Lumbar Imaging with Reporting of Epidemiology (LIRE) Update

Transcription

1 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

2 Acknowledgements NIH: UH2 AT ; UH3 AT AHRQ: R01HS ; 1R01HS PCORI: CE Disclosures (Jarvik) Physiosonix (ultrasound company) Founder/stockholder Healthhelp (utilization review) Consultant Evidence-Based Neuroimaging Diagnosis and Treatment (Springer) Co-Editor

3 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

4 Prevalence of Disc Degeneration in Normals Modality Author/ Year MR MR MR MR Boden/ 1990 Stadnik/ 1998 Weishaupt/ 1998 Jarvik/ 2001 Age Range Prev % 93% % % % %

5 Disc Degeneration in Asx

6 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.

7 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.

8 Participating Systems Name # Primary Care Clinics (Randomized) # PCPs (Randomized) Kaiser Perm. N. California Henry Ford Health System, MI Group Health Coop of Puget Sound Mayo Health System Total

9 Stepped Wedge RCT

10 Wave 1 Implementation Site Sub-site Wave 1 Started Group Health April 1 st, 2014 Henry Ford April 1 st, 2014 Mayo La Crescent, Prairie du Chien St. James, Austin, Waseca April 10 th, 2014 April 24 th, 2014 Plainview August 27 th, 2014 Kaiser June 25 th, 2014

11 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

12 People: Example #1 Implementation problems resolved when IT project manager replaced Solutions rapidly found to implementation problems Improved communication Improved buy-in

13 People: Example #2 Sudden regionalizing of radiology reporting Randomization by clinic impossible UW, site-pi and local leadership found technical solution

14 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

15 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)

16 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 2 nd problem: intervention tied to where report accessed vs. where order originated

17 Structure/System: Example #3 Small Wave2 clinic closed with 2 MDs Wave1 clinic Stepped-wedge design complicates impact: timing determines exposure

18 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

19 Semantic Alignment Kari Stephens, PhD Making sure information (data) from multiple sources can be combined to conduct research

20 Semantic Alignment Longitudinally Time 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

21 Semantic Alignment Time 1 within Site 4 Site 2 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 3

22 Semantic Alignment Site 1 between Sites 4 2 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 3

23 LIRE Update/Forecast Wave 1: moderate choppy seas Wave 2: light headwinds Wave 3-5: smooth sailing Data quality check 10/15/14

24 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 OHSU Rick Deyo, MD, MPH 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

25 Why Pragmatic Trials Are Important