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Using Predictive Modeling Using Predictive Modeling to Target Interventionsto Target Interventions
Barry P. Chaiken, MD, MPHBarry P. Chaiken, MD, MPHChief Medical OfficerChief Medical Officer
ABQAURP ABQAURP -- PSOSPSOS
2
OverviewOverview
Cost and Quality Trends Cost and Quality Trends
Disease Management and ModelingDisease Management and Modeling
Predictive Modeling FundamentalsPredictive Modeling Fundamentals
Accuracy of Models Accuracy of Models -- Case StudyCase Study
3
02468
1012141618
1991 1993 1995 1997 1999 2001 2003*
Change Per Capita In Health Care Change Per Capita In Health Care Spending and GDPSpending and GDP
Source: B. Strunk and P. Ginsburg, “Tracking Health Care Costs: Trends Stabilize But Remain High in 2002,” Health Affairs (Web Exclusive June 11, 2003); B. Strunk and P. Ginsburg, Tracking Health Care Costs: Trends Slow in First Half of 2003, Center for Studying Health System Change, December 2003.
Percent
Health Care Spending
GDP
* Data for January through June 2003, compared with corresponding months in 2002
8.5%
2.9%
4
0
3
6
9
12
15
18
1985 1988 1991 1994 1997 2000 2003*
Growth in Per Enrollee Premiums and BenefitsGrowth in Per Enrollee Premiums and Benefits
Source: Heffler et al., “Health Spending Projections for 2002-2012,” Health Affairs (Web Exclusive February 7, 2003) for 1985–2001; Employer Health Benefits 2003 Annual Survey, The Kaiser Family Foundation and Health Research and Educational Trust, September 2003 for 2002–2003.
Percent
Premiums per enrollee
Benefits per enrollee
* Data for growth between Spring 2002 and Spring 2003
5
Drivers of Care ManagementDrivers of Care Management
50% 50% -- preventive carepreventive care
30% 30% -- lack recommended acute carelack recommended acute care
40% 40% -- lack recommended chronic carelack recommended chronic care
30% 30% -- receive contraindicated acute carereceive contraindicated acute care
20% 20% -- receive contraindicated chronic carereceive contraindicated chronic care
6
Recommended Care and Quality VariesRecommended Care and Quality Varies
Source: McGlynn et al., “The Quality of Health Care Delivered to Adults in the United States,” The New England Journal of Medicine (June 26, 2003): 2635–2645.
55
68 6554
49 45
0
20
40
60
80
Overall CAD Hypertension Asthma Hyperlipidemia Diabetes
Percent Receiving Recommended Care
7
0%10%20%30%40%50%60%70%80%90%
100%
U.S. Population Health Expenditures
Health Care Costs Concentrated in Sick FewHealth Care Costs Concentrated in Sick Few
1%5%
10%
55%
69%
27%
Source: AC Monheit, “Persistence in Health Expenditures in the Short Run: Prevalence and Consequences,” Medical Care 41, supplement 7 (2003): III53–III64.
50%
97%
$27,914
$7,995
$4,115
$351
Expenditure Threshold (1997 Dollars)
8
OverviewOverview
Cost and Quality Trends Cost and Quality Trends
Disease Management and ModelingDisease Management and Modeling
Predictive Modeling FundamentalsPredictive Modeling Fundamentals
Accuracy of Models Accuracy of Models -- Case StudyCase Study
9
DM DefinedDM Defined
“Knowledge“Knowledge--based process intended to based process intended to improve continuously the value of health care improve continuously the value of health care delivery from the perspectives of those who delivery from the perspectives of those who receive, purchase, provide, supply and receive, purchase, provide, supply and evaluate it.”evaluate it.”
----James B. Couch, MDJames B. Couch, MD
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DM Criteria SpecificDM Criteria Specific
High dollar and volumeHigh dollar and volume
Preventable complicationsPreventable complications
Short time frame for resultsShort time frame for results
Treatment variabilityTreatment variability
Extensive patient nonExtensive patient non--compliancecompliance
Practical guidelinesPractical guidelines
Measurable quality metricsMeasurable quality metrics
11
DM ProcessesDM Processes
Identify patientsIdentify patients
Develop therapeutic programsDevelop therapeutic programs
Improve outcomesImprove outcomes
Achieve acceptable cost levelsAchieve acceptable cost levels
Provide evidence based careProvide evidence based care
12
Spectrum of CareSpectrum of Care
PatientsProvider
Home Care
InstitutionsLong Term
CareNursing Homes
HospitalsCenters ofExcellence
SpecialistOutpatient
Clinics
PCPAllied HealthProfessionals
Self-Directed SecondaryPrimary Long TermCareTertiary
Disease Management Lower Costs Higher Costs
13
Risk Measurement PyramidRisk Measurement Pyramid
CaseMgmt.
NeedsAssessment
QualityImprovement
Payment/Finance
PracticeResource
Mgmt.
DiseaseMgmt.
HighDiseaseBurden
Single HighImpact Disease
Users
Users & Non-Users
Population Segment Source: Weiner JP. Presentation at National BCBS Association meeting, Chicago, 1/30/03.
14
OverviewOverview
Cost and Quality Trends Cost and Quality Trends
Disease Management and ModelingDisease Management and Modeling
Predictive Modeling FundamentalsPredictive Modeling Fundamentals
Accuracy of Models Accuracy of Models -- Case StudyCase Study
15
Predictive Modeling DefinedPredictive Modeling Defined
Use of clinical information available for all Use of clinical information available for all members of a population to predict future members of a population to predict future healthcare needs, overall or for specific healthcare needs, overall or for specific types of services.types of services.
Source: CB Forrest, “Population-based Predictive Modeling Using ACGs: Application to Disease and Case Management”, International ACG User’s Conference, November 11, 2003
16
Reasons for Predictive ModelingReasons for Predictive Modeling
Existing high utilization by the fewExisting high utilization by the few
Uses available data to identify highUses available data to identify high--riskrisk
Allows intervention early in disease cycleAllows intervention early in disease cycle
Enhances case and disease managementEnhances case and disease management
17
Predictive Modeling FocusPredictive Modeling Focus
Case management targetingCase management targetingIdentify persons for care programsIdentify persons for care programs
Disease management risk stratificationDisease management risk stratificationIntensity tiersIntensity tiers
Financial forecastingFinancial forecastingActuarial riskActuarial risk
Source: CB Forrest, “Population-based Predictive Modeling Using ACGs: Application to Disease and Case Management”, International ACG User’s Conference, November 11, 2003
18
What Predictive Modeling DoesWhat Predictive Modeling Does
Stratify membersStratify membersEnhance impact of interventionsEnhance impact of interventions
Probabilistic identification of high Probabilistic identification of high utilizersutilizersAssign risk scoresAssign risk scores
–– Describe comparative severity of illnessDescribe comparative severity of illnessIdentify members not receiving proper careIdentify members not receiving proper careHighlight inconsistency of careHighlight inconsistency of careProspectively identify adverse eventsProspectively identify adverse eventsAllow focused interventionsAllow focused interventions
Maximize benefits of disease managementMaximize benefits of disease managementDiscover inefficient careDiscover inefficient care
19
Additional Uses of ModelingAdditional Uses of Modeling
Influence adoption of best practicesInfluence adoption of best practices
Track effectiveness of interventionsTrack effectiveness of interventions
Establish pay for performanceEstablish pay for performance
Set more accurate premiumsSet more accurate premiums
Develop contracts with providersDevelop contracts with providersActuarialActuarial
Help plan network compositionHelp plan network compositionBased on member needsBased on member needs
20
Target PopulationsTarget Populations
Risk stratifyRisk stratifySubpopulationSubpopulation
Risk factorsRisk factors
Identify most likely to benefitIdentify most likely to benefit
Develop specific, targeted interventionsDevelop specific, targeted interventionsProbabilities for certain outcomesProbabilities for certain outcomes
Practice guidelinesPractice guidelines
Practice standardizationPractice standardization
–– Decrease variationDecrease variation
21
Modeling Utilizes Available DataModeling Utilizes Available Data
Traditional Traditional –– demographic datademographic dataAge, sex, occupation, prior costsAge, sex, occupation, prior costs
Clinical data from claimsClinical data from claims
Pharmacy dataPharmacy data
Diagnostic testsDiagnostic tests
Health risk appraisalsHealth risk appraisals
QuestionnairesQuestionnaires
22
Components of ModelingComponents of Modeling
Entire population dataEntire population dataBaseline assessment used to predict futureBaseline assessment used to predict future
Risk assessment periodRisk assessment periodStatic: year 1 predicts year 2Static: year 1 predicts year 2Rolling: assign score every periodRolling: assign score every period
Outcomes of interestOutcomes of interestChanges in health statusChanges in health statusCostly healthcare eventsCostly healthcare eventsOverall healthcare expendituresOverall healthcare expenditures
Source: CB Forrest, “Population-based Predictive Modeling Using ACGs: Application to Disease and Case Management”, International ACG User’s Conference, November 11, 2003
23
Statistics in Predictive ModelingStatistics in Predictive Modeling
Screening TestTP
(True Positive)
FN(False Negative)
TN(True Negative)
FP(False Positive)
Specificity
Sensitivity
Predicative Pos. Value
Predicative Neg. Value
TN/FP + TN
TN/FN + TN
TP/TP+FN
TP/TP + FP
+
+ -
-
24
Sensitivity Versus Predictive ValueSensitivity Versus Predictive Value
51%51%25%25%Top 10 %Top 10 %
36%36%36%36%Top 5%Top 5%
14%14%69%69%Top 1%Top 1%
% All True % All True Cases IdentifiedCases Identified
% True Cases % True Cases Among GroupAmong GroupScore CutScore Cut--PointPoint
Predictive Pos. Value
Sensitivity
25
First Generation ModelingFirst Generation Modeling
Utilize demographic dataUtilize demographic dataAge, sex, diagnosesAge, sex, diagnoses
Rely upon historical financial dataRely upon historical financial data
Predict riskPredict risk
26
Second Generation ModelingSecond Generation Modeling
Utilize first generation data sourcesUtilize first generation data sources
Incorporate second generation sourcesIncorporate second generation sourcesPharmacy dataPharmacy data
Lab dataLab data
Test dataTest data
Predictions based on risk adjustmentPredictions based on risk adjustmentDCGsDCGs, , ACGsACGs, , ETGsETGs
27
Third Generation ModelingThird Generation Modeling
Utilize first and second generation sourcesUtilize first and second generation sourcesIncorporate other sources and modelsIncorporate other sources and models
Health risk appraisals, questionnairesHealth risk appraisals, questionnairesSurveysSurveysRegional variabilityRegional variabilityACGsACGs, , DCGsDCGs, , ETGsETGs
Model the modelsModel the modelsChoose the best modeling of models for resultsChoose the best modeling of models for resultsLearn from previous data modelingLearn from previous data modeling
28
Perform dataPerform data ccleanupleanup
Split data into 2 yearsSplit data into 2 years
Use Year1 data to predict Year2 costUse Year1 data to predict Year2 cost
Identify modeling clusters and select best driversIdentify modeling clusters and select best driversDiseases, enrollment groups, product line Diseases, enrollment groups, product line
Episodes of Care (ETGs)
MEDai’s Clinical
Groupings
Patient Characteristics•Age•Gender•Enrollment•Insurance type
Provider Specialty
Selected Resource Measures
Timing & Frequency of
Services
Risk Score
DrugsEpisodes of Care (ETGs)
MEDai’s Clinical
Groupings
Patient Characteristics•Age•Gender•Enrollment•Insurance type
Provider Specialty
Selected Resource Measures
Timing & Frequency of
Services
Risk Score
Drugs
Third Generation ProcessThird Generation Process
Source: MEDai Inc.
29
Select model for optimum training of each clusterSelect model for optimum training of each clusterLinear & Nonlinear / Regression, Neural Networks….,Linear & Nonlinear / Regression, Neural Networks….,
Modeling of ModelsModeling of Models
Source: MEDai Inc.
30
OverviewOverview
Cost and Quality Trends Cost and Quality Trends
Disease Management and ModelingDisease Management and Modeling
Predictive Modeling FundamentalsPredictive Modeling Fundamentals
Accuracy of Models Accuracy of Models -- Case StudyCase Study
31
0
5000
10000
15000
20000
25000
30000
35000
40000
1 4 7 10 13 16 19 22 25 28 31 34 37 40 43 46 49 52 55 58 61 64 67 70 73 76 79 82 85 88 91 94 97 100
Fu
ture
Do
llars
Predicted
Actual
Accuracy of Third Generation ModelAccuracy of Third Generation Model
• Predicted Costs By Memberversus
• Actual Costs Experienced that Year
Each data point represents a single group of members within a range of predicted costs from the lowest predicted group to the highest predicted group (100 groups each with 1900 members)
Source: MEDai Inc.
32
Diabetes Model AccuracyDiabetes Model Accuracy
95 members per observation95 members per observation
0
10000
20000
30000
40000
50000
60000
70000
80000
90000
0 20000 40000 60000 80000 100000 120000 140000
Predicted $
Ac
tua
l $
Source: MEDai Inc.
33
0
5000
10000
15000
20000
25000
30000
35000
40000
0 10000 20000 30000 40000 50000 60000 70000
Predicted $
Ac
tua
l $
Depression Model AccuracyDepression Model Accuracy
105 members per observation105 members per observation Source: MEDai Inc.
34
HypertensionHypertension Model AccuracyModel Accuracy
230 members per observation230 members per observation
0
5000
10000
15000
20000
25000
30000
35000
40000
45000
50000
0 10000 20000 30000 40000 50000 60000 70000
avg predicted $
av
g a
ctu
al $
Source: MEDai Inc.
35
Underwriting Impact for Employer GroupsUnderwriting Impact for Employer Groups
$6 $6 $124 $124 $118 $118 36 36 KK
($36)($36)$168 $168 $204 $204 3939JJ
$5 $5 $135 $135 $131 $131 4141II
$15 $15 $126 $126 $111 $111 4444HH
($36)($36)$134 $134 $170 $170 4848GG
($32)($32)$113 $113 $145 $145 5151FF
($21)($21)$93 $93 $114 $114 5555EE
($43)($43)$124 $124 $167 $167 6161DD
$28 $28 $149 $149 $121 $121 7878CC
($12)($12)$139 $139 $151 $151 7878BB
$6 $6 $119 $119 $114 $114 8080AA
DifferenceDifferenceInternal Actuary Internal Actuary Premium SPMPMPremium SPMPM
MEDai forecast MEDai forecast $PMPM$PMPMMembersMembers
Sample of Sample of Employer Employer GroupGroup
• Health plan XYZ compared their premiums for a sample of employer groups using actuary vs. MEDai’s. • The actuarial model underestimated on the majority of small employer groups in comparison to MEDai.• This creates substantial losses, since 80% of the employers are small-group. The MEDai forecasting provides a
savings opportunity that approximated $11 million for 100,000 lives.• The client states that “in the final quarter of 2001, the actual cost for these groups shows clearunderestimation by the internal actuary forecasting.” Source: MEDai Inc.
36
37
38
Successful Predictive ModelingSuccessful Predictive Modeling
Identify clear goalsIdentify clear goalsModels fit some better than othersModels fit some better than others–– Actuarial versus care managementActuarial versus care management
Assess available data inputsAssess available data inputsDemographic, claims, pharmacy, lab valuesDemographic, claims, pharmacy, lab values
Secure a product championSecure a product championKey to any successful implementationKey to any successful implementation
Apply effective change managementApply effective change managementAdjustment of approach to care managementAdjustment of approach to care management
39
ReferencesReferences
MonheitMonheit AC, Persistence in health expenditures in the short run: AC, Persistence in health expenditures in the short run: Prevalence and consequences, Medical Care 41, supplement 7 Prevalence and consequences, Medical Care 41, supplement 7 (2003): III53(2003): III53––III64.III64.StrunkStrunk B, Ginsburg P, Tracking health care costs: Trends stabilize B, Ginsburg P, Tracking health care costs: Trends stabilize but remain high in 2002, Health Affairs (Web Exclusive June 11, but remain high in 2002, Health Affairs (Web Exclusive June 11, 2003)2003)StrunkStrunk B, Ginsburg P, Tracking health care costs: Trends slow in B, Ginsburg P, Tracking health care costs: Trends slow in first half of 2003, Center for Studying Health System Change, first half of 2003, Center for Studying Health System Change, December 2003.December 2003.HefflerHeffler et al., Health spending projections for 2002et al., Health spending projections for 2002--2012, Health 2012, Health Affairs (Web Exclusive February 7, 2003) for 1985Affairs (Web Exclusive February 7, 2003) for 1985––2001.2001.HefflerHeffler et al., Employer health benefits 2003 annual survey, The et al., Employer health benefits 2003 annual survey, The Kaiser Family Foundation and Health Research and Educational Kaiser Family Foundation and Health Research and Educational Trust, September 2003 for 2002Trust, September 2003 for 2002––2003. 2003. McGlynnMcGlynn et al., et al., ““The Quality of Health Care Delivered to Adults in The Quality of Health Care Delivered to Adults in the United States,the United States,”” The New England Journal of Medicine (June 26, The New England Journal of Medicine (June 26, 2003): 26352003): 2635––26452645
40
ReferencesReferencesWeiner JP, Weiner JP, ““Predictive modeling and risk measurement: Predictive modeling and risk measurement: Paradigms, potential and pitfalls.Paradigms, potential and pitfalls.”” Presented at a symposium Presented at a symposium on on ““Predictive ModelingPredictive Modeling”” sponsored by the National Blue sponsored by the National Blue Cross/Blue Shield Association, Chicago, January 30, 2003.Cross/Blue Shield Association, Chicago, January 30, 2003.Forrest CB, Forrest CB, ““PopulationPopulation--based predictive modeling using based predictive modeling using ACGsACGs: : Application to disease and case management,Application to disease and case management,”” International International ACG UserACG User’’s Conference, November 11, 2003.s Conference, November 11, 2003.LeGrowLeGrow G, Metzger J, G, Metzger J, EE--Disease ManagementDisease Management, First Consulting , First Consulting Group, Group, ©© 2001, California HealthCare Foundation2001, California HealthCare FoundationCouch JB, Couch JB, The Physician’s Guide to Disease ManagementThe Physician’s Guide to Disease Management. © . © 1997, Aspen Publishing, Inc., Gaithersburg, MD1997, Aspen Publishing, Inc., Gaithersburg, MDKongstevdtKongstevdt PR, PR, The Managed Health Care Handbook, 3The Managed Health Care Handbook, 3rdrd. ©. ©1996, Aspen Publishers, Inc., Gaithersburg, MD1996, Aspen Publishers, Inc., Gaithersburg, MDwww.medai.comwww.medai.com