Department of Health and Human Services 

OFFICE OF 

INSPECTOR GENERAL 

MEDICARE RISK HMO


PERFORMANCE INDICATORS


sswrcl~.b ~. $ 

w 

# 4gJUNE GIBBS BROWN ~ 

s Inspector General 

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++ OCTOBER 1995 4~daa > OEI 06-91-00734 
OFFICE OF INSPECTOR GENERAL 

The mission of the OffIce of Inspector General (OIG), as mandated by Public Law 95-452, as 

amended, is to protect the integrity of the Department of Health and Human Services’ (HHS) 

programs as well as the health and welfare of beneficiaries served by those programs. This 

statutory mission is carried out through a nationwide network of audits, investigations, and 

inspections conducted by three OIG operating components: the OffIce of Audit Services, the 

OffIce of Investigations, and the OffIce of Evaluation and Inspections. The OIG also informs the 

Secretary of HHS of program, and management problems, and recommends courses to correct 

them. 

OFFICE OF AUDIT SERVICES 

The OIG’S Ofllce of Audit Services (OAS) provides all auditing services for HHS, either by 

conducting audits with its own audit resources or by overseeing audit work done by others. Audits 

examine the performmce of HHS programs and/or its grantees and contractors in carrying out 

their respective responsibilities and are intended to provide independent assessments of HHS 

programs and operations in order to reduce waste, abuse, and mismanagement and to promote 

economy and efilciency throughout the Department. 

OFFICE OF INVESTIGATIONS 

The OIG’S Ofllce of Investigations (01) conducts criminal, civil, and administrative investigations


of allegations of wrongdoing in HHS programs or to HHS beneficiaries and of unjust enrichment


by providers. The investigative efforts of 01 lead to criminal convictions, administrative


sanctions, or civil money penalties. The 01 also oversees State Medicaid fraud control units which


investigate and prosecute fraud and patient abuse in the Medicaid program.


OFFICE OF EVALUATION AND INSPECTIONS 

The OIG’S OffIce of Evaluation and Inspections (OEI) conducts short-term management and


program evaluations (called inspections) that focus on issues of concern to the Department, the


Congress, and the public. The findings and recommendations contained in these inspection reports


generate rapid, accurate, and up-to-date information on the efficiency, vulnerability, and


effectiveness of departmental programs. This report was prepared in the Dallas regional office


under the direction of Chester B. Slaughter, Regional Inspector General.


Project staff


REGION HEADQUARTERS 

Michelle Adams Judith Tyler Hugh Hetzer 

Carolyn Neuwirth Nancy Watts Mark Krushat 

Sarah Taylor Brian Ritchie 

Barbara Tedesco 

For additional copies of this report, please contact the Dallas Regional Ofllce at (800) 848-8960. 
Department of Health and Human Services 

OFFICE OF	

INSPECTOR	

MEDICARE


PERFORMANCE


*4. S6WICJ$ 

“% 

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% 

% ~ ‘%za
 +*2

GENERAL 

RISK HMO


INDICATORS


JUNE GIBBS BROWN 

Inspector General 

OCTOBER 1995 

OEI 06-91-00734 
EXECUTIVE SUMMARY 

PURPOSE: 

To determine the usefulness of HMO disenrollment rates as performance indicators of 

Medicare risk HMOS, in light of our recent beneficiary survey data. 

BACKGROUND AND METHODOLOGY: 

The rapidly increasing participation of Medicare and Medicaid beneficiaries in managed 

care has heightened the need to fmd valid measures and performance indicators for 

HMOS. Two measures which have been considered by researchers and policy analysts are 

HMO disenrolhnent rates and direct surveys of HMO members. We previously reported 

results from a survey of 2,882 enrolled and disenrolled beneficiaries in 45 Medicare risk 

HMOS. Using our survey data, coupled with disenrollment data for these HMOS, we 

conducted both beneficiary and HMO-level analyses to assess the validity of using 

disenrollment rates as indicators of HMO performance, and participant survey data to 

predict disenrollment. This report presents our analyses and conclusions regarding the 

viability of these two performance indicators. 

FINDINGS 

Disenrollrnent Rates as a Pe~orrnance Indicator 

HMO disenrollment rates, once properly adjusted, may provide an early alert of possible 

problems among Medicare risk HMOS. HMOS with higher disenrollrnent rates had more 

enrollees who reported service access problems. However, the following adjustments to 

HMO disenrolhnent rates are needed to accurately reflect HMO trends: 

� Annualize the rates to more accurately portray disenrollment activity among newer 

HMO risk contracts. 

� Adjust rates for administrative disenrollments (e.g. beneficiaries moving out of the 

service area). 

� Recognize disenrolhnent rates will be understated because they do not capture 

those beneficiaries who want to leave but cannot. 

HMO Disenrollment Palterns 

HMOS with more experience in the Medicare risk HMO program experienced the largest 

decreases in their disenrollment rates over time. 

i 
Beneficiary Disenrollment Patterns 

Beneficiary-level survey data showed beneficiaries who are more likely to disenroll tend to 

report: 1) declining health due to HMO care; 2) being disabled or having end-stage renal 

disease (ESIU)); 3) perceiving that their HMO places more importance on holding down 

the cost of care rather than giving the best medical care possible; and 4) experiencing long 

waits in a primary HMO doctor’s office. 

Beneficiaries leaving Medicare risk HMOS ofien re-enroll directly, or shortly thereafter, in 

another HMO. 

RECOMMENDATIONS 

HMO disenrollment rates, in conjunction with beneficiary survey data, appear to be useful 

HMO performance indicators. Overall, we recommend that HCFA use systematically 

developed HMO disenrolhnent rates and beneficiary survey data to improve its monitoring 

activities. We believe this is a particularly important step in light of the anticipated rapid 

growth of the Medicare risk HMO program. We spcciilcally recommend that HCFA: 

Use Disenrolhnent Data 

F Tmck disenrollment rates over time to detect potential problems among HMOS. 

F	 Use tijusted disendrnent ties, along with other available HMO information 

(e.g. beneficiary complaints and appeals lodged against HMOS) to target reviews of 

HMOS. Adjustments must include: 1) annualizing rates for Medicare risk HMO 

contracts less than 2 years old to more accurately measure newer HMOS’ 

disenrollment activity, and 2) excluding administrative disenrollees, which 

overstate disenrollment rates due to such factors as enrollees moving or HMO plan 

discontinuation. 

�	 Conduct disenmllment surveys that fully capture all the beneficiary’s reasons for 

leaving the Medicare risk HMO. 

Use Beneficiary Survey Data 

F	 Survey enroUees systematically and routinely on key questions and on their desire 

to leave/remain with an HMO, to complement disenrollrnent data. Such survey 

data might be captured nationally, to assist in targeting HMOS for in-depth 

reviews, or in the HMOS which have been targeted as part of the in-depth review 

itself. 

F	 Monitor Medicare risk HMOS with high disenrollment rates and repotted service 

access problems and work with HMOS to respond to the needs of k-neficiaries at 

risk of disenrolling. This should include activities that: 1) give more attention to 

the care delivered to disabled/ESRD beneficiaries; 2) reduce the waiting times in 

ii 
primary HMO doctor offices; and 3) address HMO practices that cause 

beneficiaries to report declining health status as a result of their HMO care and 

their sense that the HMO gives too much priority to holding down costs versus 

giving the best care. 

Use Key Questions 

Several key questions successfully predicted future disenrollment and HMO disenrollment 

rates, along with beneficiaries who wanted to leave but felt they could not. 

�	 The questc”onswe found most predictive of benejiciuries’ future disenrollment 

included: 

� Were complaints taken seriously by their HMO doctors? 

�	 Did their primary HMO doctors provide Medicare services, admit them to 

the hospital, or refer them to a specialist when needed? 

�	 Did they perceive their HMOS as giving too high a priority to holding down 

the cost of medical care compared to giving the best medical care? 

�	 Did they perceive their health got worse as a result of the medical care they 

received in their HMO? 

�	 Did they experience long waits (1 hour or more) in their primary HMO 

doctors’ offices? 

AGENCY COMMENTS 

HCFA concurred with the report’s recommendations. They noted several projects 

underway by the OffIce of Managed Care (OMC) and other work groups addressing many 

of the recommendations. We applaud their efforts. We would emphasize the importance 

of conducting systematic and ongoing national surveys that are statistically sound and 

contain a sufficient representation from individual Medicare HMOS to obtain comparable, 

nonbiased data. 

... 

111 
TABLE OF CONTENTS 

PAGE


EXECUTIVE SUMMARY . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ..i


INTRODUCTION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ...1


FINDINGS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ...5


Disenrollment Rates asaPerformance Indicator . . . . . . . . . . . . . . . . . . . . . 5


HMO Disenrollment Patterns. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ...7


Beneficiary Disenrollment Patterns . . . . . . . . . . . . . . . . . . . . . . . . . . ...7


RECOMMENDATIONS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10


AGENCY CO MMENTs .....a.............. . . . . . . . . . . . . . . ...12


ENDNOTES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13


BIBLIOGRAPHY . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ..18


APPENDICES


A: Adjusted CY92-93Disenrollment Rates by Key Variables . . . . . . . . . . . A-1


B: Annualized CY91-92and CY92-93 Disenrollment Rates for NewHMOs . . . B-1


c: CY92-93Disenrollment Rates Adjusted for Administrative Disenrollees . . . C-1


D: CY91-92and CY92-93Disenrollment RatesbyHMO . . . . . . . . . . . . . D-1


E: Analysis ofBeneficiaries More Likely toDisenroll. . . . . . . . . . . . . . . . .E-l


F: Key Linear Regression Variables and Results . . . . . . . . . . . . . . . . . . . F-1


G: Agency Comments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . G-1

INTRODUCTION 

PURPOSE 

To determine the usefulness of HMO disenrollment rates as performance indicators for 

Medicare risk HMOS, in light of our recent beneficiary survey data. 

BACKGROUND 

As of February 1995, the Health Care Financing Administration (HCFA) reported 157 

Medicare risk HMO plans served approximately 2.4 milLion beneficiaries, an increase of 

70 contracts in just two years. The rapidly increasing participation of Medicare and 

Medicaid beneficiaries in managed care has heightened the need to fmd valid measures 

and performance indicators for HMOS. Two measures which have been considered by 

researchers and policy analysts are HMO disenrollment ratesl and direct surveys of HMO 

members. 

Prior research on disenrolhnent rates and patterns for Medicare risk HMOS has generally 

been retrospective and limited to disenrollee data. Using this approach, they found 

disenrollment rates were lower for plans that were nonprofit, not federally qualifkd, and 

group models (Brown et. al. 1986), and higher for plans that were networldmixed models, 

for-profit, lacking a chain affiliation, and having fewer than 1,000 or more than 10,000 

Medicare beneficiaries enrolled (HCFA 1990). Reasons for disenrollment were discontent 

with physicians, often caused by poorly established relationships with them; financial 

constraints, such as high premiums; and HMO pmceduml constraints, such as needing a 

primary HMO doctor’s referral for other services (Porell et al. 1992; Rossiter et al. 1988, 

1989; Ward, R.A. 198’7). Studies exploring the geneml characteristics of disenrollees 

have found that those reporting access problems were more likely to have Mdlcare 

entitlement through disability, reported fair or poor health status, had out-of-plan use, had 

either very high or no HMO physician/specialist use, and disenrolled within three months 

of enrollment (Porrell et al. i992). 

One study on Medicare risk HMOS, which used demographic information for both 

enrollees and disenrollees, concentrated on beneficiary satisfaction and how this related to 

disenrollment from social/HMOs2 @rrington et al. 1993). Disenrollees, compared to 

enrollees, were more likely to not live alone, have some other type of health insurance or 

Medicaid, have an employer who pays for the insurance, and report having inadequate 

information when they joined the HMOS. Disenrollees were also more likely to have 

better health status, have no impairments in activities of daily living, and have required no 

hospital or chronic care. 

1

continuity and consistency with our frost report, we separated enrollees and disenrollees 

for most HMO-level data analysis. Also, since our individual-level data was a 

disproportionate sample of enrollees and disenrollees, we had to account for varying 

response rates per HMO. Specitlcally, enrollees were distributed among all 45 HMOS, 

but disenrollee representation was limited. Four HMOS had less than 20 of 50 

disenrollees return their survey. Thus, for disenrollees only, these 4 HMOS were dropped 

from fimtber analysis. Our intent was not to generalize our findings to all Medicare risk 

HMOS but to use our data to detect trends within our own sample, especially to illustrate 

the utility of HMO-level analyses. 

Regression Analysis of Data 

We analyzed the data at the beneficiary-level and at the HMO-level, using both linear and 

logistic regression models. Due to the continuous nature of the dependent variables (e.g. 

HMO disenrollmefit rates), linear regression models were used for any data aggregated at 

the HMO-level. Logisitic regression models were used for the beneficiary-level data 

because the dependent variable in this analysis was binary, either a one (1) if the event 

occurred or a zero (0) if otherwise. 

b Linear remwsion analyses - Beneficiary responses from key survey questions were 

linked to their respective HMOS and became variables used in all regression analyses, 

along with structural factors for each HMO. Except for some structural factors, all 

variables were continuous, measuring the percentage of beneficiaries reporting a particular 

occurrence within each HMO. Only the dependent variable measuring HMO 

disenrollment rates is presented as a finding in the main body of this report. Several 

additional analyses were conducted but are only referenced as an endnote or included in 

the Appendix material. These analyses included models with dependent variables 

measuring the percentage of HMO beneficiaries who want to leave but cannot, and those 

who actually lefi within one year of our survey. 

Key survey questions, used to identify potential problems in our companion reports, are 

included for each HMO in our regression analysis and show the percent of beneficiaries: 

� receiving fee-for-service prior health care


� self-reporting serious health problemsg


� being asked inappropriate questions about health at application


� waiting an hour or more to see a primary HMO doctor


� perceiving declining health as a result of HMO care


� saying their complaints were not taken seriously by a primary HMO doctor


� seeking out-of-plan medical care while in the HMO


� reporting it was more important to their HMOS to hold down the cost of


medical care than to provide the best medical care possible 

� saying their primary HMO doctors did not provide Medicare services, 

admit them to the hospital, or refer them to a specialist when needed 

3

� Logistic remession analvsis - For the logisitic regression, all variables, except age, 

used individual-level beneficiary data coded as binary variables, i.e., either a 

characteristic existed (coded as 1) or otherwise (coded as O). The dependent variable, 

being a sampled disenrollee or otherwise (enrollee), measured the change in the referent 

category (disenrollee) produced by each independent variable (see Appendix E). 

Additional independent variables used in logisitic regression analysis include: 

� Medicare category: beneficiary was disabled or had end-stage renal disease 

(ESRD) 

� beneficiary’s age 

� non-competitive area: beneficiary lived in a county with no other Medicare 

risk HMO contract 

� propensity: beneficiary’s propensity to use medical services was low or 

medium 

� prior care: beneficiary was enrolled in an HMO before joining sampled 

HMo 

� health worsened: beneficiary reported health declined due to care provided 

by the sampled HMO 

This inspection was conducted in accordance with the Quality Standar& for Inspection 

issued by the President’s Council on Integrity and Eftlciency. 

4

FINDINGS 

DISENROLLMENT RATES AS A PERFORMANCE INDICATOR 

HMO disenrollment raies, once properly tijusted, may provide an early alert of possible 

problems among Medicare risk HMOS. 

HMOS with high disenrollment mtes could be targeted for follow-up analysis and further 

investigation by HCFA. However, disenrollment rates, like other performance indicators, 

should not be used as a sole measure of performance but are most helpful when used in 

conjunction with beneficiary survey data. 

HMOS wilh higher disenrollment rates had more enrollees who reported service access 

problems. 

Enrollees who said they experienced poor service, whose complaints were not taken 

seriously, and who were in for-profit HMOS, were more likely to come from HMOS with 

higher disenrollment rates.9 These 3 factors helped explain much of the variation in our 

HMOS’ Calendar Year 1992-93 disenrollment ratesl”, even after controlling for such 

structural characteristics as HMO type and enrollment size (see Appendix A and Appendix 

F, Tables 1 and 5).:1 

Poor service 

�	 Poor service (e.g. primary HMO doctor did @ provide Medicare services, admit 

to the hospital, or refer to a specialist when needed) had the largest impact on 

disenrollrnent rates. 12 HMOS with the 5 highest disenrollment rates were 1.5 

times more likely to have beneficiaries who reported poor service (18 percent vs. 

12 percent). 13 

Complaints not taken seriouslv 

�	 The perception that a primary HMO doctor did @ take a beneficiary’s complaints 

seriously strongly affects disenrollment rates. 14’15The 3 HMOS with the highest 

disenrollment rates also have the highest percentages of beneficiaries reporting 

their complaints were not taken seriously (see Appendix A). 

Profit status 

�	 Profit status also significantly influences fi.dnre disenrollrnent rates. Four of the 5 

HMOS with the highest disenrollment rates were for profit HMOS. 

5

HMO disenrollment ties need adjustment to more accurately reflect trends within 

HMOS. 

HMO disenrollment rates, in their raw form, do not accurately reflect beneficiary 

disenrollment patterns. They may overstate or understate the true rate. For example, 

limited GHP information regarding beneficiaries’ reasons for disenrollment would 

overstate the true disenrollment rate. 16 To correct some of these problems, adjustments 

are needed before using HMO disenrollment rates. 

Annualize HMO disenrollment rates. 

We found the length of the HMO contract sigtilcantly influenced large changes in 

disenrollment rates over time (see Appendix F, Tables 1 and 2). For the time periods we 

measured, HMOS with risk contracts less than two years old experienced enormous 

increases in disenrolhnent rates. These HMOS had disenrollrnent rates that were 

understated because not enough time had elapsed to fully measure their disenrollment 

rates. However, after annualizing disenrolhnent rates for our 10 newer HMOS, these 

increases were less dramatic’ (see Appendix B). 

Account for administrative disenrolhnents. 

We found administrative disenrollments overstate true HMO disenrollment rates. Almost 

one-third of disenrollees surveyed left solely for administrative reasons, such as moving, 

procedural errors, or an HMO no longer participating in the Medicare risk HMO progmm 

or in a company’s retirement plan. Adjusting for administmtive disenrcdlments resulted in 

substantial decreases in disenrollment rates for several HMOS; 17 HMOS experienced a 

drop of 33 percent or more, one declined 71 percent. The average change in 

disenrollment rate, after adjusting for administrative disenrolkxs, was a decrease of 32 

percent17 (see Appendix C). 

Using disenrollment rates without feedback on why beneficiaries left can be very 

misleading. As mentioned, sometimes beneficiaries have only moved or were disenrolled 

in error. In a more dramatic example, 1 HMO experienced a 725 percent increase in its 

disenrollment rate from Calendar Years 1991-92 to Calendar Years 1992-93, with a rise 

from 4 to 33 percent. One year after our survey, 74 percent of surveyed beneficiaries lefl 

this HMO. Interestingly, our survey feedback for this HMO showed w widespread 

beneficiary discontent. Upon further examination, we found this increase resulted from 

the HMO splitting into 2 HMOS. Beneficiaries were administratively transferred from one 

HMO risk contract to the new risk contract operated by the same company .18 

* Annualizeddisenrollmentrates were used in the analysis on page 5 and throughoutthe remainder of 

the report, allowing for comparisonsof older and newer HMO risk contracts. 

6 
Recomize disenrollment rates will not ca@ure information from those enrollees who want 

to leave but cannot. 

Enrollees wanting to leave their HMOS, but saying they cannot, comprise an unmeasured 

group in any assessment of HMO disenrollment rates, thus, understating the true rate. 

Ten percent of surveyed enrollees wanted to leave but could not, primarily because they 

thought they could not afford to leave. One HMO had 26 percent of its beneficiaries 

reporting this situation+ Since only 9 percent of enrollees wanting to leave actually left 

within one year after our survey, many of this group’s concerns will not be measured by 

future HMO disenrollment rates. 19 

HMO DISENROLLMENT PATTERNS 

HiWOs with more experience in the Medicare risk HMO program experienced the largest 

decreases in their disenrollment &es over time. 

Although disenrollment rates averaged a 6 percent increase over the two time periods 

measured, individual HMO disenrollment rates fluctuated rather widely, with an average 

26 percent change in absolute value. 20 HMOS with older risk contracts experienced the 

largest decreases in their disenrollment rates. The largest decrease, 62 percent, occurred 

in an HMO which began its risk contract in 1987. The largest increase in disenrollment 

rate, 150 percent after annualizing, occurred in an HMO which began its risk contract in 

1992 (see Appendix D). 

BENEFICIARY DISENROLLMENT PATTERNS 

By using beneficiary survey data responses we were able to determine: 1) which 

beneficiaries were more likely to be an enrollee or a non-administrative disenrollee based 

on their answers to key survey questions (see Apyendix E) and 2) beneficiary enrollment 

patterns before joining and after leaving a sample HMO. 

Beneficiaries who are more likely to disenroll tend to repoti declining hedh due to 

HMO care received, being disabled/ESRD, being in HMOS perceived as @“vingmore 

impotiance to holding down the cost of care, and experiencing long waits in a primary 

HMO doctor’s office 

In this section, beneficiary responses were analyzed as sampled (e.g. enrolled or 

disenrolled), and not in reference to their disenrollment status after we surveyed them. 

Beneficiaries health worsened 

�	 Beneficiaries saying their health got worse as a result of the care they received 

from their HMO were 5 times more likely to disenroll.21 This factor was the 

strongest predictor of disenrollment status. 

7

Disabled/ESRD beneficiary 

�	 Disabled/BSRD beneficiaries were 3.5 times more likely to disenroll.zz This 

group reported more access and service problems compared to aged 

beneficiaries. 23 

Holdimz down costs perceived as more imDortant to HMO than zivin~ the best medical 

care Dossible 

�	 Beneficiaries perceiving their HMOS give more importance to holding down the 

cost of care were 3.4 times more likely to disenroll.x 

Long waits in primarv HMO doctor’s office 

�	 Long waits in their primary HMO doctor’s office made beneficiaries 2.8 times 

more likely to dlsenroll. 

Beneficiaries leaving Medicare risk HMOS ofien re-enroll immediately, or shortly 

thereafter, in another HMO. 

Beneficiaries switch frequently between fee-for-service and HMOS after they disenroll. 

However, this disenrollment ofien does not mean beneficiaries leave HMOS for long 

periods of time. Enrollment patterns among Medicare risk HMOS suggest market factors 

(e.g. expensive premiums/co-payments) strongly influence beneficiary disenrollment. 

However, survey data also show perceived service access problems are a major influence 

on whether beneficiaries disenroll from their HMO. 

Previous HMO exwrience: 

Of the 31 percent of beneficiaries with prior HMO experience, 69 percent went directly 

from the prior HMO to our sample HMO, while 31 percent went into fee-for-service. 

This latter group received care them for an average of 25 months.25 Seven percent of 

beneficiaries left an HMO in 1 state and enrolled in a new HMO in another state. 

Nine percent of beneficiaries disenrolled from their previous HMO, where they averaged 

14 months of care, and received fee-for-service care for just over one year. These 

beneficiaries then re-enrolled in the same HMO, receiving an average of 23 months of 

care. 

8

!Mnmle HMO ex~erience: 

Beneficiaries stayed in our sample HMOS an average of 26 months: 18 percent were


enrolled between 1 and 3 months; 22 percent, four months to 1 year; 19 percent, at least


1 year; and 41 percent, more than 2 years. Of the beneficiaries who were surveyed as


disenrollees, the following were some of the most frequent reasons for leaving:


� 31 percent said their premium/co-payments were too expensive


� 30 percent did not like their choice of doctors


� 25 percent moved


� 25 percent wanted to use the doctor they had prior to this HMO


24 percent did not like going through their primary HMO doctor for other services 

Of the 5 percent who said they planned to leave their HMO, ahnost half (47 percent) 

actually left within me year. Over one-third who planned an administrative deparhme 

(i.e. moving), in fact, left their HMO. Thus, information on beneficiaries planning to 

leave may provide powefil insight for predicting future beneficiary disenrollment. 

Post HMO ex~erience: 

Sixty-five percent of beneficiaries went directly back into fee-for-service after disenrolling 

from their HMOS.2GJ27However, 18 percent of this group stayed in fee-for-service an 

average of 5 months and then returned to an HMO. Therefore, nearly half of 

beneficiaries leaving their HMO, either initially or eventually, enrolled in an Hl 10. Ten 

percent of these beneficiaries enrolled in a new HMO in another state. Another 12 

percent of beneficiaries switched back to the same HMO they were in prior to joining our 

sampled HMo. 

Twelve percent of beneficiaries received care through fee-for-service for an average of 5 

months before m-enrolling in the same HMO in which they were sampled. Their reasons 

for leaving include: 1) moving; 2) wanting to use the doctor they had prior to this HMO; 

3) not liking their choice of doctors; and 4) the premium/co-payment being too costly. 

9


c 
RECOMMENDATIONS 

HMOdisenrollrnent rates, inconjunction with beneficia.ry survey data, appear to be useful


HMO performance indicators. High disenrollment rates or frequent reports of service


access problems should be a warning to HCFA of a potentially problematic HMO.


Overall, we recommend that HCFA use systematically developed HMO disenrollment


rates and beneficiary survey data to improve its monitoring activities. We believe this is a


particularly important step in light of the anticipated rapid growth of the Medicare risk


HMO progmm. We specifically recommend that HCFA:


USE DISENROLLMENT DATA 

�	 Track disenrollment rules over time to detect potential problems among HMOS. 

HMOS experiencing larger than average increases in disenrollment rates should be 

monitored more closely. 

�	 Use tijusted disenrollment rates, along with other available HMO information 

(e.g. beneficiary complaints and appeals lodged against HMOS) to target reviews of 

HMOS. Adjustments must include: 

�	 Annualizing rates for Medicare risk HMO contracts less than 2 years old to 

more accurately measure newer HMOS’ disenrolhnent activity. Waiting to 

evaluate HMOS until they become fidly operational might jeopardize the 

health of and service to Medicare beneficiaries, especially since nearly half 

of risk contracts are less than 2 years old. Alternative methods, such as 

annualizing, will allow HCFA and other researchers to keep abreast of 

potential problems of all HMOS and not just the HMOS with older 

contracts. 

�	 Excluding administrative disenrollees, which overstate disenrollment rates 

due to such factors as enrollees moving or HMO plan discontinuation. 

�	 Conduct disenrollment surveys that fully capture all the beneficiary’s reasons for 

leaving the Medicare risk HMO. This information should be included in the GHP 

data fdes. 

USE BENEFICIARY SURVEY DATA 

As discussed in previous OIG reports providing results of our beneficiary survey data, this 

information can be useful in a number of different ways. We emphasize here two uses: 

supplementing disenrollment data to assess HMO performance, and pinpointing problem 

areas that might lead beneficiaries to disenroll. 

10

�	 Survey enrollees systemah”cally and routinely on key questions and on their desire 

to leave/remain with an HMO, to complement disenrollment data. Such survey 

data might be captured nationally, to assist in targeting HMOS for in-depth 

reviews, or in the KMOS which have been targeted as part of the in-depth review 

itself. 

�	 Monitor Medicare risk HMOS with high disenrollment nrtes and repotied service 

access problems and work with HMOS to respond to the needs of beneficiaries at 

risk of disenrolling. This should include activities that: 1) give more attention to 

the care delivered to disabled/ESRD beneficiaries; 2) reduce the waiting times in 

primary HMO doctor ofilces; and 3) address HMO practices that cause 

beneficiaries to report declining health status as a result of their HMO care and 

their sense that the HMO gives too much priority to holding down costs versus 

giving the best care. 

USE KEY QUESTIONS 

Several key questions successfully predicted future disenrollment and HMO disenrollment 

rates, along with beneficiaries who wanted to leave but felt they could not. 

F	 The questions we found most predictive of beneficiaries’ fidure disenrollment 

included: 

� Were complaints taken seriously by their HMO doctors? 

�	 Did their p~imary HMO doctors provide Medicare services, admit them to 

the hospital, or refer them to a specialist when needed? 

*	 Did they perceive their HMOS as giving too high a priority to holding down 

the cost of medical care compared to giving the best medical care? 

�	 Did they perceive their health got worse as a result of the medical care they 

received in their HMO? 

�	 Did they experience long waits (1 hour or more) in their primary HMO 

doctors’ offices? 

11

AGENCY COMMENTS 

We received comments from the Health Care Financing Administration (HCFA). The full 

text of their comments is in Appendix G. 

The HCFA concurred with all of the report’s recommendations. HCFA noted that the 

Office of Managed Care (OMC) has developed a plan profde report which incorpomtes a 

number of data elements on disenrollrnent. These profdes are reviewed on an ongoing 

basis by OMC’S Operations and Oversight Team. HCFA reports it is currently 

implementing a new disenrollment form to capture signiilcant data regarding motivations 

for disenrollment. With the addition of this information, we believe that HCFA should be 

able to adjust HMO disenrollment rates by accounting for administrative disenrollments. 

As it pursues this initiative, we suggest that HCFA: 1) attempt to capture all the 

beneficiaries’ significant reasons for leaving the Medicare risk HMO; 2) include 

disenrollment information in the GHP data files so that researchers can properly adjust 

disenrollment rates when studying Medicare risk HMOS; and 3) annualize disenrollment 

rates to improve comparability of newer HMO contracts with older ones. 

A work group within HCFA is also currently developing a strategy on beneficiary 

satisfaction surveys. We applaud their efforts. Due to the difficulties involved in 

measuring satisfaction, we urge that the survey focus more on measuring enrollee service 

access than on beneficiary satisfaction. We would emphasize the importance of 

conducting systematic and ongoing national surveys that are statistically sound and contain 

a sufficient representation from individual Medicare HMOS to obtain comparable, 

nonbiased data. These surveys should ask key questions that we know are predictive of 

future disenrollment and also focus on service access for disabled/ESRD beneficiaries. 

The results of these surveys should be included in the HMO plan profde report and used 

proactively by HCFA to detect potential problems among HMOS. 

12

EN DNOTES


1.	 Several researchers have suggested Medicare risk HMOS should be required to 

disclose disenrolltnent rates to serve as a proxy for such things as enrollment and 

service access problems (Prasad and Javalgi 1992; Porrell et al. 1992). 

2.	 Social/Health Maintenance Organizations offer expanded benefits in addition to 

those normally offered by Medicare risk HMOS, such as outpatient prescription 

drugs, eyeglasses, and/or chronic care benefits. In return for these expanded 

benefits, beneficiaries am charged premiums. 

3.	 We did not speciilcally ask beneficiaries about their satisfaction with the HMOS, as 

the concept of satisfaction is less objective than, and sometimes independent of, the 

issues of membership in a Medicare risk HMO. 

4.	 Since our primary focus was Medicare beneficiaries’ perceptions, we collected 

information from them and did not contact HMOS or their staffs, nor did we 

attempt to assess the quality or propriety of medical care rendered by the HMOS to 

these beneficiaries. 

5.	 We selected a stratifkxl random sample from HCFA’S Group Health Plan (GHP) 

data base. First, we sampled 45 HMOS from the 87 HMOS under a risk contract 

with HCFA as of February 1993. Beginning with the GHP data, we counted the 

number of enrollments occurring within calendar years 1991 and 1992. For this 

cohort, we then calculated the proportion of disenrollments within the following 12 

months. Based on this disenrollment rate, we divided the 87 risk HMOS into three 

strata of 29 HMOS each. Within each strata, we selected 15 HMOS by simple 

random sampling. Second, from each sampled HMO, we randomly selected 50 

Medicare beneficiaries who were enrolled as of February 28, 1993 and 50 who had 

disenrolled between November 1992 and February 1993 inclusive. When the total 

number per HMO for either group was less than 50, we selected them all. Using 

HCFA’s Enrollment Data Base, we excluded, from the sampling universe, 

beneficiaries who had died or who appeared as current enrollees, but had actually 

disenrolled since the last update to the GHP fde. This process resulted in 2,217 

enrollees and 1,915 disenrollees for a total of 4,132 beneficiaries. A total of 2882 

surveys were deemed usable, yielding an unweighed return rate of 70 % overall, 

77% for enrollees (N= 1,705) and 61% for disenrollees (N= 1, 17’7). 

6.	 See “Beneficiary Perspectives of Medicare Risk HMOS, ” OEI-06-9 1-00730 and 

“Medicare Risk HMOS: Beneficiary Enrollment and Service Access Problems, ” 

OEI-06-91-00731 . 

13

7. 

8. 

9. 

10. 

11. 

12. 

13. 

14. 

HMO model types are divided into three categories. Group models contract with 

independent, multispecialty physician groups. Individual practice associations 

(IPAs) contract with independent physicians or small, single specialty physician 

groups who also maintain private practices co-jointly with their HMO contract. 

Staff models directly employ salaried physicians to serve patients. 

Beneficiaries with serious health problems had one or more illnesses, e.g., heart 

attack, cancer, or pneumonia. 

As found in past surveys, disenrollees are consistently more dissatisfied than 

enrolled beneficiaries (OIG/OEI 1994; Barrington 1993). We found survey data of 

enrollees more powerfully differentiates problematic HMOS. We were able to 

explain 20 percent more of the variation in disenrolhnent rates for CY92-93 by 

using the experiences of just enrollees. 

HMO disenrollment rates for 1992-93 were adjusted as discussed on page — 6 to

more accurately reflect each HMO’s true dlse~ollment rate. 

Table 1 below shows the sharp contrast between the average percentage of 

beneficiaries reporting service problems at the 5 HMOS with the highest 

disenrolhnent rates compared to other surveyed HMOS. 

Table 1: Comparisonof HMOSWith Top 5 Dknrolhnent Rates Compared 

To AU Other Surveyed HMOs by Key Variables 

Top 5 HMOSwith 

Highest Disenrollment Other Surveyed 

Key Variables Rates HMOS 

Receivedpoor service 18.0% 12.1% 

Complaintsnot taken seriouslyby 

primary HMO doctor 21.4% 15.4% 

* Six HMOSwere excludedin the analysis of disenrolleesand disenrolhnentrates because 

three had less than 20 disenrokes of the 50 suweyedper HMO;2 HMOSdiscontinued 

theirHMOrisk contractsin 1993; and 1 HMO split into 2 risk HMOS,transferringmany 

beneficiaries. 

For every 1 percent increase in HMO beneficiaries reporting poor service, HMO 

disenrollment rates changed .68 percent. 

Poor service also signitlcantly influenced the percentage of beneficiaries who 

wanted to leave but could not (see Appendix F, Tables 1 & 3). 

Future HMO disenrolhnent rates increased .51 percent for every 1 percent increase 

in HMO beneficiaries’ complaints not being taken seriously. 

14

15.	 We also found beneficiaries whose complaints were not taken seriously significantly 

affected the percentages of beneficiaries leaving their HMO one year later (see 

Appendix F, Tables 1 & 6). 

16.	 HMO disenrolhnent rates have typically only reflected movement out of an HMO 

(#of disenrollees -+ # of new enrollees in the year prior) with no regard to such 

factors as administrative reasons for a beneficiary to leave (moving, company 

dropped the HMO) or the length of time the Medicare HMO risk contract has been 

operating. 

17.	 Table 2 below shows the change in overall HMO disrollment rates for CY91-92 

and CY92-93 after adjusting for administmtive disenrollments. Four HMO’s 

CY92-93 disenrollment rates decreased more than 6 percent, while 23 HMOS 

decreased between 3 and 6 percent, and 12 HMOS had decreases less than three 

percent after adjusting for administmtive disenrollees. 

Table 2: Comparison of HMOS’ Unadjusted CY91-92 & CY92-93


Dkenrollment Rates with HMOS’ Adjusted Rates,


Excluding Administrative Dkenrollees”


Unadjusted Adjusted 

Rates Rates !%Change 

CY91-92 14.6% 10.6% -27% 

CY92-93 13.6% 9.8% -28 % II 

* SIXHMOSwere excludedin the analysis of disenrolleesand disenrollmentrates because 

three had less than 20 disenrolleesof the 50 surveyedper HMO; 2 HMOSdiscontinued 

their HMO risk contractsin 1993; and 1 HMO split into 2 risk HMOS,transferring many 

beneficiaries. 

18.	 This HMO was excluded from all i%ture analyses of disenrolhnent rates or the 

percent of beneficiaries leaving their HMO. 

19.	 Research has shown beneficiaries respond to dissatisfaction by either “exiting” or 

through complaints, or “voice” (Wrrington 1993; Hirschman 1970). Beneficiaries 

with financial constraints or without other insurance options are often forced to 

remain in their HMO because they lack alternatives. 

20.	 We excluded 3 HMOS from our analysis because 2 HMOS discontinued their HMO 

contracts in 1993 and 1 split into 2 Medicare risk HMOS. 

21.	 Beneficiaries’ health getting worse is ne~ativel~ correlated at the .86 level with the 

variable measuring poor service. This relationship suggests some beneficiaries are 

provided Medicare services and referrals to specialist care but stiLlfeel their health 

worsened due to the care they received in their HMO. Why poor service did not 

significantly predict disenrollment status may be explained by the fact that 

15 
variables correlated in excess of .80 tend to weaken the effect of one or both 

variables. 

In addition, beneficiaries’ health getting worse is positivelv correlated with 

beneficiary complaints not taken being seriously at the .71 level. This suggests 

there is a strong relationship between beneficiaries’ health getting worse and their 

complaints not being taken seriously by their primary HMO doctor. 

22.	 This finding is consistent with prior research reported in “Beneficiary Perspectives 

of Medicare Risk HMOS” (OEI 06-91-00730). 

23.	 Table 3 below shows the sharp differences in reported access and service problems 

of disabled/ESRD beneficiaries compared to aged beneficiaries. 

Table 3: Geneflciary Perspectives of Aged or Dkabled/ESRD* by Key Variables 

Key Variables Disenrollees Enrolled 

Aged Disabled/ESRD Aged Disabled/ESRD 

Primary HMO doctor did 38% 48% 11% 20% 

not tie healthcomplaints (7,892) (976) (104,185) (4,671) 

seriously. 

PrimaryHMOdoctor 20% 39% 3% 4% 

failedto provideMedicare (4,366) (823) (30,648) (1,285) 

servicesthat wereneeded. 

* 

The data presented in Table 3 was weighted to approximate 70% of the universe. A 

previous OEI report (OEI 06-91-00730)includes a completedescriptionof the weighting 

methodology. 

Disabled/FZSRD disenrollees also seem to be disproportionately represented among 

disenrollees. In the entire sample and among enrollees, the weighted proportion of 

disabled/ESRD beneficiaries is 3 percent. However, disabled/ESRD disenrollees 

account for 8 percent of all disenrollees, compared to only 3 percent of aged 

beneficiaries who are disenrolling. After analyzing HMOS that had at least 4 

disabled/ESRD beneficiaries, we found 8 HMOS had mom than 50 percent of their 

disabled/ESRD beneficiaries disenroI1. 

24.	 In additional analyses, we found beneficiaries who were in HMOS that found it 

more important to hold down the cost of care signifkantly affected the percentages 

of beneficiaries leaving their HMOS one year later (see Appendix F, Tables 1 & 

6). 

25.	 Prior HMO care includes various arrangements: 1) enrolling directly in our 

sampled HMO from another HMO; 2) enrolling directly from fee-for-service; and 

3)enrolling in an HMO, returning to fee-for-service, and then enrolling in our 

sampled HMo. 

16

26. SeeEndnote #10. 

27.	 One study of disenrolhnent patterns of Medicare risk beneficiaries found 

beneficiaries were less likely to enroll in another HMO if they had some type of 

“other insurance” (Barrington 1993). 

17

BIBLIOGRAPHY 

Brown, R. S. 1988. Biased Selection in the Medicare Competition Demonstrations. 

Princeton, N. J.: Mathematical Policy Research. 

Brown, R. S., K. Langwell, K. Berman, A. Ciemenecki, L. Nelson, A. Schreier, and A. 

Tucker. 1986. Enrollment and Disenrollment in Medicare Competition Demonstration 

Plans: A Descriptive Analysis. Mathematical Policy Research, under Health Care 

Financing Administration contract # 500-83-0047. 

Barrington, C., R.J. Newcomer, and S. Preston. 1993. “A Comparison of S/HMO 

Disenrollees and Continuing Members, ” Inquiry 30:429-440. 

Health Care Financing Administration. 1990. Disenrollment Experience in the Medicare 

HMO and (34P Risk Program: 1985 to 1988. Report to Congress, Department of Health 

and Human Services. 

Hirschman, A. O. 1970. Exit, Voice and Loyalty: Responses to Decline in Firms, 

Organizations and States. Cambridge, Mass.: Harvard University Press. 

OffIce of Inspector General, Department of Health. and Human Services. 1994. 

Beneficiary Perspectives of Medicare Risk HMOS. OEI-06-91-00730. 

Porrell, F., C. Cocotas, P. Perales, C. Tompkins, and M. Glavin. 1992. Factors 

Associated w“thDisenrollment j?om Medicare HMOS: Findings @om a survey of 

DisenroUees. Cooperative Agreement to the Health Policy Research Consortium of 

Brandeis University under contract # 99-C99256/l 1-06. 

Porrell, F. and C. Tompkins. 1993. “Medicare Risk Contracting: Identifying Factors 

Assoicated with Market Exit, ” Inquiry 30:157-169. 

Prasad, V.K. and J.G. Rajshekhar. 1992. “Understanding Needs and Concerns of the 

Elderly Regarding Medicare Health Maintenance Organizations, ” Z%eJourrud of 

Consumer A#airs 26:1:47-67. 

Rossiter, L., K. Langwell, T.H. Wan, and M. Rivnyak. 1989. “Patient Satisfaction 

Among Elderly Enrollees and Disenrollees in Medicare Health Maintenance 

Organizations: Results from the National Medicare Competition Evaluation, ” Journul of 

the American Medical Association 262:1:57-63. 

18

Rossiter, L., T. Wan, K. Langwell, J. Hadley, A. Tucker, M. Rivnyak, K. Sullivan, and 

J. Narcross. 1988. Nationul Medicare Competition Evaluation: An analysis of Patient 

Satisfaction for Enrollees and Disenrollees in Medicare Risk-Based Plans. Williamson 

Institute for Health Studies, Medical College of Virginia and Mathematical Policy 

Research, under contract # 500-83-0047. 

Ward, R.A. 1987. “HMO Satisfaction and Understanding Among Recent Medicare 

Enrollees, ” Journal of Health and Social Behuvior 24:325-337. 

19

APPENDIX A 

Table 1: AdjustedCY92-93D~enrolbnentRates By Key Survey 

(Enrollees onlv} 

Adjusted 

Disenrollment Rate Poor 

CY92-93 Service*** 

HMO Ownership** (Percent) (Poor) 

Group ~ 

#1 12.7 

#2 13.9 

#3 NS 

#4 25.7 

#5 16.5 

#6 18.2 

#7 NS 

Group B 

#8 2.2 

#9 6.4 

# 10 NA 

#11 NA 

# 12 NS 

Group C 

# 13 8.4 

# 14 7.4* 

# 15 7.6 

Group D 

# 16 6.5 

# 17 3.2 

Group E 

# 18 9.3 

# 19 4.1 

Group F 

# 20 6.8 

# 21 10.1 

Group G 

# 22 4.4 

# 23 5.0 

#24 I 4.9* 

# 25 I 8.0 

# 26 I 37.0* 

8.0 

6.9 

13.3 

11.8 

2.6 

13.8 

5.6 

2.8 

2.8 

2.9 

13.8 

3.1 

0.0 

0.0 

12.0 

13.2 

Variables 

ComplaintsNotTaken 

Seriously 

(Percent) 

7.7 

10.7 

20.7 

17.9 

15.0 

10.3 

19.0 

2.8 

8.1 

6.3 

16.1 

20.0 

6.3 

3.6 

3.8 

15.0 

5.9 I 8.1 

17.6 15.0 

6.7 I 16.1 

6.1 11.1 

8.1 I 17.1 

3.8 I 12.9 

3.7 I 11.1 

3.2 I 12.1 

11.8 I 23.8 

6.7 I 10.3 

23.5 I 21.7 

7.0 I 13.3 

6.1 8.6 

A-1

Table 1: 

HMO Ownership** 

# 30 

#31 

# 32 

# 33 

# 34 

# 35 

# 36 

# 37 

# 38 

# 39 

#40 

#41 

# 42 — 

# 43 

#44 

# 45 

* 

Adjusted CY92-93 Disenrollment Rates By Key Survey Variables 

(Enrollees only) 

Adjusted 

Disenrollment Rate Poor Complaints Not Taken 

CY92-93 Service*** Seriously 

(Percent) (Poor) (Percent) L 

5.9 5.1 7.5 

15.5 10.3 6.5 

20.2 12.5 7.7 

4.2 10.0 13.6 

1.9 5.6 5.7 

3.5 7.5 7.1 

10.2 10.3 13.8 

13.4 16.2 12.8 

15.0 10.8 18.4 

5.4 6.1 8.6 

8.1* 6.9 8.6 

NA 9.7 6.3 

4.2 6.1 5.6 

6.6 8.3 4.9 

3.4 7.9 7.9 

2.3 9.7 25.0 

\ 

Annualized diaemolhnent rate based on the number of disenrollees for the period of time the HMO risk 

contract existed. This technique allows for comparison among newer and more established HMOS. 

** 

HMOS are grouped together if they are owned by the same parent company. 

*** 

Primary HMO doctor did not provide Medicare services, admit to the hospital, or refer to a specialist when 

needed. 

NS	 Not Sufficient Data - Five HMOS were excluded in the analysis of disemollees and disenrollment rates because 

less than 20 of the 50 disenrollees returned their survey. 

NA	 Not Applicable - Three HMOS were not included in this analysis, H0616 and H0617 discontinued their risk 

contract in 1993 and H9049 split into two risk HMOS, transferring many beneficiaries into H3856. 

A-2

Table 2: Top 5 Adjusted CY92-93 Disenrollment Rates by Key Survey Variables 

Adjusted 

Disenrollment Poor Complaints Not Taken 

Rate CY92-93 Service** Seriously 

HMO Ownership (Percent) (Percent) (Percent) 

# 26 37.0* 11.8 23.8 

# 28 28.1 23.5 21.7 

#4 25.7 11.8 17.9 

# 32 20.2 12.5 7.7 

#6 18.2 13.8 10.3 

* 

Annualized disemollment rate based on the number of disemollees for the period of time the HMO risk 

contract existed. This technique allows for comparison among newer and more established HMOS. 

** 

Primary HMO doctor did not provide Medicare services, admit to the hospital, or refer to a specialist when 

needed. 

A-3

APPENDIX B 

Table 1 : Unannualiied and Annualized CY91 -92 & CY92-93 Disenrollment Rates 

For the 10 HMOS with Risk Contracts for Less Than Two Years 

Unannualiied Rates Annualized Rates 

Month/Year 

Risk HMO CY91-92 CY92-93 % CY91 -92 CY92-93 % 

Contract Rate Rate Change Rate Rate Change 

HMO Began (Percent) (Percent) in Rate (Percent) (Percent) in Rate 

# 24 5192 3.0 6.0 100.0 9.0 7.2 -20.0 

# 26 6{92 10.7 31.0 189.7 36.7 39.2 6.8 

# 27 10/92 0.5 4.9 880.0 4.0 7,9 95.0 

# 32 11/91 20.0 26.5 32.5 34.3 26.5 -22.7 

# 33 8/91 4.4 5.3 20.5 6.2 5.3 -14.5 

# 14 9/92 0.8 8.0 900.0 4.8 12.0 150.0 

# 12 4/92 5.6 13.1 133.9 14.9 15.0 0.7 

# 40 4/92 6.5 10.4 60.0 17.3 11.9 -31.2 

# 22 2/9 1 

# 43 419 ‘ 

mm -::,::2: 1:: l:; : 

B-1

APPENDIX C 

Table 1: CY92-93 Dkenrollment Rates, Percent Administrative Disenrollment, 

and CY92-93 Adjusted Dsenrollment Rates by HMO 

Dkenrollment 

Rate 

CY92-93 

HMO Ownership** (Percent) 

Group A 

#1 20.1 

#2 19.8 

#3 29.6 

#4 30.0 

#5 17.6 

#6 24.3 

#7 22.6 

Group B 

#8 5.2 

#9 10.6 

# 10 NA 

#11 NA 

# 12 15.0* 

Group C 

# 13 12.2 

# 14 12.0* 

# 15 10.9 

Group D 

# 16 11.3 

# 17 7.7 

Group E 

# 18 11.9 

# 19 9.2 

Group F 

# 20 9.7 

# 21 13.6 

Group G 

# 22 5.5 

# 23 8.1 

#24 7.2* 

#25 12.8 

# 26 39.2* 

# 27 7.8* 

# 28 32.8 

# 29 16.3 

Percent 

Administrative 

Disenrolhnent 

37.0 

30.0 

NS 

14.3 

6.3 

25.0 

NS 

58.3 

39.3 

NS 

25.0 

NS 

31.0 

38.1 

30.8 

42.9 

58.3 

22.2 

56.0 

30.3 

25.8 

20.0 

38.9 

31.6 

37.5 

5.6 

18.5 

14.3 

50.0 

Adjusted 

Disenrollment 

Rate % Decrease in 

CY92-93 CY92-93 

(Percent) Rate 

12.7 36.8 

13.9 29.8 

NS NS 

25.7 14.3 

16.5 6.3 

18.2 25.1 

NS NS 

2.2 57.7 

6.4 39.6 

NA NA 

NA NA 

NS NS 

8.4 31.1 

7.4* 38.3 

7.6 30.3 

6.5 42.5 

3.2 58.4 

9.3 21.8 

4.1 55.4 

6.8 29.9 

10.1 25.7 

4.4 20.0 

5.0 38.3 

4.9* 31.9 

8.0 37.5 

37.0* 5.6 

6.4* 17.9 

28.1 14.3 

8.2 49.7 

c-1 
Table 1: CY92-93 DisenroUment Rates, Percent Adminiitrative Disenrollment, 

and CY92-93 Adjusted Disenrollment Rates by HMO 

Adjusted 

DEenrollment DBenroUment 

Rate Percent Rate % Darease in 

CY92-93 Administrative CY92-93 CY92-93 

HMO Ownership** (Percent) Disenrollment (Percent) Rate 

# 30 7.1 16.7 5.9 16.9 

#31 20.2 23.1 15.5 23.3 

# 32 26.5 23.8 20.2 23.8 

# 33 5.3 20.0 4.2 20.8 

# 34 6.6 71.9 1.9 71.2 

# 35 3.5 0.0 3.5 0.0 

# 36 15.7 34.8 10.2 35.0 

# 37 15.9 16.0 13.4 15.7 

# 38 18.4 18.5 15.0 18.5 

# 39 10.5 48.3 5.4 48.6 

# 40 11.9* 32.0 8.1* 31.9 

# 41 NA NA NA NA 

# 42 9.4 55.2 4.2 55.3 

# 43 9.9 33.3 6.6 33.3 

# 44 6.6 48.6 3.4 48.5 

# 45 5.1 54.8 2.3 54.9 

Average % Change 32.3 

* 

Annualized diseorolhnent rate based on the number of disenrollees for the period of time the HMO risk 

contract existed. This technique allows for comparison among newer and more established HMOS. 

** 

HMOS are grouped together if they are owned by the same parent company. 

NS	 Not Sufficient Data - Five HMOS were excluded in the analysis of disenrolkes and disenrollment rates because 

less than 20 of the 50 disenrollees returned their survey. 

NA	 Not Applicable - Three HMOS were not incIuded in this analysis: H0616 and H0617 discontinued their risk 

contract in 1993 and H9049 split into two risk HMOS, transferring many beneficiaries into H3856, 

c-2

APPENDIX D	

Table 1: 

HMO 

Ownership** 

Group A


#1


#2


#3


#4


#5


#6


#7


Group B


#8


#9


# 10


#11 

# 12 

Group C 

# 13 

# 14 

# 15 

Group D 

# 16 

# 17 

Group E 

# 18 

# 19 

Group F 

# 20 

# 21 

Group G 

# 22 

# 23 

# 24 

# 25 

# 26 

#27 

# 28 

#29 

# 30 

Unadjusted CY91-92 

Month/Year 

Risk HMO 

Contract 

Began 

6/88 

4185 

7185 

12/87 

1/90 

1/86 

6188 

10/85 

7185 

8/88 

8188 

4192 

4{86 

9192 

4186 

1/88 

1187 

8186 

2186 

10I85 

7185 

2191 

1/88 

5192 

6/86 

6192 

10/92 

4/85 

1/86 

7186 

Disenrollment Rates 

CY91-92 

Disenrollment 

Rate 

(Percent) 

21.8 

18.8 

21.0 

47.5 

10.6 

28.4 

29.3 

5.9 

11.0 

5.4 

5.4 

14.9* 

10.1 

4.8* 

8.8 

9.0 

9.2 

10.8 

7.1 

22.9 

14.5 

6.2* 

8.7 

9.0* 

11.3 

36.7* 

4.0* 

29.5 

15.3 

9.7 

D-1 

& ‘92-’93 Disenrollment Rates by HMO 

CY92-93 

Disenrollment % Change in 

Rate Dkenrollment 

(Percent) Rate 

20.1 7.8 

19.8 + 5.3 

29.6 + 41.0 

30.0 36.8 

17.6 + 66.0 

24.3 14.4 

22.6 22.9 

5.2 11.9 

10.6 3.6 

NA NA 

NA NA 

15.0* + 0.7* 

12.2 + 20.8 

12.0* + 150.0* 

10.9 + 23.9 

11.3 + 25.6 

7.7 16.3 

11.9 + 10.2 

9.2 + 29.6 

9.7 57.6 

13.6 6.2 

5.5 11.3* 

8.1 6.9 

7.2* 20.0* 

12.8 + 13.3 

39.2* + 6.8* 

7.8* + 95.0* 

32.8 + 11.2 

16.3 + 6.5 

7.1 26.8 I 
Table 1: Unadjusted CY91-92 DisenrolJment Rates & ‘92-’93 Dfienrollment Rates by HMO 

Month/Year CY91-92 CY92-93 

Risk HMO Disenrollment Disenrollment % Change in 

HMO Contract Rate Rate Duenrollment 

Ownership** Began (Percent) (Percent) Rate 

#31 4/85 18.4 20.2 + 9.8 

# 32 11/91 34.3* 26.5 22.7* 

# 33 8/9 1 6.2* 5.3 14.5* 

# 34 7/85 5.6 6.6 + 17.9 

# 35 6/86 5.5 3.5 36.4 

# 36 1/87 41.8 15.7 62.4 

# 37 3/90 16.1 15.9 1.2 

# 38 7185 10.6 18.4 6.: 

# 39 818-? 9.9 10.5 + 6.1 

#40 4[92 17.3* 11 .9* 31 .2* 

# 41 3188 3.5 NA NA 

# 42 11/85 7.1 9.4 + 32.4 

# 43 4191 6.4* 9.9 + 54.7* 

# 44 11/87 4.9 6.6 + 34.7 

#45 1/89 5.0 5.1 2.0 

Average % Change 6.0% 

Absolute % Change 26.0% 

* 

Annualized disenrolhnent rate based on the number of disenrollees for the period of time the HMO risk 

contract existed. This technique allows for comparison among newer and more established HMOS. 

** 

HMOS are grouped together if they are owned by the same parent company. 

NA Not Applicable - Three HMOS were not included in this analysis, H0616 and H0617 discontinued their risk 

contract in 1993 and H9049 split into two risk HMOS, transferring many beneficiaries into H3856. 

D-2

Table 2: Top 5 HMOS with the Largest Percent DECREASE in the 

Unadjusted Disenrollment Rate From CY91-92 to CY92-93 

Month/Year CY91-92 CY92-93 

Risk HMO DisenroUment DisenroUmentRa % Change in 

Contract Rate te DisenroUment 

HMO Ownership Began (Percent) (Percent) Rate 

# 36 1/87 41.8 15.7 - 62.4 

# 20 10/85 22.9 9.7 - 57.6 

#4 i2187 47.5 30.0 - 36.8 

# 35 6/86 5.5 3.5 - 36.4 

# 40 4/92 17.3* 11 .9* - 31.2* 

Table 3: Top 5 HMOS with the Largest Percent lNCREASE in the 

Unadjusted DisenroUment Rate From CY91-92 to CY92-93 

MonthNear CY91-92 CY92-93 

Risk HMO DisenroUment DisenroUment % Change in 

Contract Rate Rate DMenroUment 

HMO Ownership Began (Percent) (Percent) Rate 

# 14 I 9192 4.8* 12.0* + 150.0* 

# 27 10/92 4.0* 7.8* + 95.0* 

#5 1/90 10.6 17.6 + 66.0 

# 43 4/9 1 6.4* 9.9* 4- 54.7* 

#3 7185 21.0 29.6 + 41.0 

* 

Annualized disenroilment rate based on the number of disenroUees for the period of time the HMO risk 

contract existed. This technique allows for comparison among newer and more established HMOS. 

D-3

APPENDIX E 

ANALYSIS OF BENEFICIARY DISENROLLMENT STATUS 

Introduction 

This logistic regression model measures the eff~l of beneficiaries’ negative HMO 

experiences on the likelihood of disenrollment, even after controlling for a number of 

demographic characteristics, such as age, sex, and health status. 

Model Construction 

Methods 

The variables created for this anlaysis are binary (except age), characterizing beneficiaries 

with either a one (1) if the characteristic existed or a zero (0) if otherwise (see Table 1). 

For this regression model, beneficiaries or events coded as zero represent the referent 

category. The dependent variable estimated in this model, disenrollment status, measured 

the change from the referent category produced by each independent variable. 

This linear logistic model was created using SUDAAN to compute the correct standard 

errors, based on weighted datab. This model allows us to estimate the probability of a 

beneficiary disenrolling ~rom their HMO or remaining enrollefl, (p) depending on the 

model, based on the linear combination of independent variables. That is, 

where pi is the coefficient estimated by the equation, Xi is the value of the independent 

variable, and k is the number of independent variables in the equation. 

Using the logistic regression model, the interpretation of the coefficients, pi can be 

translated easier using the exponential of the coefficient, known as the odds ratio. The 

estimated odds ratio reflects the extent to which the refenmt category increases or 

decreases the odds that a beneficiary disenrolled from their HMO or not. An odds ratio 

of 5.0 for a particular independent variable, such as HEALTH’, is translated as: the 

beneficiary is 5.0 times more likely to have disenrolled if their health got worse as a 

result of the care they received by their HMO. 

b	 SUDAAN - Release 6.34, Research Triangle park, North Carolinu: Research Triangle Institute, 

1993. 

c	 HEALTH measures Lythe beneficiaries’ health got worse as a result of the care received by their 

HMO. 

E-1

Results of the Regression Model 

Tables 1 and 2 provide the logistic results for the regression model. Beneficiaries with 

missing data for any of the variables were excluded from the analysis. Each table provides 

the estimated odds ratios and the estimated lower and upper limits of the 95 percent 

confidence intervals. Odd ratios with values greater than one (1) indicate that 

beneficiaries with the characteristic have a greater likelihood of being disenrolled than 

beneficiaries in the referent group. Confidence intervals including the value one (1) 

indicate the characteristic does not have a significant effect at the 95 percent confidence 

interval on whether a beneficiary has disenrolled or not. 

Table 2 includes the full model measuring the probability of a beneficiary being 

disenrolled or not. Highlighted variables are statistically sigtilcant at the 95 percent 

confidence interval or better. 

Discussion of the Results 

Variables influencing beneficiaries to disenroll, after adjusting for everything else include: 

1) beneficiaries’ health getting worse as a result of the care received by their HMO, 2) 

beneficiaries having prior HMO experience, 3) beneficiaries’ HMO prioritizing holding 

down the cost of medical care, and 4) beneficiaries usually waiting an hour or more 

before seeing primary HMO doctor. 

E-2

Table 1: VARIABLE DESCRIPTIONS 

DEPENDENT VARIABLE: DISENROLLEE 

Variable Name I Description 

SEX [ 1 = Beneficiary was male, O = female 

DISABLED 1 = Disabled, O = aged beneficiary ~ 

AGE Beneficiaries’ age 

I 

COMPET_AREA ] 1 = Beneficiary lived in a non-competitive area, 

IO = beneficiary lived in competitive area 

PROPENSITY_USE 1= Low/Medium use of medical services by beneficiary, 

I O = high use of medical services 

SICK 1 = Beneficiary was rated as very sick, O = beneficiary was not rated as 

moderately sick or not sick -. 

PRIOR —HMO	 ] 1 = Beneficiary came previously from a HMO, O = beneficiary came 

Ipreviously from fee-for-service 

COMPLAINTS	 I 1 = Beneficiaries’ complaints were@ take. seriously, O = beneficiaries’ 

I complaints were taken seriously 

HMO_PRIORITY 1 = Beneficiary reported their HMO most concerned with holding down the 

I cost of their medical care, O = Beneficiary reported their HMO most concerned 

Iwith providing the best care possible 

HEALTH_WORSENED	 ] 1 = Beneficiaries’ health got worse as a result of care received by HMO, O = 

I beneficiaries’ health staYed the same or got better 

POOR_SERVICE = Beneficiaries’ HMO doctor not providing Medicare services, hospital care, I or referral to specialist care, O = otherwise

1 

HOUR_WAIT I

1=Beneficiaries usually waited an hour or more before seeing their primary 

HMO doctors, O = beneficiaries did not wait an hour 

OWN_SERVICES = Beneficiaries got services on their own, O = beneficiaries did not get I services on their own.

1 

E-3

Table 2: All beneficiaries who are enrolkd or had a 

non-administrative disenrollment (N = 674) 

Full Model 

Dependent Variable = Disenrolled Beneficiary 

Variable 

SEX 

DISABLED 

AGE


COMPET_AREA


PROPENSITY_ USE


SICK


PRIOR_HMO


COMPLAINTS


HMO_PRIORITY 

HEALTH_WORSENED 

POOR_SERVICE 

HOUR_WAIT 

OWN SERVICES 

Odds 95% C.1. 

Ratio 

Lower Upper 

1.07 .62 1.86 

3.53 1.04 11.94 

1.02 .98 1.06 

.90 .52 1.55 

.92 .53 1.60 

.55 .18 1.70 

1.92 .81 4.54 

1.35 .61 3.00 

3.39 1.55 7.42 

5.00 1.48 16.95 

1.63 .72 3.72 

2.75 1.12 6.75 

1.55 .73 I 3.32 

E-4 
APPENDIX F 

KEY LINEAR REGRESSION VARIABLES 

All regression models in this report were created using the SPSS for Windows 6.1 

software package. Each analysis includes the following information about beneficiaries 

from those FINIOs with complete information for each variable. 

Table 1: VARIABLEDESCRIPTIONS 

Variable Name Description 

STAFF HMO 1 = Staff HMO, O = IPA HMO 

GROUP HMO 1 = Group HMO, O = IPA HMO 

HMO_SIZE # of Me&care Risk Beneficiariesenrokd in HMO 

SOUTH 1 = HMO located in South, O = HMO located in West 

NEAST 1 = HMO located h Northeast, O = Hmo located in West 

MIDWEST 1 = HMO located in Midwest, O = HMO located in West 

FEE-FOR-SERVICE Percent of beneficiaries receiving prior care through fee-for-service 

PROFIT 1 = HMO is for profit, O = non-profit. HMO 

COMPETITIVE 1 = HMO is in competitive area, O = HMO is in non-competitive area 

POOR_SERVICE	 Percent of beneficiaries with HMO doctor not providing Medicare 

services, hospital care, or referral to specialist care 

SICK Percent of beneficia~-reported serious health problems 

Q_HEALTH	 Percent of beneficiaries asked questions about health problems, when they 

applied for HMO membership 

HOUR_WAIT	 Percent of beneficiaries usually waiting an hour or more before seeing 

their primary HMO doctors 

COMPLAINTS	 Percent of beneficiaries reporting their primary HMO doctor did not take 

their complaints seriously 

OWN_SERVICES	 Percent of beneficiaries in last year, getting Medicare covered services on 

their own without primary HMO doctor or HMO first approving 

HMO_PRIORITY	 Percent of beneficiaries reporting their HMO most concerned with holding 

down the cost of medical care 

DAYS_OPEN	 Days HMO has operated current risk HMO contract (used only in 

regression model #1) 

WANT_LEAVE	 Percent of beneficiaries wanting to leave but feel they can’t (used only in 

regression model #4) 

F-1 
LINEAR REGRESSION RESULTS


The following tables show the overall regression results of all our reduced models used in 

this report. It includes the estimate, standard error, t-value for all variables and the 

significance level (probability > ~t [). The t-value for each variable tests for the effect of 

each independent variable on the dependent variable. The last column gives the 

probability of the t-value. The t-values and the associated probabilities (probability > 

~t ~) test the hypothesis that the parameter is actually zero and answers the question: If 

the true slope and intercept were zero, what would the probability be of obtaining, by 

chance alone, a value as large or larger than the one actually obtained? 

Reduced Models: 

Table 2	 Dependent Variable = Percent change in disenrollment rate from 

time period 1 (’91 & ’92) to time period 2 (’92 & ‘93)” 

N = 39 HMOS, all enrollees & disenrollees” 

R2 = .29 & Adjusttxl R* = .27 

Variable Parameter Standard t-Value Probability 

Estimate Error > /tj 

I I I I 

Intercept ~ 227.96 ! 62.56 [ 4.44 

1 

.Ooo1 

Days Open I -0.12 I 0.03 I -3.91 I ,0004 

* Excludes 6 HMOS; H0616 & H(M 17 dropped their risk HMO contract, H3249, H45 10, H 1406 had 

a low number of disenroiled respondents, & H9049 split into two risk I-IMOS, transferring many 

beneficiaries into H3856. 

Table 3: Dependent Variable = Percent beneficiaries wanting to leave but can’t


N = 45 HMOS, enrollees only


R2 = .39& Adjusted R2 = .34


Variable Parameter Standard t-Value Probability 

Estimate Error > ~t~ 

Intercept 3.84 1.61 2.38 .0218 

Hour Wait or 

more for Primary 

HMO Doctor 0.43 0.13 3.16 .0030 

Poor Service 0.35 0.16 2.13 .0391 

Southern HMO 0.04 0.18 2.36 .0230 

F-2

Table 4: Dependent	 Variable = Annualized adjusted disenrollment rates for ’92 & ’93” 

N = 39 HMOS, all enrollees & disenrolleesR’ = .23& Adjusted R2= .21 

Variable Parameter Standard t-Value Probability 

Estimate Error > ~t~ 

I I I I 

Intercept 0.79 2.88 0.28 ,7842 

I { 1 1 

Complaints I 0.55 0.17 3.37 .0018 

. The adjusted disenrollment rate excludes the administrative dise~ollees (based on a formula created 

by our survey data, see Methodology seetion for further explanation) 

-.. . . .. . . . . . .. -—.
 bxcludes 6 HMOS; H(I616 & H0617 dropped their risk HMO contract, H3249, H45 10, H 1406 had


a low number of disenrolled respondents, & H9049 split into two risk HMOS, transferring many 

beneficiaries into H3856. 

Table 5: Dependent Variable = Annualized adjusted disenrollment rates for ’92 & ’93-


N = 39 HMCS, enrollees only-


R2 = .43 & Adjusted Rz = .38


Variable Parameter Standard t-Value Probability 

Estimate Error > jt~ 

Intercept -5.17 3.36 -1.54 .1331 

Poor Service 0.68 0.24 2.89 .0066 — 

Complaints 0.51 0.21 2.46 .0189 

Profit 0.05 0.02 2.10 .0429 

* The adjusted disenrollmentrate excludesthe administrativedisenrollees(based on a formula created 

by our survey data, see Methodology section for further explanation) 

Excludes 6 HMOS; H0616 &H0617 dropped their risk HMO contract, H3249, H45 10, H 1406 had 

a low number of disenrolled respondents, & H9049 split into two risk HMOS, transferring many 

beneficiaries into H3856. 

Table 6: Dependent Variable = Percent Leaving HMO one year after survey 

N = 42 HMOS, enrollees only* 

R2 = .37 & Adjusted R2 = .33 

Variable Parameter Standard t-Value Probability 

Estimate Error > ~t~ 

Intercept -2.05 2.48 -0.83 .4139 

complaints 0.58 0.15 3.79 .0005 

HMO_Priority 0.24 0.10 2.44 .0191 

* Excludes 3 HMOS; H0616 & H0617 dropped their risk HMO contract, & H9049 split into two risk 

HMOS, transferring many beneficiaries into H3856. 

F-3— 
Table 4: Dependent Variable = Annualized adjusted disenrollment rates for ’92 & ’93” 

N = 39 HMOS, all enrollees & disenrolleess 

Rz = .23& Adiusted Rz = .21 

Variable Parameter Standard t-Value Probability 

Estimate Error > ~t~ 

Intercept 0.79 2.88 0.28 .7842 

Complaints 0.55 0.17 3.37 .0018 

. The adjusted disenrollment rate excludes the administrative disenrollees (based on a formula created 

by our survey data, see Methodology section for further explanation) 

Excludes 6 HMOS; H0616 & H0617 dropped their risk HMO contract, H3249, H45 10, H 1406 had 

a low number of disenrolled respondents, & H9049 split into two risk HMOS,trmsfeming m~Y 

beneficiariesinto H3856. 

Table 5: Dependent Variable = Annualized adjusted disenrollment rates for ’92 & ’93-

N = 39 HM@s, enrollees onlyR2 = .43 & Adiusted R’ = .3S 

Variable Parameter Standard t-Value Probability 

Estimate Error > ~t~ 

Intercept -5.17 3.36 -1.54 .1331 

Poor Service 0.68 0.24 2.89 .0066 _— 

Complaints 0.51 0.21 2.46 .0189 

Profit 0.05 0.02 2.10 .0429 

* The adjusted disenrollmentrate excludesthe adminktrative disenrollees(based on a formula created 

by our survey data, see Methodology section for further explanation) 

Excludes6 HMOS;H0616 &H0617 dropped their risk HMO contract, H3249, H4510, H1406had 

a low number of disenrolledrespondents,& H9049 split into hvo risk HMOS,transferringmanY 

beneficiariesinto H3856. 

Table 6: DependentVariable = Percent LeavingHMO one year after survey


N = 42 HMOS, enrolleesonly*


R’ = .37 & Ad@sted R2 = .33


Variable Parameter Standard t-Value Probability 

Estimate Error > ~t~ 

Intercept -2.05 2.48 -0.83 .4139 

complaints 0.58 0.15 3.79 .0005 

HMO_Priority 0.24 0.10 2.44 .0191 

. Excludes 3 HMOS; H0616 & H0617 dropped their risk HMO contract, & H9049 split into two risk 

HMOS, transferring many beneficiariesinto H3856. 

F-3 

II 
APPENDIX G 

TEXT OF AGENCY COMMENTS 

G-1 
W8-2S


# ~


DEPARTMENT OF HEALTH& HUMAN SERVICES 

-@
 ‘%. \ 

DATE 

FROM 

; 

SUB~CT 

TO 

SET I 1995 

Bruce C. Vladec I LP@ 

Administrator %- “ 

Office of Inspector General (OIG) Draft Report: “Medicare 

Maintenance Organization (HMO) Performance Indicators,” 

June Gibbs Brown 

Inspector General 

We reviewed the above-referenced report which analyzes the 

OIG beneficiary su~ey data and fiture HMO disenrollment 

service access relates to disenrollment status. Our comments 

recommendations are attached. 

Health Care Fin~n~ing Administration 

The Administrator 

Washington, D.C. 20201 

Risk Health 

(OEI-06-9 1-00734) 

relationship between 

and, specifically, how 

on the report 

Thank you for the opportunity to review and comment .n this report. 

Attachment 

G-2 
Comments of the Health Care Financing Administration (HCFA)


on Office of Insuector General (OIG) Draft Reoort:


“Medicare Risk Health Maintenance Organization (HMO) Performance.”


(OEI-06-9 1-00734)


OIG Recommendation


Track disenrollment rates over time to detect potential problems among HMOS. I-lMOs


experiencing larger than average increases in ciisenrollment rates Should be monitored more


closely.


HCFA Re.monse


We concur. HCFA has been tracking disenrollment rates over time to detect potential problems


among HMOS. Our Office of Managed Care (OMC) developed a plan profile report in 1991


which incorporates various key indicators of a Medicare contractor’s HMO performance and


includes a number of significant data elements on disenrollment. OMC’s Operations and


oversight Team reviews these data on an ongoing basis as well as before conducting a monitoring


site visit of a Medicare contractor. OMc also tracks disenrollment via other data; e.g., HCFA


Medicare Hotline data, reconsiderations.


OIG Recommendation


Use adjusted disenrollment rates, along with other availab]e HM() information (e.g., beneficiary


complaints and appeals lodged against HMOS) to target review-s of HMOS,


HCFA Response


We concur. The C)Mc puts disenrollment rates in context with other ~f10 information as part of


targeting reviews of contractors. We believe, however, that the OIG may & underestimating the


degree to which disenrollment occurs due to shopping for benefits on the part of beneficiaries.


OIG Recommendation


Conduct disenrollment surveys that folly capture all the beneficiary’s reasons for leaving the


Medicare risk HMO. This information should be included in the Group Health Plan data files.


HCFA ResDonse


We concur and are implementing a new disenro]lment form that has been designed to captu[e


significant data regarding motivation for disenrollment.


OIG Recommendation


Survey enrollees systematically and routinely on key questions and on their desire to leave/remain


with an HMO, to complement disenrollment data.


G-3

Page 2 

HCFA ResDonse


We concur. A work group is currently developing a strategy on the conduct of beneficiary


satisfaction surveys. Items being considered are a national survey versus individual HMO


surveys, subject areas to cover, types of questions to ask, approaches for evaluation of surveys,


existing surveys, as well as other relevant areas.


(. 

OIG Recommendation


Monitor Medicare risk HMOS with high disenrollment rates and reported service access problems


and work with HMOS to respond to the needs of beneficiaries at risk of disenrolling.


HCFA ResDonse


We concur. DisenroIlment rates are monitored on an ongoing basis with special sensiti v;.Y and


follow up with HMOS which show high disenrollment rates and reported access problems.


Comments on Kev O uestions 

The key questions offered by the OIG as predictive of beneficiaries’ future disenrollment will be 

shared with the work group referenced above. 

G-4