Mckesson payor conf implementing a pred. model

CRMS & PREDICTIVE MODELING AT BCBSLA

Blue Cross Blue Shield of Louisiana

Felix J. Bradbury, RN, MHA, ScD*, CHEDirector, Medical Management & Reporting Department

September, 2003

AGENDA

What is a predictive model? Introduction and overview BCBSLA health plan facts How is Predictive Modeling Used to Support Our

Care Management Initiatives? Predictive model implementation issues Our high-risk algorithm for care management Using CRMS Fundamentals to improve reliability

and validity Using CRMS Fundamentals to enhance the

predictive model referral process

What is a Predictive Model?

A software application which generates a prospective index of risk and a probability of admission from a combination of medical, pharmacy and laboratory claims data. This data allows us to compare the expected future health status of an individual member, or the aggregate health status of a group, product or LOB.

What is a Predictive Model, Cont’dTwo Important Definitions

1.1. Relative Risk Score: Also called RR Score, it is a standardized score, or index, Relative Risk Score: Also called RR Score, it is a standardized score, or index, which enables us to make individual as well as aggregate comparisons between which enables us to make individual as well as aggregate comparisons between the health of members, groups, and products . In our population, RR Scores the health of members, groups, and products . In our population, RR Scores range from approximately 0.2-101.range from approximately 0.2-101.

– Example: A RR Score of “1.00” means a person is as healthy as “average”. Example: A RR Score of “1.00” means a person is as healthy as “average”. A RR Score of “1.89”means a person is 89% sicker than average.A RR Score of “1.89”means a person is 89% sicker than average.

2.2. Probability of Admission: Also called POA, this is the probability that a person Probability of Admission: Also called POA, this is the probability that a person will experience at least one inpatient hospital admission in the coming year. will experience at least one inpatient hospital admission in the coming year.

– Example: RR Score of 0.90 means a member has a 90% chance of at least Example: RR Score of 0.90 means a member has a 90% chance of at least one admission. POA scores range from approximately 0.03-0.95 in our one admission. POA scores range from approximately 0.03-0.95 in our population.population.

Forces driving strategic change in the health insurance industryIntroduction and Overview

$

$

Increasing EmployerCosts

Increasing ConsumerKnowledge

Direct to ConsumerMarketing

$

$

Can’t ReduceBenefits Resistance to

Cost-Shifting

Defensive Medicine

Strategic Implications

Questionable value of Insuranceto Healthy MembersCost of

New Technology

OVERVIEW: Why Do We have a Predictive Model?

To analyze prospective health risk for our entire population,

To prospectively identify members who may:– experience catastrophic medical and financial

outcomes, or– be admitted to a hospital in the next year,

To stratify patients for care and case management, To help evaluate the ROI of our Care

Management processes, and To support the small-group renewal underwriting

process.

Why Do we Have Predictive Model, Cont’dOverview of Member Risk Stratification

Entire MemberPopulation

Average-Risk,RR=1.00

Low-Risk

Moderate

HIGH

TransitioningMbrs

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2.0e

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3.0e

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4.0e

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5.0e

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Num

ber

of M

embe

rs

0 20 40 60 80 100R-R Score Distribution

(Source: BCBSLA MMRD)

Predictive model data, June 2003

N=561,076 mbrs, mean=1.0192Relative Risk Distribution

05.

0e+

041.0

e+05

Num

ber

of M

embe

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N=5,606 mbrs, 99th p-tile=6.77, mean=12.8699th P-tile Relative Risk Distribution

01.

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Num

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embe

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0 .2 .4 .6 .8Probability of Admission



N=561,076 members, mean=0.0272POA Distribution

020

0400

6008

00N

umbe

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Mem

bers

.2 .4 .6 .8 1Probability of Admission



99th P-tile>=0.22, N=5,207 members, mean POA=0.3971POA Distribution for Mbrs in 99th P-tile

BCBSLA Healthplan Facts(Enrollment data current through June 2003)

| ProductLOB | BCBSLA PO BCBSLA PP BCBSLA Tr HMOLA HMO HMOLA POS | Total

----------------------+-------------------------------------------------------+----------ASO | 33,936 30,229 2,496 2,641 4,886 | 74,188Association | 15 46,221 125 1,538 3,566 | 51,465Community | 1,251 54,496 656 7,690 14,499 | 78,592Cost Plus | 0 2,633 573 2,076 625 | 5,907FEP | 4 36,336 0 0 0 | 36,340Individual Ble Value | 0 20,787 4,840 0 0 | 25,627Individual Blue Max | 1 84,383 11,640 0 0 | 96,024Individual Conversion | 0 0 130 0 0 | 130Individual Regular | 0 0 210 18,068 10,645 | 28,923Individual Value Plus | 0 0 226 0 0 | 226Merit Rated | 7,096 32,139 11 14,957 6,729 | 60,932Met | 746 59,044 1,128 5,300 18,706 | 84,924National Control ASO | 0 5,352 3,708 0 0 | 9,060National Control Insu | 0 0 39 0 0 | 39Rate Stabilization | 1,078 7,316 305 0 0 | 8,699----------------------+-------------------------------------------------------+---------- Total | 44,127 378,936 26,087 52,270 59,656 | 561,076

• Over 1 million total covered lives throughout LouisianaOver 1 million total covered lives throughout Louisiana• Approximately 561,076 managed members as of June, 2003Approximately 561,076 managed members as of June, 2003• BCBSLA began using predictive modeling in August, 2002BCBSLA began using predictive modeling in August, 2002

How is Predictive Modeling Used to Support Our Care Management Initiatives?

We use the predictive model at the population level and then drill-down to the high-risk member level. This drill down process includes:

– Analysis of an employer group’s health status Peer Comparison, i.e., self-funded vs. fully-funded accounts Blue Cross Blue Shield book of business comparison

– Analysis of specific diseases which drive employer’s costs

– Identification of the most prevalent and costly conditions

– Identification of actionable and predictable conditions

– Identification of high-risk members

– Stratification of actionable high-risk members into the most appropriate care management program

– Referral of high-risk members into the appropriate program

Predictive ModelImplementation Issues

Differences in expectations of clinical and reporting staff– Mathematical models and clinical relevance– Referral lists and finding actionable, predictable

members Determining the optimal predictive model algorithm Difficulty determining the ideal volume of members to

manage Impersonal and unfamiliar technology Lack of confidence in predictions Impatience with the iterative developmental processes Questions about how Care Management ROI will be

measured

Implementation Issue 1: Differences in Expectations of Clinical and Technical Staff

Mathematical models and clinical relevance “Perfect” referral list consists of members who are:

Predictable (Trauma vs. Chronic Disease) Actionable (ESRD vs. Diabetes Compliant with treatment regimen Teachable Easy to contact Represent an ideal caseload for clinical staff Likely to yield high return for resources

consumed – High ROI

Implementation Issue 2: Difficulty Determining the Ideal Volume of Members to Manage

How many members can your care management staff manage?

Volume of members referred to care management depends on:– High-Risk selection process– Specific disease prevalence in your

population, e.g., COPD, Diabetes, CHF– Acuity of referred members– Available nursing resources

Implementation Issue 3:Impersonal Technology

The predictive model may be perceived as:– A contradiction to the “hands-on” personal

approach that clinical staff are used to,– Impersonal because it may be used to target

members based on numbers.

Implementation Issue 4:Lack of Confidence in Predictions

If the model accurately predicts high-risk members 30% of the time, what about the other 70%?

Why use it if it isn’t 100% accurate? Age and sex predict only about 2% of

cases

Implementation Issue 5:Impatience with the Predictive Modeling Process

Staff may expect perfect identification of high-risk members, i.e.., members who are actionable, predictable, and teachable.

Implementation Issue 6:Questions About How Care Management

ROI will be Measured

Value of Care Management is a significant issue Care Management staff may question how ROI

from predictive model referrals to case management is calculated

Impatience with adhering to a referral process that may not yield immediate savings

Concerns regarding member retention: Why waste resources on members who may move on?

Determining the OptimalPredictive Model Algorithm

Consider the following issues:– Clinical resources – How many Case Management and Disease

Management referrals can you handle?– Business priorities– What criteria will your nursing staff use to review clinical profiles?– Employer groups – increasing demand for information from large self-

funded accounts– Political issues

Referral exclusions for:– Company, product, or LOB?– Medicare primary beneficiaries?– Age group?– Disease category, e.g., ESRD, HIV, NICU, or Cancer?

Carve outs - members managed by vendors and PBMs

Determining the OptimalPredictive Model Algorithm, Cont’d

Identifying “Actionable” and “Predictable” members Five approaches to identifying high-risk members:

– Disease-specific approach– Relative risk score approach– Probability of admission (POA) approach– Low-to-Moderate-to-high cost transition approach– Combination of any of the above

There is no “best” approach: – Begin with the end in mind: How will you measure ROI?– Ask the question: “Is this member actionable?”– Focus on a developing a consistent, measurable process– Use your clinical judgment and training– Pick an approach that makes sense to you from a business and

clinical perspective– Approach must be defensible to employer groups

Approaches and Examples ofReferral Algorithms

Disease-specific approach Relative risk score approach Probability of admission (POA) approach Low-to-Moderate-to-high cost transition

approach The current BCBSLA predictive model

referral algorithm

Example of Employer Group Disease-Specific Approach

MPC Description Cases Services/1,000 Mbrs

ORTHOPEDICS & RHEUMATOLOGY 335 32.15

CARDIOLOGY 312 29.95

OTOLARYNGOLOGY 127 12.19 ENDOCRINOLOGY 165 15.84

DERMATOLOGY 300 28.79 GASTROENTEROLOGY 283 27.16

PULMONOLOGY 217 20.83 PREVENTATIVE AND

ADMINISTRATIVE 152 14.59 PSYCHIATRY 165 15.84

GYNECOLOGY 186 17.85 UROLOGY 296 28.41

NEUROLOGY 229 21.98 OPHTHALMOLOGY 195 18.72 ISOLATED SIGNS &

SYMPTOMS 196 18.81 HEMATOLOGY 107 10.27

OBSTETRICS 156 14.97 HEPATOLOGY 336 32.25

INFECTIOUS DISEASES 183 17.56 NEPHROLOGY 287 27.55

LATE EFFECTS, ENVIRONMENTAL TRAUMA

AND POISONINGS 312 29.95 NEONATOLOGY 153 14.68

CHEMICAL DEPENDENCY 301 28.89

Total 4,993

.9992%3.951%3.974%5.068%

8 .891 %

11.01%

14.88%24.2%

27.02%

>6.77 RR .09-.15 RR3.1-6.77 RR 2.16-3.1 RR.15-.19 RR .19-.30 RR1.16-2.16 RR .57-1.16 RR.30-.57 RR


Predictive Model data, June 2003

(N=561,076 managed Members)

Percentage of Members by RR Score Category

.9999%4%5%

15%

25%

50.01 %

>=$25,857 <$25,857<$8,180 <$4,522<$1,562 <$410


Predictive Model data, June 2003

(N=561,076 managed Members)

Percentage of Members by Total Observed Cost

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N=5,606 mbrs, 99th p-tile=6.77, mean=12.8699th P-tile Relative Risk Distribution

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99th P-tile>=0.22, N=5,207 mbrs, mean POA=0.39POA Distribution for Mbrs in 99th P-tile

Example of the Low-to-High and Moderate-to-High Cost Transition ApproachMEMBE

R Age Sex Obs Costs POARR Score

1 64 M $ 121,263.00 0.9 58.4822 56 M $ 43,740.00 0.9 33.423

3 62 F $ 23,127.00 0.877 7.1244 48 M $ 85,391.00 0.869 13.0045 48 M $ 100,846.00 0.86 16.979

6 63 M $ 90,034.00 0.845 24.4797 46 F $ 98,559.00 0.813 10.575

8 49 M $ 72,896.00 0.793 32.8669 2 F $ 264,299.00 0.788 19.12610 60 F $ 23,824.00 0.757 8.8711 63 F $ 33,682.00 0.739 16.93612 52 M $ 101,641.00 0.734 32.59313 70 M $ 27,919.00 0.724 17.90514 46 F $ 55,315.00 0.722 21.07715 57 F $ 16,420.00 0.715 21.19216 60 M $ 33,893.00 0.699 11.75617 53 M $ 64,704.00 0.686 20.30118 42 F $ 26,013.00 0.685 40.3919 66 F $ 13,611.00 0.684 7.805

This slide is an example of the list of high-risk members referred to case management on a monthly basis.

The BCBSLA Predictive Model Referral Schema, June 2003

ESRD?To be Determined:

Transplants>=1 year?NICU?

HIV/AIDSCA?

MEMBERS(N=561,076 Managed Care

Mbrs)

No

NO

99th Percentile RR Score(RR Score>=6.7)

99th Percentile POA(POA>=22.0%)?

Does Mbr have:COPD? And/or

Heart Disease? And/ orDiabetes?

Yes

No

Is clinical pictureappropriate for

CM or DM?

Yes

Exclude from Referral


Refer to appropriateCM or DM programn=1,529 HIGH-RISK

Mbrs

Yes

Medicare Prime?

Previous Referral?Calais Health, LLC?

Exclude from ReferralYes

Yes



Yes

Exclude from Referralas Non-Actionable

No

Yes

Exclude from ReferralNo

No

No

PROCESS START

CaseManagement

DiseaseManagement

HighRisk

ModerateRisk

LowRisk

HighRisk

ModerateRisk

LowRisk

MedcoHealth(PBM)

UM

Predictive Model Referral Schema, Cont’d

Refer Individual Memberto Case Mgt Nursing Staff

with Disease-SpecificExpertise

Review Predictive ModelReferral List

DiabetesLow vs. High

Acuity

CaseManagement

Nurse

Refer to appropriateCM or DM programn=1,529 HIGH-RISK

Mbrs


CM or DM?

CardiacDisease

Low vs. HighAcuity

COPDLow vs. High

Acuity


CM or DM?

PROCESSTO BE

FINALIZED

DiabetesLow vs. High

Acuity

CardiacDisease

Low vs. HighAcuity

COPDLow vs. High

Acuity

CA

SE

MA

NA

GE

ME

NT

RE

FE

RR

ALS

DIS

EA

SE

MA

NA

GE

ME

NT

RE

FE

RR

ALS

Using CRMS Fundamentals to Improve Reliability and Validity

BCBSLA clinical staff use Fundamentals to:BCBSLA clinical staff use Fundamentals to:– Review claims-level detail of membersReview claims-level detail of members– Determine if members referred via predictive model Determine if members referred via predictive model

were appropriately referredwere appropriately referred– Develop a log of members who were:Develop a log of members who were:

Identified as actionable high-risk but may not beIdentified as actionable high-risk but may not be Not identified as actionable high-risk but should have beenNot identified as actionable high-risk but should have been

Using CRMS toEnhance the Referral Process

The current BCBSLA referral methodology identifies The current BCBSLA referral methodology identifies specific ETGs for referral to Care Managementspecific ETGs for referral to Care Management

You may need more granularity than is available via You may need more granularity than is available via ETGsETGs

CRMS claims-level data may be used to identify CRMS claims-level data may be used to identify members with specific ICD-9 and CPT-4 codesmembers with specific ICD-9 and CPT-4 codes– Example: Member on Celexa not being treated for Example: Member on Celexa not being treated for

depression. ETG says the member is “depressed” depression. ETG says the member is “depressed” while analysis of ICD-9 may say PVD.while analysis of ICD-9 may say PVD.

Using CRMS toEnhance the Referral Process, Cont’d

We use CRMS claims-level data to identify members with the We use CRMS claims-level data to identify members with the following diseases for a Case Management vendor:following diseases for a Case Management vendor:– DepressionDepression– CHFCHF– Hepatitis-CHepatitis-C– High-risk pregnancyHigh-risk pregnancy– DiabetesDiabetes

Once high-risk members are identified:Once high-risk members are identified:– CRMS member-personal data are used to provide contact CRMS member-personal data are used to provide contact

information on high-risk membersinformation on high-risk members– CRMS provider data may be used to contact the member’s CRMS provider data may be used to contact the member’s

primary care providerprimary care provider

Question and Answer Session

Mckesson payor conf implementing a pred. model

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