Health literacies in marginalised communities LILAC 24.pptx
Full automation
2. Full Automation
Dr. Jayesh Warade
MBBS MD PGDHHCM DCRM PGDip (Endo)
Consultant Biochemistry and Molecular Biology
Quality Manager
Laboratory Services
Meenakshi Mission Hospital and Research Center, Madurai
3. Automation
It is automation of manual processes and
involves automated or robotic equipment.
Automation is the use of control
systems and information technologies to
reduce the need for human work in the
production of goods and services
4. Laboratory Automation
Laboratory automation is the use of
instrument and specimen processing
equipment to perform clinical assay
with only minimal involvement the
technologist
6. Operations in Laboratory
Divided into three phases
preanalytic,
analytic, and
postanalytic
The analytic phase is the most automated, and more
research and development efforts are focusing on
increasing automation of the preanalytic and
postanalytic processes.
7. Inidividual Steps in Process
Specimen acquistion
specimen identification
specimen delivery to laboratory
specimen preparation
specimen loading and aspiration
on analyzer specimen delivery
reagent handling and storage
reagent delivery
chemical reaction phase
measurement approches
signal processing, data handling and process control
16. Laboratory Infromation System
Computerized information management
system designed for laboratories
Manages lab data from sample log-in to
reporting
Interfaces with analytical instruments
Sorts and organizes data into various
report formats
Stores data for future reference and use
19. Autoverification
What is Autoverification? (CAP)
“Autoverification is the process by which patient results
are generated from interfaced instruments and sent to
the LIS, where they are compared against laboratory-
defined acceptance parameters.
If the results fall within these defined parameters, the
results are automatically released to patient reporting
formats without any additional laboratory staff
intervention.
Any data that fall outside the defined parameters is
reviewed by laboratory staff prior to reporting.”
20. Why Autoverification?
↑ Consistency:
Autoverification removes technologist’s “subjectivity” and improves
consistencyof reporting (regardless of the number and skill set of the
technologists in the lab)
↑Quality:
Autoverification reduces errors/mistakes, and improves quality
↓TAT:
Autoverification reduces amount of labor required for validation of
results
About 60-80% of results could be automatically verified, while 20-40%
require further attention.
Holy Trinity of Lab Testing
1.Increase Patient Safety
2.Decrease TAT
3.Cost-Savings
21. Guideline is Available for
Autoverification?
CLSI AUTO 10-A; Volume 26 Number 4
Autoverification of Clinical Laboratory
Test Results Guideline
23. Integrated Automation
Speciment Input Area
Bar code reading Sations
Transport System
High Level sorting or routing device
Automated Centrifuge
Level Detections and evalution of speciment adequacy
Decapping Station
Recapping station
Aliquoter
Interface to Automated Analyzer
Sorter
Take Out Station
Storage and retrival system
25. Middleware
Middleware is connectivity
software that provides a
mechanism for processes to
interact with other
processes running on
multiple networked
machines.
Middleware Application
Programming Interfaces
provide a more functional
set of capabilities than the
OS and network services
provide on their own.
26. Middleware
Sample tracking & routing of samples to different
instruments/work stations.
Differentiation of STAT & Routine samples to be handled efficiently
for fast TAT
Monitoring TAT for STAT & Routine samples
Auto-validation of results based on configurable set of criteria (QC &
Calibration pass, Delta check, Moving average, etc)
Monitoring QC performance of all configured instruments & alert to
user on ‘outliers’
Monitoring ‘instrument maintenance protocols’, error & event logs on
linked instruments
Inventory management on all instruments linked to the middleware
Instant access to patient test orders and results
Instrument reports (events, calibration, reagent status)
27. Systematic Approach to
Automation
Evaluation of needs (move current state to desired state)
Logistics and handling issues
Facilities and space consideration
Temperature considerations
Mapping workflow, timing workflow
Finding bottlenecks and time wasters
Identify possible solutions to meet needs
Evaluation of alternatives
Progress measures
Cost justification
29. WHY AUTOMATION?
Reduce human error
Safety
decrease laboratory costs
improve turnaround time
increase productivity
Run more tests
Test in fewer sites
Operate with fewer instruments.
Retain lower operating costs.
Employ relatively less skilled labor.
Use more automation in a paperless environment
30. Specimen Volumes and Workload
What is laboratory’s specimen volume?
Chart specimen count by hour of day and day
of week
What percentage are centrifuged?
What percentage are aliquotted?
What percentage of specimens are shared
between two lab sections?
What percentage of specimens are
refrigerated or frozen?
31. Handling Considerations
How and where do specimens arrive?Courier
vehicles, tube system, dumb waiter, window,
phlebotomists, patient walk-ins, nurse delivery? Are
these near each other or in separate areas?
Patient registration -is it required, is it before or
after processing, where is it located, who does it -
lab personnel or hospital personnel?
Patient identification: is there a wrist band bar code
system linked to the LIS?
How do phlebotomists verify patient ID?
32. Handling Considerations
Do nurses or patient care assistants (i.e., employees not
under lab control) draw or collect specimens?
For tests ordered on the floors, do LIS labels print on the
floors or in the lab?
Where are tubes centrifuged? Specimen Processing or
Chemistry?
Pour-offs and aliquotting –what is the workload?
Sorting -how much sorting of specimens occurs -in
Specimen Processing and in lab sections?
Transport -delivery by Specimen Processing or pick-up by
labs? What are the distances covered?
33. Handling Considerations
How, where, and for how long are
archived specimens stored?
Centralized or decentralized?
Manual system or using bar codes ?
What is the percentage of repeat testing?
What is the percentage of additional
testing requested to be added to archived
specimens?
34. Facilities and Space
If there is the opportunity to design a new facility, great.
Whether yes or no, here are several worthwhile ideas:
Arrange the facilities in a manner that follows the flow of the
specimens.
Position highest volume testing (Chemistry, Hematology,
etc.) closest to Specimen Receiving and lowest volume
testing furthest away.
Avoid having all lab traffic go through a key area such as
Specimen Receiving.
Position client service and exception handling activities in or
close to Specimen Receiving.
35. Workflow Mapping
Material flows (specimens)
Process flows
Data flow diagram-done at different layers
of detail
Workload map-can be used in simulation
studies
39. Steps For Tracking
Registration
Consultation
Time of prescription by
physician
Billing time
Time of collection
Time of transport to
collection
Receiving at reception and
barcode
Transportation to
segregation
Segregation and transfer to
department
Department reception
Transfer to centrifuge
Centrifuge
Transfer to instrument
Waiting and processing
Updating
Approval
40. Non-Track Automation Possibilities
Wristband bar code systems for
phlebotomy
Document management systems
Autoverification, middleware, and QC
software
PC or LIS-based specimen storage and
retrieval
42. Identifying Possible Solutions to
Meet Needs
Use quality and turn-around time measures, workflow, and
timing studies to find bottlenecks and potential areas for re-
engineering.
Re-engineering of processes should precede introduction of
automation.
Not all solutions need to involve automation
Several seemingly small, low-cost re-engineering projects
sometimes have more impact on laboratory performance
than an expensive automation project.
“Automating a poor process still leaves one with a poor
process.”
43. Re-Engineer Processes
Use continuous quality improvement (CQI) tools such as
Lean and Six Sigma to foster process improvements
Standardize processing procedures to “best practice” solutions
with fewest “hand-offs.”
Reduce or eliminate non-value added handling and sorting.
Eliminate “running around” to find shared specimens.
Redesign workstations so that individuals process orders
from start to finish.
Maximize the number of specimens at test run start times.
44. Evaluation of Alternatives
Define and rank objectives (needs to be filled).
Identify alternative solutions, some of which may not
involve automated equipment.
Match the key features of alternative solutions to the most
important needs of your lab that are solved by those
solutions.
Emphasis in any solution that is selected should be on
process control and process improvement.
A solution with several small steps sometimes is better than
a major implementation of automation.
45. Progress Measures
Median turn-around time
95th percentile turn-around time
Stat turn-around time
Lost specimens
Mislabeled specimens
Billed units per FTE
Rate of hiring of technical employees
46. Objectives To be Included...
To compare and contrast the TAT pre-
and post-LAS
To compare and contrast laboratory errors
pre- and post-LAS
To gauge the level of staff satisfaction and
their feedback post-LAS
To document the advantages and
limitations of the LAS and the continuous
improvement process for the first 6
months.
49. Reasons Why Automation Projects
are Not Successful
Incomplete understanding of current environment...processes, costs,
customer expectations
Loss in flexibility due to fixed processes and limited throughput
Unrealistic expectations of system...cost reduction, throughput, return
on investment
Unplanned and poorly developed ‘workarounds’ required to interface
automation with manual processes
Unclear expectations of system functionality
Overbuilt and unnecessarily complicated system design
Inadequate technical support
Credible and realistic impact analysis never conducted
Hidden costs...labor, supplies, maintenance
Failure to optimize current processes prior to automation→never
automate a poor process!
61. Future Concept Lab Automation
Biorepository-sized automated storage
systems, inexpensive radiofrequency
identification (RFID), Drone - based
sample dispatching, development of
new positions such as
"Robotechnologist", Multilayer
Perception Neural Network
68. Automation Lessons and Take Home
Messages
Know your laboratory’s business!
Map workflow to find bottlenecks
Determine your primary and secondary objectives
Use your workflow map and objectives to authenticate
vendor proposals
Focus on process improvement
Re-engineering processes may have just as much impact
on operations as automation
Maximize use of information technology
Consider alternatives
Justify all costs
Take your time