Neurology/Nuclear Brand Developed for Pharmaceutical Management Science Association

Presented by Mert Sahin, PhD, Chief Marketing Officer, GE Healthcare Ashish Patel, Sales, CareSet SystemsApril 15th, 2019

Targeting and Segmentation in Specialty Pharmaceuticals with Calibrated Messaging

1. About CS/GE, What’s in Medicare, What’s Out, What’s New2. Proxy Calculation, Targeting, and Segments3. Market Access4. Calibrated messaging with Natural Language Generation5. Results and Lessons

Contents

Recognized by Biden Foundation during White House Cancer Moonshot Initiative

CareSet Decodes Medicare Data

Seema Verma recognized 1st researcher with Medicare Advantage.

Among a vast portfolio of products, GE Healthcare manufactures injectable pharmaceuticals used to diagnose Neurological disorders.

Like other pharmaceutical firms, those diagnostic injectables are “buy and bill,” and face the same challenges.

1. Medicare datasets have no blackedout markets or accounts.

2. Neurology, Cardiology, Oncology, Immunology, and conditions prevalent in 65+ age group.

3. No proprietary identifiers. Physician/Hospital NPI is backward/forward compatible.

4. First to obtain data from CMS, quarterly, with quarter lag.

5. Generating and publishing CMS PUFs like Teaming

Breadth and Fit

✓ See Buy & Bill administrations

✓ Follow patients journeys

✓ Map Physician affiliations

A complete picture across all

care settings

Closed Network

Part B (Institutional Outpatient) ● Medical Benefits - billed on UB-04/CMS-1450● Hospital-based Outpatient Setting Examples:

○ Rural Health Clinic○ Hospital Outpatient Services and Imaging○ Community Mental Health○ Outpatient Rehab Facility○ Renal Dialysis

Part B (Carrier Outpatient) ● Medical Benefits - billed on CMS-1500● Non-Institutional (Private) Setting Examples:

○ Private Practice, ○ Medical Groups, ○ Free Standing Clinic, ○ Labs and X-rays ○ Durable Medical Equipment (DME).

Part D (Pharmacy)● Prescription Benefits

Part A (Inpatient) ● Hospital Benefits - billed on UB-04/CMS-1450● Inpatient Examples:

○ General Acute Care Hospitals○ Long Term Acute Care○ Skilled Nursing Facility (SNF)○ Home Health (HHA)○ Hospice

Med

ical

+ H

osp

ital

Ben

efits

- P

art

C

CMS Data UseWe must prevent and CMS prohibits:

1. Fraud or abuse

2. Deduction of the identity of any individual

○ 11-patient threshold protection

3. Exploiting or marketing directly to patients

What’s the Summary Data?1. Included all specialties

2. Patient counts based on ICD code groups

3. Performed procedure counts based on HCPCS/CPT groups

4. Referred procedure counts based on explicit referrals

5. Upstream HCP identified by Teaming method

6. Hospital/practice affiliations

Mapping Explicit B&B ReferralsOutpatient EncountersCMS-1500 Claim Form (Part B)

Referring HCP

Rendering HCP

Billing HCO

Site/Facility HCO

Outputs to Summary Data

Referring Rendering Brand X Referred Patient Count

Brand X Referred Claim Count

Referring HCP NPI

Administering HCP

+1 +1

Referring HCP NPI

Billing HCO +1 +1

Referring HCP NPI

Site/Facility HCO +1 +1

Filters based on:• ICD Diagnosis• ICD Procedures• HCPCS Codes• Date

Specialist Target List Data ModelServices

Number of patients perform:

Product

Imaging CPT 1

Imaging CPT 2

Imaging CPT 3

Number of patients referred:

Product

Imaging CPT 1

Imaging CPT 2

Imaging CPT 3

Target Demographic

Target NPI Number

Name

Specialty

Phone and Fax

Practice and Mailing Addresses

Treatment / E&M

Patients with Indication 1

Patients with Indication 2

Patients with Indication 3

Patients with Indication 4

Total Patients

New Office Visits

Established Office Visits

Prescribing Market Basket

NewProxy

ProxyTotal Rx’s

}

(12% of HCPs are both seeing the indicated

patients and prescribing from the market basket)

Building the Proxy Model to Predict Brand ActivationIntent: Estimate Brand utilization by a Target over next 2 years

Proxy Score = Number of Indicated Patients (x) * % New Patients (m)

Indication 1 U Indication 2New Patients

All Patients Patients Seen

Rx Volume / Total Rx Patients

● Whether the Target was managing meds for the indication (Past method)● Number of indicated patients they treat and practice growth (New Proxy)● Panel Activation by Target Model

(~40k Targets > 30%)

# Providers by Region TotalCentral xxxxxxEast xxxxxxWest xxxxxx

Grand Total ~80,000

# Providers by Region TotalCentral xxxxxEast xxxxxWest xxxxx

Grand Total ~5,600

BeforeAll Providers Found

AfterTargets with Scores > 20

Narrowing the TargetsAll Providers vs Targets with Proxy > 20

Segment BUser, High Growth

PotentialProxy: 50+

Segment DNon-User, High

Growth PotentialProxy: 50+

Segment CNon-User, Medium Growth Potential

Proxy: 20-49

Segment AUser, Medium Growth

PotentialProxy: 20-49

Practice Growth Potential

Y

N

Pro

du

ct U

se

13%

12%

25%

50%

Customer Segment Map

Allocating the Target to an Account

Problem: How can we improve efficiency of our calls?

Solution: Know how many potential Targets are at each Account

Target

Site Acct

Figure 4. Results from Analysis at Metro-levelFigure 4. Results from Analysis at Metro-level

Allocating the Target to an Account

Targets

Accounts

Billing

Market Access Can Leverage Affiliations Build data-driven plans and tactics

1. Identify local key opinion leaders (KOLs) who utilize Brand.

2. Exposes all Organizational affiliations between the Target and hospitals, medical groups, and other physicians.

3. Invite KOL to local payor account conversations.

3 Targeting Strategies

Strategy Overview

Mass marketing(Undifferentiated)

- Targets the WHOLE market, ignoring segments- Products focus on common customers needs

Segmented(Differentiated)

- Targets several market segments within the same market- Products are designed and targeted at each segment- Requires separate marketing plans

Concentrated(Niche)

- Business focuses on smaller segments or customers- Aims achieve a strong fit within the niche

HOW DO WE USE SO MUCH DATA?!

1. Capture ROI with your favorite CRM

2. Maximize ROI with natural language generator.

a. Dynamic content enables niche messaging at scale b. Contextualizes each Target for the representativesc. Hyper-localize messaging for Market Access

Putting it all together...

Segment BUser, High Growth

PotentialScore: 50+

Segment D Non-User, High Growth

Potential

Score: 50+

Segment CNon-User, Medium Growth

PotentialScore: 20-49

Segment AUser, Medium Growth

PotentialScore: 20-49

Score and Classify Customer Segments

Pro

du

ct U

se

Y

N

Growth Potential Proxy Score

1. Dr. [Last name], with a [segment name] score of [score], has [Product 1

Patient Count] Brand 1 indicated patients out of [Total Patients] total

patients from all settings.

2. They used Brand 2 [Brand 2 Claim Count] times and referred it [Brand 2

Referred Claim count] times. They used Brand 3 [Brand 3 CLAIM Count]

and referred Brand 3 [Brand 3 Referred Claim Count].

3. Etc.

Paragraph 1: Indications and Brand

Paragraph 2: Affiliations1. At [Organizational Name], Dr. [Last name]’s KOL rank is [Rank]

among [Affiliated Target Count/Affiliated HCP Count] Targeted and

Total HCPs at this sites.

2. They graduated in [year], did their residency at [Residency Location]

and have a location match type of [Settings].

3. They are most often working on [Day or Week Array].

Dr. PATEL (NPI#) has a score of 133.91 and is in Segment D with 643 patient(s), 293 with Indication 1, and 103 with Indication 2 in the last 12 months. He prescribed within the market basket for 126 patients in 2017, referred 141 for Brand X, and performed 51 administrations of Brand X. He used 45 Diagnostic X, 632 Diagnostic Y, and 207 Diagnostic Z. He has 43.76% of office visits with new patients.

He shared 51 patient(s) with Dr. TROTTER (NPI#), 33 with Dr. OZIGBO (NPI#) ….He is affiliated with MERTS MEDICAL GROUP (NPI#) and 1 other hospital(s). He is among 23 targets of 616 providers at MERTS MEDICAL GROUP.He graduated in 1997, and did his residency at Cornell University. He most often works on Monday and Thursday.

New CRM Field

Summary of Actions1. Built and filtered targets based on a proxy formula

2. Segmented the into categories for customized messaging

3. Enabled Market Access using affiliations and referrals

4. Used NLG to improve physician calls made by field team

Impacts to Brand1. Double revenue within one quarter of launch through better

targeting

2. Reached correct target audience that was prescribing and referring but was previously unknown

3. New targeting and segmentation protocol rolled out across portfolio products within pharmaceutical unit

4. Wider benefits:

a. Market dynamics

b. Procedure flow

c. KOL mapping

Impacts to Team - from Pilot to Scale1. Teamwork involving partners, field representatives and

managers

2. Pilot results

3. Iterative planning and strategy allowing confidence to grow

4. Data updates

5. Foster the innovation: There should be no point where the manufacturer says “we have a way that you must follow”, teamwork is more beneficial.

Lessons Learned1. Having the proxy model first could have sped things up.

2. “One size fits all” across the portfolio is a false assumption:

a. Some brands rely on finding referrals and performing

b. Other brands rely on finding performing and centers/organizations

3. Proxy formulas depends on the stage of the product, the strategy, and whether you’re in growth mode.

Thank you!

Questions?

Contact: mert.sahin@ge.comashish@careset.com