March / April 2005
Wisconsin's New Quality Performance Measure System for Medicaid Managed Care
MEDDIC-MS is an automated, rapid-cycle managed care quality performance measure system for Wisconsin's Medicaid/BadgerCare HMO program. The system fulfills a variety of objectives for monitoring quality of care and access to care in the state's Medicaid (AFDC/TANF/HS) and BadgerCare (SCHIP) HMO programs. A companion system called MEDDIC-MS SSI is in use to monitor quality in the state's managed care program for individuals eligible for supplemental security income (SSI). A third sub-system called MEDDIC-MS CSHCN has been developed for use with child populations identified as having special healthcare needs. The system has an array of important operating characteristics:
- Electronic data such as monthly HMO encounter data is used for performance measurement.
- Data used for performance measurement is generated routinely from normal operations, not as the result of separate operations such as review of paper medical records. Unlike other performance measure systems, medical record review is unnecessary for routine performance measurement. Record review is still in use for functions such as data validity audits and external quality review audits.
- Multiple electronic data streams are merged with HMO encounter data to improve data accuracy and completeness.
- Patient privacy protection
- MEDDIC-MS is less intrusive from a privacy standpoint than systems with measures that require medical record review, or the so-called "hybrid" method of reporting. Thus, MEDDIC-MS supports HIPAA compliance.
- Performance measures can be calculated at various points in time from the continuous data stream allowing performance measure cycle times to be shorter or longer than the typical calendar year now in common use. In addition, an automated system allows time-specific measurement; for example, MEDDIC-MS can be used to provide comparative performance data for summer and winter months to assess seasonal differences in asthma care.
- Measures can be calculated 182 days after the last date of service to be included in the measure, allowing rapid-cycle measurement. This makes performance data available rapidly enough to be of interventional value, rather than merely historical interest; closer to the goal of "real time."
- The state can introduce new measures or revise measures in as little as 90 days, making the system much more responsive to changing program needs than proprietary systems controlled by third parties.
- Unlike other performance measure systems, HMOs do not calculate or report any performance measure results; that is all done by a data services vendor under contract with the DHFS. Elimination of HMO "self-reporting" prevents inaccuracy and inconsistency caused by variations in HMO data systems or interpretations of measure specifications or "gaming" the results. These are critical attributes in any pay-for-performance incentive programs the DHFS may wish to implement. Most important, it eliminates redundant data extraction and calculation work by all the HMOs, allowing resources previously expended on performance measurement to be redirected to performance improvement.
- MEDDIC-MS includes an integrated quality improvement goal-setting system. The goal-setting system is designed to drive program-wide performance improvement by "ramping up" performance on an individual HMO level. Adapting the "performance gap" formula in the Department of Health and Human Services (HHS) Quality Improvement System for Managed Care (QISMC), the DHFS can establish intermediate performance goals for each HMO on the Targeted Performance Improvement Measures. This allows the DHFS to work with individual HMOs to drive performance improvement using goals that are realistic, achievable and that are tightly targeted to topics of highest priority or most in need of improvement.
- HMOs received their goal statements from the DHFS for 2005 on the selected Targeted Performance Improvement Measures in 2004, in time to allow the HMOs to incorporate the goal statements in their 2005 quality improvement annual work plans.
MEDDIC-MS: A Response to Changing State and National Needs
In 1997, Wisconsin expanded its successful Medicaid HMO program from several southern counties to include much of the remainder of the state. Further changes in the HMO program's scope and scale occurred with statewide implementation of the BadgerCare program in 1999. In addition, the state has operated a special managed care pilot program called Independent Care (iCare) for individuals eligible for SSI in Milwaukee County since 1994. Planning for expansion of managed care options for the SSI population is currently under way. Serving ever-larger and more diverse populations in these programs within ever-tighter budgetary constraints dictated that routine quality management functions such as clinical performance measurement and public reporting be as administratively sleek and low-cost as possible.
Timely, high-quality encounter data is at the heart of the MEDDIC-MS system. The DHFS had been working with participating HMOs to secure accurate, complete encounter data since 1996. By 1999, the process of implementing encounter data reporting was complete, and 2000 saw the first full calendar year of HMO encounter data reporting. Annual audits of data validity by the DHFS help to assure that HMO encounter data is of sufficient completeness and accuracy to meet contract requirements and operate encounter data-driven performance measures. In January 2001, the DHFS launched the migration of performance measurement from the outmoded record-review system then in use to the automated encounter-data system. Testing of the technical specifications of the measures was completed in July 2002. Development of the MEDDIC-MS SSI version of the system was under way simultaneously.
Performance reporting is designed to provide comprehensive data in a user-friendly format that allows the data to be analyzed in several contexts. The results are presented in a report called the MEDDIC-MS Data Book, which is divided into three volumes. Volume 1 presents aggregate, program-wide results on each measure. It also provides data charts with results from past years to reveal data trends. Volume 2 breaks the results down into program-specific portions to allow comparison of the results on each measure between the Medicaid and BadgerCare populations. Volume 3 provides the results of the measures for each individual HMO. Each volume presents the data using a brief narrative about the measure topic and why it is important, as well as a description of results. Simple bar charts are used with the narrative for graphic presentation of the data.
This makes the data useful to program managers for quality assessment and performance improvement decision-making ranging from program-wide scope down to specific areas of weakness with an individual HMO. It also makes it possible for consumers, advocates, policymakers, and providers to use the data to shape informed decisions and opinions about the managed care system. To view the full reports, go to http://www.dhfs.state.wi.us/medicaid7/providers/index.htm.
National Needs Call for New Approaches
Wisconsin's DHFS wasn't the only entity to perceive the need for automated rapid-cycle performance measurement in publicly funded healthcare systems. In 2002, the Institute of Medicine (IOM) published a landmark book on the subject, Leadership by Example: Coordinating Government Roles in Improving Health Care Quality. In it, the IOM recommended that:
- Measures be "derived from computerized data and public reporting of comparative quality information."
- "Providers should not be burdened with reporting the same patient-specific performance data more than once to the same government agency."
- "Finally, effective performance measurement demands real-time access to sufficient clinical detail and accurate data. By the time retrospective performance measures reach decision-makers, it is too late for them to be useful. The current health information environment is far too fragmented, technologically primitive, and overly dependent on paper medical records."
In large measure, MEDDIC-MS is an effective response to the IOM's call, moving performance measurement closer to "real time," based on routinely generated computerized data and providing comparative public reporting.
Other federal entities apart from the IOM have taken an interest in performance measurement and quality improvement in Medicaid managed care. In June 2002, HHS published the Medicaid Managed Care Final Rule in the Federal Register. The rule implements quality improvement provisions for states' Medicaid managed care programs that Congress included in the Balanced Budget Act of 1997.
The rule requires that each state's quality assessment and performance improvement strategy include state-specified standardized performance measures for all state Medicaid managed care programs. Specifically, 42 CFR Ŗ438.240(c) requires that states monitor managed care organization (MCO) performance using standardized performance measures specified by the state and that HMOs submit data necessary for the performance measures to operate. MEDDIC-MS provides a practical system that facilitates compliance with the performance measurement requirements in the rule.
The federal Agency for Healthcare Research and Quality (AHRQ) is another entity with an interest„and an important role„in facilitating the advancement of healthcare quality performance measures and in improving quality of healthcare nationally. The AHRQ Web site (www.ahrq.gov) is a rich resource on a variety of topics related to healthcare quality and performance improvement. It includes a link to one of the latest resources AHRQ provides, the National Quality Measures Clearinghouse (NQMC). Through the NQMC, AHRQ reviews and catalogues performance measures and measure systems in use in various healthcare settings. MEDDIC-MS and MEDDIC-MS SSI have been accepted for inclusion in the NQMC. To view the measure summaries, go to http://www.qualitymeasures.ahrq.gov/resources/measureindex.aspx and scroll down to State of Wisconsin.
How Does It Work?
MEDDIC-MS and MEDDIC-MS SSI are each divided into two subsets of measures„targeted performance improvement measures (TPIM) and monitoring measures. The TPIMs are on topics that the DHFS, together with stakeholder input, has defined as the measures of highest priority for monitoring and improvement. The TPIMs are the measures where performance goal-setting occurs. Monitoring measures are supporting measures to give a broad view of system performance, access and, in some instances, provide additional information related to TPIM topics. Because enrollees served in the SSI managed care program have different healthcare needs and demographic characteristics (for example, children are not enrolled in the program), the list of TPIMs in MEDDIC-MS SSI and some parts of their technical specifications differ from MEDDIC-MS.
Monitoring measures are similar between the two systems, but there are differences there as well, due to differences in populations. The monitoring measure topics are the same in both systems, except as noted in the listing on the following page.
MEDDIC-MS CSHCN is organized somewhat differently. Instead of TPIMs and monitoring measures, the measures are organized into domains similar to those recommended by the American Academy of Pediatrics Committee on Early Childhood, Adoption and Dependent Care in Health Care of Young Children in Foster Care (2002). This particular measure set is designed primarily for programs serving children, so none of the measures include adult-age cohorts. Programs that may use MEDDIC-MS CSHCN in Wisconsin are not yet operational, so no reporting using the measure set has occurred.
MEDDIC-MS CSHCN Measures
Initial and monitoring health screens
- Comprehensive EPSDT examinations, birth to age 2 years
- Comprehensive EPSDT examinations, age 3 to 20 years
- Comprehensive EPSDT examinations resulting in referral
- Non-EPSDT well-child encounters by age cohort
Comprehensive health assessment and preventive services
- Primary care encounters
- Vision care encounters
- Audiology encounters
- Dental encounters (if included in managed services)
- Childhood immunizations
- Blood lead toxicity screening at age 1 and 2 years
Developmental and mental health
- Outpatient mental health and/or substance abuse evaluations
- Mental health day/outpatient treatment
- Substance abuse day/outpatient treatment
- Readmission for selected mental health/substance abuse care within one year of inpatient care
- Inpatient psychiatric and substance abuse care
Electronic Data Supports
HMOs participating in the Wisconsin Medicaid and BadgerCare programs are required by the contract with the DHFS to provide encounter data for the services provided to their enrollees. The HMOs submit the data to the DHFS each month. Each HMO's monthly submission is subjected to a variety of quality and completeness edits prior to the submission being uploaded to the DHFS data warehouse. Overall error rates have been low, and when edit failures occur, the HMO is informed. Corrections must be completed before the data is accepted.
Through memoranda of understanding with other divisions of the DHFS, multiple data streams are available to help make the database for performance measurement as robust as possible. Other data streams include Lead Poisoning Prevention Program data, Wisconsin Immunization Registry data from the Department of Public Health, and fee-for-service program data. Encounter data from any previous HMOs individuals may have been enrolled in while in Medicaid or BadgerCare is also available. All of these data sources are existing data streams that require no additional work by providers in order to support quality assessment. These data sources help displace the need for medical record review for performance data acquisition and add value to the existing data streams themselves.
Reducing or ending the reliance on medical record review for performance data acquisition dramatically reduces the hassle factor for providers. Review of paper medical records represents a major problem for provider clinics because the clinics usually must devote staff time to the task of pulling the records for reviewers and either setting the records aside somewhere for review or copying records for the reviewers. All of this imparts costs„for both clinic staff and reviewer staff time.
Patient privacy is also an issue whenever third-party reviewers are allowed to range through charts in search of the required performance data elements. Signatures on confidentiality agreements notwithstanding, this process allows reviewers to see much more information than is required for the acquisition of the data elements.
Conversely, electronic queries made of encounter data reveal only the specific coded data necessary for the performance measure, nothing more. This provides an additional level of confidentiality protection for patients, thereby supporting HIPAA compliance.
When electronic medical records (EMR) come into much wider use, it will be possible for a system like MEDDIC-MS to extract not only diagnosis and procedure codes, but lab results and other outcome-related data as well. Until then, the system tends to focus primarily on process measures. Reliance on electronic data streams helps move the assessment of performance as close as possible to "real time."
MEDDIC-MS and Future
Wisconsin has implemented the performance measures, goal-setting process, and public reporting of results. Closing the loop on performance measurement requires using the MEDDIC-MS tools for in-depth assessment of individual HMO performance, and using the data to drive quality improvement. This includes assessment of HMO compliance with contract provisions, achievement of sustained performance improvement, and overall improvement in program-wide quality of care. This may involve the development of pay-for-performance incentives based on the measures and performance assessment tools that use measure data to drill down on the quality of care and services provided by each HMO.
MEDDIC-MS is not an answer to all the problems associated with healthcare quality performance measurement, but it does address some of the most nettlesome issues. It provides a working model of automated performance measurement that can function cost-effectively in a very large publicly funded healthcare system such as Medicaid managed care.