Using Administrative Data to Assess Quality of Care in the State Children s Health Insurance Program

Transcription

1 Using Administrative Data to Assess Quality of Care in the State Children s Health Insurance Program Elizabeth Shenkman, Ph.D. Institute for Child Health Policy University of Florida November 2003 Prepared with support from the David and Lucile Packard Foundation CHIP 21

2 Using Administrative Data to Assess Quality of Care in the State Children s Health Insurance Program Elizabeth Shenkman, Ph.D. Institute for Child Health Policy University of Florida November 2003 by National Academy for State Health Policy 50 Monument Square, Suite 502 Portland, ME Telephone: (207) Facsimile: (207) info@nashp.org Website: Prepared with support from the David and Lucile Packard Foundation CHIP 21

3 TABLE OF CONTENTS Introduction...1 The Content of Administrative Databases...2 Advantages and Disadvantages of Administrative Databases...4 Assessing the Quality of Administrative Data...6 Aspects of Care That Can Be Measured and Quality of Care Indicators...9 Calculating the Quality of Care Indicators...27 List of Tables and Figures Table 1. The Institute for Medicine Components of Health Care Quality...9 Table 2. Suggested Quality of Care Indicators...17 Figure 1. Percent of Enrollees Classified as Healthy...12 Figure 2. Per Member Per Month Health Care Expenditures...13 Figure 3. HEDIS Children s Access to Primary Care Providers...14 Figure 4. HEDIS Well-Child Visits in the 3 rd, 4 th, 5 th, and 6 th Years of Life...15 Figure 5. HEDIS Use of Appropriate Medications for Children with Asthma...16 Appendices Appendix A: Analysis of Selected Fields Found in Encounter Data Appendix B: Recommended Standards for Evaluating the Quality of Encounter Data Appendix C: Sample Expenditure Profile for a Title XXI Population Appendix D: Description of the Clinical Risk Groups (CRGs) Appendix E: Mental and Behavioral Health Diagnostic Codes

4 INTRODUCTION During initial implementation of the State Children s Health Insurance Program (SCHIP), states tended to focus much of their efforts on accurately identifying potential enrollees and ensuring that the application and enrollment process was as convenient as possible for families. As states programs have matured, the focus has shifted to other issues including assessing the quality of care that children enrolled in SCHIP are receiving. As states develop quality assessment systems, they need to make a number of key decisions about: the components of health care quality that will be measured, the specific measures that will be selected, and the data sets that are necessary to calculate the measures. The type of data used to assess quality can have a significant influence on the costs of any quality assessment program. Indicators that provide more data (e.g., telephone surveys and medical record reviews) are typically more costly to obtain than those indicators that are readily available through health plans claims and encounter databases. State agencies and health plans routinely collect administrative data such as enrollment and claims and encounter files, for billing and program operations purposes. These data were not originally intended for quality assessment purposes and are often dismissed by clinicians because, if used alone, they do not contain all of the information necessary to make good quality assessments. However, administrative data can form a strong foundation to support quality assessment activities. As Lisa Iezzoni has noted in the Annals of Internal Medicine with detailed clinical information buried deep within medical records that are expensive to extract, administrative data posses important virtues. They are readily available, are inexpensive to acquire; are computer readable; and typically encompass entire regional populations or large, well-define sub-populations. 1 The purpose of this report is to assist states in using administrative databases to develop quality assessment programs for SCHIP. It examines the following issues: The content of administrative databases, The advantages and disadvantages of administrative databases, Assessing the quality of administrative data, Aspects of care that can be measured and quality of care indicators, Calculating the quality of care indicators, Sharing findings to improve health care quality, and What states should expect from groups managing their claims and encounter data. 1 Lisa Iezzoni, Assessing quality using administrative data, Annals of Internal Medicine 8: (1997). National Academy for State Health Policy November

5 THE CONTENT OF ADMINISTRATIVE DATABASES Three primary administrative datasets will be discussed in this section: enrollment files, claims and encounter data, and databases from waiver programs or programs offering specialized services such as State Title V Children with Special Health Care Needs (CSHCN) Programs. Other administrative databases such as immunization databases and birth certificate data can also be used to assess quality of care and also will be discussed. Enrollment files contain important information about the enrollees sociodemographic characteristics such as age, gender, place of residence, income, program eligibility category, and the months in which they were enrolled. Some states also collect information about enrollees race and ethnicity. Enrollment files are essential for the calculation of several quality of care indicators and also contain information that can be used for basic rate-setting purposes. Claims and encounter data contain information about: diagnoses assigned to an enrollee at the time of the health care encounter using International Classification of Diseases, 9 th Revision, Clinical Modification (ICD-9-CM) codes, procedures or services provided during the encounter, the place where the service was provided, information about the provider rendering the service, and the amount or quantity of the service rendered. Depending on the state program, payment or reimbursement information may also be provided. States and their participating health plans also maintain pharmacy information that can be used in quality of care analyses. Most states have claims and/or encounter data for their Medicaid Programs and their SCHIP initiatives. However, some states only maintain complete data for portions of their Medicaid Programs, such as their primary care case management (PCCM) program and not their Medicaid health maintenance organizations (HMOs). Databases on unique enrollee groups. Special programs that the state may have such as waiver programs or State Title V CSHCN Programs may maintain additional databases that provide detailed information about unique enrollee groups. For example, some states have specialized behavioral health programs that collect indepth information about enrollees program eligibility, functional status, and treatment programs. State Title V CSHCN Programs may collect information about unique services provided to CSHCN such as respite care, specialized equipment, and supplies. Some states have special immunization databases. The number of immunizations given may be greatly underestimated when relying only on administrative data from MCOs to make the calculations. This can happen for many reasons. For example, some providers encourage CHIP enrollees to obtain their immunizations at public health departments and community health National Academy for State Health Policy November

6 centers citing the high cost of keeping and administering the vaccines as the reason. In other instances, in an attempt to encourage immunizations, some MCOs and providers participate in health fairs where immunizations are administered. These immunizations would never be recorded in the MCOs administrative data. In an attempt to better track immunizations, some states keep immunization databases that may be used to assess compliance. The quality of the data in the immunization database needs to be carefully assessed. In addition, a common identifier for each child, such as a social security number, is needed so that immunization information from different databases can be linked and used together to determine immunization compliance. Birth certificate data also contain valuable information about an infant s birth outcome that can be used in quality assessments. These data are usually complete and often can be linked, using social security numbers, to the information found in claims and encounter data. Each of these databases can be used alone or in combination to conduct many analyses that can be used to assess and improve the quality of care in a SCHIP program. However, the quality of the administrative databases must be carefully assessed before they are used and their advantages and disadvantages understood. National Academy for State Health Policy November

7 ADVANTAGES AND DISADVANTAGES OF ADMINISTRATIVE DATABASES There are several advantages associated with using administrative databases to assess health care quality. Because they are readily available, administrative data are cost-effective to use and far less costly than other quality indicators such as medical record reviews or telephone surveys. Medical record reviews can cost $50 to $100 per record to review, and telephone surveys can cost $30 to $50 per completed survey, depending on the enrollee population and the length of the survey. In contrast, assessments based on administrative data can cost less than a dollar per enrollee. In addition to the costs, medical record reviews and telephone surveys require the cooperation and participation of providers and enrollees. Unless managed carefully, serious selection bias can be introduced into the quality assessment findings, if certain segments of the provider or enrollee population refuse to participate in data collection activities. For example, if providers from large, well-staffed practices were more likely to participate in the medical record review process than those from smaller practices, then the quality of care findings from the medical record review would be biased toward larger physician practices. Administrative data have the potential to overcome these problems because they are already collected and require no additional effort on the part of providers or enrollees. Administrative databases also provide the opportunity to track individuals over time to assess care before and after a major treatment episode, for example. In addition, care for a census of enrollees meeting a particular quality of care indicator definition can be assessed. For example, the care received by all women meeting the eligibility criteria for breast cancer screening can be documented, rather then focusing on a subset for whom it was possible to obtain medical records. Administrative databases do have several disadvantages that often can be reduced by supplementing them with medical record and telephone survey data, and strategies incorporating multiple data sources to assess health care quality are discussed later in this report. Perhaps the two greatest concerns raised about administrative data are coding accuracy and completeness. 2 Studies examining administrative databases for their coding accuracy of diagnoses and procedures vary widely in their findings. Several studies comparing the contents of outpatient medical records to those of administrative databases have found a reasonably high level of agreement between the two sources (80% or better). However, studies focusing on inpatient care found coding discrepancies in 15% to 21% of the cases, and these coding differences led to the assignment of more severe conditions to the enrollee than were actually contained in the medical record. Information found in claims and encounter data may also be incomplete. For example, typically five to seven fields are allocated to record diagnoses and procedures. Some MCOs submit 2 J.P. Weiner, et al., Applying insurance claims data to assess quality of care: A compilation of potential indicators, Quality Review Bulletin, 16: (1990). National Academy for State Health Policy November

8 claims and encounter records with several of these fields populated, others only submit a primary diagnosis code, for example. In addition, providers may only record the acute condition that was the immediate cause of the health care encounter, whereas others will also record the presence of any chronic conditions. It is obvious that these practices could lead to different assessments of the numbers of enrollees with chronic conditions from MCO to MCO based on the completeness of the claims data and not based on the actual situation. In addition, such practices could lead to variable reporting of services rendered. Finally, claims and encounter data contain inherent limitations that cannot be overcome by improved coding practices. These databases were never developed as clinical systems. Thus, important information such as particular lab values for enrollees with diabetes, for example, or information about enrollees functional status are not available and must be obtained from other sources. Using administrative data for quality purposes requires that analysts and others evaluating health care quality undertake a multi-step process that begins with assessing the quality of each MCO s data. The steps that should be taken to assess data quality are described in the following section. National Academy for State Health Policy November

9 ASSESSING THE QUALITY OF ADMINISTRATIVE DATA A careful assessment of the quality of the administrative data is essential to ensure that enrollees quality of care is being measured as completely and accurately as possible. The Center for Medicare and Medicaid Services (CMS) has developed guidelines to follow when assessing the quality of data received from MCOs. The steps described below are adapted from those guidelines and include some additional recommendations for assessment strategies. 3 In addition to assessing the quality of claims and encounter data, the quality of the information found in enrollment files and any other administrative data that may be used for quality assessment purposes should be assessed. Enrollment or coverage files are essential for the accurate calculation of most quality of care indicators and for the assessment of health care use rates and expenditures. Understanding the content of the enrollment files and how critical issues such as assigning member numbers to enrollees who change MCOs or who drop out and later re-enroll is necessary. The following steps are recommended to assess the quality of encounter data: Step 1: Examine the Data Files to Determine Completeness of the Data Fields and Validity of the Data in the Fields A report should be generated listing each field found in the administrative data, the percentage of times the field is populated, and an assessment of the percentage of invalid information. In the case of encounter data, the percent of invalid diagnostic, procedure, and place of service codes should be assessed. For enrollment files, the percentage of invalid birthrates and the percentage of invalid information in any other critical fields should be documented. The results of these findings should be compared to a list of required fields, as specified by the state. For encounter files, tables should be prepared detailing the number of records seen in the database each month. Some fluctuations in the number of records received will undoubtedly exist, but very large changes, 25 percent or more, may indicate that the MCO is having difficulty processing the information they receive. In addition, tables should be prepared showing the number of records in the database by service type, such as inpatient, outpatient, and emergency room use. The data should be distributed in a pattern that is consistent with the expected use of care by children (i.e., the majority of care should be for outpatient services with little inpatient and emergency room use). This basic pattern should be seen for all months in which data are available. Appendix A contains an example of tables prepared for one state to examine the consistency of the data received month by month from a MCO. In order to identify invalid codes, the diagnostic and procedure codes found in the encounter data were compared to a master list of approved codes provided by the state in which this MCO was delivering care to CHIP enrollees. Inconsistencies in the diagnostic or procedure codes, such as males with a diagnosis of pregnancy or hysterectomies in children, were also used to assess the number and percentage of 3 The additional strategies are based on the experience of staff at the Institute for Child Health Policy in assessing the quality of administrative databases. National Academy for State Health Policy November

10 invalid codes. Appendix B contains recommendations about the percentage of invalid and missing fields that should be allowed. This table was adapted from the CMS guidelines. In the case of enrollment files, a table detailing the number of children enrolled should be prepared for each month. Enrollees with the same first and last name, birth date, place of residence, and parent names should be identified as possible duplicates and referred to the state or the enrollment broker. In addition, children who drop out and later re-enroll should be examined to determine the percentage that are reassigned the same SCHIP identification number. Children should keep the same identification number at re-enrollment and also at renewal periods. It is not possible to identify with complete accuracy the numbers of duplicate children in the enrollment files or the numbers of children who are reassigned their original identification number after a disenrollment spell. However, it is essential to assess the percentage of occurrences where duplicates might exist or where the same child might have different identification numbers in order to minimize the impact of such duplications on quality of care assessments. If duplications are a significant problem, then it may not be possible to generate accurate information about the children s coverage periods to calculate some of the quality of care indicators. Step 2: Examine Health Care Use Patterns CMS specifically recommends conducting an extensive analysis of the use data and comparing the health care use statistics to other available data when possible. This is especially important when working with encounter data where there may be concerns about potential underreporting. Statistics should be prepared showing health care use rates per 1,000 member months (MM) for inpatient, outpatient and emergency room (ER) use. A table or graph showing the trends by month also should be developed. The State may also wish to analyze health care use patterns for ancillary services or other specific service categories to determine if the amount of use is reasonable. This summary information should be prepared for each MCO participating in SCHIP and for the pool of SCHIP enrollees overall. In addition to preparing the basic summary information, more detailed analyses can also be conducted. Appendix C contains an example of the health care use patterns for SCHIP enrollees in one state, where the enrollees are grouped into different categories using the Clinical Risk Groups (CRGs). The CRGs is a classification and risk adjustment system that uses diagnostic and procedure codes found in claims and encounter data to classify enrollees according to the expected cost and consequences of their conditions. 4 An overview of the CRGs is contained in Appendix D. In the example, in Appendix C, the SCHIP enrollees are grouped into one of nine CRG categories, and health care use rates were calculated for children in each of the categories. This type of analysis allows the state to examine the distribution of its enrollees in the various categories (healthy, acute, single chronic condition, and so on). Further the state can examine 4 J. Neff, et al. Identifying and Classifying Children With Chronic Conditions Using Administrative Data With the Clinical Risk Group Classification System, Ambulatory Pediatrics 2(1):71-79 (2001). National Academy for State Health Policy November

11 the children s health care use rates in each of the categories. As expected, the majority of children are healthy and their health care use rates are considerably lower than those of children in any of the other categories, especially compared to those children with malignant or catastrophic conditions. In all instances, the majority of the care is in the outpatient setting with inpatient use rates increasing with more severe condition categories (i.e., malignancies and catastrophic conditions). Step 3: Conduct Medical Record Reviews States should also plan to conduct medical record reviews to compare the information found in the encounter data to that found in the medical record. The correspondence between diagnoses recorded, services rendered, and procedures conducted should be documented. The state should work with an experienced group to develop the sampling strategy for the medical record reviews, conduct the reviews, and analyze the results. Step 4: Summarize the Results and Develop a Quality Improvement Plan The results obtained from Steps 1 though 3 should be summarized and shared with the MCOs participating in SCHIP. A plan should be developed jointly to improve the data quality. In addition to preparing a report summarizing the results of the preceding steps, the state should proceed with using the data to calculate quality of care measures and share these results with the MCOs. In a report entitled Getting to Yes, 5 experts working with encounter data note that very often improvements in data quality are not seen until the data actually are used to calculate quality of care measures or for risk adjustment purposes. 5 R. Halpern, D.J. Knutson, J.B. Fowles. Getting to Yes: How Encounter Data Become Good Enough for Health-Based Risk Adjustment (Health Research Center: Park Nicollet Institute, 2001). National Academy for State Health Policy November

12 ASPECTS OF CARE THAT CAN BE MEASURED AND QUALITY OF CARE INDICATORS Recently the Institute for Medicine (IOM) defined a framework to assess health care quality. This framework contains the following four major components that can be used to assess the quality of care that children receive within SCHIP: safety, effectiveness, patient centeredness, and timeliness. The IOM Committee further defined a set of subcategories for assessment within each of these major categories. Table 1 shows the IOM categories and subcategories for assessing health care quality. Table 1 Components of health care quality and their subcategories (The Institute for Medicine) Safety Effectiveness Patient Centeredness Timeliness 1. Diagnosis 1. Preventive care 1. Experience of care 1. Access to the system of care 2. Treatment a. Medication b. Follow-up 3. Health care environment 2. Acute, chronic, and end-of-life care 3. Appropriateness of procedures 2. Effective partnership 2. Timeliness in getting care for a particular problem 3. Timeliness within and across episodes of care A conceptual framework such as the one above helps to organize the development of quality assessment indicators for SCHIP. Selecting which particular indicators of care to measure is then the next critical step in the process. Many good quality of care indictors exist. Although not child-specific, some are recommended in the IOM s Envisioning the National Health Care Quality Report. 6 Other good sources of information about potential quality of care indicators include Health Plan Employer Data and Information Set (HEDIS), 2003; 7 the Bright Future Guidelines; 8 the Rand Quality Measurement System; 9 National Heart, Blood, and Lung (NHBL) Institute Guidelines for Asthma Care; and the PERMS 2.0 Performance Measures for Managed 6 Institute for Medicine, Envisioning the National Health Care Quality Report (Washington, D.C.: National Academy Press, 2001). 7 National Commission on Quality Assurance, HEDIS 2003 Technical Specifications (Washington, D.C., 2003). 8 National Center for Education in Maternal and Child Health, Guidelines for Health Supervision of Infants, Children, and Adolescents (2 nd ed, revised) (Washington, D.C.; Georgetown University; 2002). 9 E.A. McGlynn, et al. (Editors), Quality of Care for Children and Adolescents: A Review of Selected Clinical Conditions and Quality Indicators (Santa Monica, CA: Rand Health; 2000). National Academy for State Health Policy November

13 Behavorial Healthcare Programs as recommended by the American Managed Behavorial Healthcare Association (AMBHA). 10 The selection of quality of care indicators should be guided by several factors including 1. The relevance of the indicator for assessing the quality of health care that SCHIP enrollees receive, 2. The scientific evidence available demonstrating that the indicator is a reliable and valid marker of health care quality, 3. The availability of affordable data about the indicator, and 4. The availability of comparison data from other relevant populations. Decisions about which indicators to measure should be made collaboratively among staff involved in program operations, quality assessment and improvement, and evaluation as well as in consultation with health care providers and consumers. Table 2 (which appears at the end of this section) contains a summary of indicators that can be used to assess the quality of care SCHIP enrollees receive. These indicators are organized according to the IOM conceptual framework categories and subcategories. Further, these indicators can be calculated using administrative databases and can be further enhanced by supplementing the administrative data with information from telephone surveys and medical record reviews. Quality of care indicators related to access to care and effectiveness of care are described because these indicators can be calculated using information found in administrative databases. Other important quality of care indicators that involve the collection of patient satisfaction information or medical record review data are not the focus of this report and thus are not included in Table 2. The following sections contain in-depth descriptions of some of the indicators outlined in Table 2 and provide illustrative examples of how these indicators can be applied to SCHIP. Timeliness of Care: Access to Care According to the IOM framework, timeliness of care can be assessed by measuring enrollees access to the health care delivery system. One of the measures that can be used to assess access to care the health care use and expenditure profile. An essential component of health care quality is the extent to which health care services are used in a manner consistent with the expected pattern of use for a given population, in this case a given population of enrolled children. 11 Assessing health care use as an indicator of quality of care is particularly important when contracting with MCOs because of the perception that financial and utilization review arrangements with providers may restrict the enrollees access to needed health care. 12 For 10 The AMBHA Committee on Quality Improvement and Clinical Services, Performance Measures for Managed Behavioral Healthcare Programs (American Managed Behavioral Healthcare Association, 1998). 11 E. Shenkman, et al., Children s Health Care Use in the Healthy Kids Program, Pediatrics, 100: (1997). 12 P.W. Newacheck, et al. Monitoring and evaluating managed care for children with chronic illnesses and disabilities, Pediatrics, 98: (1996). National Academy for State Health Policy November

14 example, MCOs often require a physician to seek prior authorization before rendering certain types of services in an effort to reduce health care use and control costs. Concern has been raised that some of the reduction in use and costs may be excessive and possibly detrimental to the enrollee. 13 Adequate financing for any health care program provides the foundation for ensuring access to care. Inadequate financing, particularly for enrollees with special health care needs, can restrict their access to care because MCOs and providers may be unwilling to care for these enrollees if the financial burden is too great. Health care use and expenditure profiles can allow SCHIP administrators 1) to identify any MCOs that may be enrolling a higher percentage of enrollees with special needs and 2) to compare the expenditure and use patterns of those with special needs across MCOs. In addition, health care use and expenditure profiles can be used to assess use of particular services such as inpatient and emergency room care. Although comparison data showing health care use rates for SCHIP enrollees nationally are not available, the National Commission on Quality Assurance (NCQA) publishes health care use information for some Medicaid plans. This information can be used to provide a general comparison to the health care use patterns of SCHIP enrollees. Health care use and expenditure profiles are particularly useful if they are developed using software programs that group enrollees into health status categories based on their diagnoses and expected resource consumption. Grouping enrollees based on health status, as defined by diagnoses found in claims and encounter data, allows for a more accurate assessment of health care use and expenditures. Figures 1 and 2 illustrate how SCHIP enrollees health care use and expenditure patterns can be assessed by grouping enrollees into health status categories. 13 D.C. Hughes et al., Medicaid managed care: can it work for children? Pediatrics, 95: (1995). National Academy for State Health Policy November

15 Figure 1 Percent of Enrolless Classified as Healthy by Using the Clinical Risk Groups for SCHIP Overall and By MCO 100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% All CHIP MCOs MCO 1 MCO 2 MCO 3 MCO 4 MCO 5 Percent Healthy 84.17% 85.46% 81.42% 86.00% 83.03% 87.22% Figure 1 shows the percentage of children classified as healthy using the CRGs. The healthy category includes those who are enrolled but have not used health care services during the assessment period as well as those seen for preventive care and other routine health care needs. As expected, the majority of children in the program are healthy. However, there are some variations noted between the MCOs. For example, MCO 2 has a lower percentage of healthy enrollees when compared to the overall average for the state of 84 percent; whereas MCO 5 seems to enjoy a somewhat favorable group of enrollees in terms of health status, with 87 percent classified as healthy. National Academy for State Health Policy November

16 Figure 2 Per Member Per Month Care Expenditures by CRG Category $5,000 $4,500 $4,000 $3,500 $3,000 $2,500 $2,000 $1,500 $1,000 $500 $0 Healthy Significant Acute Single Minor Chronic Multiple Minor Chronic Single Dominant or Moderate Pair Dominant or Moderate Chronic Triplet Dominant or Moderate Malignancies Catastrophic PMPM Expenditures $24.25 $ $ $ $ $1, $4, $4, $2, Figure 2 illustrates the health care expenditures per member per month (PMPM) for each of the health status categories included in the CRGs. The PMPM expenditures vary dramatically between the health status categories. While this graph shows the expenditures for SCHIP enrollees overall, the same profile can be developed for each MCO. When considering Figures 1 and 2 together, one can see that MCOs with a higher percentage of healthy enrollees face less financial risk and may be better able to provide access to care for their enrollee pool. Conversely, in the absence of risk-adjusted payments, MCOs with a lower percentage of healthy enrollees (or more children in the acute and chronic condition categories) face greater financial risk, which could result in reduced access to care for the enrollees if the MCOs and their providers face too great a financial burden. The Health Plan Employer Data and Information Set (HEDIS) manual contains many excellent quality of care indicators, including several that assess children s access to care. Figure 3 illustrates access in one state to primary car practitioners for SCHIP enrollees. As previously noted, national comparisons for SCHIP enrollees are not readily available for the HEDIS measures; therefore, the NCQA Medicaid results are shown in Figure 3 to provide some general comparison. The majority of children in the three age cohorts of 12 to 24 months, 25 months to 6 years, and 7 to 11 years saw a primary care practitioner. Access to care was highest for those National Academy for State Health Policy November

17 12 to 24 months of age and declined as children grew older. With the exception of MCO 2, all of the MCOs had more positive results than the Medicaid 2002 findings. Figure 3 HEDIS Children's Access to Primary Care Practitioners 100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% SCHIP HEDIS Medicaid 2002 Mean MCO 1 MCO 2 MCO 3 MCO 4 MCO 5 12 to 24 mths 92.41% 90.00% 94.25% 91.25% 94.32% 95.01% 92.75% 25 mths to 6 yrs 80.68% 78.70% 84.83% 74.02% 84.18% 82.23% 81.94% 7 to 11 yrs 84.88% 79.10% 87.66% 77.21% 86.60% 84.90% 87.17% During the 2003 legislative session, some states made changes to their SCHIP programs, changes that have included increased cost sharing for the family share of premiums and/or for copayments for health care visits. In addition, some states have reduced or eliminated certain benefits. The impact of these changes is not known, but they could decrease access to care for children. Because of these changes, understanding children s disenrollment patterns from the program becomes even more critical. A great deal can be learned from disenrollees about their satisfaction with the program and also about their access to care. As described in Table 2, enrollment files can be linked to claims and encounter data to assess the health and sociodemographic characteristics of children who disenroll from SCHIP compared with those who remain enrolled. Effectiveness of Care Measures: Preventive Care Access to preventive care visits is a fundamental component of pediatric health care. Preventive care visits that meet the American Academy of Pediatrics (AAP) periodicity schedule are associated with a decrease in avoidable inpatient admissions for infants, across various racial and National Academy for State Health Policy November

18 ethnic groups, income levels, and health status. 14 Preventive care visits are also critically important given the marked increase in the incidence of learning difficulties, accidents, and violence among children, a cluster of conditions that are called the new morbidities of childhood. These visits provide an opportunity for anticipatory guidance to parents about issues such as normal developmental changes, home safety, and seat belt and car seat use. Such interventions have been shown to increase parents awareness of important developmental milestones and to reduce injury. Moreover, preventive care visits may be especially important for low-income children who are more likely than their more affluent counterparts to have these new morbidities. 15 In addition, preventive care visits are a critical time to provide immunizations and to screen for anemia and lead poisoning. HEDIS has guidelines that can be used to calculate compliance with well-child or preventive care visit recommendations. One of the HEDIS indicators is the percentage of children with well-child visits in the third, fourth, fifth, and sixth years of life. An example of one state s findings on this measure is shown in Figure 4. The findings show that while the performance of this state s SCHIP initiative in the area of wellchild visits for the three- to six-year-old age cohort is somewhat higher than the HEDIS Medicaid average, almost one-half of the children did not receive recommended well-child visits. Figure 4 HEDIS Well-Child Visits in Years 3, 4, 5, and % 90.00% 80.00% 70.00% 60.00% 50.00% 40.00% 30.00% 20.00% 10.00% 0.00% CHIP Overall HEDIS 2000 Medicaid Mean MCO 1 MCO 2 MCO 3 MCO 4 MCO 5 Percent 55.87% 50.47% 61.66% 51.97% 51.99% 59.06% 56.85% 14 R.B. Hakim, B.V. Bye, Effectiveness of compliance with pediatric preventive care guidelines among Medicaid beneficiaries, Pediatrics 108:90-97 (2001). 15 S. Busey, T.R. Schum, J.R. Meurer, Parental perceptions of well-child care visits in an inner-city clinic, Archives of Pediatric and Adolescent Medicine 156:62-66 (2002). National Academy for State Health Policy November

19 Safety and Effectiveness of Care Measures: Asthma Medications Asthma is the most common childhood chronic condition and contributes to morbidity, mortality, and high health care costs. The use of asthma medications as recommended by the National Heart Blood and Lung (NHBL) Institute can contribute to positive health outcomes for children with this chronic condition. HEDIS measures can be used to assess the percentage of children receiving asthma medications as recommended by the NHBL Institute. An example of findings for one SCHIP program is shown in Figure 5. The results displayed in Figure 5 show that a high percentage of children received appropriate medications for asthma. However, almost one-third did not. Further quality assurance studies for this program should focus on addressing factors contributing to the lack of appropriate medications for these children. Figure 5 HEDIS Use of Appropriate Medications for Children with Asthma 100% 90% 80% 70% 60% 50% 40% 30% 20% 10% CHIP Mean HEDIS 2002 Benchmark 0% All CHIP MCOs HEDIS Medicaid 2002 Mean MCO 1 MCO 2 MCO 3 MCO 4 MCO 5 Ages % 54.20% 74.34% 74.18% 69.23% 71.43% 67.78% Ages % 57.20% 70.79% 76.61% 69.79% 68.49% 67.97% National Academy for State Health Policy November

20 Table 2 Suggested Quality of Care Indicators Indicators Rationale for Use Definition Data Source Suggested Frequency of Reporting Access to Care 1. Health care use & expenditure profiles Understanding health care use and expenditure patterns for various diagnostic categories is important for program planning and resource allocation. Adequate program planning and resource allocation is an essential component of ensuring quality health care. Information about health care use and expenditures can provide important information about access to care. The expenditure profile could contain information about the following: 1. High volume diagnoses; 2. High cost diagnoses; 3. Organization of diagnoses into categories using a profiling software such as the Clinical Risk Groups or the Chronic Disability Payment System; 4. Detail of all inpatient, outpatient, emergency room, and pharmacy use and charges; and 5. Further breakdowns of care to reflect laboratory, radiology, therapy use and other major categories. Enrollment and claims and encounter data Quarterly with annual summaries 2. Actual versus expected health care use and charges 3. Access and availability of care measures: HEDIS indicators Assessing the degree to which SCHIP enrollees receive the amount of care that is expected based on their illness burden or case-mix is the cornerstone to any quality assessment program. Actual versus expected health care use can be assessed for the following categories: 1. For regions within a state, 2. For each MCO serving SCHIP enrollees, and 3. For providers with large panels within SCHIP. HEDIS has a series of measures for various populations that address access to care measures. The following is a measure for children: Children s access to primary care practitioners for: month olds, 25 months to 6 year olds, and 7 to 11 year olds. Actual versus expected health care use can be calculated using one of the major risk adjustment packages intended for such use. The Chronic Disability Payment System (CDPS) is used in several states and has been used in SCHIP evaluations nationally. Other systems are available and can also be used including the Clinical Risk Groups (CRGs). The exact specifications for this indicator are outlined in the HEDIS 2003 Technical Specifications Manual. Claims and encounter data and enrollment files The calculations rely on claims and encounter data and enrollment files. Medical records should be used to verify the accuracy of the claims data. Annually Annually National Academy for State Health Policy November

21 Table 2 Suggested Quality of Care Indicators Indicators Rationale for Use Definition Data Source Frequency of Reporting Quarterly 4. Incidence of emergency room (ER) use and inpatient Some hospitalizations and emergency ER visits are called ACSC admissions or visits because there is consensus that the condition usually can be managed successfully in the outpatient The total number of inpatient stays and ER visits for ACSC as a percentage of the total number of inpatient stays and ER visits and as a function of the total Claims and encounter data and enrollment files. stays for ambulatory care sensitive conditions (ACSCs) setting. 16 The Institute for Medicine (IOM) has member months can be calculated. specifically recommended that ACSC inpatient discharge rates be used as an indicator of access to primary care services among populations. 17 The following conditions will be assessed. 1. Immunization preventable conditions such as pertussis, rheumatic fever, tetanus, polio, and hemophilus meningitis; 2. Chronic conditions such as asthma, diabetes with ketoacidosis or hyperosmolar coma, diabetes with specified manifestations, diabetes without specified complications, grand mal seizures, and hypoglycemia; and 3. Acute conditions such as cellulitis; dehydration; gastroenteritis; pneumonia and kidney/urinary tract infections; ear, nose, and throat infections; ruptured appendix; and untyped conditions such as failure to thrive, congenital syphilis, and nutritional deficiency. Variations by racial and ethnic groups can be reported, if the state collects these data. MCO variation will be reported where there are sufficient occurrences of the conditions. 16 F.W. Powell, A comparison of ambulatory care-sensitive hospital discharge rates for Medicaid HMO enrollees and nonenrollees, Medical Care Research and Review, 58(4): (2001). 17 Institute of Medicine, Access To Health Care In America (Washington DC: National Academy Press, 1993). National Academy for State Health Policy November

22 Table 2 Suggested Quality of Care Indicators Indicators Rationale for Use Definition Data Source Frequency of Reporting 5. Analysis of disenrollment patterns Administrative data can be used to obtain valuable information about factors contributing to program disenrollment. For example, enrollment files can be linked to claims or encounter data to assess the sociodemographic and health characteristics associated with disenrollment. Analyses in two states have revealed that children with diagnoses indicative of special health care needs are less likely to disenroll than those without such conditions. This finding has implications for program financing and also for family education. If sick enrollees tend to stay in the program and healthy ones leave, medical costs could be higher than expected. In addition, families may need outreach and education about the importance of insurance for their children even for those who are healthy. Variables of interest from the enrollment files (such as income, race and ethnicity, place of residence urban or rural) and from the encounter files (diagnoses indicative of a special health care need) must be identified. Disenrollment episodes must be identified from the enrollment files and statistical models developed to examine the health and sociodemographic characteristics related to the odds of disenrollment. Claims and encounter data and enrollment files. Quarterly National Academy for State Health Policy November

23 Table 2 Suggested Quality of Care Indicators Indicators Rationale for Use Definition Data Source Frequency of Reporting 6. HEDIS use-ofservice measures HEDIS has a series of measures addressing health care use. These can be calculated and reviewed to address potential access to care issues. They include: Inpatient utilization to include discharges and days per member month and average length of stay); Ambulatory care use; Non-acute inpatient care use in: hospice, nursing home, rehab facilities, skilled nursing facilities, transitional care, and respite; Mental health use inpatient and average length of stay; Mental health use percentage of members receiving services; Chemical dependency use inpatient discharges and average length of stay; Chemical dependency use percentage of members receiving services; Outpatient drug use; and Dental use. The exact specifications for this indicator are outlined in the HEDIS 2003 Technical Specifications Manual. More information about the manual and instructions for ordering it may be found at The calculations rely on claims and encounter data and enrollment files. Quarterly National Academy for State Health Policy November

24 Table 2 Suggested Quality of Care Indicators Indicators Rationale for Use Definition Data Source Frequency of Reporting Effectiveness of Care 1. Effectiveness of care measures: HEDIS indicators HEDIS has a series of measures for various populations that address effectiveness of care measures. The following are child-specific measures: Well-child visits in the first 15 months of life, Well-child visits in the 3 rd, 4 th, 5 th, and 6 th years of life, and Adolescent well-care visits. The exact specifications for this indicator are outlined in the HEDIS 2003 Technical Specifications Manual. The calculations rely on claims and encounter data and enrollment files. Medical records should be used to verify the accuracy of the claims data. Annually HEDIS also has immunization measures that can be calculated. Each state must make a determination about whether its administrative data can be used for these calculations. Some providers in some states encourage enrollees to go to health departments or other public facilities for their immunizations. This information is not captured in the MCO claims or encounter files and can contribute to under reporting of immunization compliance. National Academy for State Health Policy November

25 Table 2 Suggested Quality of Care Indicators Indicators Rationale for Use Definition Data Source Frequency of Reporting Behavioral Health Access and Effectiveness of Care 1. Percentage of specific mental health (MH) and substance abuse (SA) diagnoses as proportion of total enrollees Description of the patient pool is important for program planning and resource allocation. Many of the behavioral health quality of care indicators are based on PERMS 2.0 standards suggested by the American Managed Behavioral Healthcare Assn. (AMBHA). 18 These Performance Measures for Managed Behavioral Healthcare Programs are included in HEDIS 3.0 and include 14 measures of access to and quality of care for mental health. Numerator: Number of patients with specific diagnosis. Denominator: Number of enrolled patients. Claims and encounter data should be searched for the diagnoses delineated in Appendix E. Quarterly 2. Effectiveness of care measures: HEDIS indicators mental health The provision of mental health care can be a significant component of SCHIP. In one state s program, almost 25 percent of those with special health care needs had a mental or behavioral health problem. HEDIS has two measures that address the importance of outpatient follow-up for those who have had an inpatient stay for mental or behavioral health and for those receiving antidepressant medications: Follow-up after hospitalization for mental illness Antidepressant medication management The exact specifications for this indicator are outlined in the HEDIS 2003 Technical Specifications Manual Administrative Data Annual 3. Monthly rate of inpatient admissions for MH and SA PERMS 2.0. Access to care guidelines Average number of admissions per month. Overall number of inpatient admissions should be calculated, as well as for specific high-volume diagnoses. Quarterly 18 The AMBHA Committee on Quality Improvement and Clinical Services, Performance Measures for Managed Behavioral Healthcare Programs (American Managed Behavioral Healthcare Association, 1998). National Academy for State Health Policy November

26 Table 2 Suggested Quality of Care Indicators Indicators Rationale for Use Definition Data Source Frequency of Reporting 4. Average length of stay for SA and MH inpatient stays PERMS 2.0. Access to care guidelines. Average number of days per admission. Overall days per admission should be calculated as well as for specific high volume diagnoses. Quarterly 5. Monthly rate of visits to emergency department (ED) for those with substance abuse and mental health conditions Description of patient utilization of various types of care, including high cost care such as ED care, is essential for program planning and resource allocation. 1. Average number of visits per month. 2. Frequency of ED diagnoses crosstabulated by patients behavioral health diagnosis. 3. Numerator: Number of inpatient admissions that are the same or next day as ED visit Denominator: Number of ED visits. Claims and encounter data ED diagnoses should be profiled to assess whether ED utilization is for behavioral health diagnosis or whether it is due to some other condition that the behavioral health diagnosis puts the enrollee at risk for (e.g., injuries related to manic episodes, suicide attempt as result of depression, and so on). Quarterly In addition, the percentage of ED visits that result in inpatient stay for those with substance abuse and mental health conditions will be calculated. National Academy for State Health Policy November