inclusion in the measure as an index admission if the patient was 18 years old or greater at the time of discharge. 

     

    (10) The discharge disposition was death.

    Rationale: A patient must be discharged alive from an index admission in order to be readmitted. Therefore, any record with a discharge disposition of death cannot serve as an index admission. 

     

    (11) The discharge disposition was leaving the hospital against medical advice.

    Rationale: A discharge disposition of leaving against medical advice indicates that a patient left care before the hospital determined that the patient was ready to leave. 

     

    (12) The hospital has less than 80% of records with complete patient identifier, admission date, and discharge date or less than 80% of records with complete principal ICD-10 diagnosis codes. (Records for these hospitals are still assessed as possible readmissions, but readmission rates are not calculated for these hospitals due to their lack of complete data.)

     

    Rationale: Readmission rates are not calculated for hospitals missing large amounts of data for the above variables because these hospitals have limited data to accurately apply measure cohort exclusions and calculate case-mix-adjusted readmission rates. Assessing eligibility for the measure cohort and performing case-mix adjustment requires information on admission dates, end-of-service dates, and diagnosis codes. Identifying readmissions requires information on admission dates and end-of-service dates and the ability to link unique patient identifiers across inpatient claims records. 

     

    (13) The hospital is in a state not being analyzed.

    Rationale: A claims database used for readmission analysis may contain records for hospitals located in states that are not included in the database (because covered patients may sometimes be admitted to out-of-state hospitals). Records for these out-of-state hospital admissions are not excluded from the measure dataset because these records may meet criteria for being counted as readmissions as part of an in-state hospital’s readmission rate. However, readmission rates are not calculated for out-of-state hospitals due to the lack of complete data for these hospitals. 

     

    (14) Thirty days of follow-up data are not available for assessing readmissions.

    Rationale: Identifying readmissions within 30 days requires a full 30 days of follow-up data.

    Denominator Exclusions Details

    DATA PREPARATION AND APPLICATION OF EXCLUSIONS TO THE MEASURE COHORT (NUMERATOR AND DENOMINATOR) AND TO THE DENOMINATOR ONLY

     

    Steps 1 through 8, below, describe the data preparation steps to implement and the exclusions to apply to the measure cohort (numerator and denominator) and to the denominator only before fitting the pediatric all-condition readmission model to inpatient claims data.

     

    STEP 1: IDENTIFY HOSPITALS ELIGIBLE FOR INCLUSION IN THE MEASURE

    This measure focuses on calculating pediatric readmission rates for general acute care hospitalizations. Criteria for retaining only hospitals identified as general acute care facilities are specified below.

     

    Exclusions at the Hospital Level: 

    • Drop records for specialty and non-acute care hospitals: For the list of American Hospital Association (AHA) hospital codes and Centers for Medicare & Medicaid Services (CMS) taxonomy codes for general acute care hospitals eligible for inclusion in the measure, see the Data Dictionary submitted in Section S.2b. Drop records for a hospital if the records contain only an AHA code or only a CMS code and the code is NOT for a general acute care hospital. If a hospital’s records include both an AHA and a CMS code, drop the records for the hospital if either code is NOT for a general acute care hospital. 
    • Drop records for which hospital type is missing. 

     

    STEP 2: IDENTIFY HOSPITALS FOR WHICH READMISSION RATES SHOULD NOT BE CALCULATED

    Hospitals with very incomplete data may lack adequate information to calculate accurate readmission rates. Readmission rates should therefore not be evaluated for these hospitals (i.e., their admissions should not be included in the measure as index admissions). To provide an accurate assessment based on the full dataset, data completeness at the hospital level should be assessed before excluding individual records for data quality or clinical criteria. Criteria for identifying hospitals for which readmission rates should not be calculated are listed below.

     

    Exclusions at the Hospital Level for Calculating Readmission Rates: 

    • Hospitals with less than 80% of records with complete unique patient identifier, admission date, and end-of-service date 
    • Hospitals with less than 80% of records with complete principal ICD-10 diagnosis code 
    • Out-of-state hospitals 

     

    Create a dichotomous variable named “hosp_noindex,” coded 1 for hospitals meeting the above exclusion criteria (this variable will be used to exclude these hospitals’ admissions from being evaluated as index admissions) and 0 for all other hospitals. Although readmission rates should not be calculated for these hospitals, these hospitals’ records should remain in the dataset so that their admissions can be evaluated as potential readmissions for other hospitals.

     

    STEP 3: EXCLUDE PATIENTS WHO HAVE MISSING OR INVALID DATA FOR ANALYZING READMISSIONS

    Exclusions at the Patient Level: 

    • Drop all records for a patient if ANY record is missing patient identifier, hospital identifier, admission date, end-of-service date, or disposition status. 
    • Drop all records for a patient if date of birth is missing in ALL records. 
    • Drop all records for a patient if date of birth is not consistent across records. 
    • Drop all records for a patient if ANY record has an end-of-service date prior to the admission date. 
    • Drop all records for a patient if ANY record has an admission date or end-of-service date prior to the date of birth. 
    • Drop all records for a patient if ANY record uses codes other than ICD-10 codes for the primary procedure. 
    • Drop all records for a patient if gender is missing in ALL records. 
    • Drop all records for a patient if gender is not consistent across records. 

     

    STEP 4: SPECIFY VARIABLES DEFINED AT THE RECORD LEVEL

    The variables listed in the Data Dictionary provided in Section S.2b are used to construct the measure cohort and/or to calculate readmission rates. These variables must be named and coded as specified in the Data Dictionary and should be created prior to identifying episodes of care and applying further exclusions to the data.

     

    STEP 5: DEFINE EPISODES OF CARE

    Data for a single period of inpatient care may be contained in more than 1 claims record. It therefore may be necessary to collapse instances of multiple claims for the same hospitalization into a single episode of care prior to applying some exclusion criteria and evaluating readmissions. This allows all data relevant to a given hospitalization to be appropriately evaluated for measure cohort exclusion. The process for defining episodes of care is detailed below.

     

    1. IDENTIFY TRUE DUPLICATES AND DROP ALL BUT 1. 

    • True duplicates are records that have identical values for all key variables needed to assess cohort eligibility and calculate case-mix-adjusted readmission rates, where these key variables include all variables listed in the Data Dictionary (provided in Section S.2b) except hasprimary. Combine true duplicates, using the MAXIMUM value of hasprimary. 

    2. IDENTIFY AND COMBINE MULTIPLE VALID RECORDS FROM THE SAME HOSPITAL FOR THE SAME HOSPITALIZATION. 

    • Sort records by the following variables, in the specified order: patientid, hospitalid, admit_dt, end_service_dt, and disp_status. 
    • Define records to be part of the same hospitalization at the same hospital if (a) patientid and hospitalid are equal to those in the previous record and (b) admission dates and end-of-service dates indicate consecutive time periods or nesting of 1 time period within another in that any of the following is true: 
      • admission date is before the previous record’s end-of-service date 
      • admission date is equal to the previous record’s end-of-service date AND the previous record’s disposition status is other (i.e., disp_status = 0) or transfer to an acute care hospital (i.e., disp_status = 2) 
      • admission date is 1 day after the previous record’s end-of-service date AND the previous record’s disposition status is other (i.e., disp_status = 0) or transfer to an acute care hospital (i.e., disp_status = 2) 
      • admission and end-of-service dates are both the same as those of the previous record, and admission date is equal to end-of-service date (i.e., the records are for a same-day discharge) 

     

    If the above criteria for multiple valid records from the same hospital for the same hospitalization are met, combine all of the records. Retain the variables patientid, dob, hospitalid, male, and hosp_noindex, which will be the same across records by this step. Use the MINIMUM value for admit_dt. Use the MAXIMUM value for end_service_dt, hasprimary, cci1-cci10 and cci12-cci18, planned, chemo, mh, obstetric, and newborn. Use the value of disp_status and ins_end (this variable is only used in single-payer analyses) from the record with the maximum end-of-service date. If multiple records have the same maximum end-of-service date but inconsistent values for disp_status, use the MAXIMUM value of disp_status within those records. Using the maximum value for end_service_dt captures the discharge date that serves as the starting point for the 30-day follow-up period for evaluating readmissions. Using the maximum value for chronic condition indicator and clinical exclusion variables across records captures the presence of a chronic condition or clinical exclusion for the entire episode of care. For example, if 1 record contains a primary principal ICD-10 mental health diagnosis, this diagnosis will be applied to the entire episode of care, and the entire episode of care will be excluded.

     

    3. IDENTIFY AND COMBINE MULTIPLE VALID RECORDS FROM MULTIPLE HOSPITALS FOR HOSPITALIZATIONS THAT INCLUDED TRANSFERS. 

    • Sort records by the following variables, in the specified order: patientid, admit_dt, end_service_dt, and disp_status. 
    • Define records to be in the same episode of care if (a) patientid is equal to patientid in the previous record, (b) the previous record’s disposition status is transfer to an acute care hospital (i.e., disp_status = 2), and (c) the admission date is equal to or is 1 day after the previous record’s end-of-service date. 

     

    If the above criteria for connected hospitalizations are met, combine all of the records. Retain the variables patientid, dob, and male, which will be the same across records by this step. Use the MINIMUM value for admit_dt. Use the MAXIMUM value for end_service_dt, hasprimary, cci1-cci10 and cci12-cci18, planned, chemo, mh, obstetric, and newborn. Use the value of hospitalid, disp_status, ins_end, and hosp_noindex from the last record.

     

    4. IDENTIFY AND EXCLUDE INVALID EPISODES OF CARE

    There may be episodes of care that are temporally overlapping (i.e., in which it appears that a patient was in 2 different hospitals at the same time). These episodes should be dropped. 

    • Drop all episodes of care that share the same patient identifier, admission date, and end-of-service date but have different hospital identifiers. 
    • For each patient identifier, drop all temporally adjacent episodes of care if there are overlapping dates (i.e., admission date is before the end-of-service date for the preceding episode of care) but different hospital identifiers. 

     

    STEP 6: SPECIFY VARIABLES DEFINED AT THE EPISODE-OF-CARE LEVEL

    Because multiple records may be combined to create an episode of care, some variables used for measure cohort exclusions and readmission analysis should be defined only after defining valid episodes of care. This sequencing assures that the variable values accurately represent information for the entire hospitalization, rather than capturing only a subset of information for the hospitalization. These variables should be created as specified in the Data Dictionary provided in Section S.2b, prior to applying further exclusion criteria to the data.

     

    STEP 7: DEFINE EPISODES OF CARE ELIGIBLE FOR INCLUSION IN MEASURE COHORT

    The exclusions listed below are applied only after defining episodes of care (in Step 5) and defining variables at the episode-of-care level (in Step 6).

     

    Exclusions at the Patient Level Based on Data Completeness Criteria: 

    • Drop all episodes of care for a patient if the principal ICD-10 diagnosis code is missing (i.e., hasprimary = 0) for ANY episode of care for that patient. 

     

    Exclusions at the Episode-of-Care Level Based on Data Quality Criteria: 

    • Drop episodes of care with admission dates that occur after a discharge status of death during a prior episode of care. 

     

    Exclusions at the Episode-of-Care Level Based on Clinical Criteria: 

    • Drop episodes of care for patients greater than 18 years, 29 days old at the time of admission. 
    • Drop episodes of care for birth of healthy newborns (i.e., newborn = 1). 
    • Drop episodes of care with a principal ICD-10 non-delivery obstetric diagnosis or any labor and delivery diagnosis or procedure (i.e., obstetric = 1). 
    • Drop episodes of care with a principal ICD-10 mental health diagnosis (i.e., mh = 1). 

     

    STEP 8: DEFINE INDEX ADMISSIONS AND READMISSIONS

    A clean dataset containing only eligible admissions must be prepared before defining index admissions and readmissions. This dataset should consist of all admissions that are eligible for inclusion in the measure cohort based on the criteria detailed in data preparation steps 1 through 7, above.

     

    Exclusions at the Episode-of-Care Level for Defining Index Admissions: 

    • Episodes of care for patients 18 years, 0 days old or greater at the time of discharge 
    • Episodes of care with a discharge disposition of death 
    • Episodes of care with a discharge disposition of leaving the hospital against medical advice 
    • Episodes of care for which 30 days of follow-up data are unavailable, either (a) because the dataset’s time range for claims does not include the full 30 days, or (b) because, for single-payer analyses, the patient was not enrolled with the payer for the full 30 days (i.e., the difference between ins_end and end_service_dt is less than 30 days) 

     

    The above exclusions are applied without deleting the records from the dataset as these episodes of care may still meet criteria for readmissions.

     

    Exclusions at the Hospital Level for Defining Index Admissions: 

    • Hospitals with less than 80% of records with complete unique patient identifier, admission date, and end-of-service date 
    • Hospitals with less than 80% of records with complete principal ICD-10 diagnosis code 
    • Out-of-state hospitals 

     

    Hospitals meeting the above exclusion criteria were identified in Step 2, above. The dichotomous variable hosp_noindex was created in Step 2 and coded 1 for hospitals meeting the above criteria and 0 for all other hospitals. Episodes of care for hospitals with hosp_noindex = 1 are therefore excluded from index admissions.

     

    Although these hospitals’ episodes of care should not be evaluated as index admissions (i.e., readmission rates should not be calculated for these hospitals), their episodes of care should remain in the dataset so they can be evaluated as potential readmissions for other hospitals.

    Type of Score
    Rate/proportion
    Measure Score Interpretation
    Better quality = Lower score
    Calculation of Measure Score

    PREPARATION OF DATA AND IDENTIFICATION OF MEASURE COHORT

     

    Identify Hospitals Eligible for Inclusion in the Measure

    1. Starting with the complete set of claims from the time period being analyzed, exclude hospitalizations that occurred at specialty or non-acute care hospitals or at hospitals for which hospital type is missing.

     

    Identify Hospitals for which Readmission Rates Should Not Be Calculated

    2. Identify and flag out-of-state hospitals and hospitals with incomplete data for key variables for more than 20% of records.

     

    Exclude Patients Who Have Missing or Invalid Data for Analyzing Readmissions

    3. Exclude patients whose records use procedure codes other than ICD-10 codes or have missing or invalid data for 1 or more of the following variables: patient identifier, hospital identifier, admission date, end-of-service date, disposition status, date of birth, and gender.

     

    Specify Variables Defined at the Record Level

    4. Define variables for measure cohort exclusions and readmission analysis that should be created at the record level.

     

    Define Episodes of Care

    5. For hospitalizations with data contained in more than 1 claim, combine the multiple claims into a single record for each hospitalization.

     

    Specify Variables Defined at the Episode of Care Level

    6. Define variables for measure cohort exclusions and readmission analysis that should be created at the episode of care level.

     

    Define Episodes of Care Eligible for Inclusion in Measure Cohort

    7. Exclude hospitalizations with a missing principal ICD-10 diagnosis code.

     

    8. Exclude hospitalizations with an admission date occurring after a previous hospitalization with a disposition status of death.

     

    9. Exclude hospitalizations for patients older than 18 years, 29 days at the time of admission.

     

    10. Exclude hospitalizations for obstetric conditions, mental health conditions, and birth of healthy newborns.

     

    DEFINE INDEX HOSPITALIZATIONS

    11. Exclude hospitalizations for patients 18 years, 0 days old or older at the time of discharge.

     

    12. Exclude hospitalizations with a discharge disposition of death.

     

    13. Exclude hospitalizations with a discharge disposition of leaving against medical advice.

     

    14. Exclude hospitalizations for which 30 days of follow-up data are not available for assessing readmissions.

     

    15. Exclude hospitalizations that occurred at hospitals that (a) have less than 80% of records with complete patient identifier, admission date, and end-of-service date; (b) have less than 80% of records with complete principal ICD-10 diagnosis codes; or (c) are located in a state not being analyzed.

     

    DEFINE READMISSIONS

    16. Identify index hospitalizations followed by one or more readmissions within 30 days.

     

    17. When identifying readmissions, exclude hospitalizations with (a) a principal ICD-10 procedure code for a planned procedure or (b) a principal ICD-10 diagnosis code or procedure code for chemotherapy.

     

    CASE-MIX ADJUSTMENT MODEL FITTING AND DIRECT STANDARDIZATION

    18. Fit the case-mix adjustment model to the prepared dataset to estimate coefficients for the case-mix variables (age, gender, presence of chronic conditions in each of 17 body systems, and number of body systems affected by chronic conditions) and a hospital random intercept for each hospital.

     

    19. Perform direct standardization by fitting the model again for each hospital. Use the hospital´s random intercept, adjusted for its own case-mix, from Step 18, but instead of using the hospital´s own case-mix data, use a hypothetical dataset in which (a) all admissions are re-coded as if they are from the hospital for which a readmission rate is being estimated and (b) the readmission outcome has been set to missing. Each hospital´s predicted probabilities for all records are summed by hospital and divided by the total number of index admissions in the dataset to produce the hospital-specific standardized readmission rate.

     

    20. The upper confidence bound for this estimate is calculated as the mean of the upper confidence bound for each index admission´s probability of leading to a readmission. The corresponding procedure is followed to estimate the lower confidence bound.

     

    21. Finally, the point estimate and bound values are multiplied by a factor that corrects for estimation error produced by transformations used during estimation. The bias correction factor is a constant value specified as the observed number of readmissions across all hospitals in the dataset divided by the predicted number of readmissions across all hospitals in the dataset.

     

    22. The resulting hospital-specific standardized readmission rate can be interpreted as the readmission rate the hospital would have if it treated a patient cohort with the case-mix composition of all eligible index admissions within the entire dataset.

     

    Detailed Methods for Implementing Direct Standardization in SAS

    One method to implement direct standardization in SAS involves obtaining the predicted values of every patient in the dataset in each hospital using the steps listed below. This is the method used in the SAS program we have prepared for the measure.

     

    1. For each hospital being standardized, create a duplicate copy of the original dataset. The duplicate dataset should contain exactly the same variables and records as in the original dataset for all hospitals.

     

    2. Set the outcome (readmission) in the duplicate dataset to missing. This prevents these duplicate records from being used in model estimation.

     

    3. For ALL records in the duplicate dataset, set the hospital identifier to the hospital identifier of the hospital being standardized. Add a variable to the dataset that indicates that these records contain hypothetical data.

     

    4. Concatenate the duplicate datasets to the original dataset. If the concatenated dataset is too large to handle, the same procedure may be conducted for subgroups of hospitals, or for 1 hospital at a time, and the results combined afterward.

     

    5. Fit the case-mix adjustment model to the dataset created in the previous step. In SAS, the model will be fitted only on the original data since the outcome is missing for the duplicate data. This process will produce a case-mix-adjusted random intercept for each hospital. However, the procedure will also produce predicted probabilities for both original and duplicate records (SAS calculates predicted probabilities for any record in which the predictors are not missing, regardless of whether the outcome is missing).

     

    6. Calculate the mean predicted probability and lower and upper bounds for only the duplicate records (those flagged as containing hypothetical data) in order to obtain the predicted readmission rate for the hospital being standardized. This rate represents the readmission rate for this hospital if it were to treat the entire dataset’s population mix.

    Measure Stratification Details

    Not applicable.

    All information required to stratify the measure results
    Off
    All information required to stratify the measure results
    Off
    Testing Data Sources
    Claims Data
    Data Sources

    The measure could be used with state Medicaid or all-payer databases. There are several options for calculating rates that could be compared nationally. CMS could analyze Medicaid claims from multiple states. A private payer with data from multiple states could compare hospitals from across state lines. Multiple states with all-payer databases could combine them to enable cross-state comparisons. Individual states could calculate nationally comparable rates using a method we have developed by which readmission rates can be estimated for Medicaid-insured patients and standardized using a MAX reference dataset. Please see the Detailed Measure Specifications (provided in the Appendix) for instructions on implementing this method.

    Minimum Sample Size

    Not applicable.

  • Evidence of Measure Importance

    Evidence suggests that readmission rates provide a useful measure of health care quality. Use of effective, evidence-based approaches to diagnosis, treatment, and monitoring of disease leads to fewer complications and decreased exacerbations, which can, in turn, result in a decreased frequency of hospitalizations. Readmission rates therefore in part reflect the quality of clinical care and resulting disease outcomes.

     

    Studies have shown that hospitals that provide care in accordance with clinical practice guidelines have lower readmission rates than those that do not.1–4 Several retrospective cohort analyses and case-control studies and a prospective pre-post observational study have demonstrated that adherence to evidence-based processes of care results in improved clinical outcomes.1,2,5–11 For example, improved adherence to the Joint Commission’s recommended 3 Children’s Asthma Care (CAC 1-3) measures was associated with improved chronic asthma symptoms and fewer exacerbations, as well as longer periods out of the hospital with fewer readmissions.9 Similarly, Get With the Guidelines (GWTG) programs have been associated with significant improvements in processes of cardiovascular care strongly linked to improved outcomes.4 Lower quality of inpatient care is associated with a higher risk of unplanned readmission.5

     

    Readmission rates also reflect the quality of key health care processes. Several studies, largely in adults, have demonstrated that interventions focused on improving the quality of the discharge process, the transition from the hospital to ambulatory or long-term care, and the provision of timely follow-up care have been associated with reduced hospital readmission rates, suggesting that the quality of these processes is associated with readmission risk.12-31 For example, hospitals that provide patient-focused, individualized pre-discharge education as well as post-discharge support have fewer readmissions than those that do not provide such services.13–15,17–25,28,31-39

     

    Project RED and the Care Transition Measure are 2 examples of initiatives that have improved the quality of discharge and care transition processes for adult patients by incorporating such interventions as a transition coach who provides assistance with medication self-management, makes home visits and telephone calls to patients after discharge, and sets up timely follow-up appointments with primary or specialty care providers. Such interventions that emphasize the importance of teaching patients about their diagnoses and reviewing their treatment and discharge plan with them throughout their hospital stay are associated with a subsequent reduction in 30-day readmission rates.19,32 

     

    Studies identify care coordination, discharge planning, and hospital-to-home care transition as key processes that influence pediatric readmission rates.40-42 Indeed, parental perception that a child is not healthy enough for discharge is associated with a greater risk of subsequent, unplanned 30-day readmission.29 Responding to parental concerns about a child’s health prior to hospital discharge may help mitigate readmission risk. In another study of both pediatric and adult patients with sickle cell disease, patients who had post-discharge follow-up within 30 days of hospital discharge were readmitted less often than those who did not have post-discharge follow-up.24 Improving consensus among families and providers, improved communication among care team members and between care teams and families, and clear definitions of care team member roles may be valuable areas for future efforts to reduce readmission.41,42

     

    References

    1. Bozic KJ, Maselli J, Pekow PS, Lindenauer PK, Vail TP, Auerbach AD. The influence of procedure volumes and standardization of care on quality and efficiency in total joint replacement surgery. J Bone Joint Surg Am. 2010;92(16):2643–2652. doi:10.2106/JBJS.I.01477.
    2. Ludke RL, MacDowell NM, Booth BM, Hunter SA. Appropriateness of admissions and discharges among readmitted patients. Health Serv Res. 1990;25(3):501–525.
    3. Heidenreich PA, Hernandez AF, Yancy CW, Liang L, Peterson ED, Fonarow GC. Get With The Guidelines program participation, process of care, and outcome for Medicare patients hospitalized with heart failure. Circ Cardiovasc Qual Outcomes. 2012;5(1):37–43. doi:10.1161/CIRCOUTCOMES.110.959122.
    4. Ellrodt AG, Fonarow GC, Schwamm LH, et al. Synthesizing Lessons Learned From Get With The Guidelines. Circulation. 2013;128(22):2447-2460. doi:10.1161/01.cir.0000435779.48007.5c
    5. Ashton CM, Kuykendall DH, Johnson ML, Wray NP, Wu L. The association between the quality of inpatient care and early readmission. Ann Intern Med. 1995;122(6):415–421.
    6. Nakamura MM, Toomey SL, Zaslavsky AM, et al. Measuring Pediatric Hospital Readmission Rates to Drive Quality Improvement. Academic Pediatrics. 2014;14(5, Supplement):S39-S46. doi:10.1016/j.acap.2014.06.012
    7. Noba L, Rodgers S, Chandler C, Balfour A, Hariharan D, Yip VS. Enhanced Recovery After Surgery (ERAS) Reduces Hospital Costs and Improve Clinical Outcomes in Liver Surgery: a Systematic Review and Meta-Analysis. J Gastrointest Surg. 2020;24(4):918-932. doi:10.1007/s11605-019-04499-0
    8. Williams JB, McConnell G, Allender JE, et al. One-year results from the first US-based enhanced recovery after cardiac surgery (ERAS Cardiac) program. The Journal of Thoracic and Cardiovascular Surgery. 2019;157(5):1881-1888. doi:10.1016/j.jtcvs.2018.10.164
    9. Fassl BA, Nkoy FL, Stone BL, Srivastava R, Simon TD, Uchida DA, Koopmeiners K, Greene T, Cook LJ, Maloney CG. The Joint Commission Children’s Asthma Care quality measures and asthma readmissions. Pediatrics. 2012;130(3):482–491. doi:10.1542/peds.2011-3318.
    10. Cheney J, Barber S, Altamirano L, Medico Cirujano, Cheney M, Williams C, Jackson M, Yates P, O’Rourke P, Wainwright C. A clinical pathway for bronchiolitis is effective in reducing readmission rates. J Pediatr. 2005;147(5):622–626. doi:10.1016/j.jpeds.2005.06.040.
    11. Pillai D, Song X, Pastor W, Ottolini M, Powell D, Wiedermann BL, DeBiasi RL. Implementation and impact of a consensus diagnostic and management algorithm for complicated pneumonia in children. J Investig Med. 2011;59(8):1221–1227. doi:10.231/JIM.0b013e318231db4d.
    12. Inglis SC, Clark RA, McAlister FA, Ball J, Lewinter C, Cullington D, Stewart S, Cleland JG. Structured telephone support or telemonitoring programmes for patients with chronic heart failure. Cochrane Database Syst Rev Online. 2010;(8):CD007228. doi:10.1002/14651858.CD007228.pub2.
    13. Phillips CO, Wright SM, Kern DE, Singa RM, Shepperd S, Rubin HR. Comprehensive discharge planning with postdischarge support for older patients with congestive heart failure: a meta-analysis. JAMA. 2004;291(11):1358–1367. doi:10.1001/jama.291.11.1358.
    14. Scott IA. Preventing the rebound: improving care transition in hospital discharge processes. Aust Health Rev. 2010;34(4):445–451. doi:10.1071/AH09777.
    15. Shepperd S, Lannin NA, Clemson LM, McCluskey A, Cameron ID, Barras SL. Discharge planning from hospital to home. Cochrane Database Syst Rev Online. 2013;1:CD000313. doi:10.1002/14651858.CD000313.pub4.
    16. Sochalski J, Jaarsma T, Krumholz HM, Laramee A, McMurray JJV, Naylor MD, Rich MW, Riegel B, Stewart S. What works in chronic care management: the case of heart failure. Health Aff (Millwood). 2009;28(1):179–189. doi:10.1377/hlthaff.28.1.179.
    17. Anderson C, Deepak BV, Amoateng-Adjepong Y, Zarich S. Benefits of comprehensive inpatient education and discharge planning combined with outpatient support in elderly patients with congestive heart failure. Congest Heart Fail. 2005;11(6):315–321.
    18. Balaban RB, Weissman JS, Samuel PA, Woolhandler S. Redefining and redesigning hospital discharge to enhance patient care: a randomized controlled study. J Gen Intern Med. 2008;23(8):1228–1233. doi:10.1007/s11606-008-0618-9.
    19. Coleman EA, Parry C, Chalmers S, Min S-J. The care transitions intervention: results of a randomized controlled trial. Arch Intern Med. 2006;166(17):1822–1828. doi:10.1001/archinte.166.17.1822.
    20. Evans RL, Hendricks RD. Evaluating hospital discharge planning: a randomized clinical trial. Med Care. 1993;31(4):358–370.
    21. Jack BW, Chetty VK, Anthony D, Greenwald JL, Sanchez GM, Johnson AE, Forsythe SR, O’Donnell JK, Paasche-Orlow MK, Manasseh C, Martin S, Culpepper L. A reengineered hospital discharge program to decrease rehospitalization: a randomized trial. Ann Intern Med. 2009;150(3):178–187.
    22. Naylor MD, Brooten DA, Campbell RL, Maislin G, McCauley KM, Schwartz JS. Transitional care of older adults hospitalized with heart failure: a randomized, controlled trial. J Am Geriatr Soc. 2004;52(5):675–684. doi:10.1111/j.1532-5415.2004.52202.x.
    23. Naylor M, Brooten D, Jones R, Lavizzo-Mourey R, Mezey M, Pauly M. Comprehensive discharge planning for the hospitalized elderly. A randomized clinical trial. Ann Intern Med. 1994;120(12):999–1006.
    24. Koehler BE, Richter KM, Youngblood L, Cohen BA, Prengler ID, Cheng D, Masica AL. Reduction of 30-day postdischarge hospital readmission or emergency department (ED) visit rates in high-risk elderly medical patients through delivery of a targeted care bundle. J Hosp Med. 2009;4(4):211–218. doi:10.1002/jhm.427.
    25. Rich MW, Beckham V, Wittenberg C, Leven CL, Freedland KE, Carney RM. A multidisciplinary intervention to prevent the readmission of elderly patients with congestive heart failure. N Engl J Med. 1995;333(18):1190–1195. doi:10.1056/NEJM199511023331806.
    26. Costantino ME, Frey B, Hall B, Painter P. The influence of a postdischarge intervention on reducing hospital readmissions in a Medicare population. Popul Heal Manag. 2013;16(5):310–316. doi:10.1089/pop.2012.0084.
    27. Flood KL, Maclennan PA, McGrew D, Green D, Dodd C, Brown CJ. Effects of an acute care for elders unit on costs and 30-day readmissions. JAMA Intern Med. 2013:1–7. doi:10.1001/jamainternmed.2013.524.
    28. Leschke J, Panepinto JA, Nimmer M, Hoffmann RG, Yan K, Brousseau DC. Outpatient follow-up and rehospitalizations for sickle cell disease patients. Pediatr Blood Cancer. 2012;58(3):406–409. doi:10.1002/pbc.23140.
    29. Bradley EH, Curry L, Horwitz LI, Sipsma H, Wang Y, Walsh MN, Goldmann D, White N, Piña IL, Krumholz HM. Hospital strategies associated with 30-day readmission rates for patients with heart failure. Circ Cardiovasc Qual Outcomes. 2013;6(4):444–450. doi:10.1161/CIRCOUTCOMES.111.000101.
    30. Harrison PL, Hara PA, Pope JE, Young MC, Rula EY. The impact of postdischarge telephonic follow-up on hospital readmissions. Popul Heal Manag. 2011;14(1):27–32. doi:10.1089/pop.2009.0076.
    31. Kripalani S, Theobald CN, Anctil B, Vasilevskis EE. Reducing Hospital Readmission Rates: Current Strategies and Future Directions. Annu Rev Med. 2013. doi:10.1146/annurev-med-022613-090415.
    32. Markley J, Andow V, Sabharwal K, Wang Z, Fennell E, Dusek R. A project to reengineer discharges reduces 30-day readmission rates. Am J Nurs. 2013;113(7):55–64. doi:10.1097/01.NAJ.0000431922.47547.eb.
    33. Berry JG, Ziniel SI, Freeman L, Kaplan W, Antonelli R, Gay J, Coleman EA, Porter S, Goldmann D. Hospital readmission and parent perceptions of their child’s hospital discharge. Int J Qual Health Care. 2013;25(5):573–581. doi:10.1093/intqhc/mzt051.
    34. Balaban RB, Galbraith AA, Burns ME, Vialle-Valentin CE, Larochelle MR, Ross-Degnan D. A Patient Navigator Intervention to Reduce Hospital Readmissions among High-Risk Safety-Net Patients: A Randomized Controlled Trial. J GEN INTERN MED. 2015;30(7):907-915. doi:10.1007/s11606-015-3185-x
    35. Rice H, Say R, Betihavas V. The effect of nurse-led education on hospitalisation, readmission, quality of life and cost in adults with heart failure. A systematic review. Patient Education and Counseling. 2018;101(3):363-374. doi:10.1016/j.pec.2017.10.002
    36. Finlayson K, Chang AM, Courtney MD, et al. Transitional care interventions reduce unplanned hospital readmissions in high-risk older adults. BMC Health Serv Res. 2018;18(1):956. doi:10.1186/s12913-018-3771-9
    37. Kripalani S, Chen G, Ciampa P, et al. A transition care coordinator model reduces hospital readmissions and costs. Contemporary Clinical Trials. 2019;81:55-61. doi:10.1016/j.cct.2019.04.014
    38. deJong NA, Kimple KS, Morreale MC, Hang S, Davis D, Steiner MJ. A Quality Improvement Intervention Bundle to Reduce 30-Day Pediatric Readmissions. Pediatr Qual Saf. 2020;5(2):e264. doi:10.1097/pq9.0000000000000264
    39. Dautzenberg L, Bretagne L, Koek HL, et al. Medication review interventions to reduce hospital readmissions in older people. Journal of the American Geriatrics Society. 2021;69(6):1646-1658. doi:10.1111/jgs.17041
    40. Congdon M, Rauch B, Carroll B, et al. Opportunities for Diagnostic Improvement Among Pediatric Hospital Readmissions. Hosp Pediatr. 2023;13(7):563-571. doi:10.1542/hpeds.2023-007157
    41. Hamline MY, Sauers-Ford H, Kair LR, Vadlaputi P, Rosenthal JL. Parent and Physician Qualitative Perspectives on Reasons for Pediatric Hospital Readmissions. Hospital Pediatrics. 2021;11(10):1057-1065. doi:10.1542/hpeds.2020-004499
    42. Rodriguez VA, Goodman DM, Bayldon B, et al. Pediatric Readmissions Within 3 Days of Discharge: Preventability, Contributing Factors, and Necessity. Hospital Pediatrics. 2019;9(4):241-248. doi:10.1542/hpeds.2018-0159
    Table 1. Performance Scores by Decile
    Performance Gap
    Overall Minimum Decile_1 Decile_2 Decile_3 Decile_4 Decile_5 Decile_6 Decile_7 Decile_8 Decile_9 Decile_10 Maximum
    Mean Performance Score 0.058 0.024 0.046 0.050 0.053 0.054 0.055 0.056 0.060 0.064 0.072 N/A 0.502
    N of Entities N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A
    N of Persons / Encounters / Episodes N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A
    Meaningfulness to Target Population

     

    Previous (2015) Submission:

    Hospitalization of a child is disruptive to families. It can affect parent/caregiver work and sibling school or daycare arrangements and expose families to various psychosocial stressors.1,2 In addition, readmission exposes patients to additional hospital days and thus increased potential for infections and medical errors that can occur during hospitalization.3,4

     

    Frequent hospitalization may have negative developmental effects, including anxiety and feelings of isolation, particularly for children who are chronically ill and return to school after prolonged hospitalizations.5 Frequently hospitalized adolescents are more likely to drop out of school than their healthy peers.6,7 School reintegration can be complicated by side effects caused by treatment or the illness itself or by increased social, emotional, and behavioral problems.8

     

    Current Submission:

    New evidence affirms that hospitalizations, including readmissions, impact the psychological well-being of patient families.9,10 Furthermore, children frequently miss school, and their parents may miss work, for multiple weeks after hospital discharge.11 School absenteeism may lower educational achievement, cause economic strain, and lead to poor health in adulthood.

     

    References

    1. Shudy M, de Almeida ML, Ly S, Landon C, Groft S, Jenkins TL, Nicholson CE. Impact of pediatric critical illness and injury on families: a systematic literature review. Pediatrics. 2006;118 Suppl 3:S203–218.
    2. Rennick JE, Johnston CC, Dougherty G, Platt R, Ritchie JA. Children’s psychological responses after critical illness and exposure to invasive technology. J Dev Behav Pediatr. 2002;23(3):133–144.
    3. Committee on Identifying and Preventing Medication Errors, Institute of Medicine. Preventing Medication Errors. (Aspden P, Wolcott J, Bootman J, eds.). National Academies Press; 2006.
    4. Committee on Quality of Health Care in America, Institute of Medicine. To Err is Human: Building a Safer Health System. (Kohn L, Corrigan J, eds.). National Academies Press; 2000.
    5. Worchel-Prevatt FF, Heffer RW, Prevatt BC, Miner J, Young-Saleme T, Horgan D, Lopez MA, Rae WA, Frankel L. A school reentry program for chronically ill children. J Sch Psychol. 1998;36(3):261–279.
    6. Weitzman M, Klerman LV, Lamb G, Menary J, Alpert JJ. School absence: a problem for the pediatrician. Pediatrics. 1982;69(6):739–746.
    7. Kearney CA. School absenteeism and school refusal behavior in youth: a contemporary review. Clin Psychol Rev. 2008;28(3):451–471.
    8. Shaw SR, McCabe PC. Hospital-to-school transition for children with chronic illness: meeting the new challenges of an evolving health care system. Psychol Sch. 2008;45(1):74–87.
    9. Rennick JE, Knox AM, Treherne SC, et al. Family Members' Perceptions of Their Psychological Responses One Year Following Pediatric Intensive Care Unit (PICU) Hospitalization: Qualitative Findings From the Caring Intensively Study. Front Pediatr. 2021;9:724155. Published 2021 Sep 7. doi:10.3389/fped.2021.724155.
    10. Logan GE, Sahrmann JM, Gu H, Hartman ME. Parental Mental Health Care After Their Child's Pediatric Intensive Care Hospitalization. Pediatr Crit Care Med. 2020;21(11):941-948. doi:10.1097/PCC.0000000000002559.
    11. Carlton EF, Donnelly JP, Prescott HC, et al. School and Work Absences After Critical Care Hospitalization for Pediatric Acute Respiratory Failure: A Secondary Analysis of a Cluster Randomized Trial. JAMA Netw Open. 2021;4(12):e2140732. Published 2021 Dec 1. doi:10.1001/jamanetworkopen.2021.40732.
    • Feasibility Assessment

      Not applicable during the Fall 2023 cycle. 

      Feasibility Informed Final Measure

      The measure uses pediatric inpatient claims data. These data are readily available to hospitals and payers, including State Medicaid programs and private insurers. In addition, several States maintain or are implementing all-payer claims databases.

       

      To address potential issues with data quality or completeness, we have provided in the measure specifications guidelines for assessing records and excluding them from the measure if they contain indicators of poor data quality (e.g., an inconsistent date of birth for the same patient across records) or have missing values for key variables.

      Proprietary Information
      Not a proprietary measure and no proprietary components
      Fees, Licensing, or Other Requirements

      No fees or licensures apply to the measure.

    • Data Used for Testing

      We developed and tested the measure using Medicaid Analytic eXtract (MAX) data for 26 states, which include Medicaid claims from children's and non-children's hospitals. We used MAX data for hospitalizations with discharge dates from 12-01-2007 -12-31-2008.

      Differences in Data

      Not applicable.

      Characteristics of Measured Entities

      The MAX dataset includes 2,111 hospitals with characteristics detailed in the table below. The median hospital volume of annual pediatric all-condition index hospitalizations for these hospitals was 42 (IQR 11–153).

       

      Table 2 ─ MAX Cohort Hospital Characteristics (Total N = 2,111)

      Hospital Characteristics

      Hospitals

      Index Hospitalizations

      Children's hospital [Number (%) of hospitals]

      82 (3.9%)

      124,702 (31.5%)

      Teaching hospital [Number (%) of hospitals] 

      137 (6.5%)

      134,051 (33.9%)

      Rural/urban location [Number (%) of hospitals]

       

       

      Urban 

      814 (38.6%)

      313,455 (79.2%)

      Suburban  

      114 (5.4%)

      4,172 (1.0%)

      Large town 

      408 (19.4%)

      50,294 (12.7%)

      Small town 

      504 (23.9%)

      21,496 (5.4%)

      Rural 

      266 (12.6%)

      6,127 (1.6%)

       

      This table displays MAX cohort hospital characteristics, including the number and frequency of children's hospitals, teaching hospitals, and a breakdown by location.

      Characteristics of Units of the Eligible Population

      There were 1,738,043 records for pediatric patients (those ≤18 years, 29 days old on admission) in the dataset with discharge dates from December 1, 2007 to December 31, 2008.

       

      We provide below the number and percentage of records for pediatric patients excluded because they met the indicated exclusion criteria for the measure (i.e., these records were excluded from both index hospitalizations and readmissions):

      1. The hospital was a specialty or non-acute care hospital: 212,593 (12.2%)
      2. Records for the hospitalization contain incomplete data for variables needed to assess eligibility for the measure or calculate readmission rates, including hospital type, patient identifier, admission date, discharge date, disposition status, date of birth, primary diagnosis code, or gender: 174,724 (10.0%)
      3. The hospitalization was for birth of a healthy newborn: 599,419 (34.5%)
      4. The hospitalization was for obstetric care, including labor and delivery: 56,281 (3.2%)
      5. The primary diagnosis code was for a mental health condition: 25,459 (1.5%)
      6. Information for some hospitalizations is contained in multiple records. These records were combined into a single record for each hospitalization, reducing the total number of records: 142,720 (8.2%)

       

      We indicate below the number and percentage of records excluded from index hospitalizations only because they met the indicated exclusion criteria for index hospitalizations:

      1. The patient was ≥18 years old at the time of discharge: 984 (0.1%)
      2. The discharge disposition was death: 11 (0.001%)
      3. The discharge disposition was an outcome other than discharged or death (e.g., left against medical advice): 7,976 (0.5%)
      4. The hospital had incomplete data or was located in a state not being analyzed: 15,038 (0.9%)
      5. Thirty days of follow-up data are not available for assessing readmissions because the discharge date of the hospitalization occurred in the last month of the dataset:18,614 (1.1%)
      6. Thirty days of follow-up data are not available for assessing readmissions because the patient was enrolled in Medicaid for <30 days after discharge from the index hospitalization: 60,220 (3.5%)
      7. A hospitalization that occurs within 30 days of an index hospitalization was not counted as a new index hospitalization: 28,250 (1.6%)

       

      After applying all of the above exclusions, 395,754 index hospitalizations remained for patients whose characteristics are described in the table below.

       

      Table 3 ─ MAX Cohort Patient Characteristics (Total N = 395,754)

      Patient Characteristic

      *

      Number (%) of Index Hospitalizations

      Age

      < 1 year

            188,929  (47.7)

      *

      1 to < 5 years

       99,776  (25.2)

      *

      5 to < 8 years

       30,289    (7.7)

      *

      8 to <12 years

       28,273    (7.1)

      *

      12 to < 18 years

       48,487  (12.3)

      Gender

      Female

           179,063   (45.3)

      Chronic Condition Indicators (CCIs)

      CCI 1 - Infectious and parasitic disease

            358    (0.1)

      *

      CCI 2 - Neoplasms

         5,653    (1.4)

      *

      CCI 3 - Endocrine, nutritional, and metabolic diseases and immunity disorders

       19,744    (5.0)

      *

      CCI 4 - Diseases of blood and blood-forming organs

       20,148    (5.1)

      *

      CCI 5 - Mental disorders

       19,419    (4.9)

      *

      CCI 6 - Diseases of the nervous system and sense organs

       28,114    (7.1)

      *

      CCI 7 - Diseases of the circulatory system

       11,986    (3.0)

      *

      CCI 8 - Diseases of the respiratory system

       59,984  (15.2)

      *

      CCI 9 - Diseases of the digestive system

       19,686    (5.0)

      *

      CCI 10 - Diseases of the genitourinary system

         3,950    (1.0)

      *

      CCI 12 - Diseases of the skin and subcutaneous tissue

         1,590    (0.4)

      *

      CCI 13 - Diseases of the musculoskeletal system

               4,929    (1.2)

      *

      CCI 14 - Congenital anomalies

             44,477   (11.2)

      *

      CCI 15 - Certain conditions originating in the perinatal period

               1,890    (0.5)

      *

      CCI 16 - Symptoms, signs, and ill-defined conditions

               1,603    (0.4)

      *

      CCI 17 - Injury and poisoning

                  320    (0.1)

      *

      CCI 18 - Factors influencing health status and contact with health services

             12,626    (3.2)

      CCI count

      0 or 1 body system

           340,438   (86.0)

      *

      2 body systems

              35,817    (9.1)

      *

      3 body systems

              13,027    (3.3)

      *

      4+ body systems

               6,472    (1.6)

      Race/ethnicity

      Asian/Pacific Islander

               5,673    (1.6)

      *

      Black

              91,251  (25.2)

      *

      Latino

            108,129  (29.9)

      *

      Mixed

               2,642    (0.7)

      *

      Native American

              12,248    (3.4)

      *

      White

            141,849  (39.2)

      Rural/urban residence

      Urban

            247,919  (62.9)

      *

      Suburban

              31,126    (7.9)

      *

      Large town

              56,273  (14.3)

      * 

      Small town

              34,882    (8.8)

      * 

      Rural

             24,068    (6.1)

      This table displays MAX cohort patient characteristics, including the number and frequency of index hospitalizations by age, gender, CCI category, CCI count, and race/ethnicity.

      *Cell intentionally left empty.

       

      The distribution by state of the index hospitalizations is shown in the table below.

       

       

      Table 4 ─ All-Condition Index Hospitalizations by State (Total N=395,754)

      State

      Index Admissions 

      Percentage

      Alabama

      21,442

      5.4%

      Arizona

      25,968

      6.6%

      Connecticut

      1,472

      0.4%

      Iowa

      5,829

      1.5%

      Idaho

      3,475

      0.9%

      Indiana

      12,485

      3.2%

      Kansas

      6,320

      1.6%

      Kentucky

      17,249

      4.4%

      Louisiana

      25,278

      6.4%

      Minnesota

      7,696

      1.9%

      Missouri

      18,291

      4.6%

      Mississippi

      16,591

      4.2%

      Montana

      1,578

      0.4%

      North Carolina

      29,048

      7.3%

      North Dakota

      1,180

      0.3%

      New Jersey

      13,380

      3.4%

      New Mexico

      7,368

      1.9%

      New York

      54,322

      13.7%

      Oklahoma

      16,879

      4.3%

      Oregon

      4,889

      1.2%

      South Dakota

      2,345

      0.6%

      Texas

      78,441

      19.8%

      Virginia

      12,172

      3.1%

      Vermont

      1,077

      0.3%

      Wisconsin

      9,568

      2.4%

      Wyoming

      1,411

      0.4%

      This table shows all-condition index hospitalizations by state.

                                                                                          

      In addition, we indicate below the number and percentage of hospitalizations excluded from readmissions only because they met the indicated exclusion criteria for readmissions:

      1. Hospitalizations with a primary ICD-9-CM procedure code for a planned procedure: 3,059 (11.4%) of 26,915 readmissions
      2. Hospitalizations with a primary chemotherapy v-code or a primary chemotherapy procedure code: 1,287 (4.8%) of 26,915 readmissions
  • Level(s) of Reliability Testing Conducted
    Accountable Entity-Level (e.g., signal-to-noise analysis)
    Method(s) of Reliability Testing

    We evaluated the reliability of hospital-level readmission rates using the following formula:

    Reliability = σ2 / (σ2 + V)

     

    where σ2 is the systematic variance among hospitals and V is the sampling variance of the sample estimate of a hospital’s rate (both on the probability scale):

    • σ2 = σL2*p2*(1-p)2

    where σL2 = variance component from model output in logit scale

    • V = p*(1-p) / N, 

    where p = the overall readmission rate across all hospitals and N = the hospital's volume

    Reliability Testing Results

    Using the MAX dataset, we found that among the 2,111 total hospitals, 607 hospitals had a readmission rate reliability ≥ 0.5; these hospitals accounted for 88% of total index hospitalizations. The readmission rate reliability was ≥ 0.7 for 314 hospitals, accounting for 74% of total index hospitalizations. It was ≥ 0.8 for 187 hospitals, accounting for 62% of total index hospitalizations.

     

    Because hospitals with few pediatric patients would be less likely to participate in measuring pediatric readmissions, we evaluated readmission rate reliability for hospitals meeting selected minimum thresholds of pediatric index hospitalizations per year. We determined that among the 692 hospitals with ≥ 100 annual index hospitalizations, readmission rate reliability was ≥ 0.5 for 607 hospitals, accounting for 97% of the index hospitalizations at hospitals in this volume category. Readmission rate reliability was ≥ 0.7 for 314 hospitals, accounting for 82% of index hospitalizations at hospitals in this volume category. It was ≥ 0.8 for 187 hospitals, accounting for 69% of index hospitalizations at hospitals in this volume category.

    Accountable Entity-Level Reliability Testing Results
    Accountable Entity-Level Reliability Testing Results
    &nbsp; Overall Minimum Decile_1 Decile_2
    Reliability
    Mean Performance Score
    N of Entities
    Interpretation of Reliability Results

    Reliability values range from 0 to 1. If perfect information from a very large sample were available for a hospital, so the hospital’s random effect could be determined with perfect precision, then the reliability of that hospital’s readmission rate would approach 1. If no information were available for a hospital, then the reliability of that hospital’s readmission rate would be 0.

     

    We found that among the 185 hospitals with ≥ 500 annual index hospitalizations, readmission rate reliability was ≥ 0.8 for all 185 hospitals, accounting for 100% of index hospitalizations at hospitals in this volume category.

  • Level(s) of Validity Testing Conducted
    Patient or Encounter-Level (e.g., sensitivity and specificity)
    Type of accountable entity-level validity testing conducted
    Empirical validity testing at the accountable entity-level (e.g., criterion validity, construct validity, known groups analysis)
    Method(s) of Validity Testing

    We evaluated the validity of the measure's case-mix adjustment model by assessing the discriminative ability of the model using the concordance (c-) statistic. Discrimination refers to how well the model distinguishes between subjects with and without the outcome (in this case, readmission). The c-statistic is a unitless measure of the probability that a randomly selected subject who experienced readmission will have a higher predicted probability of having been readmitted than a randomly selected subject who did not experience readmission. The c-statistic for our case-mix adjustment model, when applied to our 26-state MAX dataset, was 0.69, which is very much in range with results for other 30-day readmission models.

    Validity Testing Results

     

    The sensitivity and specificity of the measure for identifying eligible readmissions from administrative data (criterion validity) were 87.0% and 99.7%, respectively. The measure is able to identify eligible readmissions from administrative data with high sensitivity and specificity.1–4 We found face validity for our planned procedure algorithm.

     

    References

    1. Horwitz L, Partovian C, Lin Z, Herrin J, Grady J, Conover M, Montague J, Dillaway C, Bartczak K, Suter L, Ross J, Bernheim S, Krumholz H, Drye E. Hospital-Wide All-Cause Unplanned Readmission Measure: Final Technical Report.; 2012.

    2. Grosso LM, Curtis JP, Lin Z, Geary LL, Vellanky S, Oladele C, Ott LS, Parzynski C, Bernheim S, Suter LG, Drye EE, Krumholz HM. Hospital-level 30-Day All-Cause Risk-Standardized Readmission Rate Following Elective Primary Total Hip Arthroplasty (THA) And/Or Total Knee Arthroplasty (TKA): Measure Methodology Report.; 2012.

    3. Grady JN, Lin Z, Wang C, Keenan M, Nwosu C, Bhat KR, Horwitz LI, Drye EE, Krumholz HM, Bernheim SM. 2013 Measures Updates and Specifications Report: Hospital-Level 30-Day Risk-Standardized Readmission Measures for Acute Myocardial Infarction, Heart Failure, and Pneumonia (Version 6.0); 2013.

    4. Rice-Townsend S, Hall M, Barnes JN, Lipsitz S, Rangel SJ. Variation in risk-adjusted hospital readmission after treatment of appendicitis at 38 children’s hospitals: an opportunity for collaborative quality improvement. Ann Surg. 2013;257(4):758–765.

    Interpretation of Validity Results

    The following analyses were done at the patient/encounter-level:

     

    Construct Validity

    As detailed in Section 1a.03 of the Evidence form, many studies have provided evidence that readmission rates serve as a measure of healthcare quality. Use of approaches to diagnosis, treatment, and monitoring of disease that adhere to clinical practice guidelines has been correlated with lower readmission rates.1–3 Likewise, improvements in the quality of clinical management have been associated with reductions in readmissions.4–6 Readmission rates have also been found to reflect the quality of discharge and care transition processes. Several studies, mostly in adults, have demonstrated that interventions focused on improving these processes have been linked with decreased readmissions, suggesting that the quality of these processes is associated with readmission risk.7–26 Although the medical literature provides ample evidence for the relationship between quality of care and pediatric and adult readmission risk, assessing the construct validity of pediatric readmission rates directly by examining how rates correlate with other pediatric measures of quality does not appear to be currently feasible. To perform this analysis, pediatric inpatient claims-based quality measures or large, multi-hospital datasets of scores from pediatric quality measures would be required. However, to our knowledge, no other publicly available claims based pediatric inpatient quality measures exist, including among the Healthcare Effectiveness Data and Information Set (HEDIS) measures, the CHIPRA Initial Core Set of Children's Health Care Quality Measures, and other measure collections that we examined. There also do not appear to be large datasets with scores from pediatric inpatient quality measures.

     

    Criterion Validity

    We evaluated the ability of our measure to identify the outcome of interest, readmission, from administrative data by comparing the measure’s performance against the gold standard of chart reviews. We performed this analysis using administrative data and electronic health records for patients admitted to Boston Children’s Hospital over a 1-year period (see Section 2a.07 above for a summary of patient characteristics). We determined from the administrative data that 8,833 index hospitalizations occurred during this time period. We then identified hospitalizations that met measure criteria for readmissions (i.e., the readmissions were not for a planned procedure or chemotherapy) in 2 ways: (1) analysis of the administrative data using the measure program and (2) review of electronic health records. We assessed the health records by first examining whether each index hospitalization was followed by a readmission within 30 days based on presence of inpatient admission orders (such an order is entered for every hospitalization). We then reviewed clinical documentation, including admission notes, discharge summaries, and procedure notes, for 500 randomly selected readmissions to determine whether the readmission had been for a planned procedure or chemotherapy.

     

    Validity of Planned Procedure Algorithm

    We also verified the face validity of the planned procedure algorithm used to identify hospitalizations for planned procedures. We sought public comments on the algorithm in a Federal Register Notice.27 No comments were submitted to suggest that procedures be removed from the list of planned procedures because they are not typically planned or are not a reason for hospitalization. Twenty-four procedures were submitted with the suggestion that they be added to the list of planned procedures. Of these, 7 were already included on the planned procedure list; our expert clinicians in 3 relevant specialties reviewed the remaining 17 procedures. Most of the remaining codes were for procedures for which patients are not hospitalized. Based on the experts’ review, however, 2 procedures, both organ transplantation procedures, were added to the planned procedure list. These transplantation procedures originally had been excluded because they did not meet our operational definition of "planned" (i.e., scheduled at least 24 hours in advance), but it was agreed that they should be added as a special case of procedures for which the need is typically known in advance, even though the actual operation occurs urgently once an organ becomes available. For the same reason, 9 other transplantation procedures were also added to the planned procedure list.

     

    Identification of International Classification of Diseases, 10th Revision (ICD-10) Codes

    To identify ICD-10 codes for chronic conditions, mental health conditions, and obstetric conditions, we used AHRQ’s ICD-10 version of its Chronic Condition Indicator tool. For all other codes used in the measure, we obtained ICD-10 codes by performing conversions from the ICD-9 codes we had selected during measure development. Our goal for the conversions was to compile an ICD-10 code set that was fully consistent with the intent of the original ICD-9 set. We carried out the conversions using the 3M™ Code Translation Tool and reviewed all conversions to ensure that the resulting ICD-10 codes captured the intended concepts, removing ICD-10 codes from the code set as appropriate. This ICD-10 code set has been updated periodically, most recently in 2022.

     

    Testing Exclusions

    We chose to exclude hospitalizations with a primary mental health diagnosis from the measure cohort after evaluating the relationship between the primary diagnosis and the readmission outcome. We fitted a hierarchical random slopes regression model to the MAX data. The model consisted of patients nested within hospitals at the first level and 2 random slope indicator variables at the second level: (a) an indicator variable for the primary diagnosis of interest alone and (b) an indicator variable for all other possible primary diagnoses. 

     

    References

    1. Bozic KJ, Maselli J, Pekow PS, Lindenauer PK, Vail TP, Auerbach AD. The influence of procedure volumes and standardization of care on quality and efficiency in total joint replacement surgery. J Bone Joint Surg Am. 2010;92(16):2643–2652. 
    2. Ludke RL, MacDowell NM, Booth BM, Hunter SA. Appropriateness of admissions and discharges among readmitted patients. Health Serv Res. 1990;25(3):501–525.
    3. Heidenreich PA, Hernandez AF, Yancy CW, Liang L, Peterson ED, Fonarow GC. Get With The Guidelines program participation, process of care, and outcome for Medicare patients hospitalized with heart failure. Circ Cardiovasc Qual Outcomes. 2012;5(1):37–43. d
    4. Fassl BA, Nkoy FL, Stone BL, Srivastava R, Simon TD, Uchida DA, Koopmeiners K, Greene T, Cook LJ, Maloney CG. The Joint Commission Children’s Asthma Care quality measures and asthma readmissions. Pediatrics. 2012;130(3):482–491.
    5. Pillai D, Song X, Pastor W, Ottolini M, Powell D, Wiedermann BL, DeBiasi RL. Implementation and impact of a consensus diagnostic and management algorithm for complicated pneumonia in children. J Investig Med. 2011;59(8):1221–1227.
    6. Cheney J, Barber S, Altamirano L, Medico Cirujano, Cheney M, Williams C, Jackson M, Yates P, O’Rourke P, Wainwright C. A clinical pathway for bronchiolitis is effective in reducing readmission rates. J Pediatr. 2005;147(5):622–626.
    7. Inglis SC, Clark RA, McAlister FA, Ball J, Lewinter C, Cullington D, Stewart S, Cleland JG. Structured telephone support or telemonitoring programmes for patients with chronic heart failure. Cochrane Database Syst Rev Online. 2010;(8):CD007228. 
    8. Phillips CO, Wright SM, Kern DE, Singa RM, Shepperd S, Rubin HR. Comprehensive discharge planning with postdischarge support for older patients with congestive heart failure: a meta-analysis. JAMA. 2004;291(11):1358–1367. doi:10.1001/jama.291.11.1358.
    9. Scott IA. Preventing the rebound: improving care transition in hospital discharge processes. Aust Health Rev. 2010;34(4):445–451. doi:10.1071/AH09777.
    10. Shepperd S, Lannin NA, Clemson LM, McCluskey A, Cameron ID, Barras SL. Discharge planning from hospital to home. Cochrane Database Syst Rev Online. 2013;1:CD000313. 
    11. Sochalski J, Jaarsma T, Krumholz HM, Laramee A, McMurray JJV, Naylor MD, Rich MW, Riegel B, Stewart S. What works in chronic care management: the case of heart failure. Health Aff (Millwood). 2009;28(1):179–189.
    12. Anderson C, Deepak BV, Amoateng-Adjepong Y, Zarich S. Benefits of comprehensive inpatient education and discharge planning combined with outpatient support in elderly patients with congestive heart failure. Congest Heart Fail. 2005;11(6):315–321.
    13. Balaban RB, Weissman JS, Samuel PA, Woolhandler S. Redefining and redesigning hospital discharge to enhance patient care: a randomized controlled study. J Gen Intern Med. 2008;23(8):1228–1233.
    14. Coleman EA, Parry C, Chalmers S, Min S-J. The care transitions intervention: results of a randomized controlled trial. Arch Intern Med. 2006;166(17):1822–1828. 
    15. Evans RL, Hendricks RD. Evaluating hospital discharge planning: a randomized clinical trial. Med Care. 1993;31(4):358–370.
    16. Jack BW, Chetty VK, Anthony D, Greenwald JL, Sanchez GM, Johnson AE, Forsythe SR, O’Donnell JK, Paasche-Orlow MK, Manasseh C, Martin S, Culpepper L. A reengineered hospital discharge program to decrease rehospitalization: a randomized trial. Ann Intern Med. 2009;150(3):178–187.
    17. Naylor MD, Brooten DA, Campbell RL, Maislin G, McCauley KM, Schwartz JS. Transitional care of older adults hospitalized with heart failure: a randomized, controlled trial. J Am Geriatr Soc. 2004;52(5):675–684.
    18. Naylor M, Brooten D, Jones R, Lavizzo-Mourey R, Mezey M, Pauly M. Comprehensive discharge planning for the hospitalized elderly. A randomized clinical trial. Ann Intern Med. 1994;120(12):999–1006.
    19. Koehler BE, Richter KM, Youngblood L, Cohen BA, Prengler ID, Cheng D, Masica AL. Reduction of 30-day postdischarge hospital readmission or emergency department (ED) visit rates in high-risk elderly medical patients through delivery of a targeted care bundle. J Hosp Med. 2009;4(4):211–218. 
    20. Rich MW, Beckham V, Wittenberg C, Leven CL, Freedland KE, Carney RM. A multidisciplinary intervention to prevent the readmission of elderly patients with congestive heart failure. N Engl J Med. 1995;333(18):1190–1195. 
    21. Costantino ME, Frey B, Hall B, Painter P. The influence of a postdischarge intervention on reducing hospital readmissions in a Medicare population. Popul Heal Manag. 2013;16(5):310–316. 
    22. Flood KL, Maclennan PA, McGrew D, Green D, Dodd C, Brown CJ. Effects of an acute care for elders unit on costs and 30-day readmissions. JAMA Intern Med. 2013:1–7. 
    23. Hoffmann RG, Yan K, Brousseau DC. Outpatient follow-up and rehospitalizations for sickle cell disease patients. Pediatr Blood Cancer. 2012;58(3):406–409.
    24. Bradley EH, Curry L, Horwitz LI, Sipsma H, Wang Y, Walsh MN, Goldmann D, White N, Piña IL, Krumholz HM. Hospital strategies associated with 30-day readmission rates for patients with heart failure. Circ Cardiovasc Qual Outcomes. 2013;6(4):444–450. 
    25. Harrison PL, Hara PA, Pope JE, Young MC, Rula EY. The impact of postdischarge telephonic follow-up on hospital readmissions. Popul Heal Manag. 2011;14(1):27–32. 
    26. Kripalani S, Theobald CN, Anctil B, Vasilevskis EE. Reducing Hospital Readmission Rates: Current Strategies and Future Directions. Annu Rev Med. 2013.
    27. Department of Health and Human Services, Agency for Healthcare Research and Quality. Request for comments on pediatric planned procedure algorithm. Fed Regist. 2013;78:57639 –57640.
    28. Horwitz L, Partovian C, Lin Z, Herrin J, Grady J, Conover M, Montague J, Dillaway C, Bartczak K, Suter L, Ross J, Bernheim S, Krumholz H, Drye E. Hospital-Wide All-Cause Unplanned Readmission Measure: Final Technical Report.; 2012. 
    29. Grosso LM, Curtis JP, Lin Z, Geary LL, Vellanky S, Oladele C, Ott LS, Parzynski C, Bernheim S, Suter LG, Drye EE, Krumholz HM. Hospital-level 30-Day All-Cause Risk-Standardized Readmission Rate Following Elective Primary Total Hip Arthroplasty (THA) And/Or Total Knee Arthroplasty (TKA): Measure Methodology Report.; 2012.
    30. Grady JN, Lin Z, Wang C, Keenan M, Nwosu C, Bhat KR, Horwitz LI, Drye EE, Krumholz HM, Bernheim SM. 2013 Measures Updates and Specifications Report: Hospital-Level 30-Day Risk-Standardized Readmission Measures for Acute Myocardial Infarction, Heart Failure, and Pneumonia (Version 6.0); 2013. 
    31. Rice-Townsend S, Hall M, Barnes JN, Lipsitz S, Rangel SJ. Variation in risk-adjusted hospital readmission after treatment of appendicitis at 38 children’s hospitals: an opportunity for collaborative quality improvement. Ann Surg. 2013;257(4):758–765. 
    32. Austin PC, Steyerberg EW. Interpreting the concordance statistic of a logistic regression model: relation to the variance and odds ratio of a continuous explanatory variable. BMC Med Res Methodol. 2012;12:82.
    33. Steyerberg EW, Vickers AJ, Cook NR, Gerds T, Gonen M, Obuchowski N, Pencina MJ, Kattan MW. Assessing the performance of prediction models: a framework for traditional and novel measures. Epidemiology. 2010;21(1):128–138. 
    34. Hosmer DW, Hosmer T, Le Cessie S, Lemeshow S. A comparison of goodness-of-fit tests for the logistic regression model. Stat Med. 1997;16(9):965–980.
    35. Department of Health and Human Services. Medicare Program; Hospital Inpatient Prospective Payment Systems for Acute Care Hospitals and the Long-Term Care Hospital Prospective Payment System and Fiscal Year 2013 Rates; Hospitals’ Resident Caps for Graduate Medical Education Payment Purposes; Quality Reporting Requirements for Specific Providers and for Ambulatory Surgical Centers. Fed Regist. 2012;77(170).
  • Methods used to address risk factors
    Statistical risk model with risk factors
    Conceptual Model Rationale

    Readmissions signal how well disease is managed in hospital and home settings. Care management at home is influenced by health-related social needs. The conceptual model presented (adapted from Zumbrunn et al1) identifies patient risk factors (e.g., age, chronic condition) and social risk factors (e.g., child and family health literacy, neighborhood and community resources) that influence possibility of readmission.

     

    References

    1. Zumbrunn A, Bachmann N, Bayer-Oglesby L, Joerg R; SIHOS Team. Social disparities in unplanned 30-day readmission rates after hospital discharge in patients with chronic health conditions: A retrospective cohort study using patient level hospital administrative data linked to the population census in Switzerland. PLoS One. 2022;17(9):e0273342. Published 2022 Sep 22. doi:10.1371/journal.pone.0273342

    Risk Factor Characteristics Across Measured Entities

    There were 1,738,043 records for pediatric patients (those ≤18 years, 29 days old on admission) in the dataset with discharge dates from December 1, 2007 to December 31, 2008.

     

    We provide below the number and percentage of records for pediatric patients excluded because they met the indicated exclusion criteria for the measure (i.e., these records were excluded from both index hospitalizations and readmissions):

    1. The hospital was a specialty or non-acute care hospital: 212,593 (12.2%)
    2. Records for the hospitalization contain incomplete data for variables needed to assess eligibility for the measure or calculate readmission rates, including hospital type, patient identifier, admission date, discharge date, disposition status, date of birth, primary diagnosis code, or gender: 174,724 (10.0%)
    3. The hospitalization was for birth of a healthy newborn: 599,419 (34.5%)
    4. The hospitalization was for obstetric care, including labor and delivery: 56,281 (3.2%)
    5. The primary diagnosis code was for a mental health condition: 25,459 (1.5%)
    6. Information for some hospitalizations is contained in multiple records. These records were combined into a single record for each hospitalization, reducing the total number of records: 142,720 (8.2%)

     

    We indicate below the number and percentage of records excluded from index hospitalizations only because they met the indicated exclusion criteria for index hospitalizations:

    1. The patient was ≥!18 years old at the time of discharge: 984 (0.1%)
    2. The discharge disposition was death: 11 (0.001%)
    3. The discharge disposition was an outcome other than discharged or death (e.g., left against medical advice): 7,976 (0.5%)
    4. The hospital had incomplete data or was located in a state not being analyzed: 15,038 (0.9%)
    5. Thirty days of follow-up data are not available for assessing readmissions because the discharge date of the hospitalization occurred in the last month of the dataset:18,614 (1.1%)
    6. Thirty days of follow-up data are not available for assessing readmissions because the patient was enrolled in Medicaid for <30 days after discharge from the index hospitalization: 60,220 (3.5%)
    7. A hospitalization that occurs within 30 days of an index hospitalization was not counted as a new index hospitalization: 28,250 (1.6%)

     

    After applying all of the above exclusions, 395,754 index hospitalizations remained for patients whose characteristics are described in the table below.

     

    Table 3 ─ MAX Cohort Patient Characteristics (Total N = 395,754)

    Patient Characteristic

    *

    Number (%) of Index Hospitalizations

    Age

    < 1 year

    188,929  (47.7)

    *

    1 to < 5 years

    99,776  (25.2)

    *

    5 to < 8 years

    30,289    (7.7)

    *

    8 to <12 years

    28,273    (7.1)

    *

    12 to < 18 years

    48,487  (12.3)

    Gender

    Female

    179,063   (45.3)

    Chronic Condition Indicators (CCIs)

    CCI 1 - Infectious and parasitic disease

    358    (0.1)

    *

    CCI 2 - Neoplasms

    5,653    (1.4)

    *

    CCI 3 - Endocrine, nutritional, and metabolic diseases and immunity disorders

    19,744    (5.0)

    *

    CCI 4 - Diseases of blood and blood-forming organs

    20,148    (5.1)

    *

    CCI 5 - Mental disorders

    19,419    (4.9)

    *

    CCI 6 - Diseases of the nervous system and sense organs

    28,114    (7.1)

    *

    CCI 7 - Diseases of the circulatory system

    11,986    (3.0)

    *

    CCI 8 - Diseases of the respiratory system

    59,984  (15.2)

    *

    CCI 9 - Diseases of the digestive system

    19,686    (5.0)

    *

    CCI 10 - Diseases of the genitourinary system

    3,950    (1.0)

    *

    CCI 12 - Diseases of the skin and subcutaneous tissue

    1,590    (0.4)

    *

    CCI 13 - Diseases of the musculoskeletal system

    4,929    (1.2)

    *

    CCI 14 - Congenital anomalies

    44,477   (11.2)

    *

    CCI 15 - Certain conditions originating in the perinatal period

    1,890    (0.5)

    *

    CCI 16 - Symptoms, signs, and ill-defined conditions

    1,603    (0.4)

    *

    CCI 17 - Injury and poisoning

    320    (0.1)

    *

    CCI 18 - Factors influencing health status and contact with health services

    12,626    (3.2)

    CCI count

    0 or 1 body system

    340,438   (86.0)

    *

    2 body systems

    35,817    (9.1)

    *

    3 body systems

    13,027    (3.3)

    *

    4+ body systems

    6,472    (1.6)

    Race/ethnicity

    Asian/Pacific Islander

    5,673    (1.6)

    *

    Black

    91,251  (25.2)

    *

    Latino

    108,129  (29.9)

    *

    Mixed

    2,642    (0.7)

    *

    Native American

    12,248    (3.4)

    *

    White

    141,849  (39.2)

    Rural/urban residence

    Urban

    247,919  (62.9)

    *

    Suburban

    31,126    (7.9)

    *

    Large town

    56,273  (14.3)

    * 

    Small town

    34,882    (8.8)

    * 

    Rural

    24,068    (6.1)

    This table displays MAX cohort patient characteristics, including the number and frequency of index hospitalizations by age, gender, CCI category, CCI count, and race/ethnicity.

    Risk Adjustment Modeling and/or Stratification Results

    Age

    We found that age had a non-linear statistically significant relationship with 30-day readmission in both bivariate and multivariate analysis (see Table 5). The final specification for age is detailed below. This specification accounted for a standard deviation of hospital means equivalent to 30% of the standard deviation of hospital means from the full multivariate model.  We chose the specification because (a) the categorical variable captures the non-linear relationship of age with the outcome of readmission, (b) the specification has a high likelihood ratio chi-square relative to other less parsimonious specifications, and (c) the age group categories are clinically and developmentally meaningful.

     

    Table 6 ─ Bivariate and Multivariate Results for Age

    Age (agegroup)

    Bivariate Analysis

     

    Multivariate Analysis

     

    Likelihood ratio 410.28

    OR

    p-value

    OR

    p-value

    1 = 0 ≤ age < 1

    reference

    -

    reference

    -

    2 = 1 ≤ age < 5 

    0.89

    < .001

    0.71

    < .001

    3 = 5 ≤ age < 8 

    0.82

    < .001

    0.57

    < .001

    4 = 8 ≤ age < 12 

    1.00

     .86

    0.64

    < .001

    5 = 12 ≤ age < 18 

    1.21

    < .001

    0.70

    < .001

    This table displays the bivariate and multivariate results in terms of odds-ratios by age group.

     

    Gender

    We found that male gender was significantly associated with increased odds of readmission in bivariate and multivariate analysis (see Table 6). We found that gender accounted for a standard deviation of hospital means equivalent to 1% of the standard deviation of hospital means from the full multivariate model. We retained the variable in our model because gender had a statistically significant relationship with the outcome of readmission.

     

    Table 7 ─ Bivariate and Multivariate Results for Gender

    Gender (male)

    Bivariate Analysis

     

    Multivariate Analysis

     

    Likelihood ratio 39.13

    OR

    p-value

    OR

    p-value

    0 = female

    reference

    -

    reference

    -

    1 = male

    1.05

    < .001

    1.08

    < .001

    This table displays the bivariate and multivariate results in terms of odds-ratios by gender.

     

    Chronic Conditions

    To account for chronic disease comorbidity, we used the AHRQ CCI tool to classify ICD-9-CM diagnosis codes for chronic conditions into 18 body systems (organ systems, disease categories, or other categories). We created a dichotomous variable for each body system, with a value of 1 if ≥ 1 chronic condition was present (coded as a primary or secondary diagnosis for an index hospitalization) in that body system or 0 if no chronic condition was present in that body system. We examined each of the 18 CCI variables in relation to the outcome of readmission in bivariate and multivariate analysis, using absence of a chronic condition in the body system in question as the reference.

     

    We found that 17 of the 18 dichotomous variables were significantly related to readmission in bivariate analysis. Only CCI 16, "Symptoms, signs, ill-defined conditions," did not have a significant relationship. In multivariate analysis, CCI 16 achieved statistical significance, and CCI 15, "Certain conditions originating in the perinatal period," became non-significant.

     

    We chose to retain both cci15 and cci16 in the final model (a) to maintain, to the extent possible, the coherence of the complete AHRQ CCI tool and (b) because most of the CCI variables had a statistically significant relationship with the outcome of readmission. However, we excluded the CCI variable for body system 11, “Complications of pregnancy, childbirth, and the puerperium,” from the final model. Patients who have a primary diagnosis code for an obstetric condition or any diagnosis or procedure code for delivery are excluded from the measure cohort. We have found using various datasets that this exclusion leaves very few (or sometimes no) patients who have a secondary diagnosis code for a chronic condition within body system 11, which could create model-fitting problems if CCI 11 were included in the case-mix-adjustment model.

     

    Table 8 ─ Bivariate and Multivariate Results for CCIs

    CCI (cci)

     

    Bivariate

    Bivariate

    Multivariate 

    Multivariate

     Likelihood ratio range: 22.33 (cci16) to 3594.46 (cci18)

     

    OR

    p-value

    OR

    p-value

    1

    Infectious and parasitic disease

    1.60

       .001

    2.38

    < .001

    2

    Neoplasms

    3.23

    < .001

    3.33

    < .001

    3

    Endocrine, nutritional, and metabolic diseases and immunity disorders

    1.79

    < .001

    1.94

    < .001

    4

    Diseases of blood and blood-forming organs

    2.01

    < .001

    2.36

    < .001

    5

    Mental disorders

    1.44

    < .001

    1.35

    < .001

    6

    Diseases of the nervous system and sense organs

    1.86

    < .001

    2.05

    < .001

    7

    Diseases of the circulatory system

    1.97

    < .001

    1.88

    < .001

    8

    Diseases of the respiratory system

    0.91

    < .001

    1.24

    < .001

    9

    Diseases of the digestive system

    2.22

    < .001

    2.24

    < .001

    10

    Diseases of the genitourinary system

    2.10

    < .001

    2.02

    < .001

    11

    Complications of pregnancy, childbirth, and the puerperium

    1.39

      .02

     0.51

      .50

    12

    Diseases of the skin and subcutaneous tissue

    1.38

    < .001

    1.62

    < .001

    13

    Diseases of the musculoskeletal system

    1.34

    < .001

    1.63

    < .001

    14

    Congenital anomalies

    1.67

    < .001

    1.73

    < .001

    15

    Certain conditions originating in the perinatal period

    0.79

     .01

    0.92

      .46

    16

    Symptoms, signs, and ill-defined conditions

    1.13

     .10

    1.19

      .09

    17

    Injury and poisoning

    2.03

    < .001

    1.95

    < .001

    18

    Factors influencing health status and contact with health services

    3.43

    < .001

    2.94

    < .001

    This table displays the bivariate and multivariate results in terms of odds-ratios by CCI category.

     

    Number of body systems affected by chronic conditions

    We also evaluated a count variable of the number of body systems in which a chronic condition was present for each index hospitalization. To avoid problems with model estimation, we top-coded the variable at ≥ 4 systems affected by a chronic disease because there were few index admissions with diagnoses in ≥ 5 systems.

     

    The CCI count variable had a statistically significant relationship with readmission in bivariate and multivariate analysis. In multivariate analysis, an increasing count was associated with decreasing odds of readmission. This finding indicates that the presence of chronic conditions in multiple body systems confers a readmission risk that is lower than the sum of the risk associated with chronic conditions in each of the individual body systems, such that the CCI count variable serves to prevent overestimation of the risk associated with having chronic conditions in multiple body systems. We chose to retain cci count as a model variable based on (a) its statistically significant relationship with the outcome of readmission and (b) because it adjusts the readmission risk associated with having chronic conditions in multiple body systems.

     

    The CCI and CCI count variables accounted for a standard deviation of hospital means equivalent to 67% of the standard deviation of hospital means from the full multivariable model.

     

    Table 9 ─ Bivariate and Multivariate Results for CCI Count

    CCI Count (cci count)

    Bivariate

    Bivariate

    Multivariate

    Multivariate

    Likelihood ratio 5647.12

    OR

    p-value

    OR

    p-value

    1 = 0 to 1

    reference

    -

    reference

    -

    2 = 2

    2.33

    < .001

    0.88

    < .001

    3 = 3

    3.14

    < .001

    0.62

    < .001

    4 = 4 or more 

    3.59

    < .001

    0.30

    < .001

    This table displays the bivariate and multivariate results in terms of odds-ratios by CCI count.

    Calibration and Discrimination

    We assessed the discriminative ability of the model using the c-statistic.5,6 Discrimination refers to how well the model distinguishes between subjects with and without the outcome (in this case, readmission).5 The c-statistic is a unitless measure of the probability that a randomly selected subject who experienced readmission will have a higher predicted probability of having been readmitted than a randomly selected subject who did not experience readmission.5

     

    We assessed model calibration with a chi-square goodness-of-fit test analogous to the Hosmer-Lemeshow test.7 We used the test, which evaluates how well observed outcomes correspond to those predicted by the fitted logistic regression model,7 to determine how well observed and predicted numbers of readmissions matched for the levels of the 2 ordinal variables in our case-mix adjustment model, age and CCI count. The lack of a significant difference between observed and predicted values indicates good model calibration.

    Interpretation of Risk Factor Findings

    Please refer to fields above. 

    Final Approach to Address Risk Factors
    Statistical risk model with risk factors
    Risk adjustment approach
    On
    Risk adjustment approach
    Off
    Specify number of risk factors

    20 fixed effect variables representing 4 types of risk factors.

    Conceptual model for risk adjustment
    Off
    Conceptual model for risk adjustment
    On
  • Contributions Towards Advancing Health Equity

    Previous (2015) Submission:

     

    We developed and tested the measure using 2008 MAX data for 26 states, which include Medicaid claims from 395,754 index hospitalizations at 2,111 children´s and non-children´s hospitals. We also used 2005-2009 AHRQ Revisit data for New York and Nebraska, which include claims for all payers from 758,347 index hospitalizations at 260 children´s and non-children´s hospitals, to evaluate disparities in readmission risk associated with race/ethnicity and insurance status. We chose which states´ data to use based on assessment of data quality and completeness. Both datasets can be used to evaluate readmissions back to the same hospital or to different hospitals.

     

    DISPARITIES ASSOCIATED WITH RACE/ETHNICITY 

     

    We assessed disparities in readmission risk associated with race/ethnicity using both 2005-2009 AHRQ Revisit data for New York (all-payer) and the MAX dataset (Medicaid only). AHRQ Revisit data for Nebraska do not include a race/ethnicity variable and so could not be used in the analysis. 

     

    Race/ethnicity is recorded in AHRQ Revisit data using the categories Asian or Pacific Islander, Black, Hispanic, Native American, Other, or White. For our analysis, we combined the Asian or Pacific Islander, Native American, and Other categories into a single “Other” category because each category contained a very small number of observations. We found that compared with White patients, Black patients (odds ratio [OR] 1.18, 95% CI 1.13–1.22; p < .001) and Hispanic patients (OR 1.24, 95% CI 1.20–1.29; p < .001) had higher odds of readmission, independent of case-mix (age, gender, and chronic conditions) and index admission hospital. 

     

    Race/ethnicity is recorded in MAX data using the categories Asian/Pacific Islander, Black, Latino, Mixed race, Native American, or White. When we assessed the relationship between readmission risk and each race/ethnicity category using White patients as the reference group, controlling for case-mix (age, gender, and chronic conditions) and index admission hospital, we found no significant differences in the odds of readmission. 

     

    The finding of a higher likelihood of readmission for Black and Hispanic patients compared with White patients in our all-payer dataset but not in our Medicaid-only dataset suggests that socioeconomic status, as reflected by insurance status, might explain at least some of the apparent difference in readmission risk. To test this hypothesis, we repeated the race/ethnicity analysis in the AHRQ Revisit New York dataset and also controlled for insurance status. We found that the differences in readmission risk for Black patients (OR 1.07, 95% CI 1.05–1.13; p < .001) or Hispanic patients (OR 1.13, 95% CI 1.08–1.19; p < .001) compared with White patients were attenuated but not eliminated. This finding suggests some association, at least for this particular patient sample, of race/ethnicity with higher readmission risk, independent of insurance status. 

     

    DISPARITIES ASSOCIATED WITH INSURANCE STATUS 

     

    We assessed disparities in readmission risk associated with insurance status using 2005-2009 AHRQ Revisit New York and Nebraska data. We found that compared with Medicaid-insured patients, the odds of readmission were significantly lower for those who had private insurance (OR 0.76, 95% CI 0.75–0.78; p < .001), other types of insurance (such as Medicare or other government-sponsored insurance)(OR 0.85, 95% CI 0.78–0.92; p < .001), or self-pay status (OR 0.73, 95% CI 0.69–0.78; p < .001) independent of case-mix (age, gender, and chronic conditions) and index admission hospital. 

     

    We also evaluated whether a given hospital´s readmission performance tends to correlate among patients with different insurance statuses. We fitted the measure model to 2005-2009 AHRQ Revisit New York and Nebraska data, adding a random slope indicator variable for Medicaid, private insurance, and self-pay statuses (we were unable to include an indicator variable for other types of insurance because the model would not converge, perhaps due to low numbers of observations in this category at some hospitals). We found that the regression coefficients were highly correlated among different insurance statuses. Correlations were 0.84 for Medicaid and self-pay, 0.92 for Medicaid and private insurance, and 0.90 for private insurance and self-pay. This finding indicates that hospitals tend to have similarly high or low readmission rates for patients with different insurance statuses, suggesting that disparities in readmission risk associated with insurance status tend to occur between rather than within hospitals. 

     

    DISPARITIES ASSOCIATED WITH RURALITY/URBANICITY 

     

    Using our MAX dataset, we assessed disparities in readmission risk associated with residence in rural versus urban areas. We used patients´ 5-digit zip codes to assign rural-urban commuting area (RUCA) codes, which are a Census tract-based classification system that uses Bureau of Census Urbanized Area and Urban Cluster definitions together with work commuting information to characterize Census tracts regarding their rural and urban status.1 We then used the RUCA codes to assign the area of each patient´s residence to 1 of 5 levels of a rurality/urbanicity classification scheme created by the Dartmouth Atlas Working Group: urban core, suburban, large town, small town, or isolated rural.2 

     

    Controlling for case-mix (age, gender, and chronic conditions) and index admission hospital, we found that readmission risk did not vary significantly among the 5 levels of rurality/urbanicity (p = .22 for chi-square test).

     

    Current Submission:

     

    New evidence affirms disparities in the rate of readmissions between children insured by Medicaid and children with private insurance. In particular, despite an overall decline in readmission rates for publicly- and privately-insured children from 2010 to 2017, readmission rates remained higher among Medicaid beneficiaries than for their counterparts. Mean hospital 30-day risk-adjusted readmission rates increased for Medicaid beneficiaries, from 6.5% (SD 2.1%) in 2010 to 6.9% (SD 1.8%) in 2017 (P = .017), but remained stable for privately insured patients, from 5.9% (SD 2.0%) in 2010 to 6.0% (SD 1.1%) in 2017 (P = .14).3

     

    References

    1. WWAMI Rural Health Research Center. UW RHRC rural urban commuting area codes - RUCA. Available at: http://depts.washington.edu/uwruca/. Accessed October 2, 2013.
    2. Dartmouth Atlas Working Group. PCSA-RUCA (rural-urban commuting areas) designations. 2007. Available at: https://data.dartmouthatlas.org/. Accessed October 3, 2013.
    3. Bucholz EM, Schuster MA, Toomey SL. Trends in 30-Day Readmission for Medicaid and Privately Insured Pediatric Patients: 2010-2017. Pediatrics. 2020;146(2):e20200270.
  • Current Use(s)
    Public Reporting
    Public Health/Disease Surveillance
    Payment Program
    Quality Improvement with Benchmarking (external benchmarking to multiple organizations)
    Quality Improvement (Internal to the specific organization)
    • Name of the program and sponsor
      Center for Health Information Analysis (CHIA)
      URL of the program
      https://www.chiamass.gov/
      Purpose of the program
      Using Pediatric All-Condition Readmission measure to evaluate pediatric readmission rates in Massachusetts
      Geographic area and percentage of accountable entities and patients included
      Massachusetts Hospital Inpatient Discharge Database (HIDD) data. This includes case mix data from acute care hospitals. All children admitted to acute care hospitals included in the HIDD.
      Applicable level of analysis and care setting

      Hospital-level and individual-level inpatient setting.

    • Name of the program and sponsor
      MassHealth Acute Hospital Pay-for-Performance Program
      URL of the program
      https://www.mass.gov/doc/ry2023-masshealth-acute-p4p-technical-session-0/downlo…
      Purpose of the program
      The Pediatric All-Condition Readmission measure is being used in this pay-for-performance program for Massachusetts Medicaid (MassHealth).
      Geographic area and percentage of accountable entities and patients included
      Massachusetts children on Medicaid.
      Applicable level of analysis and care setting

      Hospital-level inpatient setting.

    • Name of the program and sponsor
      Center of Excellence for Pediatric Quality Measurement (CEPQM) Learning Collaborative
      Purpose of the program
      The learning collaborative measured readmissions across four hospitals to develop interventions to improve rates of readmissions.
      Geographic area and percentage of accountable entities and patients included
      Children admitted to four hospitals in New York and Massachusetts.
      Applicable level of analysis and care setting

      Hospital-level inpatient setting.

    • Name of the program and sponsor
      Boston Children’s Hospital
      URL of the program
      https://www.childrenshospital.org/
      Purpose of the program
      Multidisciplinary nurse-led committee to track and improve pediatric readmission rates.
      Geographic area and percentage of accountable entities and patients included
      Pediatric inpatients admitted to Boston Children’s Hospital
      Applicable level of analysis and care setting

      Unit- and hospital-level inpatient setting.

    • Name of the program and sponsor
      New York State Department of Health
      Purpose of the program
      We partnered with the New York Office of Quality and Patient Safety to test implementation of our pediatric all-condition readmission measure on its Medicaid and all-payer inpatient claims databases.
      Geographic area and percentage of accountable entities and patients included
      New York State children
      Applicable level of analysis and care setting

      Hospital-level inpatient setting.

    • Name of the program and sponsor
      MassHealth
      Purpose of the program
      We partnered with MassHealth to test implementation of our pediatric all-condition readmission measure on its Medicaid claims databases.
      Geographic area and percentage of accountable entities and patients included
      Massachusetts children on Medicaid
      Applicable level of analysis and care setting

      Hospital-level inpatient setting.

    Actions of Measured Entities to Improve Performance

    Measuring readmission rates among Medicaid-insured patients is particularly important because multiple analyses have demonstrated that they are at higher risk of readmission than privately-insured patients. An analysis of the 2007 Healthcare Cost and Utilization Project State Inpatient Databases for 10 states found that the 30-day readmission rate for pediatric Medicaid beneficiaries (ages 0 to 20 years old, including newborns but excluding obstetric patients) was 3.1%, compared with 2.0% for privately-insured children (p<0.05). Readmission rates were higher for Medicaid-insured patients than for their privately-insured counterparts in every age and gender category except for the subcategory of 13- to 20-year-old females admitted for obstetric care.1 An analysis using our candidate measure also found that compared with Medicaid-insured children, children with private insurance (odds ratio (OR) 0.76 [95% confidence interval (CI) 0.75 to 0.78], p<0.001); other types of insurance, such as Medicare or other government-sponsored insurance (OR 0.85 [95% CI 0.78 to 0.92], p<0.001); or self-pay status (OR 0.73 [95% CI 0.69 to 0.78], p<0.001) are at lower risk of readmission.

     

    Given their higher risk of readmission, Medicaid-insured children are a vulnerable population for whom targeted interventions to reduce readmissions are especially critical. Interventions that reduce hospital readmission rates by improving hospital discharge, transition and post-discharge care as well as disease management should be beneficial to all patients, including those insured by Medicaid. Interventions that specifically address the complex needs of Medicaid-insured patients may be particularly effective in reducing readmission rates in this group.

     

    The Care Transitions Innovation (C-TraIn) is a low-cost, multi-component transitional care intervention that has decreased readmission rates in uninsured and Medicaid-insured populations.2 The intervention helps remove financial barriers to care by providing inpatient pharmacy consultation, a 30-day supply of medications for use after discharge, payment for medical homes for uninsured patients who lack access to outpatient care, and access to a transitional care nurse to bridge care between the inpatient and outpatient settings. This low-cost intervention illustrates how investing a relatively small amount of resources upfront could potentially avert the much greater cost of hospital readmission. 

     

    North Carolina has demonstrated that interventions implemented via a Medicaid program can be highly effective in reducing readmissions. Its state-wide initiative focused on comprehensive transitional care for Medicaid beneficiaries with complex chronic medical conditions, with the intensity of the intervention tailored to patients' readmission risk.3 Patients who received the intervention were 20% less likely to experience a readmission during the subsequent year than clinically similar patients who received routine care. Additionally, patients who received the transitional care were less likely than routine-care patients to experience multiple readmissions. These findings suggest that transitional care interventions targeted to address the particular needs of Medicaid-insured patients can reduce hospital readmissions among this high-risk population. A pediatric readmission measure could be used to track the impact of similar interventions in Medicaid-insured children.

     

    References

    1. Jiang HJ, Wier LM. All-Cause Hospital Readmissions among Non-Elderly Medicaid Patients, 2007. In: Healthcare Cost and Utilization Project (HCUP) Statistical Briefs. Rockville (MD): Agency for Healthcare Research and Quality (US); April 2010.

    2. Englander H, Kansagara D. Planning and designing the care transitions innovation (C-Train) for uninsured and Medicaid patients. J Hosp Med. 2012;7(7):524-529. doi:10.1002/jhm.1926

    3. Jackson CT, Trygstad TK, DeWalt DA, DuBard CA. Transitional care cut hospital readmissions for North Carolina Medicaid patients with complex chronic conditions. Health Aff (Millwood). 2013;32(8):1407-1415. doi:10.1377/hlthaff.2013.0047

    Feedback on Measure Performance

    We collaborated with two states to test the measure to ensure validity, feasibility, and usability for states, health plans, and hospitals, and to further develop strategies to streamline data collection and measure reporting. Feedback from the two states on their testing experiences indicated that the measure is straightforward and can be implemented quickly. Based on helpful suggestions from the states, we improved the clarity of the detailed measure specifications, particularly with regard to use of certain ICD-9-CM codes for applying clinical exclusions and identifying chronic conditions for case-mix adjustment.

    Consideration of Measure Feedback

    Testing the measure on the states’ databases also illustrated potential model-fitting issues that may result when a variable has a very rare value. We have revised the specification of case-mix variables to help avoid these model-fitting issues and included guidance in the detailed measure specifications for evaluating and troubleshooting such issues.

    Progress on Improvement

    POTENTIAL FOR QUALITY IMPROVEMENT

     

    As described above in Section 1b.03, most evidence for strategies to improve readmission rates arises from studies of adults, particularly those greater than 65 years old with cardiac conditions.1 Results for multi-component interventions are most promising,2–8 whereas those for single-component interventions are mixed.9,10–13 Single-component interventions effective in reducing readmissions for patients with heart failure include home visits and telephone or telemonitoring follow-up.14,15 Multi-component interventions have reduced readmissions in several adult populations with various clinical conditions. These interventions include use of a primary care medical home model,3 multi-disciplinary heart failure care management,4 and provision of inpatient care in an interdisciplinary Acute Care for Elders unit.5 Effective multi-component interventions aimed at improving peri-discharge management include comprehensive discharge planning plus post-discharge support for heart failure and involvement of advanced practice clinicians.6–9 In addition, a number of national and other large-scale efforts are addressing readmission rates for adults by improving care transitions.16–20 

     

    In children, Medicaid insurance and having complex chronic conditions are associated with higher readmission rates.21–26 Non-White race has also been identified as a risk factor.24 Disease-specific readmission risk factors have been described for patients with sickle cell disease and asthma.27–30 The relationship of having a medical home with hospitalizations is variable,31–33 but having poor access to a medical home may be associated with increased readmissions.34

     

    Interventions to reduce readmissions for children have not yet been widely studied.35-37 However, as in adults, improving the quality of care during the peri-discharge period and the transition period to home, particularly with regard to knowledge reinforcement and self-management activation for children with chronic illnesses that require substantial self-management (e.g., asthma, sickle cell disease), is effective in decreasing readmissions.38–42

     

    References

    1. Santamour B. Reining in avoidable readmissions. Hosp Health Netw. 2011;85(2):8– 44.
    2. Gilfillan RJ, Tomcavage J, Rosenthal MB, Davis DE, Graham J, Roy JA, Pierdon SB, Bloom FJ Jr, Graf TR, Goldman R, Weikel KM, Hamory BH, Paulus RA, Steele GD Jr. Value and the medical home: effects of transformed primary care. Am J Manag Care. 2010;16(8):607–614.
    3. Sochalski J, Jaarsma T, Krumholz HM, Laramee A, McMurray JJV, Naylor MD, Rich MW, Riegel B, Stewart S. What works in chronic care management: the case of heart failure. Health Aff (Millwood). 2009;28(1):179–189. 
    4. Flood KL, Maclennan PA, McGrew D, Green D, Dodd C, Brown CJ. Effects of an acute care for elders unit on costs and 30-day readmissions. JAMA Intern Med. 2013:1–7. 
    5. Phillips CO, Wright SM, Kern DE, Singa RM, Shepperd S, Rubin HR. Comprehensive discharge planning with postdischarge support for older patients with congestive heart failure: a meta-analysis. JAMA. 2004;291(11):1358–1367. 
    6. Naylor MD, Brooten DA, Campbell RL, Maislin G, McCauley KM, Schwartz JS. Transitional care of older adults hospitalized with heart failure: a randomized, controlled trial. J Am Geriatr Soc. 2004;52(5):675–684. 
    7. Coleman EA, Parry C, Chalmers S, Min S-J. The care transitions intervention: results of a randomized controlled trial. Arch Intern Med. 2006;166(17):1822–1828. 
    8. Kripalani S, Theobald CN, Anctil B, Vasilevskis EE. Reducing hospital readmission rates: current strategies and future directions. Annu Rev Med. 2013. 
    9. Harrison PL, Hara PA, Pope JE, Young MC, Rula EY. The impact of postdischarge telephonic follow-up on hospital readmissions. Popul Heal Manag. 2011;14(1):27–32.
    10. Ballard DJ, Ogola G, Fleming NS, Stauffer BD, Leonard BM, Khetan R, Yancy CW. Impact of a standardized heart failure order set on mortality, readmission, and quality and costs of care. Int J Qual Health Care. 2010;22(6):437–444. 
    11. Mistiaen P, Poot E. Telephone follow-up, initiated by a hospital-based health professional, for postdischarge problems in patients discharged from hospital to home. Cochrane Database Syst Rev. 2006;(4):CD004510. 
    12. Costantino ME, Frey B, Hall B, Painter P. The influence of a postdischarge intervention on reducing hospital readmissions in a Medicare population. Popul Heal Manag. 2013;16(5):310–316. 
    13. Hansen LO, Young RS, Hinami K, Leung A, Williams MV. Interventions to reduce 30-day rehospitalization: a systematic review. Ann Intern Med. 2011;155(8):520–528. 
    14. Inglis SC, Clark RA, McAlister FA, Ball J, Lewinter C, Cullington D, Stewart S, Cleland JG. Structured telephone support or telemonitoring programmes for patients with chronic heart failure. Cochrane Database Syst Rev Online. 2010;(8):CD007228. 
    15. Scott IA. Preventing the rebound: improving care transition in hospital discharge processes. Aust Health Rev. 2010;34(4):445–451. 
    16. Boutwell AE, Johnson MB, Rutherford P, Watson SR, Vecchioni N, Auerbach BS, Griswold P, Noga P, Wagner C. An early look at a four-state initiative to reduce avoidable hospital readmissions. Health Aff (Millwood). 2011;30(7):1272–1280. 
    17. Institute for Healthcare Improvement. STate Action on Avoidable Rehospitalizations (STAAR) Initiative. Available at: http://www.ihi.org/offerings/Initiatives/STAAR/Pages/default.aspx. Accessed October 23, 2013. 
    18. Society of Hospital Medicine. BOOST, Better Outcomes for Older adults through Safe Transitions. Available at: http://www.hospitalmedicine.org/AM/Template.cfm?Section=Home&CONTENTID=27659&TEMPLATE=/CM/HTMLDisplay.cfm. Accessed October 23, 2013. 
    19. Hansen LO, Greenwald JL, Budnitz T, Howell E, Halasyamani L, Maynard G, Vidyarthi A, Coleman EA, Williams MV. Project BOOST: Effectiveness of a multihospital effort to reduce rehospitalization. J Hosp Med. 2013;8(8):421–427.
    20. Centers for Medicare & Medicaid Services. Community-based Care Transitions Program. Available at: http://innovation.cms.gov/initiatives/CCTP/. Accessed October 23, 2013. 
    21. Brousseau DC, Owens PL, Mosso AL, Panepinto JA, Steiner CA. Acute care utilization and rehospitalizations for sickle cell disease. JAMA. 2010;303(13):1288–1294. 
    22. Rice-Townsend S, Hall M, Barnes JN, Lipsitz S, Rangel SJ. Variation in risk-adjusted hospital readmission after treatment of appendicitis at 38 children’s hospitals: an opportunity for collaborative quality improvement. Ann Surg. 2013;257(4):758–765. 
    23. Liu SY, Pearlman DN. Hospital readmissions for childhood asthma: the role of individual and neighborhood factors. Public Health Rep. 2009;124(1):65–78. 
    24. Berry JG, Hall DE, Kuo DZ, Cohen E, Agrawal R, Feudtner C, Hall M, Kueser J, Kaplan W, Neff J. Hospital utilization and characteristics of patients experiencing recurrent readmissions within children’s hospitals. JAMA. 2011;305(7):682–690. 
    25. Jiang HJ, Wier LM. All-cause hospital readmissions among non-elderly Medicaid patients, 2007: statistical brief #89. In: Healthcare Cost and Utilization Project (HCUP) Statistical Briefs. Rockville (MD): Agency for Health Care Policy and Research (US); 2006. Available at: http://www.ncbi.nlm.nih.gov/books/NBK53601/. Accessed October 23, 2013. 
    26. Gay JC, Hain PD, Grantham JA, Saville BR. Epidemiology of 15-day readmissions to a children’s hospital. Pediatrics. 2011;127(6):e1505–1512. 
    27. Frei-Jones MJ, Field JJ, DeBaun MR. Risk factors for hospital readmission within 30 days: a new quality measure for children with sickle cell disease. Pediatr Blood Cancer. 2009;52(4):481–485. 
    28. Sobota A, Graham DA, Neufeld EJ, Heeney MM. Thirty-day readmission rates following hospitalization for pediatric sickle cell crisis at freestanding children’s hospitals: risk factors and hospital variation. Pediatr Blood Cancer. 2012;58(1):61–65. 
    29. Reznik M, Hailpern SM, Ozuah PO. Predictors of early hospital readmission for asthma among inner-city children. J Asthma. 2006;43(1):37–40. 
    30. Carroll CL, Uygungil B, Zucker AR, Schramm CM. Identifying an at-risk population of children with recurrent near-fatal asthma exacerbations. J Asthma. 2010;47(4):460–464. 
    31. Health information technology and the medical home. Pediatrics. 2011;127(5):978–982. 
    32. American Academy of Pediatrics Council on Children with Disabilities. Care coordination in the medical home: integrating health and related systems of care for children with special health care needs. Pediatrics. 2005;116(5):1238–1244. 
    33. Homer CJ, Klatka K, Romm D, Kuhlthau K, Bloom S, Newacheck P, Van Cleave J, Perrin JM. A review of the evidence for the medical home for children with special health care needs. Pediatrics. 2008;122(4):e922–937. 
    34. Auger KA, Kahn RS, Davis MM, Beck AF, Simmons JM. Medical home quality and readmission risk for children hospitalized with asthma exacerbations. Pediatrics. 2013;131(1):64–70. 
    35. Cooper C, Wheeler DM, Woolfenden SR, Boss T, Piper S. Specialist home-based nursing services for children with acute and chronic illnesses. Cochrane Database Syst Rev Online. 2006;(4):CD004383.
    36. Parab CS, Cooper C, Woolfenden S, Piper SM. Specialist home-based nursing services for children with acute and chronic illnesses. Cochrane Database Syst Rev. 2013;2013(6):CD004383. Published 2013 Jun 15. doi:10.1002/14651858.CD004383.pub3
    37. Coffey A, Leahy-Warren P, Savage E, et al. Interventions to Promote Early Discharge and Avoid Inappropriate Hospital (Re)Admission: A Systematic Review. Int J Environ Res Public Health. 2019;16(14):2457. Published 2019 Jul 10. doi:10.3390/ijerph16142457
    38. Fassl BA, Nkoy FL, Stone BL, Srivastava R, Simon TD, Uchida DA, Koopmeiners K, Greene T, Cook LJ, Maloney CG. The Joint Commission Children’s Asthma Care quality measures and asthma readmissions. Pediatrics. 2012;130(3):482–491. 
    39. Davis AM, Benson M, Cooney D, Spruell B, Orelian J. A matched-cohort evaluation of a bedside asthma intervention for patients hospitalized at a large urban children’s hospital. J Urban Health. 2011;88 Suppl 1:49–60. 
    40. Boyd M, Lasserson TJ, McKean MC, Gibson PG, Ducharme FM, Haby M. Interventions for educating children who are at risk of asthma-related emergency department attendance. Cochrane Database Syst Rev Online. 2009;(2):CD001290. 
    41. Leschke J, Panepinto JA, Nimmer M, Hoffmann RG, Yan K, Brousseau DC. Outpatient follow-up and rehospitalizations for sickle cell disease patients. Pediatr Blood Cancer. 2012;58(3):406–409. 
    42. Frei-Jones MJ, Field JJ, DeBaun MR. Multi-modal intervention and prospective implementation of standardized sickle cell pain admission orders reduces 30-day readmission rate. Pediatr Blood Cancer. 2009;53(3):401–405.
    Unexpected Findings

    No unintended findings (positive or negative) were identified during implementation.

  • Most Recent Endorsement Activity
    All-Cause Admissions and Readmissions 2015-2017
    Initial Endorsement
    Next Planned Maintenance Review
    Cost and Efficiency Fall 2023
    Endorsement Status
    Endorsement Removed
    E&M Committee Rationale/Justification

    Prior to the Cost and Efficiency endorsement meeting, the measure developer withdrew this measure due to data access concerns. This measure was last endorsed in 2016 and used data from 2008 for endorsement. After the measure was submitted to the Fall 2023 cycle, Battelle drew attention to the lack of more recent data, which limits the committee's ability to assess whether a performance gap remains, whether scientific acceptability (i.e., reliability and validity) of the measure is still established, and whether improvement on this measure has occurred due to its use. The developer withdrew the measure from the Fall 2023 cycle prior to the endorsement meeting and endorsement was removed at the conclusion of the Fall 2023 cycle.

    Removal Date
  • Detailed Measure Specifications
    Yes
    Logic Model
    On
    Impact and Gap
    Yes
    Feasibility assessment methodology and results
    Yes
    Measured/accountable entity (reliability and/or validity) methodology and results (if available)
    Measured entity (reliability and validity) methodology and results (if available)
    Address health equity
    No
    Measure’s use or intended use
    Yes
    Risk-adjustment or stratification
    Yes, risk-adjusted only
    508 Compliance
    On
    If no, attest that all information will be provided in other fields in the submission.
    Off

  • Submitted by Jamar Haggans,… (not verified) on Wed, 12/20/2023 - 18:03

    Permalink

    AOTA supports the Pediatric All-Condition Readmission Measure. Occupational therapy practitioners often see the effects of unplanned hospital readmissions, which can increase healthcare costs, cause anxiety in families and caregivers, be associated with adverse health outcomes, and interfere with daily routines that support meaningful engagement in a child’s life. 

    Name or Organization
    American Occupational Therapy Association