of Epidemiology (LIRE) Update Jeffrey (Jerry) Jarvik, M.D., M.P.H. - - PowerPoint PPT Presentation

Lumbar Imaging with Reporting

• f 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 Normals

Modality 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 Text

The 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 Systems

Name # 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 Implementation

Site 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
• f 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 Alignment

Kari 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 4

Longitudinally

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