Department  of  Health  and  Human  Services 
OFFICE  OF 
INSPECTOR  GENERAL 
MEDICARE  RISK  HMO

PERFORMANCE  INDICATORS

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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 OIGS 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 OIGS 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 OIGS 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	 GENERAL 
MEDICARE
 RISK  HMO

PERFORMANCE
 INDICATORS

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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 doctors  office. 
Beneficiaries  leaving Medicare  risk  ofien  re-enroll  directly,  or shortly thereafter,  in 
HMOS 
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 beneficiarys  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 questconswe 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 reports  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 doctors  referral  for other  services  (Porell et al.  1992; Rossiter  et al.  1988, 
1989; Ward,  R.A.  1987). 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. 
Linear  remwsion  analyses  - Beneficiary  responses  from key survey questions  were 
b 
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) 
  beneficiarys  age 
  non-competitive  area:  beneficiary  lived in a county with no other Medicare 
risk HMO contract 
  propensity:  beneficiarys  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 Presidents  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 beneficiarys  complaints 
seriously  strongly affects disenrollment  rates. 1415The 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 companys  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 
*  Annualizeddisenrollmentrateswereusedin the analysison page5 and throughoutthe remainderof 
thereport, allowingforcomparisonsofolderandnewerHMOriskcontracts. 
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 groups 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 doctors  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 doctors  office 
	 Long waits in their primary  HMO doctors  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

c  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
 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 beneficiarys  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  systemahcally 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. 
The questions  we found  most predictive  of beneficiaries fidure  disenrollment 
F	
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 reports  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 OMCS 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 HCFAS 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 
HCFAs  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, 177). 
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.  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. 
8.  Beneficiaries  with serious health problems  had one or more illnesses,  e.g.,  heart 
attack,  cancer,  or pneumonia. 
9.  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. 
10.  HMO disenrollment  rates for  1992-93 were adjusted as discussed on page 6 to
 
more accurately  reflect  each HMOs true dlse~ollment  rate. 
11.  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% 
* 
SixHMOSwereexcludedin the analysisof disenrolleesanddisenrolhnentratesbecause 
three had less than 20 disenrokes  of  the  50 suweyedper HMO;2 HMOSdiscontinued 
theirHMOriskcontractsin 1993;and 1HMOsplitinto2 riskHMOS,transferringmany 
beneficiaries. 
12.  For  every  1 percent  increase  in HMO  beneficiaries  reporting  poor  service,  HMO 
disenrollment  rates  changed  .68 percent. 
13.  Poor  service  also signitlcantly  influenced  the percentage  of beneficiaries  who 
wanted to leave but could not (see Appendix  F, Tables  1 & 3). 
14.  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 HMOs 
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 analysisof disenrolleesanddisenrollmentrates because 
threehad lessthan20 disenrolleesof the50 surveyedper HMO; 2 HMOSdiscontinued 
theirHMOriskcontractsin 1993;and 1HMOsplitinto2 riskHMOS,transferringmany 
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 
previousOEIreport(OEI06-91-00730)includesa completedescriptionof theweighting 
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  wthDisenrollment 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  Variables 
(Enrollees  onlv} 
Adjusted 
Disenrollment  Rate  Poor  ComplaintsNotTaken 
CY92-93  Service***  Seriously 
HMO  Ownership**  (Percent)  (Poor)  (Percent) 
Group  ~ 
#1  12.7  8.0  7.7 
#2  13.9  6.9  10.7 
#3  NS  13.3  20.7 
#4  25.7  11.8  17.9 
#5  16.5  2.6  15.0 
#6  18.2  13.8  10.3 
#7  NS  5.6  19.0 
Group  B 
#8  2.2  2.8  2.8 
#9  6.4  2.8  8.1 
# 10  NA  2.9  6.3 
#11  NA  13.8  16.1 
# 12  NS  3.1  20.0 
Group  C 
# 13  8.4  0.0  6.3 
# 14  7.4*  0.0  3.6 
# 15  7.6  12.0  3.8 
Group  D 
# 16  6.5  13.2  15.0 
# 17  3.2  5.9  I  8.1 
Group  E 
# 18  9.3  17.6  I  15.0 
# 19  4.1  6.7  16.1 
Group  F 
# 20  6.8  6.1  I  11.1 
# 21  10.1  8.1  17.1 
Group  G 
# 22  4.4  6.1  I  8.6 
# 23  5.0  3.8  12.9 
#24  I  4.9*  3.7  I  11.1 
# 25  I  8.0  3.2  I  12.1 
# 26  I  37.0*  11.8  I  23.8 
6.7  I  10.3 
23.5  I  21.7 
7.0  I  13.3 
A-1
 Table  1:  Adjusted  CY92-93  Disenrollment  Rates  By Key Survey  Variables 
(Enrollees  only) 
Adjusted 
Disenrollment  Rate  Poor  Complaints  Not Taken 
CY92-93  Service***  Seriously 
HMO  Ownership**  (Percent)  (Poor)  (Percent) 
L 
# 30  5.9  5.1  7.5 
#31  15.5  10.3  6.5 
# 32  20.2  12.5  7.7 
# 33  4.2  10.0  13.6 
# 34  1.9  5.6  5.7 
# 35  3.5  7.5  7.1 
# 36  10.2  10.3  13.8 
# 37  13.4  16.2  12.8 
# 38  15.0  10.8  18.4 
# 39  5.4  6.1  8.6 
#40  8.1*  6.9  8.6 
#41  NA  9.7  6.3 
# 42  4.2  6.1  5.6 
 
# 43  6.6  8.3  4.9 
#44  3.4  7.9  7.9 
# 45  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 
Adjusted 
Dkenrollment  Disenrollment 
Rate  Percent  Rate  % Decrease  in 
CY92-93  Administrative  CY92-93  CY92-93 
HMO  Ownership**  (Percent)  Disenrolhnent  (Percent)  Rate 
Group  A 
#1  20.1  37.0  12.7  36.8 
#2  19.8  30.0  13.9  29.8 
#3  29.6  NS  NS  NS 
#4  30.0  14.3  25.7  14.3 
#5  17.6  6.3  16.5  6.3 
#6  24.3  25.0  18.2  25.1 
#7  22.6  NS  NS  NS 
Group  B 
#8  5.2  58.3  2.2  57.7 
#9  10.6  39.3  6.4  39.6 
# 10  NA  NS  NA  NA 
#11  NA  25.0  NA  NA 
# 12  15.0*  NS  NS  NS 
Group C 
# 13  12.2  31.0  8.4  31.1 
# 14  12.0*  38.1  7.4*  38.3 
# 15  10.9  30.8  7.6  30.3 
Group  D 
# 16  11.3  42.9  6.5  42.5 
# 17  7.7  58.3  3.2  58.4 
Group  E 
# 18  11.9  22.2  9.3  21.8 
# 19  9.2  56.0  4.1  55.4 
Group  F 
# 20  9.7  30.3  6.8  29.9 
# 21  13.6  25.8  10.1  25.7 
Group  G 
# 22  5.5  20.0  4.4  20.0 
# 23  8.1  38.9  5.0  38.3 
#24  7.2*  31.6  4.9*  31.9 
#25  12.8  37.5  8.0  37.5 
# 26  39.2*  5.6  37.0*  5.6 
# 27  7.8*  18.5  6.4*  17.9 
# 28  32.8  14.3  28.1  14.3 
# 29  16.3  50.0  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:  Unadjusted  CY91-92  Disenrollment  Rates  & 92-93  Disenrollment  Rates  by HMO 
Month/Year  CY91-92  CY92-93 
Risk  HMO  Disenrollment  Disenrollment  % Change  in 
HMO  Contract  Rate  Rate  Dkenrollment 
Ownership**  Began  (Percent)  (Percent)  Rate 
Group  A

#1
 6/88  21.8  20.1  7.8 
#2
 4185  18.8  19.8  +  5.3 
#3
 7185  21.0  29.6  +  41.0 
#4
 12/87  47.5  30.0  36.8 
#5
 1/90  10.6  17.6  +  66.0 
#6
 1/86  28.4  24.3  14.4 
#7
 6188  29.3  22.6  22.9 
Group  B

#8
 10/85  5.9  5.2  11.9 
#9
 7185  11.0  10.6  3.6 
# 10
 8/88  5.4  NA  NA 
#11  8188  5.4  NA  NA 
# 12  4192  14.9*  15.0*  +  0.7* 
Group  C 
# 13  4{86  10.1  12.2  +  20.8 
# 14  9192  4.8*  12.0*  +  150.0* 
# 15  4186  8.8  10.9  +  23.9 
Group  D 
# 16  1/88  9.0  11.3  +  25.6 
# 17  1187  9.2  7.7  16.3 
Group  E 
# 18  8186  10.8  11.9  +  10.2 
# 19  2186  7.1  9.2  +  29.6 
Group  F 
# 20  10I85  22.9  9.7  57.6 
# 21  7185  14.5  13.6  6.2 
Group  G 
# 22  2191  6.2*  5.5  11.3* 
# 23  1/88  8.7  8.1  6.9 
# 24  5192  9.0*  7.2*  20.0* 
# 25  6/86  11.3  12.8  +  13.3 
# 26  6192  36.7*  39.2*  +  6.8* 
#27  10/92  4.0*  7.8*  +  95.0* 
# 28  4/85  29.5  32.8  +  11.2 
#29  1/86  15.3  16.3  +  6.5 
# 30  7186  9.7  7.1  26.8  I 
D-1  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  9192  4.8*  12.0*  +  150.0* 
I 
# 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 
I
with 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  I 1 =  Beneficiaries  HMO doctor not providing  Medicare  services,  hospital  care, 
or referral to specialist  care, O =  otherwise
1=
I
HOUR_WAIT  Beneficiaries  usually waited an hour or more before  seeing  their primary 
HMO doctors,  O =  beneficiaries  did not wait an hour 
OWN_SERVICES  I 1 =  Beneficiaries  got services  on their own,  O =  beneficiaries  did not get 
services  on their own.
E-3
 Table  2:  All beneficiaries  who  are enrolkd  or had  a 
non-administrative  disenrollment  (N  =  674) 
Full  Model 
Dependent  Variable  =  Disenrolled  Beneficiary 
Variable  Odds  95%  C.1. 
Ratio 
Lower  Upper 
SEX  1.07  .62  1.86 
DISABLED  3.53  1.04  11.94 
AGE
 1.02  .98  1.06 
COMPET_AREA
 .90  .52  1.55 
PROPENSITY_  USE
 .92  .53  1.60 
SICK
 .55  .18  1.70 
PRIOR_HMO
 1.92  .81  4.54 
COMPLAINTS
 1.35  .61  3.00 
HMO_PRIORITY  3.39  1.55  7.42 
HEALTH_WORSENED  5.00  1.48  16.95 
POOR_SERVICE  1.63  .72  3.72 
HOUR_WAIT  2.75  1.12  6.75 
OWN  SERVICES  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  cant  (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  .Ooo1 
~  !  [ 
1 
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 cant

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  & disenrollees
R  =  .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  0.55  0.17  3.37  .0018 
I 
. 
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 
* 
Theadjusteddisenrollmentrateexcludestheadministrativedisenrollees(basedona formulacreated 
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  only-
R2 =  .43 & Adiusted  R  =  .3S  II 
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 
* 
Theadjusteddisenrollmentrateexcludesthe adminktrativedisenrollees(basedon a formulacreated 
by our  survey  data,  see  Methodology  section  for  further explanation) 
Excludes6 HMOS;H0616&H0617 droppedtheirriskHMOcontract,H3249,H4510,H1406had 
a lownumberof disenrolledrespondents,& H9049split into hvorisk 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  APPENDIX  G 
TEXT  OF  AGENCY  COMMENTS 
G-1  W8-2S

# ~

DEPARTMENT  OF HEALTH&  HUMAN  SERVICES  Health  Care Fin~n~ing  Administration 
-@

%. \ 
The  Administrator 
Washington,  D.C.  20201 
DATE  SET  I  1995 
@ 
FROM  LP
Bruce  C. Vladec  I 
%-
Administrator   
; 
SUB~CT  Office  of Inspector  General  (OIG)  Draft  Report:  Medicare  Risk  Health 
Maintenance  Organization  (HMO)  Performance  Indicators,  (OEI-06-9  1-00734) 
TO  June  Gibbs  Brown 
Inspector  General 
We  reviewed  the  above-referenced  report  which  analyzes  the  relationship  between 
OIG  beneficiary  su~ey  data  and  fiture  HMO  disenrollment  and,  specifically,  how 
service  access  relates  to  disenrollment  status.  Our  comments  on  the  report 
recommendations  are  attached. 
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  contractors  HMO  performance  and

includes  a number  of  significant  data  elements  on  disenrollment.  OMCs  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

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

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

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

OIG  Recommendation

Conduct  disenrollment  surveys  that  folly  capture  all the beneficiarys  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