Using Technology to Enhance

(1)

Using Technology to Enhance

Clinical Trial Accrual

SWOG Spring Meeting

Neal J. Meropol, MD

Chief, Division of Hematology and Oncology

University Hospitals Case Medical Center

Case Western Reserve University

May 2, 2014

(2)

The Problem

• Clinical trials are the evidence base for

Clinical trials are the evidence base for

improving cancer care

• _{Clinical trials represent high quality cancer}

care

However,

• Very few cancer patients take part in

clinical trials

(3)

How many patients actually take part?

y p

y

p

• California Cancer Registry 2001‐2008

< 1%

–

< 1%

–

Al‐Refaie et al. Annals Surgery 2011

• NCI‐Sponsored Coop Group Trials Enrollment 1996‐

2002

–

1.7%

of incident cancer cases enrolled

–

Lower in racial/ethnic minorities, older patients

–

Murthy et al. JAMA 2004

• NCI Comprehensive Cancer Centers 2012

–

14%

median

–

http://cancercenters.cancer.gov/data/sum3.html

(4)

Barriers at the Point of Care

Logistical

Patient

Physician

4

(5)

Key Determinants

of Accrual for Patients

• Awareness

• _Access

• Eligibility

• _{The Decision}

(6)

Influences on Clinical Trial Decision Making

Patients

Doctors

H

lth

Communities

Healthcare

Teams/Organizations

Families

(7)

Overall Goal

To optimize patient decision making

about clinical trials by improving

preparation for consideration of

clinical trials as a treatment option

(8)

Theoretical Model

Knowledge

Theoretical Model

Knowledge

Barriers

Attit di

l

Preparation

for Decision

M ki

Physician

Encounter

Attitudinal

Barriers

Making

Encounter

Th

The

Decision

Miller, SM; Diefenbach, M. C-SHIP: A cognitive-social health information processing

approach to cancer. In: D. Krantz & A. Baum, editors. Perspectives in behavioral medicine. Lawrence Erlbaum: New Jersey; 1998. p. 219-44.

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PRE‐ACT

Survey to assess knowledge,

attitudes, and values

Automated Feedback:

Values clarification and

top barriers

Tailored Video Library

9

(10)

Welcome Screen

Thank you for your interest in the PRE-ACT study. We are asking you to take part in this study because you are coming in for your first appointment with your doctor As part of this study we will because you are coming in for your first appointment with your doctor. As part of this study, we will ask you to:

• Read and approve Informed Consent documents (Informed Consent, HIPAA Authorization) • Fill out a survey (this takes about 20 minutes)

• Look at some educational materials (this takes about 15 minutes)Look at some educational materials (this takes about 15 minutes) • Fill out a second survey (this takes about 15 minutes)

Two weeks after you meet with your doctor, we will ask you to fill out one more survey. It should take about 15 minutes

about 15 minutes.

If you have any questions or concerns about this study, you can call the study team at

1-877-404-4159 or email them at [email protected]. You will see this contact information on every screen.

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Sample PRE‐ACT Video Library

Preparatory Education About Clinical Trials

Mr. Doe, below is a list of video clips that you can watch to get information about clinical trials in general, and information about common misconceptions about clinical trials.

What is standard treatment?

Is there a clinical trial for everyone?

Click an item in the list to view a video. When

treatment?

Is taking part in a li i l t i l

Are there ways to deal for everyone?

you are done watching videos, click the Back

Button.

After you view the videos selected just for

clinical trial voluntary?

with transportation and financial issues?

What is How will clinical trials

videos selected just for you, you will be given the entire video library to watch at your convenience.

randomization? affect my family?

(12)

PRE ACT was compared to NCI text in a

PRE‐ACT was compared to NCI text in a

randomized multicenter clinical trial of

>1200 adult cancer patients

PRE-ACT improves:

• Knowledge

g

• Attitudes

• Preparation

More satisfaction with PRE-ACT vs. Control

12

(13)

PRE ACT was compared to NCI text in a

PRE‐ACT was compared to NCI text in a

randomized multicenter clinical trial of

>1200 adult cancer patients

13 Meropol et al. ASCO 2013

(14)

RESULTS

(15)

Demographics

Demographic

Control

PRE-ACT

Combined

Male 258 41.6% 255 41.5% 513 41.5% Female 363 58.4% 359 58.5% 722 58.5% White 544 545 1089 White 87.9% 89.1% 88.5% Non‐white 75 12.1% 67 10.9% 142 11.5% High school graduate or 148 143 291 High school graduate or less 148 23.8% 143 23.3% 291 23.6% Some college or college graduate 473 76 2% 470 76 7% 943 76 4% graduate 76.2% 76.7% 76.4% Metastatic disease 276 47.3% 254 44.8% 530 46.0% N t t ti di 307 313 621 Non‐metastatic disease 52.7% 55.2% 54.0%

(16)

Top 5 Knowledge Barriers at Baseline

Item

% Incorrect/Unsure

Most clinical trials involve a placebo (sugar Most clinical trials involve a placebo (sugar pill). 75.5% Side effects in clinical trials are usually worse 65 2% than with standard treatments. 65.2% “Standard treatments” are the best 61 9% treatments currently known for a cancer. 61.9% Informed Consent mainly protects researchers f l it 59.9% from lawsuits. 59 9% Patients in clinical trials must get their care at different places from patients getting standard 58.7% treatments.

(17)

Top 5 Attitude Barriers at Baseline

Item

% Agree

I'm afraid of the side effects I'll have on a clinical trial. 51.9% I'm worried that the treatment I'd receive on a clinical trial

I m worried that the treatment I d receive on a clinical trial

wouldn't work for me. 41.5%

I'm afraid I'll get a sugar pill (placebo) instead of real I m afraid I ll get a sugar pill (placebo) instead of real

medicine on a clinical trial. 39.3% I'm afraid that my health insurance won't pay for a clinical

I m afraid that my health insurance won t pay for a clinical

trial. 38.7%

I wouldn't ask about clinical trials unless my doctor

38 1%

brought them up first. 38.1%

(18)

Knowledge about Clinical Trials

Arm

(n)

Test

Mean

correct SD

p-value

(n)

(of 19)

p

Control

(573)

Pre

11.71

3.77 <0.0001

(573)

_Post

_14.28

_3.78

0.0001

PRE-ACT

(505)

Pre

11.76

3.69 <0.0001

P

t

15 07

3 07

(505)

_Post

_15.07

_3.07

Arm

Mean

SD

p-value

Arm

change

SD

p-value

Control

2.51

3.05 0.0006

PRE ACT

3 16

3 10

PRE-ACT

3.16

3.10

(19)

Attitudinal Barriers

28 items; Higher score = more agreement with barriers

Arm

T

t

Mean

SD

l

(n)

Test

(1-5)

SD

p-value

Control

Pre

2.55

0.65 <0 0001

(570)

_Post

_2.39

_0.67

<0.0001

PRE-ACT

(504)

Pre

2.54

0.66 <0.0001

(504)

_Post

_2.24

_0.67

A

Mean

SD

l

Arm

ea

change

SD

p-value

Control

-0.16

0.38 <0 0001

<0.0001

PRE-ACT

-0.27

0.45

(20)

Preparation for Decision Making

Higher score = greater preparedness

Arm

(n)

Test

Mean

(0-100)

SD

p-value

(n)

(0-100)

Control

(578)

Pre

72.65

15.42 <0.0001

Post

76.48

15.54 (

)

_Post

_76.48

_15.54

PRE-ACT

(506)

Pre

72.78

15.67 <0.0001

Post

78.02

14.12 Arm

Mean

h

SD

p-value

Arm

change

SD

p value

Control

3.37

13.52

0.09 PRE ACT

4 72

12 81

PRE-ACT

4.72

12.81

(21)

PRE‐ACT Program Satisfaction

Question PRE-ACT Mean Control Mean Favors PRE-ACT l Mean (SD) Mean (SD) p-value

How satisfied are you with the amount of

information you received? (1-5 most satisfied)

3.74 (0.77)

3.60

(0.79) 0.002 How satisfied are you with the way the

information was presented to you? (1-5 most satisfied)

3.86 (0.79)

3.65

(0.84) <0.0001 Did this program help you feel more prepared

to consider clinical trials as a way to treat your cancer? (1-5 a great deal)

3.62 (0.92)

3.43

(0.89) 0.0003 f f

Which of the following best describes your feelings about the length of this program?

(1, reasonable; 2, a little long; 3, much too long)

1.31 (0.52)

1.53

(0.63) <0.0001 Did you find this program useful for making

Did you find this program useful for making your decision about treatment for cancer? (1, yes; 2, no)

1.22 (0.41)

1.28

(22)

Conclusions

• Computer‐based approaches to patient education

before the oncologist visit can improve knowledge,

attitudes, and preparation for decision making about

clinical trials Both text and tailored video were

clinical trials. Both text and tailored video were

effective.

• PRE ACT is more effective than NCI text in improving

• PRE‐ACT is more effective than NCI text in improving

knowledge and reducing attitudinal barriers

• PRE‐ACT is associated with greater patient

PRE ACT is associated with greater patient

satisfaction than NCI text alone

22

(23)

Barriers at the Point of Care

Logistical

Patient

Physician

23

(24)

Overall Goal

To improve efficiency and accuracy of clinical

i l id

ifi

i

f

b

i

trial identification at point of care by automating

the extraction of clinical information and

hi

li i l i l li ibili

matching to clinical trial eligibility

(25)

Clinical Trials Identification Approaches

• CT repositories and search engines (e.g.

CT repositories and search engines (e.g.

clinicaltrials.gov)

–

Manual searchingg

–

Manual data entry

• Patient entry database

Patient entry database

–

Critical mass of patients/studies required

• Automated matching from electronic medical records

Automated matching from electronic medical records

(26)

(27)

Traditional Screening

Physician evaluates patient and collects data

Physician thinks about clinical trials

Access clinical trials database

Identify potential studies for disease/stage

Assess eligibility

Identify potential studies for disease/stage

Contact research staff

27

(28)

Manual Screening For Clinical Trials

(29)

What is Trial Prospector?

• An automated tool that extracts clinical and research

eligibility data, runs a matching algorithm and

displays clinical trial eligibility results at the point of

care

• Hypothesis: Trial Prospector will

–

Be accurate

–

Save time

–

Be appealing to physicians

–

Ultimately result in improved access and accrual

to clinical trials

29

(30)

Methods

• Trial Prospector uses artificial intelligence and natural

l

i

t

t h li i l t i l li ibilit t

language processing to match clinical trial eligibility to

clinical information from multiple data sources

• Trial Prospector is platform‐agnostic and HIPAA

compliant, to permit future dissemination

• Clinical criteria for matching include:

g

–

demographics, diagnosis, TNM stage, stage grouping,

metastasis, and the three most recent lab values (CBC,

renal function, liver tests, coagulation)

30

(31)

Trial Prospector Components and User Access

Physician Oncology Database Secure Web‐Based Access TP User Interface Demography, Diagnosis, TNM, Stage Grouping, Metastatic Status Matching Algorithm Metastatic Status Last 3 Lab Values Eligibility _{C it i} Lab Database Clinical Trial Database Automated Data Retrieval TP Database Values _Criteria Parchman et al. ASCO Meeting Abstracts June 2013:6538 31

(32)

Trial Prospector Search Screen

xxxx xxxx xxxx xxxx xxxx xxxx xxxx xxxx Parchman et al. ASCO Meeting Abstracts June 2013:6538 32

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Update Option xxxx xxxx p Protocol Document Eligibility g y Checklist Exclusion Report Report Parchman et al. ASCO Meeting Abstracts June 2013:6538

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Use Feedback Pilot Study

GI Oncology Clinic TP Deployment 60 New Patient Visits 60 New Patient Visits

Clinical Patient E l i

Patient Specific Survey Evaluation

Summary Experience Survey Summary Experience Survey

Parchman et al. ASCO Meeting Abstracts June 2013:6538 34

(35)

User Experience

Physician Survey Results for the Trial Prospector Group Trial Prospector (60) Trial Prospector (60) Yes No Did you review a TP report for this i ? 29 (72.5%) 11 (27.5%) patient? 29 (72.5%) 11 (27.5%) To your knowledge was the information provided by TP accurate? 22 (75.9%) 7 (24.1%) Did TP save you time in identifying potential clinical trials? 16 (57.1%) 12 (42.9%) Would you recommend utilizing TP for

( %) ( %) Would you recommend utilizing TP for

eligibility screening? 25 (89.3%) 3 (10.7%)

Parchman et al. ASCO Meeting Abstracts June 2013:6538 35

(36)

Summary Experience Survey Yes No Yes No Did TP allow you to spend more time with your patients? 2 (18.2%) 9 (81.8%) Did TP k i i fi d li ibl Did TP make it easier to find eligible clinical trials for your patients? 8 (72.7%) 3 (27.3%) Did TP help you communicate with 2 (18.2%) 9 (81.8%) patients about clinical trials? 2 (18.2%) 9 (81.8%) Did TP make it easier to review protocols and eligibility checklists? 9 (81.8%) 2 (18.2%) Is TP easy to use and navigate? 11 (100%) 0 Is the TP interface visibly pleasing? 10 (90.9%) 1 (9.1%) Would you recommend TP to other Physicians? 9 (81.8%) 2 (18.2%) Parchman et al. ASCO Meeting Abstracts June 2013:6538 36

(37)

Conclusions

• Trial Prospector is a point‐of‐care application that is a

p

pp

feasible and accurate means to screen patients for

clinical trials in a busy outpatient oncology clinic

• Physicians generally found Trial Prospector to be easy

to use and would recommend its use for clinical trial

li ibilit

i

eligibility screening

• Program enhancements (e.g. functionality, user

interface) and further testing are underway

37

(38)

Study Team

Supported by NCI _{R01 CA127655} University Hospitals Seidman Cancer Center/Case Western – Sarah Fulton Fox Chase Cancer Center – Michael Collins (MCW) Sarah Fulton – Tyler Kinzy – Tasnuva Liu S h M i i – Brian Egleston – Linda Fleisher (CHOP) – Sharon Manne (CINJ) – Seunghee Margevicius – Neal Meropol – Dawn Miller Sharon Manne (CINJ) – Dave Poole – Suzanne Miller E i R – Mark Schluchter Karmanos Cancer Institute – Terrance Albrecht – Eric Ross – Yu‐Ning Wong Cleveland Clinic Terrance Albrecht Robert H. Lurie Comprehensive Cancer Center, Northwestern

Al Bowen Benson III

– Anne Flamm International Myeloma Foundation – Michael Katz – Al Bowen Benson, III Cancer Support Community – Joanne Buzaglo Michael Katz Fight Colorectal Cancer – Nancy Roach

(39)

Study Team

Jill Barnholtz‐Sloan Sarah Fulton Robert Lanese Patrick Merglerg Neal J Meropol Dawn Miller Andrew Parchman

Supported by NIH

UL1TR000439,

P30CA043703

Andrew Parchman Satya Sahoo Shiaqiang Tao

P30CA043703

James Warfe GQ Zhang 39

(40)