Predictive Acquisition

(1)

Predictive Acquisition Cost

q

Introduction to PAC

i

(2)

Agenda

• Five key criteria for new price type

• Five

key

criteria

for

new

price

type

• What

is

PAC?

• Key

milestones

since

launch

(3)

Five Key Criteria for a

New Price Type

• Transparency – benchmark bears a genuine

l i hi h l i i i f h

Led by key NCPDP

stakeholders/committees:

relationship to the actual acquisition cost of the

drug. (In addition to this “relevance” definition,

transparency also sometimes refers to being

“understandable”.)

stakeholders/committees:

industry found fundamental

short‐comings in each of the

existing pricing benchmarks,

• Accessibility– benchmark readily accessible

and can be readily adopted by the pharmacy

industry.

consensus was reached on

what criteria any suitable

price type must satisfy

• Comprehensiveness – benchmark available for

all branded + generic drug groups.

• Timeliness– benchmark updated with a

frequency appropriate to the quickly changing frequency appropriate to the quickly changing

actual acquisition costs, especially for generic

products.

(4)

What is PAC?

Pharmacy Distribution System

• PAC estimates drug Acquisition Costs

Pharmacy

Distribution

System

in a transparent and defensible way

• PAC is more closely aligned with true

drug acquisition cost than any other

available drug price type available drug price type

• PAC supports both Pricing Analytics

and Contractual Requirements

– UseUse of of PACPAC(with(with PACPAC_low_l andand PACPAC_{hi h}_highrange)range)

to determine performance of contracts,

guide reimbursement rates, improve

negotiating position

– Use of PACas formal reference in “cost

plus” contracts orPAC Retailas formal plus contracts or PAC Retailas formal

(5)

The PAC Offering

g

The PAC output file delivers PAC (and a range

PAC_lowto PAC_high) for each drug on a daily basis.

PAC tracks acquisition‐cost far better than any of

the traditional pricing benchmarks (e.g. AWP).

The PAC Dashboard is a convenient browser‐

based tool that allows you to look up the PAC

value for any particular drug. Other information

about the drug, including some of the items that

(6)

Key Milestones Since Launch

y

Launch First PBM First LTC First 2012Q1 2012Q2 2012Q3 2012Q4 2013Q1 First Retail Chain First Health Plan First Wholesaler Go Live! PAC Dashboard Model Update (data design) Model Update (NADAC) Q Q Q Q Q Model Update (codes)

(7)

Expanding Set of Use Cases

p

g

•Operational •Grade out PBMs, challenge/negotiation h •State Medicaids:

•Replace or validate AAC

•Tune SMACs

•Employer / Plan Sponsor:

PBM l l ti research

•Wholesaler RFP and

contract pricing

consistency

• U&C/cash pricing

•Loss File analysis

P f t l AC

PBM proposal evaluation

/contract performance

(consultants)

•Proxy for actual AC;

acceptable alternative to

submitting invoices to

CMS, states, others?

•Defend pricing when

challenged by payer/PBM +

challenge aggressive

payer/PBM MACs

•Proxy for actual acquisition

•Pharmacy MAC

optimization: balanced;

spread

cost; acceptable alternative

to submitting invoices to

CMS, states, others? spread

•Identify opportunities to

create savings for clients

•Improve GER predictability

•Justify pricing (PBM vs

(8)

PAC Addresses Two Fundamental

Drug Pricing Activities

• _Establishing

_or

_assessing

_specific

_drug

_prices

(9)

Use Case – Maximum Allowable Cost (PBM/Payer)

MAC Optimization

PAC range can translate into a range within which the MAC should lie:

MAC Optimization

Value Proposition

• Balanced and Fair Pricing

which the MAC should lie: *Reduce drug spend‐ Identify

situations where reimbursement is high

*Pharmacy relations‐Identify

situations where reimbursement is low

Factor: pharmacy

per‐script profit target

• Explainable methodology

PAC_low PAC_high MAC_low MAC_high

Drug Current MAC PAC PAClow PAChigh MAC Low MAC High Proposed Target Price Impact Potential

amlodipine besylate tab 10 mg 0.0915 0.0318 0.0195 0.0441 0.0758 0.1004 Unchanged 0.0915 $ ‐

gabapentin cap 300 mg 0.1235 0.0678 0.0473 0.0883 0.0697 0.1107 Reduce 0.1107 $ 13,040.84

glimepiride tab 4 mg 0.3981 0.0616 0.0425 0.0807 0.0814 0.1196 Reduce 0.1196 $ 31,319.04

glyburide tab 5 mg 0.2741 0.1565 0.1157 0.1973 0.1391 0.2207 Reduce 0.2207 $ 5,788.63

loratadine tab 10 mg 0.0925 0.0667 0.0456 0.0878 0.1234 0.1656 Raise 0.1234 $ (25,653.50)

l t b 1 0 0812 0 0266 0 0155 0 0376 0 0630 0 0851 U h d 0 0812 $

lorazepam tab 1 mg 0.0812 0.0266 0.0155 0.0376 0.0630 0.0851 Unchanged 0.0812 $ ‐

zolpidem tartrate tab 10 mg 0.0706 0.0320 0.0197 0.0444 0.1011 0.1258 Raise 0.1011 $ (6,436.90)

Assumptions:

Min $‐profit/Rx (NOT including dispensing fee): $ 2.50 Min %‐profit/Rx (NOT including dispensing fee): 10%

(10)

Use Case – Loss-File Analysis (Retail Pharmacy)

Purchasing and Reimbursement

For

any

specific

claims,

a

pharmacy

can

use PAC and ranges (PAC

_low

and PAC

_high

) to

Value

Proposition

• Quickly identify if loss file issues

use

PAC

and

ranges

(PAC

low

and

PAC

high

)

to

identify

which

party

it

should

direct

its

attention

towards:

i.

Identify

claims

where

the

• Quickly identify if loss‐file issues

are due to:

(a) Payer/PBM reimbursement

OR

Payer/PBM

is

paying

less

than

PAC

_low

ii.

Identify

claims

where

the

Wholesaler/Manufacturer

is

h

i

th

PAC

(b) Procurement

charging

more

than

PAC

_high

• 58% of claims had a reimbursement price lower than AC

• Claims which satisfied one of the criteria above

AC_low AC_high

(i) Reimburse < PAC‐low (ii)Acquisition > PAC‐high

(11)

Use Case – Generic Effective Rate (GER)

GER Relevance and Stability

GER to measure Price list quality

• GER approach is an effective AWP

measurement of a price list’s

performance if using an underlying

reference that tracks acquisition cost

• PBM

case

study

for

a

Medicaid

book

of

business



target

74%

GER

for MAC performance measurement

for

MAC

performance

measurement

• AWP

• Because AWP is so disconnected from

i iti t d h f th $1 2

$1.4 $1.6 $1.8 $2.0

AC vs AWP (zoomed in)

acquisition cost, and hence from the

PBM’s MAC, the GER varies

dramatically across GCNs when based

on AWP $0.2 $0.4 $0.6 $0.8 $1.0 $1.2 AW P

(12)

Use Case – Generic Effective Rate (GER)

GER Relevance and Stability (cont’d)

GER Relevance and Stability (cont d)

AWP

• GER approach is an effective

• PBM

case

study

for

a

Medicaid

book

of

business



target

74%

GER

for MAC performance measurement

for

MAC

performance

measurement

• AWP

‐

based

GER

measure

sensitive

to

utilization

mix

movement

• E.g. ~68% for Acute meds, while ~%85

(13)

Use Case – Generic Effective Rate (GER)

GER Relevance and Stability (cont’d)

GER Relevance and Stability (cont d)

AWP

• GER approach is an effective

• PBM

case

study

for

a

Medicaid

book

of

business



target

74%

GER

for MAC performance measurement

for

MAC

performance

measurement

• Basing

GER

on

an

PAC

Retail

(i.e.

AC

“MSRP”)

reference

PAC Retail

• Looking across GCNs, a PAC Retail

based GER is tightly centered around

(14)

Use Case – Generic Effective Rate (GER)

GER Predictability

Beyond AWP’s disconnect with true acquisition cost, another issue exists when

measuringg MAC performancep based on GER:

When looking across NDC’s within a GCN, the AWP often varies even though PBM’s MAC is fixed at the GCN level

Outcome: Resulting GER depends in part on which manufacturers a pharmacy

Drug Label PBM MAC NDC AWP GER

TRETINOIN 0 05% CREAM 1 1860 45802036142 2 09511 43%

g p p f p y

purchases from (i.e. which NDCs within a GCN are utilized), adding a degree of

uncertainty for PBM when targeting GER‐based performance metrics

TRETINOIN 0.05% CREAM 1.1860 45802036142 2.09511 43%

TRETINOIN 0.05% CREAM 1.1860 43478024220 2.51100 53%

HYDROCODON‐ACETAMINOPHEN 5‐500 0.0440 00406035705 0.19686 80%

HYDROCODON‐ACETAMINOPHEN 5‐500 0.0440 00591034901 0.50650 92%

PAC Retail exhibits little to no variance across NDCs within a GCN

Therefore, while the PAC Retail‐based GER is fixed at 74%, the same GCN utilization

could result in an AWP‐based GER of anywhere from 68% to 77% (depending on

(15)

Use Case – Retail Pharmacy

Grading out Payers/PBMs

Plan GER (PAC)

Plan 1 55.6%

Plan 2 57.4%

Value

Proposition

• Rank Payers/PBMs based on

Plan 3 60.6% Plan 4 59.2% Plan 5 59.8% Plan 6 57.6% Plan 7 58.7% Plan 8 59 3% y / reimbursement

• Quickly identify opportunities to

seek improvement

• Compare

each

PBM’s

reimbursement

to

some

common,

independent

measure

Plan 8 59.3% Plan 9 66.6% Plan 10 63.0% Plan 11 67.4% Plan 12 51.0% Plan 13 24.9%

– Difficult to use AWP because resulting GER is so

sensitive to drug mix, and even to manufacturer

from whom drug is purchased.

• For each plan an overall GER based on PAC

Plan 14 43.1% Plan 15 60.1% Plan 16 58.7% Plan 17 58.4% Plan 18 53.9% Plan 19 66.2%

For

each

plan,

an

overall

GER

based

on

PAC

Retail

was

calculated

Plan 9 66. % Plan 20 41.2% Plan 21 66.3% Plan 22 69.2% Plan 23 72.8% Plan 24 44.6%