Lumbar Imaging with Reporting

of Epidemiology (LIRE) Update

Jeffrey (Jerry) Jarvik, M.D., M.P.H.Professor of Radiology, Neurological Surgery and Health Services

Adjunct Professor Orthopedic Surgery & Sports Medicine and Pharmacy

Director, Comparative Effectiveness, Cost and Outcomes Research

Center (CECORC)

Kari Stephens, Ph.D.Assistant Professor, Psychiatry & Behavioral Sciences

Adjunct Assistant Professor, Biomedical Informatics & Medical Education

Disclosures (Jarvik)• Physiosonix (ultrasound company)

– Founder/stockholder

• Healthhelp (utilization review)– Consultant

• Evidence-Based Neuroimaging Diagnosis and Treatment (Springer)– Co-Editor

•NIH: UH2 AT007766-01; UH3 AT007766

•AHRQ: R01HS019222-01; 1R01HS022972-01

•PCORI: CE-12-11-4469

Acknowledgements

Background and Rationale

• Lumbar spine imaging frequently

reveals incidental findings

• These findings may have an

adverse effect on:

–Subsequent healthcare utilization

–Patient health related quality of life

Prevalence of Disc

Degeneration in NormalsModality Author/

Year

Age

Range

Prev

MR Boden/ 1990

20-60 60-80

44% 93%

MR Stadnik/ 1998

17-60 61-71

52% 80%

MR Weishaupt/

1998

20-50 72-100%

MR Jarvik/ 2001

35-70 91%

Disc Degeneration in Asx

Intervention TextThe following findings are so common in normal,

pain-free volunteers, that while we report their

presence, they must be interpreted with caution and

in the context of the clinical situation. Among people

between the age of 40 and 60 years, who do not

have back pain, a plain film x-ray will find that about:

• 8 in 10 have disk degeneration

• 6 in 10 have disk height loss

Note that even 3 in 10 means that the finding is

quite common in people without back pain.

UH3 Hypothesis• For patients referred from primary care,

inserting epidemiological benchmark data

in lumbar spine imaging reports will

reduce:

–subsequent cross-sectional imaging (MR/CT)

–opioid prescriptions

–spinal injections

–surgery.

Participating SystemsName # Primary

Care Clinics

(Randomized)

# PCPs

(Randomized)

Kaiser Perm.

N. California

20 865

Henry Ford

Health

System, MI

26 228

Group Health

Coop of Puget

Sound

19 245

Mayo Health

System

36 345

Total 101 1683

Stepped Wedge RCT

Wave 1 ImplementationSite Sub-site Wave 1 Started

Group Health April 1st, 2014

Henry Ford April 1st, 2014

Mayo

La Crescent,

Prairie du Chien

April 10th, 2014

St. James,

Austin, Waseca

April 24th, 2014

Plainview August 27th, 2014

Kaiser June 25th, 2014

Problems Encountered

• People

–Wrong skills

–Lack of buy-in

–Personality fit (or lack thereof)

–Political/leadership issues

• Structure/System

–Multiplicity of data systems

–Distributed administration vs. centralized

People: Example #1

• Implementation problems

resolved when IT project

manager replaced

–Solutions rapidly found to

implementation problems

– Improved communication

– Improved buy-in

People: Example #2

• Sudden regionalizing

of radiology

reporting

• Randomization by

clinic impossible

• UW, site-PI and local

leadership found

technical solution

People- Lessons

• Leverage pre-existing good

relationships

• Need familiarity w/data

systems + personalities

• Find team members who are

a better fit ASAP

• Work with local stakeholders

to identify possible

interference on horizon

Structure/System: Example #1

Distributed vs. Centralized• Distributed

– Clinic autonomy

standardization for

implementation difficult (e.g.

multiple RIS)

• Centralized

– Standardization efforts can

also interfere with

implementation (e.g.

initiative to standardize

radiology reporting)

Structure/System: Example #2

• Dynamic rendering vs. permanent part of EMR

– Only way to implement in a timely manner

– Required manual verification

– For Wave 2, programmer was able to permanently

insert intervention into EMR

– Uncovered 2nd problem: intervention tied to where

report accessed vs. where order originated

Structure/System: Example #3

• Small Wave2 clinic

closed with 2 MDs

Wave1 clinic

• Stepped-wedge

design complicates

impact: timing

determines exposure

Structure/System Lessons• Centralized vs. Distributed

–More centralized systems started on-time

–Consider longer start-up for

distributed/complex systems

• Communication key in learning about and

remedying problems (dynamic rendering,

system regionalization)

• Build on existing relationships

Semantic AlignmentKari Stephens, PhD

• Making sure information (data)

from multiple sources can be

combined to conduct research

Semantic Alignment

• Now: Planning for pulling data repeatedly over time

– Clear and frequent communication with sites

– Same data file format repeated, test with index files

– Document validation process

• Long term: repeat data extractions

– Conduct validation checks between extractions

– Document process to create library of procedures

(who / what / how)

– Determine validation best practice methods

Time 1

2 3 4Longitudinally

Semantic Alignment

within Site

• Now: multiple systems of care within sites

– e.g. proprietary radiology report codes

– Staff turnover increases potential error and effort

– Validation with primary / centralized research team

• Long term: replicability

– Track and document process for extraction and

alignment; difficult to maintain post funding

– Stabilize methods within sites as much as possible

Site

Time 1

2

3

4

Semantic Alignment

between Sites• Now: defining variables

– Outcome variables: NLP for reports, RVUs (BOLD)

• Review of index files

– ↑ sites and variability = ↑ time / effort / complexity

– Validate that independent variables mean the same thing

(i.e., orders, PCP, clinic, gender, age, etc.)

– Stepped wedge design reduces burden

• Long term: usable dataset for analyses

– Adjust analytic plan for variability

Site 1

2

3

4

LIRE Update/Forecast

• Wave 1: moderate choppy seas

• Wave 2: light headwinds

• Wave 3-5: smooth sailing

• Data quality check 10/15/14

UW

Jerry Jarvik, MD MPH-PI

Zoya Bauer, MD, PhD

Brian Bresnahan, PhD

Bryan Comstock, MS

Janna Friedly, MD

Laurie Gold, PhD

Patrick Heagerty, PhD

Katie James, PA-C, MPH

Sean Rundell, PT, PhD

Kari Stephens, PhD

Judy Turner, PhD

Henry Ford

Safwan Halabi, MD- site PI

Dave Nerenz, PhD- site PI

Jim Ciarelli

Bryan Macfarlane

Brooke Wessman

Rachel Blair

DeShawn Mahone

Group Health

Dan Cherkin, PhD-site PI

Heidi Berthoud

Dwipen Bhagawati

Kristin Delaney

Lawrence Madziwa

Camilo Estrada

Mayo

Dave Kallmes, MD-site PI

Beth Connelly

Kevin Erdal

Patrick Luetmer, MD

Jyoti Pathak, PhD

Todd Sheley

Dan Waugh

Todd Wohlers

Kaiser

Andy Avins, MD MPH-site PI

Luisa Hamilton

Mike Matza

John Rego, MD

Cliff Sweet, MD

Mary Muth

Patrick Chang

OHSU

Rick Deyo, MD, MPH

Why Pragmatic Trials Are Important