IMS Health Clinical Trial Optimization Solutions

Clinical Trial Optimization @IMS HealthLeveraging 360⁰ insights to deliver trials on time and on budget

Linda Drumright, General ManagerClinical Trial Optimization Solutions, IMS Health

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2 October 2014IMS Health Clinical Trial Optimization Solutions - Patient Recruitment and Retention Summit

Session Objectives

• How different types of information can be used to influence

What I hope you will learn

ypthe trial planning process

• How to validate trial assumptions and evaluate the operational implications of various performance variables operational implications of various performance variables through the use of data, technology and predictive analytics

• How some sponsors and CROs are successfully leveraging these optimization techniques in their trial operations to these optimization techniques in their trial operations to achieve better outcomes and deliver their trials more predictably


Delivering Trials On Time and On Budget

• Becoming more predictable requires an organization to test

A Data-Driven Approach to Predictability

and validate as many assumptions as possible that can impact timelines and costs or create volatility during execution

• Many types of insights drive answers to key questions:Many types of insights drive answers to key questions:− Are there patients in the world that match the I/E criteria?− Where are they and who has access to them?

How many do I think I can get and how fast?− How many do I think I can get and how fast?− What will it cost me?− What are the risks in my operational plan?

Wh t ld b th ti l t d ff f t ti ?− What would be the optimal tradeoffs of cost versus time?

• Predictive analytics can be used to measure and weigh the tradeoffs so the most effective courses of action are chosen


Assessment of Operational Feasibility

Patient Profile

Validate / Challenge

• Epidemiology• Competition • Standard of Care• Regulatory Landscape

• Demographics – age, insurance status, income

• Treatment Behaviors and Patterns specialists?

Patient Profile• Patient Access• Facilities• Capabilities• Performance• Experience

Market Analysis

Patterns – specialists? GPs?

p

Site Profile

Enrollment ModelsCost Considerations

5

Site Selection

October 2014IMS Health Clinical Trial Optimization Solutions - Patient Recruitment and Retention Summit

Real World Evidence

Feasibility - assess and ti i I/E it i

Apply I/E criteria against relevant patient population

Select best data source for your specific protocol

optimize your I/E criteriaLongitudinal

Rx data(10 countries)Health

plan data(US)

OncologyEMR data

(US)

400m Globali i

Non-Oncology EMR data

( )

Medical pharmacy

(US)

Country Allocation & Site Selection – find

countries/sites with relevant patients

Patient Lives EMR data(US, UK, FR, DE)claims

(US)

Medical Lab data

(US)Oncology

survey data(11 countries)

Medical survey data(44 countries)


Customer Case Study: Global Pharmaceutical Co #1

• Situation− All subjects failing screening by not meeting a lab requirement in protocol

Optimizing a Protocol

• Total Testosterone <5nmol/L at screening

− Question of what is normal vs abnormal and whether to change protocol

• Analysisy− Normal TOTAL testosterone levels in females who are not pregnant:

• PREmenopausal women: 0.347 nmol/L to 1.9085 nmol/L• POSTmenopausal women: 0.2429 nmol/L to 1.388 nmol/L

− Client range seems appropriate but may be high (Total Testosterone <2nmol/L might be more appropriate, but would not yield different results.)

− It appears they are looking for low Total testosterone, however, criterion #2 requires Free Testosterone to be above normal a contradiction If Free #2 requires Free Testosterone to be above normal - a contradiction. If Free testosterone is above normal, it is unlikely that total will be below normal.

• RecommendationR i t t t ti i t l− Re-review testosterone assumptions in protocol


Customer Case Study: Global Pharmaceutical Co #2U d t di th S iti it f I/E C it iUnderstanding the Sensitivity of I/E Criteria

Asthma attrition funnel: inclusion criteria


Customer Case Study: Global Pharmaceutical Co #2U d t di th S iti it f I/E C it iUnderstanding the Sensitivity of I/E Criteria


Key lessons using EHR data to evaluate asthma patients

• Asthmatics can be identified using EHRs

• Data on a substantial proportion of pharmacological asthmatic management is il blavailable

• The majority of elements of a clinical trial protocol can be translated into a selection process to apply to EHR data

• Assessing continuity of asthmatic medication requires a more flexible definition or a probabilistic or predictive approach

• Severe asthmatics may not receive all of their care in a GP setting, so choice of clinical data source is critical

• Data relating to severe exacerbations may be missing from claims and GP recordsg y g

• Successful translation of clinical criteria into a selection algorithm requires an appreciation of limitations of the data source

10 October 2014IMS Health Clinical Trial Optimization Solutions - Patient Recruitment and

Retention Summit

Creating an Optimal Plan

Who

Validate Assumptions Protocol Optimization

Exclusion

Inclusion

Patient Definition Site Definition Id l C t i Global Ideal CountriesMetrics Number of sites

Patient

1572

Number of countries

Patient Access Site Selection

ExperiencedInvestigators

USA

Sit S l tiTime/Cost Trade-offs

Site Selection/

December 201311IMS Health Clinical Trial Optimization Solutions - Patient Recruitment and Retention Summit

Site Profiles with Patient Access

Find sites with recent trial experience

Find sites with patients Find sites with patients meeting the

inclusion/exclusion criteria


Patient Access Proximity


Geographic Cost Implications

14© IMS Health, GrantPlan 2013


Startup and Enrollment Implications


Customer Case Study: Global Pharmaceutical Co. #3• Challenge

− Limited historical data to support enrollment planning

• Reliant on CRO plan and enrollment status/projections 35

40

Benchmarks

Benchmarks

• Lack of accurate forecasting capabilities

− Critical trial with 13+ month delay• Delay not recognized until very late• Leading to increased enrollment timelines and budget

S t i C t d Sit P f i20

25

30

Canada

US− Systemic Country and Site Performance issues• Most sites not starting until end of enrollment• Many countries with significant start up delays

• SolutionR t ti l i i b h k t t

5

10

15

− Retrospective analysis using benchmarks on start-up and enrollment, as well as predictive analytics along the course of the trial, proved a data-driven approach would have yielded significantly better results

0Planned Achieved

RESULTS:• 11+ month delay identified prior to study start through benchmark data• Projected LSE 10 days from actual LSE at 60% of subjects enrolled

16

Projected LSE 10 days from actual LSE at 60% of subjects enrolled


Optimal Site Selection

Public domainCt gov 3rd party

Data Sources

Industry Site Benchmarks

Claims pha macies Client systemsEDC CTMS IVRS

IMS HealthG Pl

Your own site performance

Industry-wide site performance

Patient access, site affiliations

Site experience and capacity

Ct.gov, 3 party data providers.

Site Selection C it i

Claims, pharmacies, health plans, EMRs,

etc.

EDC, CTMS, IVRS, internal warehouses

GrantPlan cost benchmarks

GrantPlan

Criteria

Sit O ti i• Fact driven process• Intuitive, visual analysis

• Common interface for site selection• Access to “Naïve Sites” performance

SiteOptimizer

• Predictive analytics• 2-way CTMS integration• Enables StudyOptimizer

ccess to a e S tes pe o a ce• Patient availability and Site contact info• Integrated cost insights• Global Unique identifiers

WHY IMS


Compare Investigator Performance and Experience


Segment by performance categories to build roster


Scenario Modeling – Weighing the Options


Baseline and Execute to Plan

Track progress and see projections for site initiation.

T k d Track progress and see projections for screening.

Track progress and see j ti f projections for

randomization..


Customer Case Study: Global Pharmaceutical Co. #4

Challenge• Limited recruitment performance predictability• Lack of visibility into the ‘truth’ (multiple data

Studies Recruiting to Plan65%

sources, manual processing)• Financial pressures (cost and capacity)

Solution• Implemented IMS Health’s Global Enrollment 50%

55%

60%

head

of T

ime

ead

of

Tim

e

Implemented IMS Health s Global Enrollment Planning and Tracking platform

• Automated multiple data feeds into single data warehouse

• Trained staff, mandated use40%

45%

% o

f Stu

dies

on/

ah

Client mandates use of the IMS Health platform

for all studies

Client mandates use of the IMS Health platform

for all studies

tud

ies

on

/ah

• Established approved recruitment plans prior to start of recruitment− Managed entire trial thru the platform 30%

35%

Jan

Apr Jul

Oct

Jan

Apr Jul

Oct

Jan

Apr Jul

OctYear 1 Year 2 Year 3

PilotPilot

% o

f S

tRESULTS:• 100% increase in studies recruiting to plan (exceeded 50% target)• Automated reporting, improved visibility• 1/3 decrease in non-performing sites

22

1/3 decrease in non performing sites


Conclusion

• Data and technology combined with the right analytics can help you gain a 360-degree perspective of this very complex p y g g p p y pset of problems: − You create a protocol that is feasible from the start− Even if you can’t change the protocol, you can execute it more y g p , y

knowledgably− You can plan for what a patient is most likely to look like rather than one

that is conceptual− You can leverage your own and others’ experience through real world

historical data− You can predict more accurately what will happen and you can respond

more quickly to what actually will happen before it happensmore quickly to what actually will happen before it happens− You can set and manage to more realistic expectations


Questions?


Thank You!

[email protected] l hwww.imshealth.com

and stop by the IMS Health booth


IMS Health Clinical Trial Optimization Solutions

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