Download The Economic Impact of Utilization Management on Health Plan

The Economic Impact of Utilization
Management on Health Plan Expenditures:
An Example of Non-Price Rationing
by
Stephen T. Parente
University of Minnesota
H. Lawrence Van Horn
University of Rochester
Gerald Wedig
Indiana University
David Bradford
Medical University of South Carolina
Outline of Presentation
•
•
•
•
•
•
Rationale for Study
Economic Model Overview
Data
Econometric Specification
Results & Discussion
Next Steps
What is Utilization Management?
• Restrictions from a health insurer on the
reimbursement for a medical service.
• Takes three basic forms:
– Prospective or pre-authorization of service.
– Concurrent
– Retrospective: after the service has been rendered.
• Effectively non-price rationing taking the
operation form of a denied claim or
‘redirected’ service.
Utilization Management
Taxonomy
LOCATION OF
SERVICE
Inpatient
Outpatient
Time of Service
Before
 Pre-admission
certification
 Second surgical
opinion
 Pre-procedure or
pre-certification
review

During
Concurrent review

Case management

Concurrent review
(episode of
treatment review)


After
Retrospective discharge
or claims review
Retrospective discharge
or claims review
Rationale for Study
• At least 95% of U.S. health insurers use some
form of utilization management
• 75% of physicians require preauthorization for
procedures (Kerr et al, 1995).
• 59% of patients experience UM in health plans
(Remler, 1997)
– 59% with length of stay reviewed
– 45% with site of care reviewed
– 39% with treatment appropriateness reviewed
Recent UM Activity
• Denial of care from UM activities sparked a
managed care backlash in the late 1990s.
– Examples:
• ‘Drive-by deliveries’
• As Good as it Gets (film audience derides HMO)
• HMO Hell (Newsweek cover)
• Industry counter-punch: United Healthcare’s 1999
announcement to curtail ‘UM’ activities.
• Patient Bill of Rights to be federal law soon (?).
Prior Work
• Feldman (1988): Reductions in hospital use by 10%
to 15% attributed to UM activities.
• IOM (1989): Reductions from preauthorization and
concurrent review are largely a one-time savings.
• Case management programs were found in several
studies to have little effect in containing costs.
• Dietzen & Bond (1993): Results suggested a
caseload threshold effect for effectiveness of UM.
• Other studies showing effect: (Wickizer, et al 1989;
Wickizer 1991, 1992; Scheffler et al., 1991).
Unexplored Areas
• Decomposing the UM effect by a managed
care plan’s targeted populations: (e.g., general
hospital, mental health, ambulatory care).
• Accounting for patient selection effects.
• Identifying UM threshold and interaction
effects.
Research Objectives
• Identify the effect of a large health plan’s UM
program on cost and utilization.
• Identify the effectiveness of separate UM
activities.
Data
• Data provided from a large managed care
organization (MCO) for four states members
including:
–
–
–
–
Claims for current members in markets of interest
Authorization file for current markets
Provider file
Characteristics of potential subscribes in new market.
• Non-MCO (Area Resource File, AHA file,
Bureau of Labor Statistics)
Key Variables from Data
• Dependent Variables (from claims):
– Use (encounters)
– Cost (paid by government)
• Independent Variables
– Utilization Management (county level)
– Market Factors (county level)
– Patient Case-mix
Utilization Management
Measures
•
•
•
•
•
Rate of claims pre-authorization
Rate of case management
Rate of claims denied
Percent of physicians enrolled ‘in network’
Experience of physicians in network
MEAN VALUES OF MARKET
CHARACTERISTICS
Study Population Characteristics
STATE
Central Gulf South Central Gulf South
South #1
#1
South #2
#2
Active Nonfederal physicians per 100,000
186
138
152
182
Community Hospitals - Beds per 100,000
503
414
396
332
Private Non-farm employment
61,775
34,506
95,546
332,252
Private non-farm establishments
3,781
2,440
6,740
19,633
Persons 65 or over
15,989
12,823
27,868
73,953
Median Age
34
31
33
31
Population used to calculate educational attainment rates.
79,100
64,974
139,296
453,170
Income per-capita
$11,038
$9,994
$11,525
$12,595
Percent of population under 65
86%
89%
87%
89%
Percent of Persons 25 and over completing at least a BS
13%
12%
15%
16%
Total non-farm employers with over 100 employees
513
197
350
1,774
Number of HMOs 1993
1
0
1
2
Percent of population enrolled in an HMO –1993
6.4%
0.7%
4.8%
10.9%
Note: Calculations are made with weights on each county being county enrollment to total state
enrollment. Population data are derived from the Area Resource File, Bureau of Labor Statistics and U.S.
Census File for the most recent available year 1994. While this may not correspond directly to the study
period, this data is valuable for conducting comparisons across regions.
Theoretical Approach
• Health care use and cost modeled as three
sequential decisions:
– the selection of either an HMO or Standard plan;
– the decision to utilize any services, conditional on
one’s choice of plan; and
– conditional on the use, the cost of service.
• Expressed as:
E(Cost) = Prob(HMO)*Prob(Utilize/HMO)*E(Cost/Use)
Econometric Approach
• Measure effect of UM in equations #2 (use)
and #3 (cost).
• Control for endogeneity of UM effect using
county-wide estimates of UM rates.
• Control for selection bias effect by use (eq. 2)
and cost (eq. 3) conditional on plan choice
from (eq. 1). Modeled using Heckman
correction (1978) to account for prior
selection effects.
Equation 1: Choice of Plan
Dependent Variable:
0/1 indicator of
whether individual
holds HMO or
FFS/POS plan during
the time period
Independent Variables
• Individual
characteristics
• Market characteristics
• Plan characteristics
• Utilization
management
Eq #1: Plan Choice Estimation
The Plan Choice is specified as:
COVERAGE   (  Z 1 )  
Z represents a vector of explanatory variables
represents the parameters to be estimated.
With parameters, * , the inverse Mill’s ratio is calculated as:
P 
*
 (* Z1 )
1   (* Z1 )
where ( . ) represents the cumulative density function of the standard normal
distribution.
With choosing Standard the inverse of choosing HMO, the definition for the inverse
Mill’s ratio for the Standard population is simply
 (* Z1 )
*
S 
1   (* Z1 )
Equation 2: Use/No-Use
Dependent Variable:
Independent Variables
“0/1 indicator of whether Same variables as
individual uses any
equation 1; exclude
services in a particular
some market
category during the
characteristics
time period”*
• Add remaining
deductible and prior
year use
* Separate measures for
benefit type
Eq #2: Use/No Use
Utilization takes the form
UTILIZATION   (  P Z 2   2 *P )  
where UTILIZATION is a variable which equals 1 when the person used a service.
Z2 is a vector of explanatory variables (a sub-set of Z1).
P is a vector of corresponding parameters to be estimated
*P is the inverse Mill’s ratio for those individuals who chose HMO coverage
2 is the remaining parameter to be estimated.
Subscripts are specific to those who chose HMO coverage.
A second inverse Mill’s ratio is calculated to the probabilities of utilizing any service,
conditional on selecting HMO coverage taking the form
UP
*
 (  P * Z 2   2 *P )

*
1   (  P Z 2   2 *P )
Equation 3: Cost Per Unit Service
Dependent Variable:
“Average amount paid by
HMO per service
unit”*
*Separate values for
Different product lines
• Independent
Variables (Drivers)
• Individual
characteristics
• Market characteristics
• UM measure
• Plan payment rates
Eq #3: Cost
The final model of the costs of care conditional on utilization taking
the form:
COSTS  F ( 1P Z 3   1 *UP )  
where F( . ) represents the functional form,
Z3 is a vector of explanatory variables (which is a sub-set of Z2),
i is a set of parameters to be estimated,
*UP is the inverse Mill’s ratio calculated from the prior stage, and i
represents its parameter which is to be estimated.
Case-Mix Adjustment Strategy
• Want to account for
case-mix differences.
• Claims data breadth
allow us to go beyond
age and gender.
• Use case-mix software
for regression results.
% of Variance Total $$$
Explained:
• 0.03 to 0.06 (age &
gender alone)
• 0.15 to 0.38 (age,
gender & ACGs)
Patient Case-mix measured
using ACGs
• Ambulatory Care Groups (ACGs) were
developed by Johns Hopkins University
• Based on combination of diagnosis, age,
gender information during a period of time.
• Can explain variation in utilization as well as
risk-adjustment for premium calculation.
• By-product is diagnostic clustering system
called Ambulatory Diagnostic Groups.
Examples of the 34 Ambulatory
Diagnostic Groups
ADG
Common Diagnosis
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
01: Time Limited: Minor
03: Time Limited: Major
09: Likely to Recur: Progressive
10: Chronic medical: Stable
11: Chronic medical: Unstable
23: Psychosocial: Chronic
26: Sign & Symptoms: Minor
32: Malignancy
Dermatitis
Synovitis
Diabetic Ketoacidosis
Hypertension
Coronary Atherosclerosis
Depression
Headache
Maliginant Skin Neoplasm
Regression Results Overview
 Regression results for equations 1, 2 & 3
 Plan Choice
 Log paid charges
 Focus on Inpatient & Outpatient General
Medical Care and Mental Health
 Highlight key regression findings
 Effects of UM
 Effects of other key variables
Effects of Key Variables on Odds
of Selecting HMO
VARIABLE
CHANGE IN
VARIABLE
Benefit Type #1
Prior Use
ADG33
ADG34
Catchment
Prior MCO Experience
MDs/Capita
Period
Period*Catchment
Has Benefit Type #1
Used in Previous Period
Has ADG33
Has ADG34
In Catchment Area
Had prior MCO exp.
100 More MDs/10,000
1 Period Later
1 Period Later and in
Catchment
76 More MDs/10,000
Number of PPO
Physicians
CHANGE IN
PROBABILITY OF
SELECTING HMO
24.5%
-3.5%
-11.3%
-9.6%
21.5%
-3.5%
5.1%
7.2%
-1.1%
ODDS
RATIO
0.6%
1.02
2.18
.876
.572
.631
3.17
.866
1.20
1.28
.959
Inpatient Results
Eq. #2: Use / No Use
VARIABLE
Observations
Key Non-UM Variables
Period
Age
Sex
Catchment
Salary
Travel Distance
Prior Use
UM Variables
Denial Percentage
Case Management Percentage
Percentage Prime MDs
Authorization Percentage
GENERAL
58,898
MENTAL
HEALTH
5,164
.043*
.000
-.020
-.208*
-.092
.102*
-.765*
.044
-.003
.139
-.436*
.318
-.019
-.556*
-.037*
.006
-.090
.000
-.009*
-.004*
.602*
-.003
Outpatient Results
Eq. #2: Use / No Use
VARIABLE
Observations
Key Non-UM Variables
Period
Age
Sex
Catchment
Salary
Travel Distance
Prior Use
UM Variables
Denial Percentage
Case Management Percentage
Percentage Prime MDs
Authorization Percentage
PHYSICIAN
168,070
MENTAL
HEALTH
67,428
.075*
.003*
-.027*
-.185*
.357*
.010
.481*
.040*
.001
.048*
-.298*
.255*
.032*
.941*
-.045*
-.006
.191*
-.010*
-.009
.014*
.355*
-.004
Inpatient Results
Eq. #3: Paid $$
VARIABLE
Observations
Key Non-UM Variables
Sex
Catchment
Salary
Prior Use
UM Variables
Case Management Percentage
Catchment* Case Management Percent
Denial Percentage
Catchment* Denial Percentage
Percentage MDs participating
Catchment*Percentage MDs participating
Experience With HMO
Catchment*Experience With HMO
Authorization Percentage
Catchment*Authorization Percentage
GENERAL
9,961
MENTAL HEALTH
1,236
.119*
.262*
-.433
.034
.043
.292
-.406
-.186
.020*
-.006
.011
-.008
-.092
.347
-.026
-.014
-.001
-.008*
.003
-.004
-.006
-.005
-.185
.496
.155
-.243*
-.007
.002
Outpatient Results
Eq. #3: Paid $$
VARIABLE
Observations
Key Non-UM Variables
Sex
Catchment
Salary
Prior Use
UM Variables
Case Management Percentage
Catchment* Case Management Percent
Denial Percentage
Catchment* Denial Percentage
Percentage MDs participating
Catchment*Percentage MDs participating
Experience With HMO
Catchment*Experience With HMO
Authorization Percentage
Catchment*Authorization Percentage
PHYSICIAN
168,118
MENTAL HEALTH
1,236
.043*
.232*
-.220*
-.077*
-.090*
.292*
.324*
-.039
-.001
-.005
-.032*
.046*
-.088*
-.080*
-.043*
.034*
.002*
-.004*
-.012
.042*
.136*
-.257*
-.007
-.216*
-.089
.026
.008*
.000
Results - Inpatient
• Use
– General: Denied claims lowers use by near 4%.
– Mental Health: Denied claims and case management
lower use (slightly). HMO experienced physicians drive
use.
• Cost
– General/Physician: Ambiguous UM effect
– Mental health: UM lowers costs through the use of an
provider panel with experience in the HMO.
Results - Outpatient
• Use
– General: Denied claims and authorization surveillance
lowers use rate.
– Mental Health: Authorization surveillance slightly
lowers use. HMO experienced physicians drive use.
• Cost
– General/Physician: UM lowers costs, particularly when
patients are being treated in catchment areas.
– Mental health: Mixed results, but HMO providers lower
costs as well as denying claims for providers in catchment
areas where the HMO has greater control of provider
resources.
Contribution to Literature
• Structural approach to modeling UM
response identified impact cost, dependent
upon use.
• Identification of the cost-saving UM effect
of ‘in network’ providers combined
traditional UM activities.
• Most recent action (late 1990s) in UM is in
the outpatient setting.
Health Policy & Management
Applications
• MCOs bidding for government contracts.
• MCOs/Integrated delivery systems bidding
for large employer contracts.
• Identify methods to pre/post response to
changes in UM activities (e.g., United Health)
Outpatient Physician $$ Simulations
1
0.9
0.8
0.7
0.6
% Paid 0.5
0.4
0.3
0.2
0.1
0
Start
Option
#1
Option
#2
HMO
Option
#3
FFS
Option
#4
Option
#5
Outpatient physician
$ Simulations
HMO
Start
Option 1
Option 2
Option 3
Option 4
Option 5
BASE CASE NDOCPEN CMPER UP DENPER UP AUTHPER
up 25%
25%
25%
UP 25%
1.0000
1.0000
1.0000
1.0000
1.0000
0.7926
0.7875
0.7917
0.7906
0.7588
0.7669
0.7613
0.7660
0.7651
0.7353
0.7426
0.7363
0.7417
0.7409
0.7130
0.7195
0.7127
0.7187
0.7180
0.6918
0.6976
0.6903
0.6969
0.6963
0.6716
FFS
BASE CASE NDOCPEN CMPER UP DENPER UP AUTHPER
up 25%
25%
25%
UP 25%
Start
1.0000
1.0000
1.0000
1.0000
1.0000
Option 1
Option 2
Option 3
Option 4
Option 5
0.9679
0.9683
0.9686
0.9690
0.9693
0.9753
0.9757
0.9760
0.9763
0.9766
0.9753
0.9756
0.9759
0.9762
0.9765
0.9724
0.9727
0.9730
0.9733
0.9736
0.9679
0.9683
0.9686
0.9690
0.9693
Next Steps
• Does increasing size of PPO network increase or
decrease costs to the MCO?
– Not just the effect of UM but overall.
• Use model to include effects of copay &
deductible changes.
• Identify modeling consequences of leaving Eq. #1
(plan choice) out of the model or developing
estimates for Eq. #1 to enable approach to be
provided on a wider set of data.
• Work with longer period of data.
Data and Variable Measurement
• Review of Key Data Bases (Contents, Strengths and
Weaknesses)
 Key Categories of Variables and Their Relationships
to Data Bases
 Construction of Select, Key Variables (ACGs, UM)
 Problems With Missing Data (Ex. plan choice and
nonusers)
 Final ‘Working’ Specifications
Model Assumptions
• UM activity applied proposed region will be
the same as that employed in present
markets.
• Negative time trends in observed costs are
attributed to UM.
• Model can only predict with precision up to
3 years in the future. Managerial discretion
may be warranted for periods 4 & 5.
Diagram of process
Overall UM Effect:
Category1
Prime
Extra
Standard
Equation 2:
Equation 2:
Use / No Use
Use / No Use
Equation 3:
Equation 3:
Log of Cost
Log of Cost
Reg 2 Sim
Reg 5 Sim
Period 1-5
UR on
UR off
Difference in
Prob. Wgt. Cost.
Equation 3:
Log of Cost
The Basic Regression Model w/ACGs
•
•
Model: MODEL1
Dependent Variable: LNPDAMT
•
Analysis of Variance
•
•
Source
•
•
•
Model
Error
C Total
•
•
•
Sum of
Squares
Mean
Square
60 41250.34357
127104 176649.09756
127164 217899.44113
687.50573
1.38980
DF
Root MSE
Dep Mean
C.V.
1.17890
4.13408
28.51657
•
•
•
Parameter Estimates
Parameter
Variable DF
Estimate
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
INTERCEP
PER
AGEDUM2
AGEDUM3
AGEDUM4
SEX
ADFM
CAREA
PRIOR
SALARY
PRIORUSE
MDIST
MMONDUM
NDOCPEN
EXPERA
INT
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
2.819837
0.018242
-0.099926
-0.222581
-0.385802
0.019478
0.226269
0.005800
0.002056
0.000044815
0.070027
0.000182
0.004519
-0.061988
-0.008869
-0.011197
R-square
Adj R-sq
F Value
Prob>F
494.680
0.0001
0.1893
0.1889
Standard
Error
T for H0:
Parameter=0
Prob > |T|
0.14408409
0.00646076
0.01097104
0.01187214
0.04145682
0.00741437
0.00993134
0.00997010
0.00990511
0.00000372
0.00738015
0.00002769
0.05622991
0.02226988
0.00866062
0.00904502
19.571
2.824
-9.108
-18.748
-9.306
2.627
22.783
0.582
0.208
12.051
9.489
6.588
0.080
-2.783
-1.024
-1.238
0.0001
0.0048
0.0001
0.0001
0.0001
0.0086
0.0001
0.5607
0.8356
0.0001
0.0001
0.0001
0.9359
0.0054
0.3058
0.2157
Variable
Label
Intercept
period 0-6
1 if sponsor male
cat area
1 if prior
salary of spons
mean distance to prov
rate of auth
network/total docs
tenure of particip
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
HMOPEN93
HE42091D
HE04090D
HIGHED
BZ44093D
OVER65
UNEMPRT
HHI
NHOSPS
MOCC
MEXPADM
ADG01
ADG02
ADG03
ADG04
ADG05
ADG06
ADG07
ADG08
ADG09
ADG10
ADG11
ADG12
ADG13
ADG14
ADG15
ADG16
ADG17
ADG18
ADG19
ADG20
ADG21
ADG22
ADG23
ADG24
ADG25
ADG26
ADG27
ADG28
ADG29
ADG30
ADG31
ADG32
ADG33
ADG34
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
0.165754
-0.000053441
-0.000035791
0.300071
0.000726
0.182085
1.126164
-0.081618
-0.003458
-0.142999
-0.000000792
0.119877
0.071282
0.207521
0.161844
0.250753
0.220410
0.186564
0.114595
0.424842
0.183475
0.350556
0.175933
0.274498
0.143965
0.192683
0.257850
0.016823
0.248276
0.106662
0.023981
0.213156
0.311214
0.076171
0.106676
0.116351
0.379097
0.217063
0.334007
0.113150
0.095594
0.274176
0.449687
0.532485
0.042311
0.03929617
0.00003774
0.00010385
0.13537091
0.00017585
0.14549595
0.19709147
0.02102686
0.00215523
0.04140786
0.00000260
0.00816775
0.00788720
0.01324167
0.01263572
0.01020996
0.01455330
0.00870894
0.00833611
0.02167448
0.00818431
0.00886408
0.02086764
0.02635974
0.02057518
0.05726306
0.02036620
0.01435281
0.01415729
0.03525019
0.01379482
0.01135294
0.01282756
0.01080527
0.01843844
0.01701046
0.00766209
0.00977495
0.00717941
0.00993796
0.01925589
0.00733022
0.01639601
0.02225990
0.04996367
4.218
-1.416
-0.345
2.217
4.126
1.251
5.714
-3.882
-1.604
-3.453
-0.305
14.677
9.038
15.672
12.808
24.560
15.145
21.422
13.747
19.601
22.418
39.548
8.431
10.414
6.997
3.365
12.661
1.172
17.537
3.026
1.738
18.775
24.261
7.049
5.786
6.840
49.477
22.206
46.523
11.386
4.964
37.404
27.427
23.921
0.847
0.0001
0.1568
0.7304
0.0266
0.0001
0.2108
0.0001
0.0001
0.1086
0.0006
0.7606
0.0001
0.0001
0.0001
0.0001
0.0001
0.0001
0.0001
0.0001
0.0001
0.0001
0.0001
0.0001
0.0001
0.0001
0.0008
0.0001
0.2412
0.0001
0.0025
0.0821
0.0001
0.0001
0.0001
0.0001
0.0001
0.0001
0.0001
0.0001
0.0001
0.0001
0.0001
0.0001
0.0001
0.3971
% of pop. in HMO
beds per 100,000
phys per 100,000
%of pop with BS
employers with > 100
% of pop under 65
unemployment rate
herf index
num of hospitals
avg. occ. rate
avg. exp. per admis