Illinois Department of Public HealthPat Quinn, Governor
Quality Process Measures
The Centers for Medicare & Medicaid Services (CMS) publishes a set of data every quarter covering several measures each for heart failure, heart attack and pneumonia care. This data is submitted to CMS by the hospitals using clinical chart data.
IDPH compiles these data into one composite measure for each condition. A weighted average is computed by adding up all the numerators and dividing by all the denominators. The numerator for the composite score is calculated by summing the number of patients who were both candidates for and received any of the aspects of care. If a patient received all four aspects of heart failure care, the patient would be counted four times. If the patient was a candidate for only three aspects of care, he/she would only be counted three times.
The denominator for the composite scores is calculated by summing the number of patients who were eligible for any of the four aspects of heart failure care. If a patient was eligible to receive all four aspects of heart failure care, the patient would be counted four times in the denominator.
The individual measures for heart failure, heart attack and pneumonia care published by CMS are linked to the composite measure with results for each displayed.
Readmission rates
Readmission rates includes patients readmitted to a hospital within 30 days of discharge from a previous hospital stay for heart attack, heart failure, or pneumonia. Readmission rates are reported for Medicare patients only. Readmissions rates displayed on this site reflect 3 years of data. For more information, visit the CMS Hospital Compare Web site.
Thirty Day Mortality
CMS 30-day mortality rates take into account deaths within 30 days from all causes after an initial hospitalization with a principal diagnosis of heart attack, heart failure, or pneumonia. Mortality rates are reported for Medicare patients only. Mortality rates displayed on this site reflect 3 years of data. For more information, visit the CMS Hospital Compare Web site.
Inpatient Quality Indicators
The inpatient quality indicators (IQIs) are a set of measures that can be used with hospital inpatient discharge data to provide a perspective on quality. Mortality indicators for inpatient procedures include procedures for which mortality has been shown to vary across institutions and for which there is evidence that high mortality may be associated with poorer quality of care. Utilization indicators examine procedures whose use varies significantly across hospitals and for which questions have been raised about overuse, underuse, or misuse.
Discharge data submitted by the hospitals are entered by IDPH into software provided by the Agency for Healthcare Research and Quality (AHRQ). The measures of care listed are authored by AHRQ.
AHRQ Version 4.2 software is utilized with risk adjustment as appropriate. For additional information about AHRQ measures and risk adjustment, read about the Quality Indicators at http://www.qualityindicators.ahrq.gov/.
Pediatric Quality Indicators
The Pediatric Quality Indicators (PDIs) are a set of measures that reflect quality of care inside hospitals and identify potentially avoidable hospitalizations among children (PDI). They focus on potentially preventable complications and iatrogenic events for pediatric patients treated in hospitals, and on preventable hospitalizations among pediatric patients.
Discharge data submitted by the hospitals are entered by IDPH into software provided by the Agency for Healthcare Research and Quality (AHRQ). The measures of care listed are authored by AHRQ.
AHRQ Version 4.2 software is utilized with risk adjustment as appropriate. For additional information about AHRQ measures and risk adjustment, read about the Pediatric Quality Indicators at http://www.qualityindicators.ahrq.gov/.
Surgical Care Improvement Project (SCIP) Process Measures
Hospitals submit monthly data on a quarterly submission cycle from specified surgical measure categories to the Illinois Department of Public Health and the federal Centers for Medicare & Medicaid Services (CMS). Some of these measures were selected for display and summed to numerator and denominator totals. These totals are then presented in the mouse-over or hover box while the percentage shown in the performance band is the percentage calculated by dividing the numerator (n) by the denominator (of N) and multiplying by 100. These measures and rates can be an indicator of how well the hospitals care for their patients. It is important to know that small differences in the percentages usually don't mean that one hospital is significantly better or worse. It is better to look at larger differences. Percentages may be affected by such factors as how many patients are included in the calculation of the rate. This doesn't necessarily reflect the quality of the care you will receive.
Psychiatric, rehabilitation and long-term care hospitals currently are not reported on this Web site, although many have agreed in principle to provide data using standard quality measures. The conditions currently measured - care of adults with a heart attack, heart failure, or pneumonia or adults having surgery - are not commonly treated in these settings.
For technical information about where the data comes from and how the measures are calculated, see Information for Professionals on Data Collection.
Health Care-associated Infection Methodology
Central Line-Associated Bloodstream Infections
The National Healthcare Safety Network (NHSN), a secure, Internet-based surveillance system managed by the Division of Healthcare Quality Promotion at the Centers for Disease Control and Prevention, is used by all Illinois hospitals with adult, pediatric and neonatal intensive care units to collect data on central line-associated bloodstream infections.
The reporting criteria and methods required by NHSN and a summary of the national level data are detailed on the NHSN Web site.
While the data collection methodology is identical in adult and pediatric intensive care settings, in neonatal intensive care unit (NICU) locations (level III or level II/III), data on central line-associated bloodstream infections are collected for each of five birth-weight categories (<750 g, 751-1000 g, 1001-1500 g, 1501-2500 g, and >2500 g) and for catheter type (central line and umbilical). The risk of bloodstream infections in neonates varies by birth-weight and type of catheter.
A summary measure for central line-associated bloodstream infection (CLABSI) is calculated as follows:
Standardized Infection Ratio:
The Standardized Infection Ratio (SIR) is a summary measure used to compare the central line-associated bloodstream infection (CLABSI) experience among one or more groups of patients to that of a standard population. It is the observed number of infections divided by the expected number of infections.
For Health Care-Associated Infection (HAI) reports, the standard population comes from NHSN data reported from all hospitals using the system. "Expected" is based on historical data for CLABSI rates at the national level. The expected number of infections for a given intensive care unit is calculated by multiplying the national CLABSI rate from the standard population by the observed number of central line days for that unit. If the expected number of infections is less than 1.0, the SIR value is not calculated for a given intensive care unit. This stipulation allows for a more precise measure of the SIR.
Effective June 6, 2011, the national CLABSI rates used to calculate the expected number of infections for a given intensive care unit no longer include clinical sepsis cases. Although clinical sepsis (CSEP) cases were excluded from the NHSN surveillance definition of CLABSI in January 1, 2010 the pooled means did not reflect this. The implementation of CSEP decremated pooled means will allow for more appropriate comparisons.
View a detailed description of the calculation of the SIR.
A general interpretation of the SIR is as follows:
If the SIR equals 1.0 (observed number of CLABSIs equals the expected number of CLABSIs based on the NHSN data) there is no difference between the observed number and the expected number.
If the SIR is greater than 1.0, the number of CLABSIs observed in a specific hospital is greater than the number of CLABSIs expected based on the NHSN pooled data.
If the SIR is less than 1.0, the number of CLABSIs observed in a specific hospital is less than the number of CLABSIs expected based on the NHSN pooled data.
To assess whether the difference between the observed number of CLABSIs is significantly different from the expected number of CLABSIs, a 95% confidence interval for the SIR is calculated. The confidence interval for a hospital’s SIR is the range of possible SIRs within which there is a 95% confidence that the real SIR for that hospital lies, given the number of infections and procedures that were observed in that hospital in a specific time period. The lower bound of the 95% confidence interval is only calculated if the observed infection count is greater than 0.
The formula used to calculate the confidence interval is:
If the 95% confidence interval includes 1.0, the hospital’s infection rate is similar (not statistically significantly different) from the “expected” (predicted).
If the SIR is greater than 1.0 and the 95% confidence interval does not include 1.0, the hospital’s infection rate is statistically significantly higher than “expected” (predicted).
If the SIR is less than 1.0 and the 95% confidence interval does not include 1.0, the hospital’s infection rate is statistically significantly lower than “expected” (predicted).
All conclusions are based on the assumption that the hospital’s patient population is similar to the NHSN pooled patient population.
Calculation of SIR for Neonatal Intensive Care Units (NICUs)
The SIR for NICUs is comprised of both central line associated blood stream infections as well as umbilical catheter-associated blood stream infection (UCABSIs). To calculate the SIR, the expected number of infections is calculated for each of the five birth-weight categories (<750 g, 751-1000 g, 1001-1500 g, 1501-2500 g, and >2500 g) by catheter type, using the methodology referenced above, summed and compared to the observed number of infections (CLABSI and UCABSI) in all five birth-weight categories.
The interpretation of the SIR is identical to that referenced above.
Methicillin-Resistant Staphylococcus aureus (MRSA) in Illinois
Analysis in these sections was conducted using hospital discharge data, which are routinely collected and provided to the Illinois Department of Public Health for all acute care hospitals in Illinois. The unit of analysis is the hospital discharge, not the person or patient. If a person is admitted to the hospital multiple times during the course of a year, that person will be counted each time as a separate "discharge" from the hospital.
Up to 25 diagnosis codes for each discharge can be included in the analysis.
The ICD-9 codes 038.12, 041.12, and 482.42, appearing anywhere in the list of discharge diagnoses, was used to identify cases of MRSA infection.
C. difficile in Illinois
Analysis in these sections was conducted using hospital discharge data, which are routinely collected and provided to the Illinois Department of Public Health for hospitals in Illinois. The unit of analysis is the hospital discharge, not the person or patient. A person admitted to the hospital multiple times during the course of a year will be counted each time as a separate "discharge" from the hospital.
Up to 25 diagnosis codes for each discharge can be included in the analysis.
The ICD-9 code 008.45, appearing anywhere in the list of discharge diagnoses, was used to identify cases of C. difficile infection.
Patient safety
The patient safety indicators (PSIs) are a set of measures that screen for adverse events that patients experience as a result of exposure to the health care system. These events are likely amenable to prevention by changes at the system or provider level.
Discharge data submitted by the hospitals, are entered by IDPH into software provided by the Agency for Healthcare Research and Quality (AHRQ). The measures of care listed are authored by AHRQ.
AHRQ Version 4.2 software is utilized with risk adjustment as appropriate. For additional information about AHRQ measures and risk adjustment, read about the Patient Safety Indicators at http://www.qualityindicators.ahrq.gov/.
Breast Feeding
The breast feeding measures are calculated from birth certificate data submitted to the Vital Records program of the Illinois Department of Public health. Illinois birth certificates were revised to include whether a newborn is breast fed at discharge from the hospital, with possible answers yes, no and unknown. Each response is shown as a percentage of all births in a hospital.
The breastfeeding data by hospital are from a newly-revised birth certificate that was implemented in Illinois beginning in January 2010. The item was added as part of the incorporation of the 2003 US Standard Certificate Revisions recommended by the National Center for Health Statistics (NCHS). These counts are based on self-reporting of the mothers and hospital staff. The numbers and corresponding percentages have no birth certificate precedent for comparison. In addition, these are preliminary counts and subject to ongoing and annual data clean-up. As such, these counts may vary for the same period based on final counts produced annually.
The question, as originally presented on the US Standard Birth Certificate reads: “Is Infant Being Breastfed at the time of Discharge?” The original intent by NCHS was to collect data on infants being breastfed at discharge from the hospital. In the latter part of 2010, NCHS revised their definition of this field. The question now asks if an infant was breastfed at any time between delivery and discharge, and not just at discharge. The field label in the Illinois Vital Records System Birth application that is in use in all Illinois birthing hospitals is being reworded and future data will reflect this change.
Satisfaction
The HCAHPS (Hospital Consumer Assessment of Healthcare Providers and Systems) survey is the first national, standardized, publicly reported survey of patients' perspectives of hospital care. HCAHPS (pronounced “H-caps”), also known as the CAHPS® Hospital Survey, is a standardized survey instrument and data collection methodology for measuring patients’ perceptions of their hospital experience.
The HCAHPS survey asks discharged patients 27 questions about their recent hospital stay. The survey contains 18 questions about critical aspects of patients’ hospital experiences (communication with nurses and doctors, the responsiveness of hospital staff, the cleanliness and quietness of the hospital environment, pain management, communication about medicines, discharge information, overall rating of hospital, and would they recommend the hospital). The survey also includes four items to direct patients to relevant questions, three to adjust for the mix of patients across hospitals, and two items that support Congressionally-mandated reports.
The HCAHPS survey is administered to a random sample of adult patients across medical conditions between 48 hours and six weeks following discharge; the survey is not restricted to Medicare beneficiaries. Participating hospitals may either use an approved survey vendor, or collect their own HCAHPS data (if approved by CMS to do so). To accommodate the needs of hospitals, HCAHPS can be implemented in four different survey modes: mail, telephone, mail with telephone follow-up, or active interactive voice recognition (IVR). Hospitals may either integrate HCAHPS with their own patient surveys, or use HCAHPS by itself. Hospitals must survey patients throughout each month of the year. The survey is available in official English, Spanish, Chinese, Russian and Vietnamese versions.
The survey itself, as well as detailed information on sampling, data collection and coding, and file submission, are contained in the HCAHPS Quality Assurance Guidelines, Version 4.0, found at the official HCAHPS Web site, www.hcahpsonline.org.
Services
The number of patients, their length of stay and the charges associated with their visit are calculated from discharge data provided by the Illinois Department of Public Health. The collection of these data includes the use of DRG grouping software from 3M to combine these discharge data records into the various listed conditions. The median stay and charges are then calculated by listed service for each facility. A patient's length of stay for any listed condition will vary. The charges may be discounted and paid by the health insurance company.
Nurse Staffing
Hospitals gather internal data related to the type and number of monthly nurse hours worked. They also gather data on the number of days patients spend within specific categories of service. This information is submitted quarterly to IDPH where the data is combined. In addition, hospitals submit annual data on the number of nurse staffing position vacancies and turnover rates.
Infection Prevention Staffing
The number of infection prevention and control staff, both total and those certified in infection control, is gathered from data submitted by hospitals to the Illinois Department of Public Health through the Annual Hospital Profile survey. The number of beds used in per 100 beds calculation reflects the total number of authorized beds established by certificate of need at the Illinois Department of Public Health.
Authorized Beds
The bed numbers refer to the number of authorized beds approved by the Health Facilities and Services Review Board. These bed numbers may change frequently as a result of the regular Board meetings, and will be updated regularly as other data is added to the site.
Emergency Room and Bypass Hours
The number of patients seen in the emergency room, both those admitted and those treated and released as outpatients, is calculated from discharge data provided by the Illinois Department of Public Health.
Emergency room bypass hours are calculated from data submitted by hospitals to the Illinois Department of Public Health through its Web-based Hospital Bypass System. Hospitals fulfill reporting requirements by accessing the system at the start of the bypass period, indicating that it is on bypass. They then access the system at the end of the bypass period to indicate that the period has ended. Not all hospitals experience the need to place their emergency room on "bypass".
The percent of patients who left the emergency room either before being seen or against medical advice is also calculated from discharge data provided by the Illinois Department of Public Health.
Patient Insurance Mix
Hospitals gather data on the type and volume of insurance providers of the patients seen in their facility. This information is submitted to the Illinois Department of Public Health annually, as part of the Annual Hospital Questionnaire (AHQ). Data on patient volume by insurance type was extracted as submitted to provide the percentage of hospital visits by major categories of insurance provider, with private pay and charity included where no insurance coverage exists.
Emergency Department Measures
Interest in the study of emergency department utilization is growing rapidly. Emergency department (ED) visit volume has surged in recent years, and many people are using these services as a primary means of obtaining medical care. Along with demographic components such as age, sex, race and ethnicity, payer and income level, it is useful to look at the type of visit associated with emergency department use. The Illinois Department of Public Health used an algorithm to help classify ED utilization developed by the New York University Center for Health and Public Service Research. Data used as input to this classification system comes from the Illinois discharge data collection system and includes all 2009 outpatient discharges with an emergency department billing code. These data currently exclude those cases known to have resulted in admission to a hospital as an inpatient; however they do include cases where an outpatient surgery and/or observation care may have been given. The algorithm classifies ED cases into four major categories of visit necessity after isolating injury, drug, alcohol and mental illness cases for separate study. Any cases not meeting any of these criteria are labeled "unclassified". The four major groups of ED care are 1) non-emergent, 2) emergent but primary care treatable, 3) emergent but primary care avoidable and 4) emergent and unavoidable. The first three groups are often combined, resulting in two final groupings labeled unnecessary and necessary ED visits. Complete documentation on the development and use of the ED utilization algorithm is detailed by the New York University research team at: http://wagner.nyu.edu/chpsr/.
Data for geographic areas (county, zip code, Cook County sub-regions and Illinois regions) with less than 20 visits in a given emergency room category were suppressed due to statistical imprecision and patient confidentiality. Data concerning emergency room visits due to "alcohol" and/or "drugs" are not presented due to very small numbers (number of cause-specific visits <20).
Data by visit category is expressed as a percentage of all Emergency Department visits and crude rate per 10,000 population within a geographic locale. Charges shown are gross charges established by hospitals each year, not actual dollar amounts received in payment. All patients are charged the same gross charge (or list price) for the same services before applying any discounts.
Prevention Quality Indicators
Prevention Quality Indicators (PQIs), developed by the federal Agency of Healthcare Research and Quality (AHRQ), identify hospital admissions that evidence suggests could have been avoided, at least in part, through high-quality outpatient care. They represent hospital admission rates for ambulatory care sensitive conditions, which are conditions for which good outpatient care can potentially prevent the need for hospitalization, further complications or more severe disease. Even though these indicators are based on hospital inpatient data, they provide insight into the community health care system or services outside the hospital setting. For Prevention Quality Indicators, lower rates usually represent better outpatient care which can potentially prevent the need for hospitalization.
PQIs used in this report consist of the following 13 ambulatory care sensitive conditions, which are measured as rates of admission to the hospital:
PQIs presented as a percentage rate of population over age 18 are:
- Diabetes Short-term Complications Admission Rate (PQI 1)
- Diabetes Long-term Complications Admission Rate (PQI 3)
- Chronic Obstructive Pulmonary Disease Admission Rate (PQI 5)
- Hypertension Admission Rate (PQI 7)
- Congestive Heart Failure Admission Rate (PQI 8)
- Dehydration Admission Rate (PQI 10)
- Bacterial Pneumonia Admission Rate (PQI 11)
- Urinary Tract Infection Admission Rate (PQI 12)
- Angina Admission without Procedure (PQI 13)
- Uncontrolled Diabetes Admission Rate (PQI 14)
- Adult Asthma Admission Rate (PQI 15)
- Rate of Lower-Extremity Amputation Among Patients with Diabetes (PQI 16)
PQIs presented as a percentage rate of total admissions for the specified condition are:
- Perforated Appendix Admission Rate (PQI 2)
Both the observed and risk adjusted measures are reported. Risk adjustment is based on county specific age and sex distribution. Further details of the PQIs can be found at: http://www.qualityindicators.ahrq.gov/pqi_overview.htm.
The PQIs are defined using AHRQ definitions and programs. The county level PQIs were created using the AHRQ Windows software Version 4.1a. As this software only defines the PQIs at the county level, the AHRQ SAS programs Version 4.1 were modified to allow for estimation of other geographical levels -- zip code, Cook county sub-regions, and Illinois regions.
Data Issues
Computation of the "Deviation from the Statewide Benchmark"
The deviation from the statewide benchmark is defined in one of three categories: better than the state, worse than the state or no difference. To make this assessment, the county-specific PQI rates are compared against the benchmark derived from the population – the statewide PQI rate. If the benchmark falls within the 95% confidence interval (CI) of a given county, it is considered to be the same, i.e., there is no difference. If the benchmark lies outside of the county-specific CI then the county-specific rate is considered to be statistically different from the statewide benchmark. The direction of whether it is better or worse, is based on how the finding is scored (i.e., a lower PQI rates is better).
When a rate is a true zero (i.e., the numerator, in this instance the number of admissions for a certain condition is equal to 0) then the lower limit of the confidence interval is set to zero. The upper limit is set according to the denominator, the county-specific population. In the few instances where this has occurred to date, the deviation for that county-specific rate is that it performed better than the state (since a lower rate is desirable as opposed to a higher rate).
The AHRQ PQIs rates and confidence intervals are generated using the AHRQ Quality Indicator Windows 4.1a and the 4.1a SAS software.
Caution is advised in interpreting the rates and deviation from the statewide benchmark for those counties reporting a rate of 0 admissions per 100,000 population. A zero rate may truly reflect zero admissions for a specific condition or may reflect incomplete case ascertainment particularly in border counties.
Stability of County Rates
The county-specific rates are influenced by numerous factors including: 1) county population size; 2) the completeness of the discharge data and 3) the prevalence of illness/conditions.
County Population Size
In this first release of the Illinois Public Health Community Map, the PQI rates and ED data are reported for each county, regardless of county population size. There are 102 counties in Illinois ranging in size from approximately 4,000 in Pope County to 5.3 million in Cook County. Rates calculated for counties with small populations are statistically unstable. A common method used to improve the reliability of the data is to suppress counties with populations fewer than 10,000 residents. A list of the 15 Illinois counties with a population size less than 10,000 (2009 U.S. Census estimates) is provided below under Data Advisory.
Caution is advised in interpreting estimates for these 15 counties. Future reports will incorporate techniques to improve the stability of these estimates such as “small area” analysis, which aggregates smaller counties into a larger population unit of analysis, and the use of statistical techniques such as funnel plot or random effects (shrinkage) analysis that are better equipped to address concerns stemming from variable population sizes to number of events (PQIs and ED measures).
Completeness of the Discharge Data
The IDPH discharge data set includes only Illinois residents discharged from Illinois hospitals and excludes discharge data from border states and beyond, as well as Veteran Administration medical centers.
Prevalence of Illness/Conditions.
A less prevalent condition such as a perforated appendix or lower leg amputation may arise less frequently in a smaller county which could result in less stable estimates.
Data Suppression
Data for geographic areas (county, zip code, Cook County sub-regions and Illinois regions) with less than 20 visits in a given emergency room category were suppressed due to statistical imprecision and patient confidentiality. Data concerning emergency room visits due to “alcohol” and/or “drugs” are not presented due to very small numbers (number of cause-specific visits <20).
Data Advisory
Use rates calculated with a small number of events or population sizes are statistically unstable. They tend to exhibit wide confidence intervals, indicative of variability. Caution is advised when interpreting estimates for 15 Illinois counties with a population size of less than 10,000 listed below, and for border counties. Counties with population less than 10,000 (2009 U.S. Census Estimates) include: Alexander, Brown, Calhoun, Edwards, Gallatin, Hamilton, Hardin, Henderson, Jasper, Pope, Pulaski, Putnam, Schuyler, Scott and Stark.
Where the Data Came From