DATA INPUTS

Leveraging Anonymous Patient Level Data to Identify Undiagnosed Rare Disease Patients

Jennifer RawdingSr. Consultant, Commercial Effectiveness (302) 463-8411; Jennifer.rawding@SymphonyHealth.com

Julieanna GubitosaSr. Manager, Commercial Effectiveness (610) 996-6760; Julie.gubitosa@SymphonyHelath.com

Jonathan WoodringPrincipal, Commercial Effectiveness (201) 421-6472; jonathan.woodring@SymphonyHelath.com

BACKGROUND

METHODOLOGY

CONCLUSION

SHS combines anonymous patient level data with disease state knowledge and machine learning techniques to identify potential rare disease patients prior to diagnosis. This enables identification of patients and physicians in “real time,” such that interventions are

timely, targeted and effective.

Extremely small populations, complex symptomatology, and lack of physician awareness can make identifying and diagnosing rare disease patientsextremely difficult.

Challenge: Identifying the appropriate time to engage a physician is paramount for diagnosing rare disease patients

SHS Solution: Real-time Promotional Analytics Identifying appropriate medical education through daily or weekly reports that monitor changes in patient level claims data patterns.

TriggerIdentification

Trigger Design

TriggerImplementation1 2

Deduction Based

Induction Based

Historical Lookback

Tracking & Impact

Deduction Based

Collaborate with brand, marketing and clinical teams to identify events that could potentially trigger greater receptivity to promotion

Induction Based

Data mining to identify events that could potentially trigger greater receptivity to promotion

Historical review of the data associated with the trigger to…

• Estimate the opportunity related to the trigger

• Identify the ideal outreach timing based on the trigger (given the trigger event and physician action)

• Determine the optimal cadence/frequency of the trigger

• Incorporate triggers into sales planning and deployment processes

• Post-implementation impact assessment at a trigger level to estimate the lift in script volume due to the trigger

• Tracking and reporting metrics associated with the triggers on a regular basis.

Build & Deploy

Operational Assessment

Rare Disease Patient Journey

Deduction Based Trigger Identification

Possible Additional Layers:• Age • Demographics

Seizures + Diagnostic testing

Juvenile Hyper-cholesterolemia+

Hepatosplenomegaly

Pre-Diagnosis Treatment

Primary Symptoms

Secondary Symptoms

Comorbidities

Misdiagnoses

Diagnostic Testing

Genetic Screening

Rare Disease Diagnosis

Diagnosis

Break in Therapy

2 Primary Symptoms + 2 Secondary

Symptoms

Beta Glucosidase Assay + Organ

Swelling

New Rare Disease Diagnosis

Pancreatitis + Metabolic Panels

Xanthoma + Cholesterol Test

Acromegaloid + Hepatosplenomegaly

+ Acanthosis

Bowel Surgery + Chronic Diarrhea +

Parenteral Nutrition

Payer Rejection

Competitive Treatment Initiation

1 Trigger Design2

Preliminary Trigger List

Analyze historical data associated with each trigger to optimize trigger definition to balance…

Trigger Volume Trigger Timing Trigger Delivery Cadence

Optimizing trigger volume by adjusting specificity/thresholds

Optimizing timing of the trigger based on trigger event and physician action

Optimizing delivery cadence of the trigger based on territory level volume & capacity

Physician Action

Trigger Event

Operational Assessment

Scanning SHS patient claims data on a continuous basis for the defined triggers…

…and delivering daily, weekly, or monthly trigger files

Relay triggers to personal and non-personal channels (Medscape, Doximity, Epocrates) and non-selling teams (MSLs, Nurse Educators, Case

Managers) on a continuous basis at a defined frequency…

Sales Force

Nurse EducatorsCase Managers

Non-personal Channels

Medical Science Liaisons

Build & Deploy

Trigger Implementation3

Anonymized patient level data can and is helping increase the awareness,

diagnosis, and treatment of patients in the rare disease space.

The scale of the SHS foundational data assets, and the strength of the deductive and inductive

algorithms deployed, are assisting leaders in the rare disease space to increase important medical

education, appropriate diagnosis, and life-changing treatment for those suffering from rare disease.

RESULTS

2%5%5%5%5%5%

7%9%9%

18%

0% 10% 20%

PED GASTROENTEROLOGYFAMILY MEDICINE

HEMATOLOGYPATHOLOGY

INTERNAL MEDICINEPEDIATRICS

PED HEM/ONCMED ONC

GASTROENTEROLOGYHEM/ONC

Physician Volume by Specialty

0102030405060708090

100

Ohi

oSo

uthe

rn C

AN

J/PA

New

Yor

k Ci

tySo

uth

Flor

ida

Loui

svill

eSo

uth

Texa

sM

I/In

dian

apol

isPa

cifi

c N

orth

wes

tAr

izon

a/U

tah

Nor

th T

exas

Nor

ther

n N

ew…

Min

neso

taSo

uth

Cent

ral

Atla

nta/

Colu

mbi

aD

CSo

uthe

rn N

ew…

Ups

tate

NY

Nor

ther

n CA

Gre

ensb

oro/

Ch…

Colo

rado

KS/M

OIo

wa

Haw

aii -

Hyb

rid

Triggers by Territory• Avg. Triggers / Territory: 27• % Triggers Aligned: 15%

Avg.

67%

34% 43% 46%

19%40%

29%39%

14%29%

19%42%

4%

1%1%

1%

3%

21%

20%

25%

3%

26%

18%

33%

6%

29%27%

21%

44%

13%18%

10%

32%

21%

29%

3%18%

33% 27% 22% 30%17% 23%

13%

49%

16% 21% 12%

Genetic Testing + Organ Swelling

Xanthoma + Cholesterol Test

Symptoms + Organ Swelling

Misdiagnosis

2 Major Symptoms + 2 Minor Symptoms

Pancreatitis + Lipid Panel

New Diagnosis

Competitive Treatment

Any Treatment + Adverse Events

Break in Therapy

Payer Rejection

Treatment + Biomarker

1

Induction Based Trigger Identification

1

2

3

Leverage Machine Learning Techniques to Identify Existing Rare Disease Patients• Collaborate with brand and medical teams to identify ‘ideal patients’• Derive all relevant variables across Rx, Tx, Px, Dx dimensions and

demographics across all SHS core data assets• Examine univariate distributions for training and hold out sample

Conduct Patient Look-Alike Modeling• Leverage advanced analytics and bootstrapping techniques • Score entire SHS (eligible) patient database • Validate model and assess model diagnostics

Patient Aggregation and Physician Attribution• Aggregate scored patient database to the physician level• Decide upon single physician attribution or allow multiple attribution• Initiate physician level targeting

Identify existing rare disease patients and score the entire SHS patient database for these characteristics, aggregate to the physician level based on the most recent data available…

1

TRIGGER when patient presents with unique

combination of symptoms

TRIGGER when physician orders tests for suspected

condition

TRIGGER when the patient is diagnosed with the

condition

Making diagnosis and treatment decisions for patients

TRIGGER when payer information for a patient

changes

HAE MarketIdiopathic Thrombocytopenic Purpura~15% increase in new patient starts on the client’s drug in the test district versus an equivalent control district

Brand’s market share among the triggered patient population was measured to be double as compared non-triggered patient base

LSD Market

Off the triggers that are delivered to Reps, ~34% got initiated on therapy; On the non-triggered patient set, only ~16% got initiated on therapy

Trigger ImplementationTracking &

Impact

Patient-centric longitudinal database enables an in-depth understanding of the patient journey

IDV® captures & connects patient, physician, pharmacy and hospital data from healthcare payment processing

Aver

age

year

s of

patie

nt

exist

ence

in

IDV

IDV highlights various Patient Touchpoints