managed care is increasingly becoming a preferred administrative vehicle for states to control their medicaid budgets and assure access to and coordination of services . managed care now serves 23 percent of the medicaid population . as reported by the kaiser commission on the future of medicaid ( 1995 ) , enrollment in some form of managed care grew from 4.8 million medicaid enrollees to 7.8 million in a single year1993 - 94 . this rapid growth is likely to continue with some of the larger states such as california and new york forecasting large expansions in 1996 . numerous states now have waivers from traditional medicaid rules ( known as 1115 or 1915b waivers ) that allow for such expansion . most of the statewide waivers require enrollment in some form of capitated plan , rather than the primary - care case - management form of managed care . one consequence of the shift to capitation is that the ffs billing mechanism that has generated much of the administrative data used in policy planning and research no longer exists , at least not universally nor for all medicaid services . states and hcfa have struggled with how best to replace ffs billing data with other comparable information on medicaid enrollees in capitated managed care plans . both levels of government face the tradeoffs between their need for data to manage the program efficiently and assure that beneficiaries are well served , versus a desire to reduce the administrative burden on the plans which contract with the program . some recent studies have shown that the ability of plans to produce encounter data is often limited . for example , the group health association of america ( 1994 ) found in its survey of 330 health maintenance organizations ( hmos ) that a substantial proportion ( more than 10 percent ) did not currently have the ability to report patient characteristics or diagnosis for a hospital encounter and that data capabilities for reporting ambulatory encounters were even weaker . a mathematica policy research study for the physician payment review commission ( gold et al . , 1995 ) problems arise most often when the data the plan collects are not automatically generated by the plan 's payment system . though most hmos that pay physicians on other than an ffs basis require encounter data , only a minority of plans with such requirements say that the required data are almost always submitted by providers . clearly a plan can not itself comply with a state 's encounter data requirements if its providers do not send in encounter records . another study of aggregate reporting showed similar problems , with some plans being unable to report aggregate measures of ambulatory care use because of the lack of such information in their administrative data base ( aizer , felt , and nelson , 1996 ) . thus the structure of an encounter data system must include , at the most basic level , the provider , then the plan , then the state , and finally the federal government ( should it choose to require medicaid encounter data , which  to date this article provides an overview of the types of encounter data currently being required of plans , and the problems and issues with providing and analyzing such data . it is based on information provided by hcfa and nine of the states which have had some of the earliest experience with obtaining encounter data as a substitute for ffs billing data . these states are arizona , california , hawaii , maryland , minnesota , new york , rhode island , tennessee , and washington . from them , we reviewed documentation of state data requirements , and we spoke with individuals about their experiences during the past few years as they struggled to implement useful reporting systems ( table 1 ) . we also spoke with individuals in states ( e.g. , massachusetts ) which have chosen to require aggregate reports from plans as an alternative to encounter data . together these states had a little more than 50 percent of the medicaid beneficiaries in managed care in june 1994 ( kaiser commission on the future of medicaid , 1995 ) . some of the states have had a relatively long experience , dating back to the early 1980s , with developing reporting systems for medicaid managed care enrollees . plans understandably have been skeptical of the need for vast volumes of data to be accumulated at the state or federal level . such skepticism has increased in states which have required encounter data but have not developed the management information systems and internal analytic expertise needed to analyze the data . there is a growing recognition , expressed by almost all the states with which we spoke , that only when data are used and used regularly will the effort to produce and submit data be worthwhile . states have not often routinely used their encounter data in the past , because managed care has been a small part of the program in most states until now . also , states that did collect data lacked a clear concept of how they planned to use them , and consequently lacked concrete plans for analysis and reporting . the precursor ffs billing data have often been used for reporting expenditures , a use that is not directly relevant for encounter data , because state expenditures for capitated plans are defined by premiums . ( however , utilization variables such as hospitalizations can be used to create ffs proxy measures . ) we spoke with many individuals about the reasons they felt it was important or necessary to routinely collect encounter data . the data needs they expressed fell into the following categories : accountability : utilization , access , and quality analysis . studies of small , high - policy - interest populations . community - wide studies . other research and evaluation studies . developing broad utilization , access , and quality measures , plans are held accountable by the state for providing or arranging all medically necessary care funded by the benefit package for a defined population . state and federal governments need to assure that patients are getting the care that they need . though oversight of this issue occurs in a variety of ways ( e.g. , external reviews , onsite inspection , consumer complaints and grievances ) , administrative data are a useful tool for broad tracking and profiling to identify potential problem areas . these measures include , for example , rates of the use of preventive services , hospitalization ( including readmissions ) , and selected surgical procedures . many of these measures are included in state aggregate reporting requirements and form the basis for some sections of the medicaid health plan employer data and information set ( hedis ) , which specifies a variety of aggregate reports that states might require plans to submit ( national committee for quality assurance , 1995 ) . critics of required encounter data reporting point out that many such utilization - access - quality measures can be obtained from aggregate reports . also , many states and plans have found that administrative data lack some of the clinical detail needed for oversight , thus making medical record review and surveys necessary . thus , states and hcfa must make a compelling case regarding why they need encounter data to develop such measures . often this case is based on the federal and state need for more flexibility than is provided by routine aggregate reports . although a certain format may be useful at the time when a report is developed , it is likely that another format may be desired subsequently . for example , mental health services for young adolescents may become an important policy topic , whereas the age breakdowns for aggregate reports may group individuals 1019 years of age . plans should consider that it may be in their long - run interest to provide encounter data , rather than to repeatedly respond to modifications in state or federal aggregate reporting requirements . ffs billing data have long been used for ratesetting or for developing risk adjustors to rates . in the past , states have developed capitation rates by a relatively simple approach of developing age - sex - eligibility group cells and averaging medicaid ffs expenditures within each cell . regardless of the method , there can be problems if plans with enrollees that are in poorer - than - average health either choose not to participate or suffer large losses under medicaid managed care . plans whose networks are made up primarily of traditional safety net providers to the medicaid population ( such as affiliations of community clinics or public hospitals ) may be particularly vulnerable . thus it is to the advantage of both the states and the plans to have the fairest rate possible . although the state - of - the - art for developing risk - adjusted rates is still developing , there are many research projects underway to refine such approaches . some of the most promising , such as ambulatory care groups , are based on using ffs billing or encounter data to adjust payments by the diagnostic mix of a plan 's panel of patients ( physician payment review commission , 1995 ) . states which have almost all enrollees in capitated plans lose the ability to test and ultimately use such approaches unless they require encounter data . another way to reimburse plans separately for high - cost outliers is through reinsurance , which is a retrospective approach that may accomplish a similar purpose to prospective risk adjustment . a plan that enrolls a high - risk medicaid population might be allowed to retrospectively receive increased reimbursement for its high - cost population . detailed encounter data will facilitate a state 's ability to use reinsurance , because the state would not otherwise have sufficient information on the distribution of utilization of certain high - cost services ( e.g. , hospitalizations ) in order to evaluate plans ' claims . related to this outlier issue is a more general need to frequently study small , high - policy - interest populations . these individuals , who tend to be sicker and more vulnerable , are the most likely to be affected by the health system , both positively and negatively . however , because of their small numbers , it may be hard to detect problems for these individuals from aggregate data only . more detailed encounter data may be important in allowing states to fulfill their fiduciary and oversight role for these populations . the experience of hcfa and the states with ffs billing data is illustrative of this need . early in the medicaid program , simple aggregate reports were frequently sufficient for both hcfa and the states ' needs . surveillance and utilization review subsystem ( surs ) and management analysis and reporting subsystem ( mars ) reports were required for certification of the state 's medicaid management information system and were often used by the states for internal reporting . a uniform report from the states to hcfa , the hcfa-2082 report , served many of hcfa 's management needs . however , as the program grew and as certain high - cost populations became of greater interest ( examples include persons with acquired immunodeficiency syndrome , pregnant women , and the mental health and substance abuse population ) , these reports no longer sufficed to answer questions from state or federal legislatures or other policymakers . state and federal analysts then began using the detailed person - level data derived from ffs billing to answer questions regarding use of services and expenditures for such groups . the detailed data were needed in order to refine analyses based on specific diagnoses or specific services . another related issue that requires encounter data is the frequent need to assess the health of whole communities or other geographic areas . a state might want to compare utilization of all medicaid enrollees in a county or zip code , or for all services provided within a given geographic area . the analysis might identify geographically based access problems in order to help the states and hcfa decide whether certain hospital or health plan services are essential to a particular geographic area . to do such a study , the analyst would need the encounter data for all individuals who lived in an area ( or who provided services in an area ) , based on patient or provider address . aggregate reports from plans would not generally be sufficient studying medicaid enrollees ' care at the community level may also facilitate cooperative identification of areas for improvement across plans . although not all - encompassing , the prior examples are illustrations of the many ways in which states , hcfa , and other policy analysts could typically use encounter data . however , such data will only be useful if they are relatively complete and an accurate representation of the care plans are providing . some of the struggle to develop successful medicaid managed care reporting systems has to do with the lack of agreement about what is the proper  unit of analysis  for reporting . when the unit of analysis is the plan , states might request aggregate reports on all of the medicaid enrollees a plan served in a particular reporting period . almost all states require some type of aggregate reports either on a monthly , quarterly , or annual basis . this form of analysis is particularly attractive because managed care aims to promote accountability for care to defined populations . plans could be requested to report aggregate counts of services or other summary data on each medicaid enrollee in the plan on a monthly or annual basis . we did not find any states that are requiring this type of reporting , although states potentially can construct such records from encounter data . finally , and most commonly one person gave the following definition :  an encounter is a bundle of services provided to one client by one provider in one time period .  using this definition , if an individual patient went to two different providers in a day , they would have two encounter records . such definitions can become difficult to operationalize when , for example , a provider is actually a hospital outpatient department with multiple clinics or a multi - specialty group practice . in such circumstances , the number of encounter records will probably depend on how the state enrolls providers and assigns unique provider identifiers . other areas that can lead to variations include , for example , such non - ambulatory services as hospital and nursing home care . we found that most states include such services and define a separate bundle of services ( e.g. the hospital stay ) as an encounter . also , very often pharmacy services are included with an individual prescription being called an encounter . in all cases which we examined , the definitions of encounters , as operationalized in the states ' data requirements , approximate closely the unit of analysis for their preexisting medicaid ffs billing system . there has been a period of evolution at both hcfa and in each of the states regarding the list of variables and other reporting specifications for their encounter data , and much of the experience is very recent . one state described a period early in their attempts to obtain encounter data in which they simply asked plans to submit data  within the parameters of our computer system . similarly , hcfa in its original terms and conditions of approval of statewide managed care 1115 waivers has required that encounter data be collected , but has not specified the content or definition of variables . states with more experience have now learned that very precise definitions of files  including lists of variables and variable definitions  and precise submission specifications are needed . also , they have learned that plans need ongoing training and technical assistance , especially early in the development of the system . finally , there needs to be intensive , ongoing editing of the data as they come in , and feedback to the plans on the quality of the data . many states define and circulate the edit specifications , along with the error tolerances for each variable . working mostly independently of each other , hcfa , and other payers , each state with which we spoke had developed a variable list for what is to be included in its encounter data set . during the same recent period , 1993 - 95 , a parallel effort was underway at hcfa to define a uniform encounter data set ( known as mcdata ) for both the medicare and medicaid programs . the draft mcdata variable list was sent on september 17 , 1995 , to 1,000 individuals and organizations for review and comment . they expect the final data set will be substantially similar to the draft version . in a survey sent out with the specifications , hcfa found that 93 percent of the 200 respondents felt that it was appropriate to have a national standard encounter data set . in addition to the standardization that will probably result from the mcdata effort , several states and hcfa have built on the efforts of the national uniform claim form committee , which developed the hcfa-1500 billing form for ambulatory services , and the national uniform billing committee , which developed the ub92 hospital billing form , as well as the american national standards institute ( ansi ) , which has developed standards for electronic submission of the hcfa-1500 and ub92 forms . indeed , the push for standardization is a major impetus for the hcfa mcdata effort . table 2 shows the draft mcdata encounter data set and some other examples of variables that occur on state encounter data sets . it shows that most of the mcdata variables are required by almost all of the nine states . the variables in this typical encounter data set include the identity , and sometimes other characteristics , of the individual receiving the care ( i.e. , the medicaid enrollee ) and the individual or institution providing the care . the reason that the characteristics of enrollees or providers are not always required is that some states plan to obtain those characteristics from their enrollment or provider reference files . ( this could be more difficult at the federal level , because hcfa might not have ready access to those files , which are often complex in structure and less uniform across states . states could also have difficulty matching to the reference files if identifiers submitted by plans have errors . ) the encounter data set also includes selected information about the service provided including the date or dates of service , the procedure code , which tells which service was provided , and the diagnosis or diagnoses , describing the condition that required the care . different types of services ( for example , hospital , outpatient , pharmacy and dentistry ) will require somewhat different variables , and often the states use different file formats for different types of encounter records . some states have very rigorous edits that are applied to data as it comes in . examples include edits to assure that all codes are valid , that numeric data are present where required , and that dates are in logical sequence . states generally specify submission schedules and tolerances which may vary for different categories of variables ( e.g. , 95 percent of primary diagnoses should be present and within valid range ) . states report both carrots and sticks that may or may not be included in plan contracts to enforce the submission of complete and accurate data . there may be financial penalties for late submission ( e.g. , a percent reduction in the capitation fee ) . alternatively , timely submittors of valid data may be allowed to expand medicaid enrollment more rapidly . most states and hcfa report substantial ambivalence about how tough such enforcement should be , because they are anxious to have plans participate in managed care and recognize that the data requirements are burdensome . as mentioned , many plans do not yet have well - developed internal systems that can generate encounter data completely and consistently . also , medicaid often is only one of a number of diverse parties making data requirements of a plan . if reporting requirements are poorly conceived , the state could drive away the plans that it most wishes to attract ( e.g. , those with substantial commercial populations ) . consequently , there may be interagency conflicts within state or federal government , with data and research officials being strongly in favor of encounter data , and managed care program officials being more sympathetic to plan difficulties . although most states recognize that their clout in requiring data has increased considerably over earlier years when medicaid hmo enrollments were lower , most states remain in a cooperative , rather than combative , mode and hope that this cooperative approach will lead to better data over time . it is important to note that encounter data systems are still in a developmental stage in all the states we talked to , with the exception of arizona , which has been working on their system for a decade . although some other states have a long history of contracting with hmos ( e.g. california , minnesota , and new york ) , their major expansions into medicaid managed care have only recently forced them into serious development of their encounter data systems . the lack of internal staff capacity is one major impediment to development of an operational encounter data system . all of the steps previously outlined ( e.g .. developing specifications , providing technical assistance to plans , editing the data , providing feedback , and using the data ) are time consuming and require staff and resources that many financially strapped states do not currently have . states are particularly challenged now since most will  at least in the short term  need to continue to maintain the administrative structure for ffs billing . hcfa has provided some help through the recent development of a technical assistance document to states ( mccall , 1995 ) . there is also a technical assistance contract to assist 1115 waiver states with the development of their encounter data . however , this type of assistance is late to arrive , is limited in scope , and will not be sufficient . all levels of organizations involved in the effort to develop workable encounter data  providers , the plans , the states , and hcfa  will need to obtain ( either directly or through contract or both ) the expertise to develop such systems . in order to make the encounter data that many plans and states will be struggling to provide and analyze most useful , it is important to consider some of the lessons learned from experience with such efforts with ffs billing data , and from recent experience with encounter data . these include the following : regular communication among providers , plans , states , and hcfa is essential . states must have clear specifications for required data , and must not change those specifications very often . attempting to develop a system without the proper resources to do so at the plan , state , or federal level will lead to questionable results . putting resources into such efforts early will prevent problems later . finally , even though the system appears to function smoothly , it is important to keep examining and refining the data as they come in . similarly , a state needs to address the same issues in relating to participating plans . and finally hcfa , to the extent that it receives data from states on a mandatory or voluntary basis , will need to develop a similar approach to data cleaning and analysis .