The Barcode-Driven Lab: Success in a Large System

1. The Barcode-Driven Lab: Success in a Large System Rodney Schmidt, MD, PhD University of Washington, Seattle April 17, 2011 Place sub-brand here

2. Topics • Why create the barcode-driven lab? • Why in a large, complex lab? • Overview of functionality • Not the detailed “how”; workflow • Achieved benefits • Error reduction • FTE savings • Important factors in success • UCLA, Sierra Pathology, NWP, NYU, OHSU • What’s down the road?

3. Disclosure • Bar-coding software developed at UW (OmniTrax and OmniImage) has been licensed by UW to Pathway Pathology Consultants for PowerPath end-users. • Dr. Schmidt and his team have a revenue-sharing agreement with UW. • Dr. Schmidt has a consulting agreement with Thermo-Fisher for educational talks.

4. Expensive $23k/gross station $10k/cutting station Software Workspaces change Wiring, networking Time investment Software fast Workspaces slow Financing slow Processes change Material handling QA Jobs change Workflow Change management Pathologists affected! Why barcode? Who needs the hassle?!

5. Large Systems – Special Factors • Multiple locations • Trainees – Residents and Fellows • Personnel turnover • Outside materials (e.g. consults) • Ancillary testing • Higher fraction of complex cases Issues: Training, complexity, communication Need: Robust systems to help people do things right

6. Bringing Bar-coding to AP • Track slides (2005) • Eliminate the “lost slide” problem • Ease conference prep • Specimen labels (2006) • Tissue discards and tracking • Drive gross photography • Block creation and labeling (2008) • Automated JIT production of barcoded blocks • Gross room QA process and tracking • Slide creation and labeling (2008) • Automated JIT creation of barcoded slides • Facilitate workflow and QA • Eliminate all manual labeling (and errors) • Facilitate workflow – JIT information display

7. Material identification (2005) • Handwritten specimen labels • Manual, off-line cassette labeling • Hand-written slide labels

8. Primary labeling errors (2004) ?

9. Targets – Gross Room • Foolproof labeling • No human labeling/data entry • Reduced dependence on support staff • Off-hours availability • Redirection of support personnel • Reduced waste of cassettes • Grossing step at least as fast as current • (Record timestamps) The unsupervised Resident!

10. Receive specimen and enter data into the LIS Generate a bar coded label for the specimen and laboratory request form. Minimum extra keystrokes (one) Targets - Accession

11. Classic Grossing Workflow Accession specimens Label specimens * Label cassettes * Group with specimens * Move to staging area Move to gross bench * Lay out cassettes * Fill cassettes Request more cassettes Store excess with specs Handling steps Rack filled cassettes Possible errors * * Reconcile with LIS Transport for processing * QA steps

12. Accession specimens Bar-code specimens Scan/print cassettes * Lay out cassettes * Fill cassettes Rack filled cassettes * * Transport for processing Just-in-Time Printing Fewer handling steps Fewer (1) error opportunities Fewer QA processes Rescan cassettes Courtesy General Data

13. Benefits • Efficiency • No manual pre-printing and sorting of cassettes • Quick just-in-time additional cassettes • Default cassettes from PowerPath specimen panels • Blocks automatically ordered in PowerPath • Quality • No manual labeling (no errors) • Scanning specimen barcode assures correct specimen • Enter cutting instructions, # pieces • Records which blocks are sent for processing

14. Q&E Benefits

15. Histology – Embedding • Target • View critical information about block and specimen • Efficient workflow • Block scan: • Embedding instructions • Number of pieces of tissue • Specimen info • (Record timestamps)

16. Histology – Cutting • Targets • Present critical information (block, specimen) • Eliminate manual slide labeling • Block/slide verification • Multiple workflows • No clutter • Efficient • Touch-screens; no keyboards • Block scan: • JIT slide printing/labeling • Info display • Slide scan: • Block/slide match

17. Cutting - Benefits • Elimination of hand labeling • Much faster than manual labeling for blocks with many slides • Fewer block/slide mismatches • Overall throughput increased ~10%

18. Slide Life Cycle Histology Pathology Offices Sendouts Faculty signout File Pull for conference Resident review Histology work order completes with scanning Deliver Ship

19. Slides – Benefits • Less staff time looking for slides • Faster to find last location than make a phone call • Fewer arguments over whether slides were delivered • Fewer recuts? • Improved job satisfaction • ** Saved me 30 min the first day! ** • Overall savings > 2.0 FTE!

20. Slides Benefits FTE Savings

21. Imaging Gross photos Photomicrographs Documents EM/IF HPV workflow Reflex testing Digene/Luminex Specimen management Discards Locations Winscribe automation Barcodes Enable…

22. Specimen Discard Workflow Device scans specimen barcode Handheld device queries AP-LIS If case signout occurred <2wks prior If case signout occurred >2wks prior If note on Req Data tab, caution light and note display

23. Barcoding Benefits • Direct personnel (FTE) • 2.0 Slide delivery and tracking • 0.75 Cassette printing • 0.1 Specimen discards • 0.1 Document scanning • TBD Fluorescence image import ~$150,000/yr assuming $50,000/FTE

24. Barcoding Benefits • Indirect personnel (FTE) • 0.5 Scanned consult document availability1 • TBD Scanned Req forms • TBD Slide location info (e.g. Pathologists) • Reduced loss of materials • Slide/Block tracking • Specimen discards 1Schmidt, RA, et al. Am J Clin Pathol 126:678-83, 2006

25. Barcoding Benefits Error Reduction • Elimination of all manual labeling steps! • Reduced labeling errors • Specimens • Blocks • ~988/yr to near 0 • “How did you manage to do that?!” • Slides • Gross photos • Scanned documents • Photomicrographs

26. Reasons for Success • Optimized workflow • Lean analysis • Close ties to users • Multiple workflows; exception trapping • LIS interoperability • Initially with PowerPath; now general • Just-in-time production of materials • Selection of appropriate equipment

27. Where Next? • Specimen transport • Within multiple sites in a large lab • Upstream all the way from the patient • Result transport • All the way back to the patient • Likely to need multiple systems  Need an industry barcode standard

28. Where Next? • Tissue banking (becoming routine) • Unique identifiers (encrypted for research) • Repository management • Maintain provenance • Pre-analytic variables • Tissue micro-arrays • Each sample linked back to patient

29. Where Next? Patient- and time-based disease data structures (Time) Treatment 1 Treatment 2 Patient Diagnosis Persistent Recurrence • All types of data • IHC • Cytogenetic • Molecular • Links between serial samples of same disease • Relation to clinical treatment • Correlated blood samples What does sample tracking mean for molecular testing?

30. Why barcode?

31. Conclusion • Barcoding is becoming an expectation • Patient safety / error reduction • It’s to your financial advantage For success, you must be sophisticated enough to know the difference between just putting a barcode on something and having a barcode-driven lab.

32. Phil Nguyen Kevin Fleming Rosy Changchien Chris Magnusson Victor Tobias General Data Thermo-Fisher Accu-Place Dr. Erin Grimm Dan Luff Steve Rath Pam Selz Kim Simmons All the Techs and Office Folks! Acknowledgements

34. Achieved Benefits • Marked reduction in labeling errors • Improved inventory control (i.e. knowledge of where things are) • Direct savings of ~ 3 FTE • Indirect savings of >> 0.5 FTE • Improved image collection and management (paperwork, gross, micro, EMs, IF, etc) • Increased job satisfaction