The Economic Impact of Utilization Management on Health Plan Expenditures:

An Example of Non-Price Rationing

byStephen T. ParenteUniversity of Minnesota

H. Lawrence Van HornUniversity of Rochester

Gerald WedigIndiana University

David BradfordMedical 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.

LOCATION OFSERVICE Time of Service

Before During AfterInpatient Pre-admission

certification Second surgical

opinion

Concurrent review

Case management

Retrospective dischargeor claims review

Outpatient Pre-procedure orpre-certificationreview

Concurrent review(episode oftreatment review)

Retrospective dischargeor claims review

Utilization Management Taxonomy

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 MARKETCHARACTERISTICS

Study Population Characteristics

STATE CentralSouth #1

Gulf South#1

CentralSouth #2

Gulf South#2

Active Nonfederal physicians per 100,000 186 138 152 182Community Hospitals - Beds per 100,000 503 414 396 332Private Non-farm employment 61,775 34,506 95,546 332,252Private non-farm establishments 3,781 2,440 6,740 19,633Persons 65 or over 15,989 12,823 27,868 73,953Median Age 34 31 33 31Population used to calculate educational attainment rates. 79,100 64,974 139,296 453,170Income per-capita $11,038 $9,994 $11,525 $12,595Percent 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 1,774Number of HMOs 1993 1 0 1 2Percent 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 stateenrollment. 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 studyperiod, 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 EstimationT h e P l a n C h o i c e i s s p e c i f i e d a s :

)( 1ZCOVERAGE

Z r e p r e s e n t s a v e c t o r o f e x p l a n a t o r y v a r i a b l e s r e p r e s e n t s t h e p a r a m e t e r s t o b e e s t i m a t e d .

W i t h p a r a m e t e r s , * , t h e i n v e r s e M i l l ’ s r a t i o i s c a l c u l a t e d a s :

)(1

)(

1*

1*

*

Z

ZP

w h e r e ( . ) r e p r e s e n t s t h e c u m u l a t i v e d e n s i t y f u n c t i o n o f t h e s t a n d a r d n o r m a ld i s t r i b u t i o n .W i t h c h o o s i n g S t a n d a r d t h e i n v e r s e o f c h o o s i n g H M O , t h e d e f i n i t i o n f o r t h e i n v e r s eM i l l ’ s r a t i o f o r t h e S t a n d a r d p o p u l a t i o n i s s i m p l y

)(1

)(

1*

1*

*

Z

ZS

Equation 2: Use/No-Use

Dependent Variable:“0/1 indicator of

whether individual uses any services in a particular category during the time period”*

* Separate measures for benefit type

Independent Variables

Same variables as equation 1; exclude some market characteristics

• Add remaining deductible and prior year use

Eq #2: Use/No UseU t i l i z a t i o n t a k e s t h e f o r m

)( *22 PP ZNUTILIZATIO

w h e r e U T I L I Z A T I O N i s a v a r i a b l e w h i c h e q u a l s 1 w h e n t h e p e r s o n u s e d a s e r v i c e .Z 2 i s a v e c t o r o f e x p l a n a t o r y v a r i a b l e s ( a s u b - s e t o f Z 1 ) .

P i s a v e c t o r o f c o r r e s p o n d i n g p a r a m e t e r s t o b e e s t i m a t e d *

P i s t h e i n v e r s e M i l l ’ s r a t i o f o r t h o s e i n d i v i d u a l s w h o c h o s e H M O c o v e r a g e

2 i s t h e r e m a i n i n g p a r a m e t e r t o b e e s t i m a t e d .S u b s c r i p t s a r e s p e c i fi c t o t h o s e w h o c h o s e H M O c o v e r a g e .

A s e c o n d i n v e r s e M i l l ’ s r a t i o i s c a l c u l a t e d t o t h e p r o b a b i l i t i e s o f u t i l i z i n g a n y s e r v i c e ,c o n d i t i o n a l o n s e l e c t i n g H M O c o v e r a g e t a k i n g t h e f o r m

)(1

)(*

22*

*22

**

PP

PPUP

Z

Z

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

T h e fi n a l m o d e l o f t h e c o s t s o f c a r e c o n d i t i o n a l o n u t i l i z a t i o n t a k i n gt h e f o r m :

)( *131 UPP ZFCOSTS

w h e r e F ( . ) r e p r e s e n t s t h e f u n c t i o n a l f o r m ,Z 3 i s a v e c t o r o f e x p l a n a t o r y v a r i a b l e s ( w h i c h i s a s u b - s e t o f Z 2 ) ,

i i s a s e t o f p a r a m e t e r s t o b e e s t i m a t e d , *

U P i s t h e i n v e r s e M i l l ’ s r a t i o c a l c u l a t e d f r o m t h e p r i o r s t a g e , a n d i

r e p r e s e n t s i t s p a r a m e t e r w h i c h i s t o b e e s t i m a t e d .

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• 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

Common Diagnosis• 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 INVARIABLE

CHANGE INPROBABILITY OFSELECTING HMO

ODDSRATIO

Benefit Type #1 Has Benefit Type #1 24.5% 2.18Prior Use Used in Previous Period -3.5% .876ADG33 Has ADG33 -11.3% .572ADG34 Has ADG34 -9.6% .631

Catchment In Catchment Area 21.5% 3.17Prior MCO Experience Had prior MCO exp. -3.5% .866

MDs/ Capita 100 More MDs/ 10,000 5.1% 1.20Period 1 Period Later 7.2% 1.28

Period*Catchment 1 Period Later and inCatchment

-1.1% .959

Number of PPOPhysicians

76 More MDs/ 10,000 0.6% 1.02

Inpatient ResultsEq. #2: Use / No Use

VARIABLE GENERAL MENTALHEALTH

Observations 58,898 5,164Key Non-UM VariablesPeriod .043* .044Age .000 -.003Sex -.020 .139Catchment -.208* -.436*Salary -.092 .318Travel Distance .102* -.019Prior Use -.765* -.556*UM VariablesDenial Percentage -.037* -.009*Case Management Percentage .006 -.004*Percentage Prime MDs -.090 .602*Authorization Percentage .000 -.003

Outpatient Results Eq. #2: Use / No Use

VARIABLE PHYSICIAN MENTALHEALTH

Observations 168,070 67,428Key Non-UM VariablesPeriod .075* .040*Age .003* .001Sex -.027* .048*Catchment -.185* -.298*Salary .357* .255*Travel Distance .010 .032*Prior Use .481* .941*UM VariablesDenial Percentage -.045* -.009Case Management Percentage -.006 .014*Percentage Prime MDs .191* .355*Authorization Percentage -.010* -.004

Inpatient ResultsEq. #3: Paid $$

VARIABLE GENERAL MENTAL HEALTHObservations 9,961 1,236Key Non-UM VariablesSex .119* .043Catchment .262* .292Salary -.433 -.406Prior Use .034 -.186UM VariablesCase Management Percentage .020* .003Catchment* Case Management Percent -.006 -.004Denial Percentage .011 -.006Catchment* Denial Percentage -.008 -.005Percentage MDs participating -.092 -.185Catchment*Percentage MDs participating .347 .496Experience With HMO -.026 .155Catchment*Experience With HMO -.014 -.243*Authorization Percentage -.001 -.007Catchment*Authorization Percentage -.008* .002

Outpatient ResultsEq. #3: Paid $$

VARIABLE PHYSICIAN MENTAL HEALTHObservations 168,118 1,236Key Non-UM VariablesSex .043* -.090*Catchment .232* .292*Salary -.220* .324*Prior Use -.077* -.039UM VariablesCase Management Percentage -.001 -.012Catchment* Case Management Percent -.005 .042*Denial Percentage -.032* .136*Catchment* Denial Percentage .046* -.257*Percentage MDs participating -.088* -.007Catchment*Percentage MDs participating -.080* -.216*Experience With HMO -.043* -.089Catchment*Experience With HMO .034* .026Authorization Percentage .002* .008*Catchment*Authorization Percentage -.004* .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

00.10.20.30.40.50.60.70.80.9

1

% Paid

Start Option#1

Option#2

Option#3

Option#4

Option#5

HMO FFS

Out pat ient physic ian$ Simul at ions

HMOBASE CASE NDOCPEN

up 25%CMPER UP

25%DENPER UP

25%AUTHPER

UP 25%Start 1.0000 1.0000 1.0000 1.0000 1.0000

Option 1 0.7926 0.7875 0.7917 0.7906 0.7588Option 2 0.7669 0.7613 0.7660 0.7651 0.7353Option 3 0.7426 0.7363 0.7417 0.7409 0.7130Option 4 0.7195 0.7127 0.7187 0.7180 0.6918Option 5 0.6976 0.6903 0.6969 0.6963 0.6716

FFSBASE CASE NDOCPEN

up 25%CMPER UP

25%DENPER UP

25%AUTHPER

UP 25%Start 1.0000 1.0000 1.0000 1.0000 1.0000

Option 1 0.9679 0.9753 0.9753 0.9724 0.9679

Option 2 0.9683 0.9757 0.9756 0.9727 0.9683Option 3 0.9686 0.9760 0.9759 0.9730 0.9686Option 4 0.9690 0.9763 0.9762 0.9733 0.9690Option 5 0.9693 0.9766 0.9765 0.9736 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 processOverall UM Effect:

Category1

Prime Extra Standard

Equation 2:Use / No Use

Equation 2:Use / No Use

Equation 3:Log of Cost

Equation 3:Log of Cost

Equation 3:Log of Cost

Reg 2 Sim Reg 5 Sim

UR on UR off

Period 1-5

Difference in Prob. Wgt. Cost.

The Basic Regression Model w/ACGs• Model: MODEL1• Dependent Variable: LNPDAMT

• Analysis of Variance

• Sum of Mean• Source DF Squares Square F Value Prob>F

• Model 60 41250.34357 687.50573 494.680 0.0001• Error 127104 176649.09756 1.38980• C Total 127164 217899.44113

• Root MSE 1.17890 R-square 0.1893• Dep Mean 4.13408 Adj R-sq 0.1889• C.V. 28.51657

• Parameter Estimates• Parameter Standard T for H0: Variable• Variable DF Estimate Error Parameter=0 Prob > |T| Label

• INTERCEP 1 2.819837 0.14408409 19.571 0.0001 Intercept• PER 1 0.018242 0.00646076 2.824 0.0048 period 0-6• AGEDUM2 1 -0.099926 0.01097104 -9.108 0.0001• AGEDUM3 1 -0.222581 0.01187214 -18.748 0.0001• AGEDUM4 1 -0.385802 0.04145682 -9.306 0.0001• SEX 1 0.019478 0.00741437 2.627 0.0086 1 if sponsor male• ADFM 1 0.226269 0.00993134 22.783 0.0001• CAREA 1 0.005800 0.00997010 0.582 0.5607 cat area• PRIOR 1 0.002056 0.00990511 0.208 0.8356 1 if prior• SALARY 1 0.000044815 0.00000372 12.051 0.0001 salary of spons• PRIORUSE 1 0.070027 0.00738015 9.489 0.0001• MDIST 1 0.000182 0.00002769 6.588 0.0001 mean distance to prov• MMONDUM 1 0.004519 0.05622991 0.080 0.9359 rate of auth• NDOCPEN 1 -0.061988 0.02226988 -2.783 0.0054 network/total docs• EXPERA 1 -0.008869 0.00866062 -1.024 0.3058 tenure of particip

• INT 1 -0.011197 0.00904502 -1.238 0.2157

• HMOPEN93 1 0.165754 0.03929617 4.218 0.0001 % of pop. in HMO• HE42091D 1 -0.000053441 0.00003774 -1.416 0.1568 beds per 100,000• HE04090D 1 -0.000035791 0.00010385 -0.345 0.7304 phys per 100,000• HIGHED 1 0.300071 0.13537091 2.217 0.0266 %of pop with BS• BZ44093D 1 0.000726 0.00017585 4.126 0.0001 employers with > 100• OVER65 1 0.182085 0.14549595 1.251 0.2108 % of pop under 65• UNEMPRT 1 1.126164 0.19709147 5.714 0.0001 unemployment rate• HHI 1 -0.081618 0.02102686 -3.882 0.0001 herf index• NHOSPS 1 -0.003458 0.00215523 -1.604 0.1086 num of hospitals• MOCC 1 -0.142999 0.04140786 -3.453 0.0006 avg. occ. rate• MEXPADM 1 -0.000000792 0.00000260 -0.305 0.7606 avg. exp. per admis• ADG01 1 0.119877 0.00816775 14.677 0.0001• ADG02 1 0.071282 0.00788720 9.038 0.0001• ADG03 1 0.207521 0.01324167 15.672 0.0001• ADG04 1 0.161844 0.01263572 12.808 0.0001• ADG05 1 0.250753 0.01020996 24.560 0.0001• ADG06 1 0.220410 0.01455330 15.145 0.0001• ADG07 1 0.186564 0.00870894 21.422 0.0001• ADG08 1 0.114595 0.00833611 13.747 0.0001• ADG09 1 0.424842 0.02167448 19.601 0.0001• ADG10 1 0.183475 0.00818431 22.418 0.0001• ADG11 1 0.350556 0.00886408 39.548 0.0001• ADG12 1 0.175933 0.02086764 8.431 0.0001• ADG13 1 0.274498 0.02635974 10.414 0.0001• ADG14 1 0.143965 0.02057518 6.997 0.0001• ADG15 1 0.192683 0.05726306 3.365 0.0008• ADG16 1 0.257850 0.02036620 12.661 0.0001• ADG17 1 0.016823 0.01435281 1.172 0.2412• ADG18 1 0.248276 0.01415729 17.537 0.0001• ADG19 1 0.106662 0.03525019 3.026 0.0025• ADG20 1 0.023981 0.01379482 1.738 0.0821• ADG21 1 0.213156 0.01135294 18.775 0.0001• ADG22 1 0.311214 0.01282756 24.261 0.0001• ADG23 1 0.076171 0.01080527 7.049 0.0001• ADG24 1 0.106676 0.01843844 5.786 0.0001• ADG25 1 0.116351 0.01701046 6.840 0.0001• ADG26 1 0.379097 0.00766209 49.477 0.0001• ADG27 1 0.217063 0.00977495 22.206 0.0001• ADG28 1 0.334007 0.00717941 46.523 0.0001• ADG29 1 0.113150 0.00993796 11.386 0.0001• ADG30 1 0.095594 0.01925589 4.964 0.0001• ADG31 1 0.274176 0.00733022 37.404 0.0001• ADG32 1 0.449687 0.01639601 27.427 0.0001• ADG33 1 0.532485 0.02225990 23.921 0.0001

• ADG34 1 0.042311 0.04996367 0.847 0.3971