Originally presented at the ThermoFisher L@unch 2017 Conference in Boston, MA This presentation describes how the Laboratory Informatics team at the Inova Translational Medicine Institute implemented a groundbreaking practice that cut their study deployment into production from two months to two hours. A video of this presentation is available: https://youtu.be/R_IBrUbESZw

1. Two Months to Two Hours
Universal Accessioning for Programmers
michael.soh@inova.org
Inova Translational Medicine Institute

2. Inova Translational Medicine Institute
● Founded in 2011 as the research arm of Inova Health Systems
● DC-Metro Area
○ Ethnically diverse
○ Large sample size
○ Subjects approached while at the hospital
● Many active studies:
○ Infant Mortality and Gut Microbiome
○ Heart and Vascular Failure
○ Genetic Disorders
○ NIH-Funded ECHO

3. A Little About Michael
● Primary Background: Web Development
○ HTML, JavaScript from 1995 with GeoCities and Tripod
○ PHP early 2000
○ Traditional LAMP (Linux, Apache, MySQL, PHP) stack Development 2005
● Skills
○ Programming (Python, PHP, Bash/sh, etc.)
○ Database (MySQL, Oracle, Microsoft SQL Server)
○ Cloud Infrastructure, primarily AWS
● Important Facts
○ Above all things, openness and transparency
○ Open Source Advocate
○ Trust the user for integrity, distrust the user for being human
○ Failed Biology; BA in English Literature and Film Studies

4. LIMS History
● Started with an in-house LIMS called SDB (Steven’s Database)
● Moved to Nautilus LIMS around 2013
● Migration of old data “completed” in 2015

5. Old and Busted Workflows
● Eight Studies, 16 Workflows, Countless Templates
● Main complaints from users:
○ “Time-Consuming logging in every single bit”
○ “Took forever to get things fixed since we couldn’t make adjustments”
○ “You were [screwed] if you made a mistake!”
○ “If you didn’t properly recycle a tube, and you forget, you could have a problem in the future
since the tube is reused.”
○ “It wasn’t intuitive. You had to do something and then undo it.”
○ “It sucked! It was not very user friendly. It was a nightmare!”
○ “Painful. Recycling barcodes and zero-ing out values was a headache.”
○ “Too many clicks!”
○ “If we didn’t use a tube, we had to create a ticket each time!”
○ “When creating a plate with the wrong name, plates couldn’t be deleted so I just deleted the
external reference.”

6. Old and Busted Workflows
● Complaints from Informatics:
○ Data Quality not consistent
○ Human Error took too much time to correct (accounted for 70% of time)
○ New Studies or workflows took months to develop (e.g. Fecal Collection Device Aliquot
Workflow took three months of development)

7. Old Workflow

8. Old Workflow - Problems
● At Accessioning
○ Hand-jammed errors (e.g. Bad Barcode, Wrong Amount, Incorrect Times)
○ Samples are hard-coded with pre-accessioned aliquots; if there is a deviation, aliquots had to
be deleted
○ Child Aliquots were pre-accessioned; if all child aliquots were not used, they had to be
manually deleted by Informatics since the barcodes are reused
● During Processing
○ Aliquot volumes not entered correctly
○ Wrong aliquot scanned
○ No ticket created to delete aliquots not accessioned

9. Old Workflow - Problems
● Post Processing
○ Aliquot not scanned correctly (Partial aliquot scan)
○ Total Processing time off due to aliquots not properly accessioned during previous steps
● Administrative
○ Months to bring new samples into Study
○ Paper logs to Nautilus incredibly difficult
○ Very little consistency in Data

10. Iteration One
● More flexible aliquot workflow that allowed lab techs to “recycle” an aliquot
○ Aliquot External Reference nulled
○ Aliquot Status set to Cancelled
● Limited aliquot details could be changed by lab techs
○ External Reference
○ Date and Time
○ Amount
● BUT!
○ This change was limited to ONE study

11. Problem
In addition to not being able to push Aliquot workflows to other studies...
● Non-Technical:
○ New studies can begin without advanced notice
○ New sample types can be accessioned without advanced notice
● Technical:
○ With multiple different workflows, errors very common
○ Very long ramp up for new lab techs

12. The User Story
“As a Lab Tech
I would like to be able to accession all samples the same way,
regardless of study
So that I can standardize my work process, increase throughput
and decrease the number of errors I make.”

13. Iteration Two
● Allow Error Correction
● Allow better aliquot recycling
● Interchangeable Workflows
● Limit overall development time
● Better data consistency
● Reduce time spent resolving errors

14. Programmer Mindset
● Much of the workflows were “snowflakes” -- very little could be reused directly
○ Modularize workflows so that they can be reused
○ Make better use of SubTree, Copy Down, and other nodes that make data retention more
consistent
● Trust the user, distrust the human
○ Trust that the user will know what to do
○ Allow the human to make mistakes by allowing them to correct it
● Increase Transparency
○ “Security by Transparency” where changes are logged
○ Simplify use, where studies are no longer closed off from each other

15. Processed Aliquot
● Secondary Aliquot, never directly accessioned (i.e. never can be the primary
aliquot)
● Copy-down as many values from parent:
○ Container Type
○ Matrix Type
○ Aliquot Template
● Allow for self-recycling if not needed
● Allow for end-user editing
● Be able to be used in any parent device with ABSOLUTELY NO
MODIFICATION

16. Collection Device Aliquot
● One Template to Rule Them All™, allows consist UX across all types of
Primary aliquots
● Modular Workflow that can be copied whenever new Primary aliquots are
required

17. Collection Device Aliquot
● To Maintain Reusability, Matrix
Type and Container Type are set
prior to login
● To enable Copy Down initiated by
the Processed Aliquot:
○ When splitting the aliquot, set the
desired values at the parent level
immediately before the split
○ Initiate the aliquot split
○ Set the value back for the parent

18. Sample Workflow
● One Sample Workflow and Template to Rule Them All™
● Groups initially used to isolate Studies to limit exposure; Studies now used
properly (SAMPLE.STUDY_ID)
● Reduce the number of workflows (one per study) down to one workflow

19. Results
● 86% decrease in number of tickets
● Tickets today concern more advanced problems
● New Study? No problem. Give us two hours, and we’ll give you the world
● New Collection Type? We got you fam. Copy Collection Aliquot, modify
matrix type, collection device.

20. Results
● No longer putting out fires
● Users have more control, blessed responsibility
● Errors still occur, but significantly less obtrusive

21. Questions?