Using Administrative Data to Assess Quality of Care in the ... · The type of data used to assess quality can have a significant influence on the costs of any quality assessment program

Using Administrative Datato Assess Quality of Carein the State Children’sHealth 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

Using Administrative Datato 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 2003by

National Academy for State Health Policy50 Monument Square, Suite 502

Portland, ME 04101Telephone: (207) 874-6524Facsimile: (207) 874-6527E-mail: [email protected]: www.nashp.org

Prepared with support from the David and Lucile Packard Foundation

CHIP 21

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 3rd, 4th, 5th, and 6th 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

National Academy for State Health Policy © November 2003 1

INTRODUCTION

During initial implementation of the State Children’s Health Insurance Program (SCHIP), statestended to focus much of their efforts on accurately identifying potential enrollees and ensuringthat 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 ofcare that children enrolled in SCHIP are receiving. As states develop quality assessmentsystems, they need to make a number of key decisions about: the components of health carequality that will be measured, the specific measures that will be selected, and the data sets thatare necessary to calculate the measures.

The type of data used to assess quality can have a significant influence on the costs of anyquality assessment program. Indicators that provide more data (e.g., telephone surveys andmedical record reviews) are typically more costly to obtain than those indicators that are readilyavailable through health plans’ claims and encounter databases. State agencies and health plansroutinely collect administrative data such as enrollment and claims and encounter files, forbilling and program operations purposes. These data were not originally intended for qualityassessment purposes and are often dismissed by clinicians because, if used alone, they do notcontain all of the information necessary to make good quality assessments. However,administrative data can form a strong foundation to support quality assessment activities. AsLisa Iezzoni has noted in the Annals of Internal Medicine “…with detailed clinical informationburied deep within medical records that are expensive to extract, administrative data possesimportant 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 qualityassessment 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 8:666-674(1997).


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 alsobe used to assess quality of care and also will be discussed.

Enrollment files contain important information about the enrollees’ sociodemographiccharacteristics such as age, gender, place of residence, income, program eligibility category, andthe 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 careindicators 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 InternationalClassification of Diseases, 9th 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 inquality of care analyses. Most states have claims and/or encounter data for their MedicaidPrograms and their SCHIP initiatives. However, some states only maintain complete data forportions 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 waiverprograms or State Title V CSHCN Programs may maintain additional databases that providedetailed information about unique enrollee groups. For example, some states have specializedbehavioral health programs that collect indepth information about enrollees’ program eligibility,functional status, and treatment programs. State Title V CSHCN Programs may collectinformation about unique services provided to CSHCN such as respite care, specializedequipment, and supplies.

Some states have special immunization databases. The number of immunizations given maybe greatly underestimated when relying only on administrative data from MCOs to make thecalculations. This can happen for many reasons. For example, some providers encourage CHIPenrollees to obtain their immunizations at public health departments and community health


centers citing the high cost of keeping and administering the vaccines as the reason. In otherinstances, in an attempt to encourage immunizations, some MCOs and providers participate inhealth fairs where immunizations are administered. These immunizations would never berecorded in the MCOs’ administrative data. In an attempt to better track immunizations, somestates keep immunization databases that may be used to assess compliance. The quality of thedata in the immunization database needs to be carefully assessed. In addition, a commonidentifier for each child, such as a social security number, is needed so that immunizationinformation from different databases can be linked and used together to determine immunizationcompliance.

Birth certificate data also contain valuable information about an infant’s birth outcome that canbe used in quality assessments. These data are usually complete and often can be linked, usingsocial 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 canbe used to assess and improve the quality of care in a SCHIP program. However, the quality ofthe administrative databases must be carefully assessed before they are used and their advantagesand disadvantages understood.


ADVANTAGES AND DISADVANTAGES OF ADMINISTRATIVE DATABASES

There are several advantages associated with using administrative databases to assess health carequality. Because they are readily available, administrative data are cost-effective to use and farless 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 cancost $30 to $50 per completed survey, depending on the enrollee population and the length of thesurvey. In contrast, assessments based on administrative data can cost less than a dollar perenrollee.

In addition to the costs, medical record reviews and telephone surveys require the cooperationand participation of providers and enrollees. Unless managed carefully, serious selection biascan be introduced into the quality assessment findings, if certain segments of the provider orenrollee population refuse to participate in data collection activities. For example, if providersfrom large, well-staffed practices were more likely to participate in the medical record reviewprocess than those from smaller practices, then the quality of care findings from the medicalrecord review would be biased toward larger physician practices. Administrative data have thepotential to overcome these problems because they are already collected and require noadditional effort on the part of providers or enrollees.

Administrative databases also provide the opportunity to track individuals over time to assesscare before and after a major treatment episode, for example. In addition, care for a census ofenrollees 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 bedocumented, rather then focusing on a subset for whom it was possible to obtain medicalrecords.

Administrative databases do have several disadvantages that often can be reduced bysupplementing them with medical record and telephone survey data, and strategies incorporatingmultiple data sources to assess health care quality are discussed later in this report. Perhaps thetwo greatest concerns raised about administrative data are coding accuracy and completeness.2

Studies examining administrative databases for their coding accuracy of diagnoses andprocedures vary widely in their findings. Several studies comparing the contents of outpatientmedical records to those of administrative databases have found a reasonably high level ofagreement between the two sources (80% or better). However, studies focusing on inpatient carefound coding discrepancies in 15% to 21% of the cases, and these coding differences led to theassignment of more severe conditions to the enrollee than were actually contained in the medicalrecord.

Information found in claims and encounter data may also be incomplete. For example, typicallyfive 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 potentialindicators,” Quality Review Bulletin, 16:424-438 (1990).


claims and encounter records with several of these fields populated, others only submit a primarydiagnosis code, for example. In addition, providers may only record the acute condition that wasthe immediate cause of the health care encounter, whereas others will also record the presence ofany chronic conditions. It is obvious that these practices could lead to different assessments ofthe numbers of enrollees with chronic conditions from MCO to MCO based on the completenessof the claims data and not based on the actual situation. In addition, such practices could lead tovariable reporting of services rendered.

Finally, claims and encounter data contain inherent limitations that cannot be overcome byimproved coding practices. These databases were never developed as clinical systems. Thus,important information such as particular lab values for enrollees with diabetes, for example, orinformation about enrollees’ functional status are not available and must be obtained from othersources.

Using administrative data for quality purposes requires that analysts and others evaluating healthcare quality undertake a multi-step process that begins with assessing the quality of each MCO’sdata. The steps that should be taken to assess data quality are described in the following section.


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 forMedicare and Medicaid Services (CMS) has developed guidelines to follow when assessing thequality of data received from MCOs. The steps described below are adapted from thoseguidelines and include some additional recommendations for assessment strategies.3 In additionto assessing the quality of claims and encounter data, the quality of the information found inenrollment files and any other administrative data that may be used for quality assessmentpurposes should be assessed. Enrollment or coverage files are essential for the accuratecalculation of most quality of care indicators and for the assessment of health care use rates andexpenditures. Understanding the content of the enrollment files and how critical issues such asassigning member numbers to enrollees who change MCOs or who drop out and later re-enroll isnecessary. The following steps are recommended to assess the quality of encounter data:

Step 1: Examine the Data Files to Determine Completeness of theData Fields and Validity of the Data in the Fields

A report should be generated listing each field found in the administrative data, the percentage oftimes the field is populated, and an assessment of the percentage of invalid information. In thecase of encounter data, the percent of invalid diagnostic, procedure, and place of service codesshould be assessed. For enrollment files, the percentage of invalid birthrates and the percentageof invalid information in any other critical fields should be documented. The results of thesefindings 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 thedatabase each month. Some fluctuations in the number of records received will undoubtedlyexist, but very large changes, 25 percent or more, may indicate that the MCO is having difficultyprocessing the information they receive. In addition, tables should be prepared showing thenumber of records in the database by service type, such as inpatient, outpatient, and emergencyroom use. The data should be distributed in a pattern that is consistent with the expected use ofcare by children (i.e., the majority of care should be for outpatient services with little inpatientand emergency room use). This basic pattern should be seen for all months in which data areavailable.

Appendix A contains an example of tables prepared for one state to examine the consistency ofthe data received month by month from a MCO. In order to identify invalid codes, thediagnostic and procedure codes found in the encounter data were compared to a master list ofapproved 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 ofpregnancy 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 inassessing the quality of administrative databases.


invalid codes. Appendix B contains recommendations about the percentage of invalid andmissing 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 beprepared for each month. Enrollees with the same first and last name, birth date, place ofresidence, and parent names should be identified as possible duplicates and referred to the stateor the enrollment broker. In addition, children who drop out and later re-enroll should beexamined 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 renewalperiods. It is not possible to identify with complete accuracy the numbers of duplicate childrenin the enrollment files or the numbers of children who are reassigned their original identificationnumber after a disenrollment spell. However, it is essential to assess the percentage ofoccurrences where duplicates might exist or where the same child might have differentidentification numbers in order to minimize the impact of such duplications on quality of careassessments. If duplications are a significant problem, then it may not be possible to generateaccurate information about the children’s coverage periods to calculate some of the quality ofcare indicators.

Step 2: Examine Health Care Use Patterns

CMS specifically recommends conducting an extensive analysis of the use data and comparingthe health care use statistics to other available data when possible. This is especially importantwhen 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) forinpatient, outpatient and emergency room (ER) use. A table or graph showing the trends bymonth also should be developed. The State may also wish to analyze health care use patterns forancillary services or other specific service categories to determine if the amount of use isreasonable. This summary information should be prepared for each MCO participating inSCHIP and for the pool of SCHIP enrollees overall.

In addition to preparing the basic summary information, more detailed analyses can also beconducted. Appendix C contains an example of the health care use patterns for SCHIP enrolleesin one state, where the enrollees are grouped into different categories using the Clinical RiskGroups (CRGs). The CRGs is a classification and risk adjustment system that uses diagnosticand procedure codes found in claims and encounter data to classify enrollees according to theexpected cost and consequences of their conditions.4 An overview of the CRGs is contained inAppendix D.

In the example, in Appendix C, the SCHIP enrollees are grouped into one of nine CRGcategories, and health care use rates were calculated for children in each of the categories. Thistype of analysis allows the state to examine the distribution of its enrollees in the variouscategories (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 DataWith the Clinical Risk Group Classification System,” Ambulatory Pediatrics 2(1):71-79 (2001).


the children’s health care use rates in each of the categories. As expected, the majority ofchildren are healthy and their health care use rates are considerably lower than those of childrenin any of the other categories, especially compared to those children with malignant orcatastrophic conditions. In all instances, the majority of the care is in the outpatient setting withinpatient use rates increasing with more severe condition categories (i.e., malignancies andcatastrophic conditions).

Step 3: Conduct Medical Record Reviews

States should also plan to conduct medical record reviews to compare the information found inthe encounter data to that found in the medical record. The correspondence between diagnosesrecorded, services rendered, and procedures conducted should be documented. The state shouldwork 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 ImprovementPlan

The results obtained from Steps 1 though 3 should be summarized and shared with the MCOsparticipating in SCHIP. A plan should be developed jointly to improve the data quality. Inaddition to preparing a report summarizing the results of the preceding steps, the state shouldproceed with using the data to calculate quality of care measures and share these results with theMCOs. In a report entitled Getting to Yes,5 experts working with encounter data note that veryoften improvements in data quality are not seen until the data actually are used to calculatequality 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 forHealth-Based Risk Adjustment (Health Research Center: Park Nicollet Institute, 2001).


ASPECTS OF CARE THAT CAN BE MEASURED AND QUALITY OF CAREINDICATORS

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 thequality of care that children receive within SCHIP: safety, effectiveness, patient centeredness,and timeliness. The IOM Committee further defined a set of subcategories for assessment withineach of these major categories. Table 1 shows the IOM categories and subcategories forassessing 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. Treatmenta. Medicationb. Follow-up

2. Acute, chronic, andend-of-life care

2. Effective partnership 2. Timeliness in gettingcare for a particularproblem

3. Health care environment

3. Appropriateness of procedures

3. Timeliness within andacross episodes ofcare

A conceptual framework such as the one above helps to organize the development of qualityassessment indicators for SCHIP. Selecting which particular indicators of care to measure isthen the next critical step in the process. Many good quality of care indictors exist. Althoughnot child-specific, some are recommended in the IOM’s Envisioning the National Health CareQuality Report.6 Other good sources of information about potential quality of care indicatorsinclude Health Plan Employer Data and Information Set (HEDIS), 2003;7 the Bright FutureGuidelines;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 (2nd 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).


Behavorial Healthcare Programs as recommended by the American Managed BehavorialHealthcare 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 enrolleesreceive,

2. The scientific evidence available demonstrating that the indicator is a reliable and validmarker of health care quality,

3. The availability of affordable data about the indicator, and4. The availability of comparison data from other relevant populations.

Decisions about which indicators to measure should be made collaboratively among staffinvolved in program operations, quality assessment and improvement, and evaluation as well asin consultation with health care providers and consumers. Table 2 (which appears at the end ofthis section) contains a summary of indicators that can be used to assess the quality of careSCHIP enrollees receive. These indicators are organized according to the IOM conceptualframework categories and subcategories. Further, these indicators can be calculated usingadministrative databases and can be further enhanced by supplementing the administrative datawith information from telephone surveys and medical record reviews.

Quality of care indicators related to access to care and effectiveness of care are describedbecause these indicators can be calculated using information found in administrative databases.Other important quality of care indicators that involve the collection of patient satisfactioninformation or medical record review data are not the focus of this report and thus are notincluded in Table 2. The following sections contain in-depth descriptions of some of theindicators outlined in Table 2 and provide illustrative examples of how these indicators can beapplied 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 accessto care the health care use and expenditure profile. An essential component of health carequality is the extent to which health care services are used in a manner consistent with theexpected pattern of use for a given population, in this case a given population of enrolledchildren.11 Assessing health care use as an indicator of quality of care is particularly importantwhen contracting with MCOs because of the perception that financial and utilization reviewarrangements 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, 100:947- 953 (1997).12 P.W. Newacheck, et al. “Monitoring and evaluating managed care for children with chronic illnesses and disabilities,” Pediatrics, 98:952-958 (1996).


example, MCOs often require a physician to seek prior authorization before rendering certaintypes of services in an effort to reduce health care use and control costs. Concern has beenraised that some of the reduction in use and costs may be excessive and possibly detrimental tothe enrollee.13

Adequate financing for any health care program provides the foundation for ensuring access tocare. Inadequate financing, particularly for enrollees with special health care needs, can restricttheir access to care because MCOs and providers may be unwilling to care for these enrollees ifthe financial burden is too great. Health care use and expenditure profiles can allow SCHIPadministrators 1) to identify any MCOs that may be enrolling a higher percentage of enrolleeswith special needs and 2) to compare the expenditure and use patterns of those with special needsacross MCOs.

In addition, health care use and expenditure profiles can be used to assess use of particularservices such as inpatient and emergency room care. Although comparison data showing healthcare use rates for SCHIP enrollees nationally are not available, the National Commission onQuality 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 ofSCHIP enrollees.

Health care use and expenditure profiles are particularly useful if they are developed usingsoftware programs that group enrollees into health status categories based on their diagnoses andexpected resource consumption. Grouping enrollees based on health status, as defined bydiagnoses found in claims and encounter data, allows for a more accurate assessment of healthcare use and expenditures. Figures 1 and 2 illustrate how SCHIP enrollees’ health care use andexpenditure 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, 95:591-594 (1995).


Figure 1 Percent of Enrolless Classified as Healthy by Using theClinical Risk Groups for SCHIP Overall and By MCO

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Percent Healthy 84.17% 85.46% 81.42% 86.00% 83.03% 87.22%

All CHIP MCOs MCO 1 MCO 2 MCO 3 MCO 4 MCO 5

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 theassessment 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 somevariations noted between the MCOs. For example, MCO 2 has a lower percentage of healthyenrollees when compared to the overall average for the state of 84 percent; whereas MCO 5seems to enjoy a somewhat favorable group of enrollees in terms of health status, with 87percent classified as healthy.


$0

$500

$1,000

$1,500

$2,000

$2,500

$3,000

$3,500

$4,000

$4,500

$5,000

Figure 2 Per Member Per Month Care Expenditures by CRG Category

PMPM Expenditures $24.25 $105.92 $129.88 $238.90 $294.87 $1,157.43 $4,304.66 $4,531.02 $2,717.62

Healthy Significant Acute

Single Minor Chronic

Multiple Minor Chronic

Single Dominant or

Moderate

Pair Dominant or Moderate

Chronic

Triplet Dominant or

Moderate Malignancies Catastrophic

Figure 2 illustrates the health care expenditures per member per month (PMPM) for each of thehealth status categories included in the CRGs. The PMPM expenditures vary dramaticallybetween the health status categories. While this graph shows the expenditures for SCHIPenrollees overall, the same profile can be developed for each MCO. When considering Figures 1and 2 together, one can see that MCOs with a higher percentage of healthy enrollees face lessfinancial 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 healthyenrollees (or more children in the acute and chronic condition categories) face greater financialrisk, which could result in reduced access to care for the enrollees if the MCOs and theirproviders face too great a financial burden.

The Health Plan Employer Data and Information Set (HEDIS) manual contains many excellentquality of care indicators, including several that assess children’s access to care. Figure 3illustrates access in one state to primary car practitioners for SCHIP enrollees. As previouslynoted, national comparisons for SCHIP enrollees are not readily available for the HEDISmeasures; therefore, the NCQA Medicaid results are shown in Figure 3 to provide some generalcomparison. The majority of children in the three age cohorts of 12 to 24 months, 25 months to6 years, and 7 to 11 years saw a primary care practitioner. Access to care was highest for those


12 to 24 months of age and declined as children grew older. With the exception of MCO 2, all ofthe MCOs had more positive results than the Medicaid 2002 findings.

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Figure 3 HEDIS Children's Access to Primary Care Practitioners

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%

SCHIPHEDIS

Medicaid 2002 Mean

MCO 1 MCO 2 MCO 3 MCO 4 MCO 5

During the 2003 legislative session, some states made changes to their SCHIP programs, changesthat have included increased cost sharing for the family share of premiums and/or for co-payments for health care visits. In addition, some states have reduced or eliminated certainbenefits. The impact of these changes is not known, but they could decrease access to care forchildren. Because of these changes, understanding children’s disenrollment patterns from theprogram becomes even more critical. A great deal can be learned from disenrollees about theirsatisfaction 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 andsociodemographic characteristics of children who disenroll from SCHIP compared with thosewho remain enrolled.

Effectiveness of Care Measures: Preventive Care

Access to preventive care visits is a fundamental component of pediatric health care. Preventivecare visits that meet the American Academy of Pediatrics (AAP) periodicity schedule areassociated with a decrease in avoidable inpatient admissions for infants, across various racial and


ethnic groups, income levels, and health status.14 Preventive care visits are also criticallyimportant given the marked increase in the incidence of learning difficulties, accidents, andviolence among children, a cluster of conditions that are called the “new morbidities” ofchildhood. These visits provide an opportunity for anticipatory guidance to parents about issuessuch as normal developmental changes, home safety, and seat belt and car seat use. Suchinterventions have been shown to increase parents’ awareness of important developmentalmilestones and to reduce injury. Moreover, preventive care visits may be especially importantfor 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 provideimmunizations and to screen for anemia and lead poisoning.

HEDIS has guidelines that can be used to calculate compliance with well-child or preventivecare visit recommendations. One of the HEDIS indicators is the percentage of children withwell-child visits in the third, fourth, fifth, and sixth years of life. An example of one state’sfindings 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 well-child visits for the three- to six-year-old age cohort is somewhat higher than the HEDISMedicaid 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 6

0.00%

10.00%

20.00%

30.00%

40.00%

50.00%

60.00%

70.00%

80.00%

90.00%

100.00%

Percent 55.87% 50.47% 61.66% 51.97% 51.99% 59.06% 56.85%

CHIP OverallHEDIS 2000

Medicaid Mean

MCO 1 MCO 2 MCO 3 MCO 4 MCO 5

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).


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 NationalHeart Blood and Lung (NHBL) Institute can contribute to positive health outcomes for childrenwith this chronic condition. HEDIS measures can be used to assess the percentage of childrenreceiving asthma medications as recommended by the NHBL Institute. An example of findingsfor one SCHIP program is shown in Figure 5.

The results displayed in Figure 5 show that a high percentage of children received appropriatemedications for asthma. However, almost one-third did not. Further quality assurance studiesfor this program should focus on addressing factors contributing to the lack of appropriatemedications for these children.

Figure 5 HEDIS Use of Appropriate Medications for Children with Asthma

CHIPMean

HEDIS2002

Benchmark

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Ages 5-9 70.02% 54.20% 74.34% 74.18% 69.23% 71.43% 67.78%

Ages 10-17 69.11% 57.20% 70.79% 76.61% 69.79% 68.49% 67.97%

All CHIP MCOs

HEDIS Medicaid

2002 MeanMCO 1 MCO 2 MCO 3 MCO 4 MCO 5

Table 2 Suggested Quality of Care Indicators


Indicators Rationale for Use Definition Data SourceSuggestedFrequency

of Reporting

Access to Care

1. Health care use& expenditureprofiles

Understanding health care use and expenditurepatterns for various diagnostic categories isimportant for program planning and resourceallocation. Adequate program planning andresource allocation is an essential component ofensuring quality health care. Information abouthealth care use and expenditures can provideimportant information about access to care.

The expenditure profile could containinformation about the following:1. High volume diagnoses;2. High cost diagnoses;3. Organization of diagnoses into

categories using a profilingsoftware such as the Clinical RiskGroups or the Chronic DisabilityPayment System;

4. Detail of all inpatient, outpatient,emergency room, and pharmacyuse and charges; and

5. Further breakdowns of care toreflect laboratory, radiology,therapy use and other majorcategories.

Enrollment and claims andencounter data

Quarterlywith annualsummaries

2. Actual versusexpectedhealth care useand charges

Assessing the degree to which SCHIP enrolleesreceive the amount of care that is expectedbased on their illness burden or case-mix is thecornerstone to any quality assessment program.Actual versus expected health care use can beassessed for the following categories:1. For regions within a state,2. For each MCO serving SCHIP enrollees, and3. For providers with large panels within

SCHIP.

Actual versus expected health care usecan be calculated using one of themajor risk adjustment packagesintended for such use. The ChronicDisability Payment System (CDPS) isused in several states and has beenused in SCHIP evaluations nationally.

Other systems are available and canalso be used including the Clinical RiskGroups (CRGs).

Claims and encounter dataand enrollment files

Annually

3. Access andavailability ofcare measures:HEDISindicators

HEDIS has a series of measures for variouspopulations that address access to caremeasures. The following is a measure forchildren:Children’s access to primary care practitionersfor: 12-24 month olds, 25 months to 6 year olds,and 7 to 11 year olds.

The exact specifications for thisindicator are outlined in the HEDIS2003 Technical Specifications Manual.

The calculations rely onclaims and encounter dataand enrollment files.

Medical records should beused to verify the accuracyof the claims data.

Annually



Indicators Rationale for Use Definition Data Source Frequencyof Reporting

4. Incidence ofemergencyroom (ER) useand inpatientstays forambulatorycare sensitiveconditions(ACSCs)

Some hospitalizations and emergency ER visitsare called ACSC admissions or visits becausethere is consensus that the condition usually canbe managed successfully in the outpatientsetting.16 The Institute for Medicine (IOM) hasspecifically recommended that ACSC inpatientdischarge rates be used as an indicator of accessto 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; and3. 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.

The total number of inpatient stays andER visits for ACSC as a percentage ofthe total number of inpatient stays andER visits and as a function of the totalmember months can be calculated.

Variations by racial and ethnic groupscan be reported, if the state collectsthese data. MCO variation will bereported where there are sufficientoccurrences of the conditions.

Claims and encounter dataand enrollment files.

Quarterly

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):404-424 (2001).17 Institute of Medicine, Access To Health Care In America (Washington DC: National Academy Press, 1993).




5. Analysis ofdisenrollmentpatterns

Administrative data can be used to obtainvaluable information about factors contributingto program disenrollment. For example,enrollment files can be linked to claims orencounter data to assess thesociodemographic and health characteristicsassociated with disenrollment. Analyses in twostates have revealed that children withdiagnoses indicative of special health careneeds are less likely to disenroll than thosewithout such conditions. This finding hasimplications for program financing and also forfamily education. If sick enrollees tend to stayin the program and healthy ones leave, medicalcosts could be higher than expected. Inaddition, families may need outreach andeducation about the importance of insurance fortheir children even for those who are healthy.

Variables of interest from theenrollment files (such as income, raceand ethnicity, place of residence –urban or rural) and from the encounterfiles (diagnoses indicative of a specialhealth care need) must be identified.Disenrollment episodes must beidentified from the enrollment files andstatistical models developed toexamine the health andsociodemographic characteristicsrelated to the odds of disenrollment.

Claims and encounter dataand enrollment files.

Quarterly




6. HEDIS use-of-servicemeasures

HEDIS has a series of measures addressinghealth care use. These can be calculated andreviewed to address potential access to careissues. 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 thisindicator are outlined in the HEDIS2003 Technical Specifications Manual.More information about the manual andinstructions for ordering it may befound at www.ncqa.org.


Quarterly




Effectiveness of Care

1. Effectiveness of care measures: HEDIS indicators

HEDIS has a series of measures for variouspopulations that address effectiveness of caremeasures. The following are child-specificmeasures:

● Well-child visits in the first 15 months of life,● Well-child visits in the 3rd, 4th, 5th, and 6th years

of life, and● Adolescent well-care visits.

HEDIS also has immunization measures that canbe calculated. Each state must make adetermination about whether its administrativedata can be used for these calculations. Someproviders in some states encourage enrollees togo to health departments or other public facilitiesfor their immunizations. This information is notcaptured in the MCO claims or encounter filesand can contribute to under reporting ofimmunization compliance.

The exact specifications for thisindicator are outlined in the HEDIS2003 Technical Specifications Manual.


Medical records should beused to verify the accuracyof the claims data.

Annually




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 forprogram planning and resource allocation. Many ofthe behavioral health quality of care indicators arebased on PERMS 2.0 standards suggested by theAmerican Managed Behavioral Healthcare Assn.(AMBHA).18 These Performance Measures forManaged Behavioral Healthcare Programs areincluded in HEDIS 3.0 and include 14 measures ofaccess to and quality of care for mental health.

Numerator: Number of patients withspecific diagnosis.Denominator: Number of enrolledpatients.

Claims and encounter datashould be searched for thediagnoses delineated inAppendix E.

Quarterly

2. Effectiveness of care measures: HEDIS indicators – mental health

The provision of mental health care can be asignificant component of SCHIP. In one state’sprogram, almost 25 percent of those with specialhealth care needs had a mental or behavioralhealth problem. HEDIS has two measures thataddress the importance of outpatient follow-up forthose who have had an inpatient stay for mental orbehavioral health and for those receivingantidepressant medications:● Follow-up after hospitalization for mental illness● Antidepressant medication management

The exact specifications for thisindicator are outlined in the HEDIS2003 Technical SpecificationsManual

Administrative Data Annual

3. Monthly rate of inpatient admissions for MH and SA

PERMS 2.0. Access to care guidelines Average number of admissions permonth.

Overall number of inpatientadmissions should becalculated, as well as forspecific high-volumediagnoses.

Quarterly

18 The AMBHA Committee on Quality Improvement and Clinical Services, Performance Measures for Managed Behavioral Healthcare Programs (American Managed Behavioral Healthcare Association, 1998).




4. Average length of stay for SA and MH inpatient stays

PERMS 2.0. Access to care guidelines. Average number of days peradmission.

Overall days per admissionshould be calculated aswell as for specific highvolume 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 varioustypes of care, including high cost care such asED care, is essential for program planning andresource allocation.

1. Average number of visits per month.

2. Frequency of ED diagnoses cross- tabulated 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 beprofiled to assess whetherED utilization is forbehavioral health diagnosisor whether it is due to someother condition that thebehavioral health diagnosisputs the enrollee at risk for(e.g., injuries related tomanic episodes, suicideattempt as result ofdepression, and so on).

In addition, the percentageof ED visits that result ininpatient stay for those withsubstance abuse andmental health conditionswill be calculated.

Quarterly




6. Percentage of members receiving inpatient, outpatient, ED, counseling and psychotherapy, and pharmacy services for mental health and substance abuse

PERMS 2.0 access-to-care measure ofmental health utilization. Description ofpatient access and needs for various types ofcare is essential for program planning andresource allocation.

Numerator: Number of patients withat least 1 inpatient (outpatient, ED,counseling, and pharmacy) claimDenominator: Number of enrolleeswith MH diagnosis

Claims and encounter datashould be searched forprocedure codes indicatinginpatient, ambulatory, ED,and psychotherapy visits.Pharmacy file should belinked and searched forrelevant drug claims (e.g.,anti-psychotics, anti-depressives, stimulants,etc.).

Quarterly

7. Readmission rates for mental health and substance abuse (30, 90, and 365 days)

PERMS 2.0 quality of care measure. 1. Numerator: Number of patients with admission following initial admission Denominator: Number of initial admissions.

2. Average number of readmissions during 30, 90, and 365 day periods.

1. Inpatient claims and encounter data should be searched for a first admission, and all subsequent admissions within 30, 90 or 365 days. A second calculation should be performed to examine the congruence between first and subsequent admission, i.e., whether readmission is for the same or different diagnosis.

2. 30, 90, and 365 rates should be calculated as the number of readmissions during the time period.

Quarterly andannually for365 day rates



Indicators Rationale for Use Definition Data Source

8. Family visits for children undergoing ambulatory mental health

PERMS 2.0 measure of quality of care. Thismeasure assumes that it is beneficial to havethe family involved in the treatment of a child.

1. Numerator: Number of childrenwith at least 1 psychotherapy visitthat includes family.Denominator: Number of childrenwith MH condition.

2. Numerator: Number of family included psychotherapy visits Denominator: Number of ambulatory mental health visits.

Claims and encounter datashould be searched forpatients who are 12 yearsor younger with any mentalhealth diagnosis. Twocalculations are made to(a) describe number ofchildren receiving familyincluded psychotherapy(90846, 90847, 90849, and90853) as percentage of allchildren with mental healthdiagnosis; (b) describepercentage of ambulatorymental health visits thatinclude family.

Quarterly

9. Percentage of individuals with MH diagnosis, and separately by high-volume diagnoses (depression, ADHD, schizophrenia, and bipolar disorder), engaging in risky behavior.

A previous study has shown that the odds ofengaging in risky behavior are dramaticallyhigher for adolescents with ADHD ascompared to those without, and thatthe healthcare costs for adolescents withADHD who are engaging in risky behavior areup to four times higher than averageadolescents, depending on service category.19

Similar analyses will also be conducted withindividuals diagnosed with depression,schizophrenia and bipolar disorder; theseconditions, because of their symptomatologymay also put individuals at higher odds forrisky behavior and associated higherhealthcare costs.

Numerator: Number of individualswith risky behavior.Denominator: Number of individualswith MH diagnosis (and separately byspecific MH diagnosis).

Individuals with diagnosesof depression, ADHD,schizophrenia and bipolardisorder should be profiledusing claims and encounterdata. Indication of riskybehavior can be madeusing a list of diagnosesindicative of risky behaviorsuch as substance abuse,sexually transmitteddiseases, and others.

Quarterly

19 L.M. Youngblade, J. Col, E.A. Shenkman, Adolescent ADD/ADHD and risky behavior: Prevalence, odds, and health-care costs (Gainesville, FL: Institute for Child Health Policy, 2002).




Asthma – Safety and Effectiveness of Care

1. Assessment of asthma care

Asthma is the most common chroniccondition in childhood affecting 4% to 7% ofchildren in the United States, with estimatedcosts of $3.6 billion annually.20 Asthmaprevalence in the United States is reported tobe higher among Blacks and Hispanics,when compared to Whites or non-Hispanics.Some, but not all of these differences inprevalence are due to variations in income,place of residence, and parental education.Cultural and linguistic barriers to health caremay also play an important role.21

Annually health care use and expenditurepatterns for children with asthma should becalculated. Children with asthma should beidentified using diagnostic codes (musthave 2 or more outpatient visits for asthmaor those with inpatient stays). Themedication use for these children based onNHBL Institute recommendations should beassessed. The percentage of children withfilled prescriptions based on claims data willbe recorded out of the number of childrenwith asthma.

The percentage of children with asthmareceiving specialty care should also beassessed.

HEDIS also contains an asthma measureand states can consider using this measurein addition to or instead of the onedescribed in this table.

Claims and encounter dataand enrollment files for theprimary calculations.

Medical records reviewsshould be used todetermine if prescriptionswere ordered ordocumented in the medicalrecord but not filled.Similarly medical recordreviews should be used todetermine if a specialtyconsult was recommendedor ordered but the familydid not make or keep theappointment.

Annually

20 K.B. Weiss, P.J. Gergen, T.A. Hodgson, “An economic evaluation of asthma in the United States,” New England Journal of Medicine, 326:862-866 (1992).21 A.A. Litonjua, et al, “Race, socioeconomic factors, and area of residence are associated with asthma prevalence,” Pediatr-Pulmonol, 28: 394- 401 (1999).


CALCULATING THE QUALITY OF CARE INDICATORS

The initial time and effort required to assess the quality of the encounter data and to develop andtest the programming code to measure the quality of care can be significant. If HEDIS measuresare used, careful consideration must be given to supplementing the HEDIS specifications withlocal codes. For example, the HEDIS technical specifications rely on diagnostic and procedurecodes used nationally. If a particular local code is used to specify inpatient mental health staysor to capture well child visits, these codes need to be incorporated into the measure. Ensuringthat the computer programming is accurate to calculate the measure and that any relevant localcodes are incorporated into the measures can be time consuming. Although local codes areeliminated with the Health Insurance Portability and Accountability Act (HIPAA), they still willappear in prior claims and encounter data and must be taken into consideration.

For most quality of care measures, at least one year of complete data is generally required. Forsome measures, two years of data are necessary. Depending on the quality of the data receivedfrom the participating health plans, the knowledge and skill level of the programmers, and otherdemands placed on the programmers, it could take as long as six months for a staff of threeprogrammers to set up a quality assurance system that relies heavily on administrative data. Thisdoes not include the time that must be spent by program administrators to work with the healthplans to improve data quality.

The reporting frequencies in Table 2 are suggestions only. Each state must decide on thefrequency of reporting based on its internal needs. For example, state programs may want totrack the use of services on a quarterly basis and then compile annual summaries. Preventivecare measures should be tracked annually. Health care expenditures could be tracked monthly orquarterly with annual summaries.

The time and investment are worth the effort. Once quarterly or annual reports developed andprogrammed, they can be readily produced and analyzed in different ways (by region, by healthplan, and by other breakdowns that can be supported by the data).

Report Preparation

Reporting formats can vary widely. The preceding section contains figures that illustrate qualityof care findings for SCHIP. In other instances, tables such as those contained in the appendices,may be the most useful way to display information. Several examples and formats from nationalorganizations are also available for states to consider. The Commonwealth Fund’s Quality ofHealth Care in the United States: A Chartbook22 uses clear graphics and brief narrativesassociated with each graph that highlight the significance of the quality of care indicator and thekey findings associated with it.

22 S. Leatherman, D. McCarty, Quality of Heath Care in the United States: A Chartbook (New York: TheCommonwealth Fund, April 2002).


What to Expect From Those Managing Your Data

At a minimum, states should expect any group managing their health care data to conduct carefulassessments of the data quality and to work with the state and with the managed careorganizations to correct any deficiencies. All data quality assessments should be documented ina formal report to the state. The frequency of these assessments should be no less than annualand perhaps more frequent if managed care organizations are submitting updated data to correctdeficiencies.

The state may also expect the group managing the data to calculate quality of care measures forthem and to prepare reports. Working with a group that has the skill and the flexibility tomanage the data and analyze quality of care indicators would be the greatest resource to the stateand would enhance its ability to provide quality health care to its pediatric enrollees.

National Academy for State Health Policy © November 2003

Appendices


APPENDIX A

Sample Summary of Analysis of Selected Fields Found in EncounterData

The number of records from month to month does fluctuate but does so in conjunction withchanging enrollment patterns seen in this particular health plan.

Date of ServiceNumber ofRecords Date of Service

Number ofRecords

Jan 2000 12104 Sept 2001 55235

Feb 2000 15234 Oct 2001 51684

March 2000 15417 Nov 2001 55852

April 2000 22141 Dec 2001 54805

May 2000 26569

June 2000 31979

July 2000 33783

August 2000 36455

Sept 2000 34311

Oct 2000 37635

Nov 2000 37896

Dec 2000 36546

Jan 2001 40741

Feb 2001 38775

March 2001 35972

April 2001 34230

May 2001 29524

June 2001 39909

July 2001 59231

August 2001 59915


Appendix A (continued):

Sample Summary of Analysis of Selected Fields Found in Encounter Data

Jan-02 Feb-02 Mar-02

Variable PercentMissing

PercentNot

Matching/Invalid

PercentMissing

PercentNot

Matching/Invalid

PercentMissing

Percent Not

Matching/Invalid

CLMSTAT 82.70 0.03 51.56 0.14 73.11 0.01MEMBNO 0.02 N/A 0.06 N/A 0.03 N/ALASTNAME 0.20 N/A 0.56 N/A 0.54 N/AFRSTNAME 0.20 N/A 0.56 N/A 0.54 N/ABDATE 0.20 0.00 0.56 0.00 0.54 0.00PLANCOD 0.00 0.00 0.00 0.01 0.00 0.00CLMNO 0.00 N/A 0.00 N/A 0.00 N/ALINENUM 0.00 N/A 0.00 N/A 0.00 N/ABPROVNO 0.19 N/A 0.30 N/A 0.14 N/ARPROVNO 80.99 N/A 50.95 N/A 72.12 N/AAPROVNO 83.44 N/A 77.10 N/A 80.45 N/ADIAGN1 0.01 0.23 0.02 0.03 0.00 0.03DIAGN2 63.43 0.06 55.49 0.02 63.05 0.07DIAGN3 87.22 0.09 78.61 0.02 84.95 0.03DIAGN4 95.04 0.03 91.01 0.05 93.96 0.02DIAGN5 98.97 0.00 97.91 0.00 98.73 0.00SVCCOD 15.72 0.44 11.67 0.04 12.59 0.03MODIF 77.09 17.55 74.76 40.73 75.38 30.30DFRDOS 4.13 0.00 0.00 0.00 2.70 0.00POSCOD 5.88 0.05 2.70 0.15 4.01 0.33TOSCOD 76.76 0.00 41.71 0.00 65.80 0.00UNITS 6.17 N/A 3.22 N/A 4.10 N/ACLMTYPE 21.85 0.21 26.90 0.61 24.48 0.54DTADJUD 60.44 N/A 13.06 N/A 44.93 N/AEOBCOD1 84.52 38.53 61.19 37.00 70.98 37.04


Appendix A (continued):


April-02 May-02 June-02

Variable PercentMissing

PercentNot

Matching/Invalid

PercentMissing

PercentNot

Matching/Invalid

PercentMissing

Percent Not

Matching/Invalid

CLMSTAT 60.11 0.04 33.13 0.00 45.32 2.66MEMBNO 0.02 N/A 0.01 N/A 0.35 N/ALASTNAME 0.42 N/A 1.37 N/A 19.83 N/AFRSTNAME 0.42 N/A 1.37 N/A 19.83 N/ABDATE 0.42 0.00 1.37 0.00 19.83 0.03PLANCOD 0.00 0.01 0.00 0.01 0.00 1.46CLMNO 0.00 N/A 0.00 N/A 0.00 N/ALINENUM 0.00 N/A 0.00 N/A 0.00 N/ABPROVNO 0.02 N/A 0.50 N/A 0.82 N/ARPROVNO 57.51 N/A 31.95 N/A 45.03 N/AAPROVNO 79.40 N/A 71.57 N/A 74.20 N/ADIAGN1 0.00 0.22 0.00 0.06 0.27 0.36DIAGN2 59.49 0.04 51.39 0.00 49.72 0.03DIAGN3 81.87 0.03 74.15 0.01 73.78 0.12DIAGN4 92.90 0.01 88.56 0.05 89.69 0.07DIAGN5 99.04 0.00 97.67 0.00 98.92 0.01SVCCOD 12.12 0.39 14.15 0.07 13.47 0.39MODIF 74.21 33.89 67.24 57.54 81.81 11.71DFRDOS 1.48 0.00 0.00 0.00 0.25 2.22POSCOD 3.08 0.39 2.78 0.64 0.52 0.06TOSCOD 51.30 0.00 25.32 0.00 28.58 0.00UNITS 4.15 N/A 3.43 N/A 0.56 N/ACLMTYPE 24.48 0.48 35.07 1.49 43.25 0.06DTADJUD 26.97 N/A 17.16 N/A 18.25 N/AEOBCOD1 67.37 28.85 44.10 42.97 22.46 8.21TYPBILL 0.00 0.34 0.00 0.48 0.82 0.39


Appendix A (continued)


July-02 August-02 Sept-02

Variable Percent Missing

Percent Not

Matching/Invalid

Percent Missing

Percent Not

Matching/Invalid

Percent Missing

PercentNot

Matching/Invalid

For Inpatient specificDTODOS 0.13 0.02 0.05 8.00 6.94 10.84ADMTDATE 0.05 0.00 0 6.61 0.01 9.42ADMTTYPE 0.03 1.96 0.23 2.74 41.04 1.56DSCHSTAT 0.00 2.00 0.00 2.40 0.01 59.57ADMTSRC 0.02 100.00 0.23 100.00 40.95 100.00ADMTDIAG 22.80 78.56 42.94 82.96 89.55 74.72PRPROCCD 20.14 7.14 19.68 19.98 45.65 3.31PRPROCDT 20.16 0.02 19.53 7.95 77.58 33.86OPROCCDA 84.68 8.59 72.13 20.99 76.96 3.05OPROCDTA 84.70 0.00 72.13 1.34 91.78 23.95OCCODE1 98.12 N/A 96.04 N/A 83.10 N/AOCDATE1 98.16 0.00 96.11 11.68 83.11 2.47REVCOD 0.09 0.09 0.03 0.06 1.50 18.40For UB 92 specificTYPBILL 0.00 0.85 0.00 1.21 0.01 2.25

Oct-02 Nov-02 Dec-02

Variable Percent Missing

Percent Not

Matching/Invalid

Percent Missing

Percent Not

Matching/Invalid

PercentMissing

PercentNot

Matching/Invalid

For Inpatient specificDTODOS 0.03 10.15 0.01 91.10 0.02 75.07ADMTDATE 0.00 9.82 1.36 90.23 1.13 73.96ADMTTYPE 1.27 0.38 1.36 1.66 1.30 14.13DSCHSTAT 0.00 1.05 0.00 1.52 0.00 1.27ADMTSRC 0.83 100.00 1.37 100.00 1.21 100.00ADMTDIAG 3.51 86.65 18.30 84.47 11.15 80.59PRPROCCD 22.22 0.75 27.96 9.13 41.05 8.98PRPROCDT 22.70 9.47 27.90 91.42 41.03 84.37OPROCCDA 97.42 8.21 96.03 42.62 86.34 37.48OPROCDTA 97.72 39.08 96.04 88.24 86.34 61.50OCCODE1 98.55 N/A 97.25 N/A 92.68 N/AOCDATE1 98.56 12.51 97.26 58.95 92.68 17.41REVCOD 0.38 1.41 1.90 0.07 1.34 0.10For UB 92 specificTYPBILL 0.00 0.11 0.41 0.67 0.32 1.17


Appendix A (continued)

Summary

There are some data elements that are missing at unacceptable levels. Claims status is missing ina large percentage of the cases. Understanding the claim status is important when calculatinghealth care expenditures. The high percentage of missing values for claim status could lead toinaccurate expenditure calculations. The billing provider number is almost always available.However, other provider information, such as the referring provider, is missing in a largepercentage of cases. The inpatient portion of the data contains many invalid dates of admission.

Overall the quality of the data are good in terms of completeness and the use of valid codes. Theareas noted above should be addressed.


APPENDIX B

Recommended Standards for Evaluating the Quality of EncounterData23

Data Element Expectation Acceptable Rate

Enrollee ID Should be valid as found in the stateeligibility file. Can use the ID or SSNif the state accepts the SSN.

100% present.

Enrollee NameShould be captured so name piecescan be separated easily.

85% present. Length should varyand there should be some lastnames with greater than 8 charactersand some first names with less than8 characters.

Enrollee Date of Birth Should not be missing and should bea valid date.

Less than 2% missing or invalid.

MCO ID Data received by MCO and clearlylabeled. Carve outs for BH clearlylabeled.

100% of the time.

Provider ID Should be an enrolled provider aslisted on the provider file.

95% of the time.

Place of Service Should be routinely coded, especiallyfor physicians.

95% of the time for physicians and85% of the time for other providers.

Principal DiagnosisWell coded except for ancillaryservices.

>90% non-missing and valid usingICD-9 look up tables and ACGs forpractitioner providers (ancillaryservices like transportation and labnot included).

23 Adapted from CMS Standards.


Appendix B (continued):

Recommended Standards for Evaluating the Quality of Encounter Data

Data Element Expectation Acceptable Rate

Other Diagnosis This is not expected on all claims butshould be present with a highfrequency.

90% valid when present.

Date of Service Dates should be distributed acrosstime and fairly evenly.

If looking at a full year, 5% to 7% ofthe records should be distributedacross all months.

Unit of Service (Quantity Codes) The number should be routinelycoded.

98% non-zero. 70% should be one ifthe CPT code is in the range of99200-99215, 99241-99291.

Procedure Code This is critical. 99% present (not blank, 8- or 9-filled). 100% should be valid stateapproved codes.

Procedure Modifier This is important to separate outsurgical procedure, anesthesia, andothers. But it is not applicable for allcodes.

20% or less can be missing. Expecta variety of modifiers but the onesthat should have some minimalfrequency are 47 (anesthesia) and80 (asst. surgeon).

EPSDT Indicator The field is for those under 21 yearsof age.

95% of enrollees <21.

Patient Discharge Status Code Should be valid codes for all claimswith the most common code“Discharged to Home.”

For inpatient claims more than 90%should indicate a discharge to home.

Revenue Code (Hospital) When UB92 Forms are used (as isthe case in Texas), this shouldalways be present.

100%.


Appendix C

Sample Expenditure Profile for a Title XXI Population

CRG ClassificationNumber ofChildren

Percentof

Children Grand Total Total InpatientInpatientPhysician

InpatientFacility

InpatientAnesthesia

InpatientOther

N % Mean Median Mean Median Mean Mean Mean MeanCRGs

Healthy 117443 85.43 85.21 36.06 4.84 0.00 0.08 2.97 1.51 0.28Significant Acute 3511 2.55 243.62 143.53 19.07 0.00 0.31 14.93 3.28 0.55Single Minor Chronic 5726 4.17 285.19 143.69 26.09 0.00 0.36 17.93 6.74 1.05Multiple Minor Chronic 248 0.18 810.31 297.60 268.57 0.00 0.81 39.17 224.08 4.50Single Dominant orModerate Chronic 9721 7.07 445.19 196.25 98.49 0.00 1.06 64.77 28.35 4.31Pair Dom. or ModerateChronic 653 0.47 1099.01 519.61 331.32 0.00 4.51 243.33 54.98 28.50Triplet Dom. orModerate Chronic 3 0.00 9306.57 10609.58 7942.71 10185.50 68.79 3861.11 118.33 3894.47Malignancies 46 0.03 4530.0 991.50 809.74 0.00 7.57 682.41 42.86 76.89Catastrophic 123 0.09 3195.6 812.65 694.50 0.00 15.18 484.94 120.30 74.07

TOTAL 137474 100.00 133.64 45.50 15.79 0.00 0.21 10.21 4.45 0.93


Appendix C (continued):

Sample Expenditure Profile for a Title XXI Population

CRGClassification Total Outpatient

Out-patientSurgery

&Anesthe.

Out-patie

ntPhysi

.

Out-patientConsult

Ancillary ER HCPCs Pharm

OT,PT &ST

OtherOutpatientServices

Mean Median Mean Mean Mean Mean Mean Mean Mean Mean Mean

CRGs 80.37 35.93 32.41 15.68 0.67 6.23 2.01 2.27 17.56 0.14 3.39Healthy 224.56 138.35 89.48 35.02 2.93 23.45 4.62 6.72 48.38 0.57 13.39SignificantAcute 259.10 141.52 111.42 27.30 4.99 24.34 3.38 6.40 56.55 2.79 21.93Single MinorChronic 541.74 288.66 278.91 39.62 8.39 5.21 7.76 8.76 97.11 4.91 39.08Multiple MinorChronic 346.70 184.40 152.70 35.18 4.77 28.57 5.68 16.40 71.86 1.87 28.68SingleDominant orModerateChronic 767.69 423.28 348.41 49.40 8.78 81.91 9.08 53.93 135.10 12.98 68.10Pair Dom. orModerateChronic 1363.87 1317.70 346.88 65.13 13.63 133.02 43.10 193.63 74.98 6.10 487.40Triplet Dom. orModerateChronic 3720.31 861.99 2874.32 87.68 13.55 346.93 7.92 122.77 136.98 10.58 119.56Malignancies 2501.12 623.29 1606.79 43.73 9.33 76.01 8.61 139.19 218.19 38.29 360.99Catastrophic

TOTAL 117.84 45.26 49.98 18.29 1.26 9.64 2.45 4.05 24.73 0.49 6.95


APPENDIX D

Description of the Clinical Risk Groups (CRGs)Clinical Risk Groups (CRGs) are a categorical clinical system that classifies individuals intomutually exclusive categories. CRGs are a relatively new system with software first released inMarch 2000. It is an integration of two previous developmental systems: 3M’s Episode Grouperand NACHRI’s Classification of Congenital and Chronic Health Conditions (CCCHC). Thereare essentially four uses of CRGs:

1. Tracking congenital/chronic disease prevalence rates and classifying enrollees,2. Profiling health service utilization and physician practices,3. Pricing and capitation risk adjustment, and4. Linkage to measures of patient satisfaction/quality tracking.

The CRG software reads all ICD-9-CM diagnosis codes from all health care encounters, exceptthose associated with providers known to frequently report unreliable codes (e.g., non-clinicianproviders and ancillary testing providers). It assigns all diagnosis codes to a diagnostic category(acute or chronic) and body system, and assigns all procedure codes to a procedure category.Each individual is grouped to a hierarchically defined core health status group and then to a CRGcategory and severity level, if chronically ill.

The CRG definition of a chronic health condition contains three components: (a) physical,mental, emotional, behavioral, or developmental disorder; (b) expected to last at least 12 monthsor longer or having sequelae that last at least 12 months or longer; and (c) requires ongoingtreatment and/or monitoring. The CRG definition of a significant acute condition is a seriousacute illness that places the individual at risk in the future for needing services of an amount andtype greater than that for not chronically ill persons, and possibly at risk for an ongoing chronichealth condition. In the CRG logic, an acute illness is only classified as a significant acute if itoccurred in the most recent six months of the base-year time period. Chronic and acute illnessesare generally classified only if there have been at least two outpatient encounters for thatdiagnosis separated by at least a day. A few diagnoses only require one outpatient encounterbased diagnosis, and these include the codes for mental retardation, Down’s Syndrome,blindness, and procedural codes such as chemotherapy and renal dialysis.

There are nine core health status groups: healthy (including non-users), significant acute, minorchronic, multiple minor chronic pairs, single dominant or moderate chronic, multiple significantchronic pairs, chronic triplets, catastrophic, and metastatic malignancy.

Significant acute conditions are those acute illnesses that could be precursors to or place theperson at risk for developing a chronic disease. Examples in this group are head injury withcoma, prematurity, and meningitis.

Minor chronic conditions are those illnesses that can usually be managed effectivelythroughout an individual’s life with typically few complications and limited effect upon the


individual’s ability, future need for medical care, and death. This category includes attentiondeficit / hyperactive disorders (ADHD), minor eye problems (excluding near-sightedness andother refractory disorders), hearing loss, migraine headache, some dermatological conditions,and depression.

Moderate chronic conditions are those illnesses that are variable in their severity andprogression but can be complicated and require extensive care and can sometimes contribute todebility and death. This category includes asthma, epilepsy, and major depressive disorders.

Dominant chronic conditions are those illnesses that are serious, and often result in progressivedeterioration, debility, death, and the need for more extensive medical care. Examples in thisgroup include diabetes, sickle cell anemia, chronic obstructive lung disease and schizophrenia.

Chronic pairs and triplets are those individuals who have multiple primary chronic illnesses intwo (pairs) or three or more body systems (triplets).

Metastatic malignancies include acute leukemia under active treatment and other activemalignant conditions that effect children.

Catastrophic Conditions are those illnesses that are severe, often progressive, and are eitherassociated with long-term dependence on medical technology, or are life defining conditions thatdominate the medical care required. Examples in this group include cystic fibrosis, spina bifida,muscular dystrophy, respirator dependent pulmonary disease, and end-stage renal disease ondialysis.


APPENDIX E

Mental and Behavioral Health Diagnostic Codes

All Codes: 290 - 319Affective disorders including depression and bi-polar 296Dysthymia 300.4Anxiety disorders V28.2, 300.02, 300.21, 308,

309.81, 300.3ADD/ADHD 314Child maltreatment 995.52, 995.53Oppositional and aggressive behaviors V71.02, 313.81, 312Pervasive developmental disorders 299.00, V40.3, 299.80, 299.10Organic psychotic conditions 290-294Schizophrenia 295.0 -295.9Other psychoses 296.9, 297, 298,299Dementia 294.8, 290, 331Personality disorders 301Neurotic disorders 300Alcohol and drug dependence 303-305Eating disorders 307.1, 307.51, 307.50Developmental delays 315Observation for MH V71.0Psychiatric exam V70.1, V70.2Special screening for MH & developmental handicaps V79Personal history of mental disorder V11Counseling: x-c V61.1, V61.2, V62, V65.5Mental retardation 317-319

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