ArticlePDF AvailableLiterature Review

Current State of Laboratory Automation in Clinical Microbiology Laboratory


Abstract and Figures

Background: Although it has been 30 years since the first automation systems were introduced in the microbiology laboratory, total laboratory automation (TLA) has only recently been recognized as a valuable component of the laboratory. A growing number of publications illustrate the potential impact of automation. TLA can improve standardization, increase laboratory efficiency, increase workplace safety, and reduce long-term costs. Content: This review provides a preview of the current state of automation in clinical microbiology and covers the main developments during the last years. We describe the available hardware systems (that range from single function devices to multifunction workstations) and the challenging alterations on workflow and organization of the laboratory that have to be implemented to optimize automation. Summary: Despite the many advantages in efficiency, productivity, and timeliness that automation offers, it is not without new and unique challenges. For every advantage that laboratory automation provides, there are similar challenges that a laboratory must face. Change management strategies should be used to lead to a successful implementation. TLA represents, moreover, a substantial initial investment. Nevertheless, if properly approached, there are a number of important benefits that can be achieved through implementation of automation in the clinical microbiology laboratory. Future developments in the field of automation will likely focus on image analysis and artificial intelligence improvements. Patient care, however, should remain the epicenter of all future directions and there will always be a need for clinical microbiology expertise to interpret the complex clinical and laboratory information.
Content may be subject to copyright.
Current State of Laboratory Automation in Clinical
Microbiology Laboratory
Kritikos Antonios,
Antony Croxatto,
and Karissa Culbreath
BACKGROUND: Although it has been 30 years since the
first automation systems were introduced in the micro-
biology laboratory, total laboratory automation (TLA)
has only recently been recognized as a valuable compo-
nent of the laboratory. A growing number of publica-
tions illustrate the potential impact of automation. TLA
can improve standardization, increase laboratory effi-
ciency, increase workplace safety, and reduce long-term
CONTENT: This review provides a preview of the current
state of automation in clinical microbiology and covers
the main developments during the last years. We de-
scribe the available hardware systems (that range from
single function devices to multifunction workstations)
and the challenging alterations on workflow and organi-
zation of the laboratory that have to be implemented to
optimize automation.
SUMMARY: Despite the many advantages in efficiency,
productivity, and timeliness that automation offers, it is
not without new and unique challenges. For every ad-
vantage that laboratory automation provides, there are
similar challenges that a laboratory must face. Change
management strategies should be used to lead to a suc-
cessful implementation. TLA represents, moreover, a
substantial initial investment. Nevertheless, if properly
approached, there are a number of important benefits
that can be achieved through implementation of auto-
mation in the clinical microbiology laboratory. Future
developments in the field of automation will likely focus
on image analysis and artificial intelligence improve-
ments. Patient care, however, should remain the epicen-
ter of all future directions and there will always be a
need for clinical microbiology expertise to interpret the
complex clinical and laboratory information.
Automation is the technology by which a procedure is
performed without human assistance or intervention. In
the laboratory context, automation refers to the switch
from manual work to machines (1). In clinical micro-
biology culture, however, “full laboratory automation”
refers to automation of the diagnostic workflow includ-
ing all steps from inoculation to final report. Therefore,
a laboratory automation system can potentially contrib-
ute to all analytical steps from inoculation, incubation,
plate reading and identification, or even antibiotic sus-
ceptibility testing (1,2).
While automation has been steadily spreading
throughout the clinical chemistry and clinical hematol-
ogy laboratories, until recently most processes for
culture-based testing in the clinical microbiology labora-
tory have been performed manually (3,4). The intro-
duction of automation in microbiology was considered
difficult to apply for several reasons such as the complexity
and variability of sample types, the variations of specimens
processing, the doubtful cost-effectiveness especially for
small and average-sized laboratories, and the perception
that machines could not exercise the critical decision-
making skills required to process microbiological samples
It was only toward the end of the 20
century that
the concept of automating the laboratory process in clin-
ical microbiology became a topic of interest (5,7,8).
Despite the substantial barriers, healthcare and clinical
laboratory trends mandated a change in the clinical
microbiology landscape. Increasing pressure to reduce
costs while maintaining or improving quality created a
demand for automation. At the same time, technological
advances converged to bring total laboratory automation
(TLA) to microbiology (3,7). Of the primary drivers for
this change, standardization of identification methods
with MALDI–TOF–MS and the adoption of liquid
microbiology specimen transport created a workflow
pattern that could be optimized with automation (5). At
the same time, overall testing volumes have been increasing
5%–10% every year, driven by an aging population (5,6),
increased numbers of immunocompromised patients,
infection control demands, and the growing challenge
resulting from detection of multidrug-resistant micro-
organisms. Despite this volume increase, the available
workforce of microbiology-trained personnel and the
University of Lausanne, Institute of Microbiology, Lausanne, Switzerland;
Reference Laboratories, Albuquerque, NM.
*Address correspondence to this author at: TriCore Reference Laboratories,
Albuquerque, NM, USA. Fax 505-938-8410; e-mail
Contributed equally.
Received August 9, 2021; accepted October 15, 2021.
CAmerican Association for Clinical Chemistry 2021. All rights reserved. For permissions, please email: 99
Clinical Chemistry 68:1 Review
99–114 (2022)
Downloaded from by ISREC Bibliotheque user on 03 January 2022
clinical laboratory training programs have been decreas-
ing over time (3,5,9).
The first semiautomated plating instrument, the
(Vista Technology, Edmonton, Alberta,
Canada), was developed in 1989. Since then, other more
sophisticated plating instruments have been developed,
such as the Innova
(BD), the InoqulA
(part of the
TLA system of BD Kiestra)
, the Walk-Away
Specimen Processor (WASP)
(Copan Diagnostics),
and the Previ Isola
(bioMe´rieux) (2,6,10).
The goals of this review are to provide an overview
of the current state of automation in clinical microbiology
and report the main developments in recent years. We
present the available hardware systems (that range from
single function devices to multifunction workstations)
and the challenging alterations to workflow and organiza-
tion of the laboratory that must be implemented in order
to optimize automation. We discuss some of the practical
considerations that a laboratory might face during the
automation implementation process. It is out of the scope
of this review to cover developments related to nucleic
acid amplification tests or automation of cultures other
than bacteria.
Description of Laboratory Automation Systems
Laboratory automation consists of 2 components: the
hardware and the workflow. In this section, we define
terms commonly used when it comes to automation in
clinical microbiology and present the key components
of a TLA system (1).
Hardware. Hardware defines all the components and
physical parts of the devices used in the diagnostic process
of the laboratory. Depending on the level of automation,
someone might find systems (hardware) capable of per-
forming only specific tasks (for example inoculation, in-
cubation) or more sophisticated systems able to provide a
higher level of automation.
Workflow. Workflow refers to a repeatable pattern of ac-
tivity performed by a person, a machine, or a combina-
tion of both. It is used for short or long sequences of
operations. The whole process involving a patient’s sam-
ple, from arrival to the end of the diagnostic procedure,
can be described as a sequence of workflows.
Software. Software includes the automation interface
with the user to review the digital images, document
and transmit results from the user, and storage of digital
images. Additionally, automated image interpretation
and artificial intelligence tools are included in the defini-
tion of software.
Time to report. It refers to the time necessary from sam-
ple’s arrival to dispatch the result. It corresponds to the
interval between the start and the end of a workflow or
a combination of workflows. This may also be referred
to as turnaround time.
Quality. The term quality can be described as a measure
of excellence and uniformity. It is a performance metric for
following established standards to obtain results without
any significant variations (1). In laboratory practice, quality
may refer to laboratory-based preanalytic errors (e.g., mis-
labeling plates, selecting incorrect media, misplacing a
plate), quality of plate streaking, and ability to recover a
microorganism (e.g., taking into account the number of
isolated colonies, accurate quantification, the need to
perform subcultures) or reproducibility (3,1113).
Total laboratory automation (TLA). TLA is used for refer-
ence to systems that are capable of automating the work-
flow of processing specimens, inoculation of broths, slides
and agar plates, and imaging of culture plates.
Currently, there are only a few companies offering com-
plete laboratory automation systems for microbiology
laboratories: BD Kiestra (USA) and Copan
Beckman Coulter with the DxA 5000
(USA) and i2a
Diagnostics with RECITALS
(France) are 2 compa-
nies that provide compact solutions for portions of the
automation process. Technical features, advantages, and
disadvantages of the major automation systems have
been extensively detailed elsewhere (1,2,6,10,14). It is
out of the scope of this review to provide a comparison of
those systems. Rather, we will provide an overview of the
main components needed for the composition of a TLA
unit with a brief description of existing technologies in
the market for each corresponding hardware.
Inoculation/streaking unit. Most fully automated streak-
ing systems require liquid transport swabs or liquid sam-
ples. Specimens can be loaded into racks and then onto
the instrument for proceeding. Plates are then inoculated
and streaked depending on what is specified for a particu-
lar pattern (2,3). This involves de/recapping specimen
containers, precise handling of liquids, and opening/clos-
ing culture plates. Streaking units can streak between 140
and 400 plates per hour and stock up to 24 different me-
dia. The most common specimen processors are the BD
Kiestra InoqulA
(USA), the Copan WASP DT
(Italy), the ALIFAX Sidecar
(Italy), the Beckman
Coulter DxM Autoplak system
(USA), the i2a
Diagnostics PreluD
(France), and the Sener diagnos-
tics Autoplak
100 Clinical Chemistry 68:1 (2022)
Downloaded from by ISREC Bibliotheque user on 03 January 2022
Incubation system and high-resolution imaging. Incubators
in microbiology need to provide a favorable environment
for growth of microorganisms, such as maintenance of
optimal temperature and atmosphere. Of note, at this
time, automated systems do not have anaerobic incuba-
tors or other specialized incubation conditions for specific
organisms (i.e., microaerophilic conditions for
Camplyobacter spp.) and, accordingly, anaerobic culture
plates must be incubated outside of the system (3).
Incubators in a TLA system must also have robotics that
enable movement of plates within the incubator so that
specific plates can be accessed, discarded, or photo-
graphed on demand. Photography modules are often ad-
jacent to or a component of the incubator. Digital
photography allows the use of multiple colors and light-
ing conditions, customization of the time interval of pic-
ture taking, and elimination of variations in background.
BD Kiestra TLA and WCA
, Copan WASPLab
and i2a Maestro
enable automated specimen process-
ing, plate incubation, and digital imaging.
Several manufacturers have developed artificial in-
telligence and image analysis algorithms for computer-
assisted culture interpretation, which enables successful
identification and report of plates showing no significant
growth, colony recognition, or even efficient identifica-
tion of microorganisms based on chromogenic media.
Copan PhenoMATRIX
(1518), Clever Culture
Systems APAS Independence
(19), and Rapidmicro
biosystems Growth Direct System
(20) are some ma-
jor systems offering digital culture’s interpretation.
Studies evaluating image analysis systems for detection
of organisms from chromogenic media have shown an
increased sensitivity for detection of organisms from
chromogenic media in routine screening for pathogens
of infection control significance such as methicillin-
resistant Staphylococcus aureus (21), Vancomycin resis-
tant Enterococcus (22), and carbapenem-resistant
Enterobacteracae (23). Two studies evaluated the perfor-
mance of chromogenic media plus image analysis sys-
tems compared to molecular detection of Group A
Streptococcus (17,24) from throat specimens and Group
BStreptococcus (15) from vaginal–rectal swabs in preg-
nancy screening. Both studies demonstrated improved
sensitivity using the image analysis system that
approaches, but did not reach, the sensitivity of molecu-
lar assays. Even on nonchromogenic media, image
analysis systems sensitive and specific image recognition
software have been able to categorize cultures by growth
and no growth cultures and characterize the growth in
the culture by organism classification or morphology
Postimaging analysis (workstations). TLA systems have
relocated technologists from the traditional
microbiology benchtop to a plate-reading workstation,
also called telebacteriology. Within a workstation, mul-
tiple images can be viewed simultaneously so that a
technologist can correlate growth patterns across multi-
ple light settings and different media types or multiple
time points for a given specimen. The technologist can
further decide whether a plate will be held for further in-
vestigation or if it will be discarded. Generally, the mon-
itor used for the workstation should meet specifications
provided by the manufacturer to ensure appropriate res-
olution and color differentiation for accurate culture
Organization of the Laboratory
The decision to move to TLA is typically associated
with a major shift in laboratory workflow and manage-
ment. Implementation of TLA in the microbiology lab-
oratory allows not only technological innovation, but
also brings the necessity for innovative approaches to
the organization of the laboratory. The clinical microbi-
ology laboratory has traditionally operated with inde-
pendent work-up benches using a one or 2 shift model
for plate reading. With the implementation of automa-
tion systems for plating, incubation, and imaging, there
are opportunities for changes in both the physical and
personnel organization of the laboratory.
Generally, there are 2 approaches to the organiza-
tion of the automation system in the laboratory, an at-
tached work cell or modular work cell approach
(Fig. 1). In the attached work cell systems, all stations
from plating, imaging, and work-up are attached via a
conveyor belt system. Specimen plates are transported
to the incubator, imaged, and called out to the technol-
ogist via this conveyor belt. The technologist can per-
form the necessary work-up at their work station and
return the plates and subsequent subcultures to the in-
cubator, if necessary, via the conveyor belt system. In a
modular work cell format, the specimen plater and incu-
bation/imaging systems are typically attached to the
line. The work-up stations are located separate from the
automation line. When technologists identify cultures
that need additional manual work-up, specimens are
called out from the incubator and sent to an output
module where the technologist retrieve the specimens
and return them to their work station. Automation sys-
tems that incorporate plating, incubation, and work-up
have options for either attached or detached work sta-
tions and the laboratory will have to determine which
meets the needs of their local setting. Systems that pro-
vide individual pieces of automation (i.e., plating only
or imaging only) may be incorporated into a modular
work cell or potentially connected through universal
conveyor belt systems to create a more attached work
cell system.
Automation in Microbiology Review
Clinical Chemistry 68:1 (2022) 101
Downloaded from by ISREC Bibliotheque user on 03 January 2022
Traditionally clinical microbiology does not operate
using a 24-h schedule for culture reading and interpreta-
tion. If laboratories are operating 24 h, generally only
culture plating and Gram stains are performed during
the overnight hours. Automation of the front-end
plating systems allows for nonspecialists to load samples
onto the automation system for plating to provide
24-hour plating of specimens in the laboratory (25).
However, this shift in plating over 24 h requires that
laboratories examine when they have technologists avail-
able to read cultures based on a 24-h or shorter incuba-
tion schedule (Table 1). Laboratories may also consider
if they continue to organize the benches based on cul-
ture type (i.e., urine bench, wound bench) or generalize
the bench assignment based on time ready to read.
All these potential changes have a large impact on
the organization and workflow of the laboratory, and
come with distinct advantages and disadvantages that
will be unique to each laboratory system (Table 2).
Factors influencing these changes may be if a laboratory
is within a hospital or part of a consolidated laboratory
system, the staffing that is available, local regulatory
requirements, and local labor law requirements.
Automation offers more than just efficiency in current
processes. The way to truly capitalize on the value of-
fered by automation is to evaluate every current process
and determine whether and how it should change.
Benefits of Laboratory Automation in Clinical
Laboratory automation has gained much interest during
the past decade with a growing number of publications
evaluating the potential impact in the clinical microbiol-
ogy laboratory. The benefits the most well studied in
recent publications represent:
improved standardization (enhanced quality and re-
producibility of bacterial cultures)
increased laboratory efficiency (reduced turnaround
times) and higher productivity
increased workplace safety
reduced long-term costs
Table 3 summarizes the most common benefits
that have been reported. Of note, a few of those studies
Fig. 1. Models of configurations of total laboratory automation systems for identification (ID) and automated susceptibility test-
ing (AST).
102 Clinical Chemistry 68:1 (2022)
Downloaded from by ISREC Bibliotheque user on 03 January 2022
Table 1. Advantages and disadvantages of various options in implementation of total laboratory automation. TAT (turnaround time).
Options Advantages Disadvantages
Work cell
Attached work cell All functions contained within system
Gained efficiency
Requires significant contiguous laboratory space
May not support future expansion
Requires complex conveyor belt systems
Modular work cell Requires less contiguous space
Modular and can grow in future
Technologists have to retrieve and return plates from system
Culture reading shifts Limited culture
reading hours
Maintain current staffing schedules Will result in delayed TAT for culture results
24-h culture reading Optimizes TAT and efficiency
gained by automation
Changes to staff schedules
Additional cost of overnight staffing should be considered
Culture reading
By culture type Maintain current training and
competency assignment
Limits the value of using automation to increase efficiency
By ready to read All cultures can be read by any staff
when the image is captured
Increases efficiency and improves
turnaround time
Staff may have to be trained on additional benches
Implementation Staged implementation Opportunity to slowly deploy
portions of the system
May take longer or stall completely to achieve full implementation
Complete implementation All sections of the laboratory gain the
efficiencies of automation
More change management required
Barriers impacting implementation of a section of the line may delay
Automation in Microbiology Review
Clinical Chemistry 68:1 (2022) 103
Downloaded from by ISREC Bibliotheque user on 03 January 2022
draw attention to potential drawbacks related with TLA.
A later section in this review raises this specific question
and addresses the challenges related to the management
change and TLA implementation.
Standardization and reproducibility are 2 crucial issues
in laboratory diagnostics. Initial efforts made after the
introduction of laboratory automation in clinical micro-
biology have been essentially focused on comparing the
quality of automated versus manual processing. Studies
assessing the quality of isolation and productivity of au-
tomated systems have consistently reported higher yield
of discrete colonies or colony distribution and separa-
tion (14,2631). In a prospective comparison in 2014
of the fully automated InoqulA (BD Kiestra) system ver-
sus the manual method, there was a higher quality of
isolation with the automated method (27). Those data
were confirmed over a year (14) in a prospective com-
parison of automated inoculation with the InoqulA (BD
Kiestra) and the WASP (Copan, Italy) against manual
inoculation. A 3- to 10-fold higher yield of discrete col-
onies was observed with automated inoculation. This
difference was mainly observed at bacterial concentra-
tions above 10
bacteria/mL. Another study (28)
evaluated quality metrics of urine clinical specimens be-
fore and after installation of the InoqulA (BD Kiestra)
automation system in 2015. The InoqulA instrument
improved quality and standardization of the isolation of
bacterial cultures.
Some of these improvements may result from the
use of flocked swabs instead of standard swabs on the
automated systems. A comparative study in 2011 (26)
evaluated the sensitivity of automated culture of
Staphylococcus aureus from flocked swabs (using the
Walk-Away Specimen Processor, Copan, Italy) versus
that of manual culture of fiber swabs from nasal swabs.
The automated culture significantly increased the detec-
tion rate by 13.1% for direct cultures and by 10.2% for
enrichment cultures. The combination of automation
and flocked swabs, significantly improved the recovery
of organisms and can potentially result in improved
diagnostic accuracy.
Another important characteristic of laboratory au-
tomation is its ability to enhance the recovery of fastidi-
ous microorganisms (32,33). This feature is likely due
to the fact that cultivation of microorganisms in TLA
system incubators decreases time from inoculation to in-
cubation, maintains a standard temperature due to the
contained incubation system, and limits the exposure of
Table 2. Factors to consider prior to implementation of total laboratory automation system.
Items to consider when implementing a laboratory automation system
Space Does the laboratory have the existing physical space to install the automated system or will con-
struction be required?
Is there enough space to conduct both automation and manual processes while the system is being
installed and validated?
Is there space to maintain some additional manual process instrumentation in the event of automa-
tion downtime?
Staffing Will staffing schedules change, will work be performed on extended hours?
How will training and competency on the automation system be managed?
Do staff need to be trained on additional work benches or tasks not previously performed?
Performance How will you conduct the validation of the automation system?
What is the throughput of the instrument and imaging system?
What is the quality of the instrument camera and reading monitors?
Maintenance What is required for routine maintenance (i.e., daily, weekly, monthly, annual)?
How often does the instrument required unscheduled maintenance?
How quickly are maintenance issues resolved and do service engineers have to be sent to the
Cost Is the laboratory able to justify the cost of the system?
What are the costs of consumables?
What is the cost of the annual service contract?
Is there labor savings expected due to increased efficiency, how will that be measured?
104 Clinical Chemistry 68:1 (2022)
Downloaded from by ISREC Bibliotheque user on 03 January 2022
Table 3. Recent studies summarizing the most common benefits of automation.
Reference Type of study Type of sample Studied outcome
Summary of main
Cherkaoui et al. (26) Retrospective compar-
ison of pre- and
periods of the
WASPLab system.
Urine specimens.
Screening ESwabs of
MRSA, VRE, and
TAT from reception of
samples to delivery
of the culture results.
Decrease in median
TAT for negative
reports (from 52.1 h
to 28.3 h for urine,
P<0.001 and from
50.7 to 26.3 h for
MRSA ESwabs, P
Decrease in median
TAT but less pro-
nounced for screen-
ing of ESBL and
VRE (P<0.001).
No significant
change in the me-
dian TAT for posi-
tive samples.
Bailey et al. (27) Comparison of 2 dif-
ferent time points to
capture the first
photograph of urine
culture plates (18 h
vs 16 h) using the
BD Kiestra TLA
Urine specimens. TAT to final results for
positive cultures.
Significant decrease
in time to final
results for positive
cultures (71.6 h me-
dian time for the
18 h timepoint vs
61 h for the 16 h
Slight decrease in
sensitivity ranging
from 2% to 7%
(depending on the
species) for the 16 h
Yarbrough et al. (28) Comparison of pre-
and postimplemen-
tation periods of BD
Kiestra TLA system.
Urine specimens. TAT for major process-
ing steps and result
Increased TAT from
receipt to inocula-
tion (23 min vs
32 min postautoma-
tion, P<0.001) and
total processing
time (28 min vs
66 min postautoma-
tion, P<0.0001).
No change in time
to identification or
Automation in Microbiology Review
Clinical Chemistry 68:1 (2022) 105
Downloaded from by ISREC Bibliotheque user on 03 January 2022
Reference Type of study Type of sample Studied outcome
Summary of main
Decrease in TAT to
final report (from
43.8 h to 42h post-
automation, attrib-
uted mainly to
decrease in TAT of
negative results, P
Choi et al. (29) Prospective compari-
son of manual ver-
sus automated
streaking with Previ
Isola system.
Clinical blood
Urine specimens.
Consistency of bacte-
rial growth between
the 2 methods.
Quantity of colony
Sample processing
time (preparation
and streaking time).
High concordance
rate in terms of
quality and quantity
among the 2
Previ Isola demon-
strated lower
hands-on-time for
inoculation per
Theparee et al. (30) Retrospective compar-
ison of pre- and
periods of the BD
Kiestra TLA þ
Urine specimens. TAT to identification.
TAT to antibiotic
TAT to final report.
Decrease in time to
identification (18.5
to 16.9 h)
Decrease in time to
AST results (41.8 to
40.8 h)
Decrease in time to
report for negative
cultures (17.7 to
13.6 h) (p >0.001).
De Socio et al. (24) Retrospective compar-
ison of 8 h incuba-
tion and digital
plate imaging with
BD Kiestra Work
Cell Automation sys-
tem vs an overnight
incubation period.
Mono-microbial posi-
tive blood cultures.
TAT to identification.
TAT to antibiotic sus-
ceptibility testing.
Duration of empiric
antibiotic therapy.
Significant reduc-
tion of time to
results (P>0.001)
with the 8 h incuba-
tion method.
Significant reduc-
tion of duration of
empirical therapy (P
<0.001) and 30-
day crude mortality
rate (P<0.037).
Graham et al. (23) Prospective compari-
son of BD Kiestra
Urine specimens. Improved standard-
ization of time of
106 Clinical Chemistry 68:1 (2022)
Downloaded from by ISREC Bibliotheque user on 03 January 2022
Reference Type of study Type of sample Studied outcome
Summary of main
TLA vs manual
TAT from inoculation
to first culture
Total incubation time.
first culture reading
and total incubation
No difference in
time from specimen
inoculation to time
a result was entered
in LIS.
Quiblier et al. (20) Prospective head-to-
head comparison of
the fully automated
WASPLab workflow
(Copan, Italy) versus
manual processing.
Urine specimens. Quality of isolation
(number of single
colonies, number of
detected morpholo-
gies, and recovered
Culture positivity
Similar positivity
rates between the 2
Automated proc-
essing resulted in
higher quality isola-
tion in compare to
the manual method
in terms of single
colonies yielded
(for 32.2% of the
samples), detected
morphologies (for
47.5% of the sam-
ples), species (for
17.4% of the sam-
ples) and patho-
gens (for 3.4% of
the samples).
Strauss et al.
(17) Comparison of pre-
and postinstallation
periods of InoqulA
(BD Kiestra).
Urine specimens. Culture positivity
Quality of isolation.
TAT to final
TAT to antibiotic sus-
ceptibility testing.
Improved quality
and standardization
of the isolation with
InoqulA instrument.
Shorter TAT to iden-
tification during au-
tomation period for
negative results (P
Slightly higher TAT
to identification for
positive cultures
with 2 results
reported with only
one of them
reported with BD
Phoenix (P¼0.04).
Automation in Microbiology Review
Clinical Chemistry 68:1 (2022) 107
Downloaded from by ISREC Bibliotheque user on 03 January 2022
Reference Type of study Type of sample Studied outcome
Summary of main
No difference in
TAT to antibiotic
susceptibility test-
ing during the 2
Croxatto et al. (14) Prospective compari-
son of the auto-
mated inoculation
with InoqulA (BD
Kiestra) and the
Specimen Processor
(WASP, Copan, Italy)
with manual
Mono- and polymicro-
bial bacterial
Urine specimens.
Yield of discrete colo-
nies and colony
Reproducibility of
each inoculation sys-
tem to produce dis-
crete colonies.
Requirement to per-
form additional
A 3- to 10-fold
higher yield of dis-
crete colonies was
observed with auto-
mated inoculation
than with manual in-
oculation (differ-
ence mainly
observed at con-
centrations >10
Mutters et al. (25) Comparison of BD
Kiestra TLA com-
bined with MALDI–
TOF–MS versus con-
ventional methods.
Clinical blood
TAT to growth
TAT to identification.
Possible clinical im-
pact to antibiotic
Kiestra TLA com-
bined with MS
resulted in 30.6 h
time gain for isolate
identification in
compared to con-
ventional methods.
Early microbial
identification with-
out susceptibility
testing led to an ad-
justment of antibi-
otic regiment in
12% (24/200) of
Froment et al. (16) Prospective compari-
son of fully auto-
mated InoqulA (BD
Kiestra) vs manual
Calibrated bacterial
suspensions and
clinical specimens
(urine or swabs).
Recovery of bacterial
Quality of colony
Total time for inocula-
tion and streaking.
Higher quality of
isolation with auto-
mated method (P
¼0.0006 and P
¼00002 for mono-
and polymicrobial
plates respectively).
No difference in
streaking rate and
inoculation time.
Mischnik et al. (18) Prospective compari-
son of manual
Wound swabs
Quality of isolation. More often individ-
ually distinct and
108 Clinical Chemistry 68:1 (2022)
Downloaded from by ISREC Bibliotheque user on 03 January 2022
microorganisms to room temperature and atmospheric
oxygen (3).
TLA is critical in a landscape of ever-increasing microbi-
ology test volume while laboratories must contend with
personnel shortages and economic restrictions. Many
studies performed over the past several years advocate
for the optimization of laboratory workflow leading to
improved productivity and decreased processing TAT
(28,3440). Given that manual inoculation and trans-
ferring of plates between benchtops and incubators oc-
cupy approximately 33% and 10% of a technologist’s
time, respectively (3,41), there is a great potential of
improvement due to an automated system. Compared
to manual streaking, a few studies have demonstrated
that automated inoculation exhibits a significant de-
crease in hands-on-plating time (14,26,27,30). A
2018 study (42) evaluated the sample processing time
(preparation and streaking time) of manual method ver-
sus automated streaking with the Previ Isola system in
blood and urine clinical specimens. While there was a
high consistency of bacterial growth between the 2
methods and a high concordance in terms of quantity of
colony counts, the Previ Isola demonstrated lower
hands-on-time for inoculation per sample. The authors
calculated that they could save about 6 min of hands-
on-time of a skilled technologist per 10 samples. While
these savings may appear to be minimal, when extrapo-
lated it showed that 1.25 8-h work days are saved per
1000 cultures. This efficiency gain can be substantial in
the face of labor shortages and increased demands on
the laboratory.
In addition to reduction in hands-on-time, TLA
offers the additional advantage of reduced processing
time for almost all the processing steps of a bacterial cul-
ture (28,3440). Compared to manual plating, in
which a technologist may inoculate several specimens
before transferring plates to an incubator, the so-called
“smart” incubators of automated systems eliminate
plates waiting in the setup area and allow direct incuba-
tion after inoculation. These incubators maintain con-
sistent temperature and gas levels with negligible heat
and CO
gas loss enabling the first plate images for cul-
ture interpretation to be ready as early as after 10 h for
urine specimens and 8 h for positive blood culture broth
subcultures (1,11,34,35,43). Digital plate imaging
can facilitate scanning for growth more frequently or
even continuously (depending on the laboratory’s work-
flow) instead of only at set times during the day.
Laboratories may consider that reducing time of report-
ing microbial growth may result in a reduction of sensi-
tivity (37). Additionally, if a laboratory chooses to
reduce the incubation time, it should also consider the
need to validate additional ancillary testing performed at
earlier timepoint than previously performed in the labo-
ratory, such as identification, susceptibility methods,
and biochemical tests.
While the positive impact of TLA on the laboratory
workflow has been shown in numerous studies, most of
these studies have evaluated the impact of TLA on the
laboratory workflow. Recent studies have demonstrated
statistically significant reduction in time to final report
(from 71.6 h to 61 h for positive urine specimens, from
Reference Type of study Type of sample Studied outcome
Summary of main
versus automated
plating with Previ
Isola system.
less confluent colo-
nies with Previ Isola
than with manual
Jones et al. (18) Prospective compari-
son of automated
culture of
Staphylococcus au-
reus from flocked
swabs versus that of
manual culture of fi-
ber swabs.
Nasal swabs. Diagnostic performan-
ces (sensitivity).
Automated culture
significantly in-
creased the detec-
tion rate by 13.1%
for direct culture
and 10.2% for en-
richment culture.
TAT, turnaround time; LIS, laboratory information system.
Start time for TAT calculation was considered the time of specimen accessioning and bullet of the test.
Automation in Microbiology Review
Clinical Chemistry 68:1 (2022) 109
Downloaded from by ISREC Bibliotheque user on 03 January 2022
52.1 h to 28.3 h for sterile urine specimens and from
55.4 h to 32.8 h for positive blood cultures, P<0.001)
(35,37,39,40) with automated systems. Combination
of the improved efficiencies gained by use of a TLA sys-
tem with rapid identification methods such as MALDI–
TOF–MS for bacterial identification can further en-
hance the reduction of reporting time. One study (36)
compared the time to identification on positive blood
cultures of conventional methods versus BD Kiestra
TLA combined with MALDI–TOF–MS, observing a
gain of 30.6 h for isolate identification with automation.
Whereas most of the studies describe reduced
reporting times with laboratory automation, a few stud-
ies have demonstrated the opposite results that might be
observed as well. In a comparison of the time to final
identification during 2 study periods (pre- and postin-
stallation of InoqulA BD Kiestra) for urine specimens
(28), the authors reported that, while a significantly
shorter time to identification during the automation pe-
riod was noted for sterile cultures, a slightly higher time
was observed for positive cultures when 2 species were
identified. A separate study also reported significant in-
creased time from receipt to inoculation (23 min vs
32 min) and total processing time (from 28 min to
66 min) postautomation using the BD Kiestra TLA sys-
tem and a similar study design as previously noted (38).
There was, on the other hand, a slight decrease in time
to final report (from 43.8 h to 42 h) attributed mainly
to the decrease in TAT of negative results. The authors
of this study argued that the workflow for these speci-
mens included a batching step in which specimens were
saved in a tabletop rack before being loaded on the TLA
instrument. The delay encountered in the post-TLA pe-
riod accounted for the time the specimen was on the
counter waiting to be loaded, or on the instrument wait-
ing to be plated rather than in active hands-on time.
Those surprising findings suggest that although there is
a great potential for decreasing the time to reporting
results using automation, TLA implementation does not
automatically eliminate human or hardware bottlenecks
or inefficient processes. To harness the maximum im-
pact of TLA, clinical microbiology laboratories might
need to adopt changes in laboratory workflow through
the entire process from specimen reception, inoculation,
incubation, and imaging whereby culture work-up can
begin as soon as cultures are ready to be read.
Large-scale studies have not yet been performed to
demonstrate a significant impact on clinical outcomes.
To our knowledge, only 2 studies have tried to eval-
uate the potential impact of TLA on patient care. One
study (36) assessed the possible clinical impact of micro-
bial identification results using TLA and MALDI–TOF
according to antibiotic treatment prescription decision
timepoints. For 33.5% (67/200) patients, there was no
impact on antimicrobial treatment based on the
identification. Antimicrobial treatment, however, was
modified following the Gram stain result (14.2%), initial
identification (12%), and identification and susceptibility
result (34.5). These results demonstrate that a significant
number of prescribing decisions are made based on the
results generated by the laboratory and optimization of
those results may influence more timely clinical decisions.
A second study retrospectively compared 8 h of incuba-
tion and digital plate imaging with BD Kiestra Work
Cell Automation versus an overnight incubation period
(35), reporting a significant reduction on duration of em-
pirical therapy (86.9 h vs 54.8 h, P<0.001), although no
differences were observed regarding the proportion of
patients receiving de-escalation or escalation therapy after
microbiological report. The crude all-cause death rate was
significantly lower (P¼0.037) during the automation pe-
riod. Nevertheless, authors did not provide data regarding
appropriateness of antibiotic therapy for both groups and
it seems difficult to assign this effect solely on the impact
of automation. Additional studies assessing the impact of
reduced turnaround time and standardization of culture
reading on clinical outcomes are needed.
Worker safety is one of the most important advantages
of automating industrial operations. Technologists do
not have to handle most specimens with automated
processing. Since the steps of transferring culture plates
to incubators, examination of cultures with digital imag-
ing, automation of identification and antimicrobial sus-
ceptibility testing, and handling of culture plates by
technologists has been limited to the bare minimum,
consequentially the exposure risk to potential pathogens
is minimized (11,12,25).
Decreased long-term costs. The laboratory budget is a pri-
mary determinant of organization and management and
thus one of the main drivers and barriers of laboratory
automation implementation. Implementation of TLA
represents an important investment, which is inevitably
associated with a short-term escalation of cost for ac-
commodating the project (12). In a real-world evalua-
tion of integrating an automated instrument for
diagnostic microbiology within a model of TLA (25),
the period of payback for integration of WASPLab in a
model of TLA was estimated to be 7.3 years. The overall
savings could be estimated from lower personnel costs,
from saving of disposables and manual tools and from
the elimination of 1 (standalone) incubator. One group
of investigators demonstrated that they were able to re-
duce staffing for both processing and working up speci-
mens, decrease time to results and costs by performing
fewer subcultures or earlier reading of cultures and in-
crease the number of specimens processed (11). In their
model, they were able to pay back the investment in
110 Clinical Chemistry 68:1 (2022)
Downloaded from by ISREC Bibliotheque user on 03 January 2022
3 years rather than their projected 5 years through labor
savings alone (11). In a separate evaluation of the finan-
cial impact of TLA at 4 facilities, there was an increase
in total staff productivity (specimens per full-time em-
ployee) ranging from 18% to 92% following implemen-
tation. Among these facilities, annual savings per year
ranged from $270 000 to $1 200 000 (44). These several
studies point to the fact that the question should not be
whether the laboratory can afford the investment in
TLA, but rather whether the laboratory can afford not
to invest in TLA given the labor shortages laboratories
Challenges for Automation Implementation
Despite the many advantages in efficiency, productivity,
and timeliness that automation offers, it is not without
new and unique challenges. For every advantage that
laboratory automation provides, there are similar chal-
lenges that a laboratory must face. The laboratory must
determine what is feasible within their current system
and constraints. Properly approached, there are a num-
ber of important benefits that can be achieved through
implementation of automation in the clinical microbiol-
ogy laboratory. Successful implementation, however,
can be a major barrier to realizing the advantages for au-
tomation systems. Before selection of an automation
system, laboratories should identify requirements criteria
to determine the automation system that should be se-
lected. Items such as space, performance, analytical cri-
teria, maintenance, and cost should all be thoughtfully
considered before selection of an automation system
(Table 2).
Laboratories should also consider and agree on the
expectations they have for the impact of the automation
system on the laboratory. Studies of automation of core
laboratory systems (i.e., chemistry, hematology, and co-
agulation) have demonstrated an 86% reduction in
manual processing steps (45). However, automation of
the microbiology laboratory still requires many manual
steps of culture interpretation, work-up, and identifica-
tion and susceptibility testing, until these processes be-
come automated in the future.
The introduction of automation in diagnostic bacteriol-
ogy is aimed at improving laboratories’ productivity,
quality, and traceability, but its integration in routine
diagnostic laboratories is challenging. Among other dif-
ficulties, the successful interface of the system with labo-
ratory information systems (LIS) and with other
automates is complex and crucial to guarantee a success-
ful achievement of the laboratory objectives and
The connection of the systems with the LIS is
mostly unique for each laboratory since multiple differ-
ent LIS are commercialized. Even laboratories using the
same LIS often have different settings based on their dif-
ferent workflows, activities, and organization. Moreover,
automated system management software and application
functionalities may not be compatible with all LIS and/
or with the different laboratories’ workflows. It is thus
strongly recommended to thoroughly determine the
compatibility of each software or imaging application
with the LIS specifications before moving to acquisition
of the systems. Due to the relative novelty of laboratory
automation in diagnostic bacteriology, technologists do
not have an extensive overall expertise in automated sys-
tems, LIS, information technology (IT) specifications,
and strict IT security settings. Thus, it is essential to in-
volve expert stakeholders from both automated systems
manufactures and the laboratory IT and LIS to design
the complex interface of the automated systems with the
LIS. A continuous collaborative work between these
experts during the entire project is absolutely required
to quickly solve any major issues that will be faced dur-
ing the installation and implementation processes.
Ideally, each manufacturer should offer an interface-test-
ing environment to test the system management soft-
ware’s connectivity and functionalities with the LIS and
IT before the installation of the automated systems.
Preinstallation interface testing represents a very inter-
esting possibility to solve most issues ahead of the instal-
lation and accelerate the postinstallation go-live of the
It is also important to consider that any upgrade or
modification of the system management software, com-
puter operating systems, and the laboratory IT or LIS
may create functional issues and failure of the bidirec-
tional interface. Thus, any minor or major upgrade re-
quire attention and should be considered as a new
project with potentially additional significant human
and financial investments to ensure continuous and scal-
able functionalities of systems.
Despite the previously mentioned financial benefits of
TLA, the significant initial investment may be a barrier
for some laboratories, along with difficulty in perform-
ing their own projections for financial justification of
the investment. There are several strategies laboratories
may use to determine how they will justify the return
on investment and payback period for TLA systems
(25). Each laboratory must perform an analysis of their
local systems to determine what may be expected with
implementation of a TLA system (Table 2). Ultimately,
each laboratory must determine what is considered a
measure of success for implementation of the system.
Automation in Microbiology Review
Clinical Chemistry 68:1 (2022) 111
Downloaded from by ISREC Bibliotheque user on 03 January 2022
Changes in workflow and staffing will lead to a require-
ment for gaining staff support and buy-in for implemen-
tation. Change management strategies should be used to
lead to a successful implementation and support among
the technical staff. Ultimately, all of the changes that
will be experienced with automation in the clinical labo-
ratory must be managed through effective leadership.
There are 2 very different ways to approach change
management within systems: motivation through penal-
ties and rewards or creating an environment that
encourages and maximizes internal motivation (46).
The laboratory setting is no different. Much of the work
that is conducted in the laboratory is based on the for-
mer, which establishes a transactional relationship based
on regulatory requirements and quality control stand-
ards that pass or fail. There is, however, opportunity to
use implementation of automation to create a transfor-
mative environment where staff gain more connection
to their work and are valued as members of the transfor-
mation in the laboratory. Effective leadership strategies
that engage staff from the beginning of the implementa-
tion of an automation system and throughout the ongo-
ing quality control and monitoring is critical to ongoing
and continued improvements in the laboratory.
Future Directions
As previously discussed, automation has brought sub-
stantial changes in clinical microbiology laboratory orga-
nization and has modernized the diagnostic approach to
a clinical sample. What does the future hold for clinical
microbiology remains yet to be seen. The near future of
laboratory automation will most likely be affected by
the spread of automated image reading expert systems
and algorithms with multiple added-value applications.
Those new technologies will allow earlier detection of
microbial growth, automated detection, and autorelease
of sterile samples, identification and quantification of
bacterial colonies, or even automated reading of antibi-
otic susceptibility testing disk diffusion assays and thus
further decrease TATs and increase productivity (13).
Further, integration of additional patient demographic
information that impact the culture interpretation (i.e.,
age, gender, other laboratory values, and previous cul-
tures) can be gathered from the LIS to aid in automated
advanced interpretation of cultures. One could consider
that expert systems are the realization of a more com-
plete automation from sample to final results with mini-
mal human intervention during the diagnostic process.
Future efforts in the field of automation should
also focus on impacting the patient management and
from this point of view efficient communication of a
microbiological result to clinicians is crucial. Shorter
TAT for final results of microbiological samples do not
automatically result a clinical decision for the patient.
Communication barriers between the microbiology lab-
oratories and clinical units due to verbal reporting of
test results, information transfer between poorly inte-
grated IT systems, limited advisory/consult services or
restricted “opening hours” must be overcome (47). A
computer-based communication of results (with an alert
system for critical results for example), together with a
reorganization in management of laboratory workflow
and clinical staff may serve as a valuable framework to
establish these changes and allow around the clock
reporting of microbiological results coupled with a faster
clinical decision aiming to improve patient’s care.
Although it has taken 30years since the first automa-
tion systems were introduced in the microbiology labora-
tory, TLA is now a recognized and valuable component
of the laboratory. As with all new advances, there contin-
ues to be need for further optimization and improvement
in the hardware and software as laboratories gain experi-
ence and needs continue to expand. It is clear, however,
that with TLA we are currently experiencing a fundamen-
tal change in the composition of the clinical microbiology
laboratory. However, despite the automation of most
specimens for culture processing, there will likely always
be unusual specimens that will be unable to be processed
on the TLA system. Similarly, while there have been
many advances in image analysis and artificial intelli-
gence, there will always be a need for clinical microbiol-
ogy expertise to interpret the complex information,
emerging microorganisms, and variety of resistance pat-
terns presented in clinical microbiology. Experienced
technologists will always be needed to augment the use of
TLA. Although the present and the future of clinical mi-
crobiology is increasingly automated, there will always be
a need for highly qualified technologists working collabo-
ratively with the automated system to complete the work
of the microbiology laboratory.
Nonstandard Abbreviations: TLA, total laboratory automation; LIS,
laboratory information systems; IT, information technology.
Author Contributions: All authors confirmed they have contributed to
the intellectual content of this paper and have met the following 4 require-
ments: (a) significant contributions to the conception and design, acquisi-
tion of data, or analysis and interpretation of data; (b) drafting or revising
the article for intellectual content; (c) final approval of the published arti-
cle; and (d) agreement to be accountable for all aspects of the article thus
ensuring that questions related to the accuracy or integrity of any part of
the article are appropriately investigated and resolved.
All authors (A. Croxatto, A. Kritikos, and K. Culbreath) performed lit-
erature research, drafted, and revised the article.
Authors’ Disclosures or Potential Conflicts of Interest: Upon manu-
script submission, all authors completed the author disclosure form.
Disclosures and/or potential conflicts of interest:
Employment or Leadership: None declared.
112 Clinical Chemistry 68:1 (2022)
Downloaded from by ISREC Bibliotheque user on 03 January 2022
Consultant or Advisory Role: A. Croxatto, Becton Dickinson.
Stock Ownership: None declared.
Honoraria: A. Croxatto, Becton Dickinson.
Research Funding: A. Kritikos reports research grants from Becton
Dickinson unrelated to the submitted work. K. Culbreath has received
research support from Copan Diagnostics. A. Croxatto, grant from
Becton Dickinson for a collaborative research project on laboratory au-
tomation (payment made to institution for the funding of laboratory
technicians and Ph.D. students).
Expert Testimony: None declared.
Patents: None declared.
1. Burckhardt I. Laboratory automation in clinicalmicrobiol-
ogy. Bioengineering 2018;5:102.
2. Croxatto A, Prod’hom G, Faverjon F, Rochais Y, Greub G.
Laboratory automation in clinicalbacteriology: what sys-
tem to choose?Clin Microbiol Infect2016;22:217–35.
3. Bailey AL, Ledeboer N, Burnham CD. Clinical microbiol-
ogy is growing up: the total laboratoryautomation revo-
lution. C lin Chem 2019; 65:634–43.
4. Mutters N. Laboratory automation in clinical microbiol-
ogy: a quiet revolution. Austin J Infect Dis 2014;1:2.
5. BourbeauPP, Ledeboer NA. Automation in clinicalmicro-
biology.J Clin Microbiol 2013;51:1658–65.
6. Greub G, Prod’hom G. Automation in clinical bacteriol-
ogy: what system to choose? Clin Microbiol Infect 2011;
7. Lina G, Greub G. Automation in bacteriology: a changing
way to perform clinical diagnosis in infectious diseases.
Clin Microbiol Infect 2016;22:215–6.
8. Middleton SR. Developing an automation conceptthat is
right for yourlaboratory. Clin Chem 2000;46:757–63.
9. SautterRL, Thomson RB Jr. Consolidated clinicalmicrobi-
ology laboratories.J Clin Microbiol 2015;53:1467–72.
10. Novak SM, Marlowe EM. Automation in the clinical mi-
crobiology laboratory. Clin Lab Med 2013;33:567–88.
11. Thomson RB Jr, McElvania E. Total laboratory automa-
tion: what is gained, what is lost, and who can afford it?
Clin Lab Med2019;39:371–89.
12. Lippi G, Da Rin G. Advantages and limitations of total
laboratory automation: a personal overview. Clin Chem
Lab Med 2019;57:802–11.
13. Croxatto A, Marcelpoil R, Orny C, Morel D, Prod’hom G,
Greub G. Towards automated detection, semi-quantification
and identification of microbial growth in clinical bacteriol-
ogy: a proof of concept. Biomed J 2017;40:317–28.
14. Croxatto A, Dijkstra K, Prod’hom G, Greub G.
Comparison of Inoculation with the InoqulA and WASP
automated systems with manual inoculation. J Clin
Microbi ol 2015;53:2298– 307.
15. Baker J, Timm K, Faron M, Ledeboer N, Culbreath K.
Digital image analysis for the detection of group B
Streptococcus from ChromID Strepto B medium using
PhenoMatrix algorithms. J Clin Microbiol 2020;59:
16. Dauwalder O, Michel A, Eymard C, Santos K, Chanel L,
Luzzati A, et al. Use of artificial intelligence for tailored
routine urine analyses. Clin Microbiol Infect 2021;27:
17. Van TT, Mata K, Dien Bard J. Automated detection of
Streptococcus pyogenes pharyngitis by use of Colorex
Strep A CHROMagar and WASPLab artificial intelligence
chromogenic detection module software. J Clin
Microbio l 2019;57:e0081 1–9.
18. Faron ML, Buchan BW, Relich RF, Clark J, Ledeboer NA.
Evaluation of WASPLab software to automatically read
chromID CPS elite agar for reporting of urine cultures. J
Clin Microbiol 2020;58:e00540–19.
19. Glasson J, Hill R, Summerford M, Olden D,
Papadopoulos F, Young S, Giglio S. Multicenter
evaluation of an image analysis device (APAS): compari-
son between digital imageand traditional plate reading
using urinecultures. Ann Lab Med 2017;37:499–504.
20. Jones D, CundellT. Method verification requirements for
an advanced imaging system for microbial plate count
enumeration. PDA J Pharm Sci Technol 2018;72:
21. Faron ML, Buchan BW, Vismara C, Lacchini C, Bielli A,
Gesu G, et al. Automated scoring of chromogenic media
for detection of methicillin-resistant Staphylococcus au-
reus by use of WASPLab image analysis software. J Clin
Microbio l 2016;54:620 –4.
22. Faron ML, Buchan BW, Coon C, Liebregts T, van Bree A,
Jansz AR, et al. Automatic digital analysis of chromo-
genic media for vancomycin-resistant-Enterococcus
screens using Copan WASPLab. J Clin Microbiol 2016;
23. Foschi C, Gaibani P, Lombardo D, Re MC, Ambretti S.
Rectal screening for carbapenemase-producing
Enterobacteriaceae: a proposed workflow. J Glob
AntimicrobResist 2020;21:86–90.
24. Foschi C, Turello G, Lazzarotto T, Ambretti S. Performance
of PhenoMatrix for the detection of Group B
Streptococcus from recto-vaginal swabs. Diagn Microbiol
Infect Dis2021;101:115427.
25. Da Rin G, Zoppelletto M, Lippi G. Integration of diagnos-
tic microbiology in a model of total laboratory automa-
tion. Lab Med 2016;47:73–82.
26. Jones G, Matthews R, Cunningham R, Jenks P.
Comparison of automated processing of flocked swabs
with manual processing of fiber swabs for detection of
nasal carriage of Staphylococcus aureus. J Clin Microbiol
27. Froment P, Marchandin H, Vande Perre P, Lamy B.
Automated versus manual sample inoculations in rou-
tine clinical microbiology: a performance evaluation of
the fully automated InoqulA instrument. J Clin Microbiol
28. Strauss S, Bourbeau PP. Impact of introductionof the BD
Kiestra InoqulA on urine culture results in a hospital clin-
ical microbiology laboratory. J Clin Microbiol 2015;53:
29. Quiblier C, Jetter M, Rominski M, Mouttet F, Bottger EC,
Keller PM, Hombach M. Performance of Copan WASP for
routine urine microbiology. J Clin Microbiol 2016;54:
30. Mischnik A, Mieth M, Busch CJ, Hofer S, Zimmermann
S. First evaluation of automated specimen inoculation
for wound swabsamples by use of the Previ Isola system
compared to manual inoculation in a routine laboratory:
finding a cost-effective and accurate approach. J Clin
Microbio l 2012;50:273 2–6.
31. Iversen J, Stendal G, Gerdes CM, Meyer CH, Andersen
CO, Frimodt-Moller N. Comparative evaluation of inocu-
lation of urine samples with the Copan WASP and BD
Kiestra InoqulA instruments. J Clin Microbiol 2016;54:
32. Lainhart W, Burnham CA. Enhanced recovery of fastidi-
ous organisms from urine culture in the setting of total
laboratory automation. J Clin Microbiol 2018;56:
33. Klein S, Nurjadi D, Horner S, Heeg K, Zimmermann S,
Burckhardt I. Significant increase in cultivation of
Gardnerella vaginalis,Alloscardovia omnicolens,
Actinotignum schaalii,andActinomyces spp. in urine
samples with total laboratory automation. Eur J Clin
MicrobiolInfect Dis 2018;37:1305–11.
34. Graham M, Tilson L, Streitberg R, Hamblin J, Korman
TM. Improved standardization and potential for short-
ened time to results with BD Kiestra total laboratory au-
tomation of early urine cultures: a prospective
comparison with manual processing. Diagn Microbiol
Infect Dis2016;86:1–4.
35. De Socio GV, Di Donato F, Paggi R, Gabrielli C, Belati A,
Rizza G, et al. Laboratory automation reduces time to re-
port of positive blood cultures and improves manage-
ment of patients with bloodstream infection. Eur J Clin
MicrobiolInfect Dis 2018;37:2313–22.
36. Mutters NT,Hodiamont CJ, de Jong MD,Overmeijer HP,
van den Boogaard M, Visser CE. Performance of Kiestra
total laboratory automation combinedwith MS in clinical
microbiology practice. Ann Lab Med2014;34:111–7.
37. Bailey AL, Burnham CD. Reducing the time between in-
oculation and first-read of urine cultures using total lab
automation significantly reduces turn-around-time of
positive culture results with minimal loss of first-read
sensitivity. Eur J Clin Microbiol Infect Dis 2019;38:
38. Yarbrough ML,Lainhart W, McMullen AR, Anderson NW,
Burnham CD. Impact of total laboratory automation on
workflow and specimen processing time for culture of
urine specimens. Eur J Clin Microbiol Infect Dis 2018;
39. Theparee T, Das S, Thomson RB Jr. Total laboratory auto-
mation and matrix-assisted laser desorption ionization-
time of flight mass spectrometry improve turnaround
times in the clinical microbiology laboratory: a retrospec-
tive analysis. J Clin Microbiol 2018;56:e01242-17.
40. Cherkaoui A, Renzi G, Martischang R, Harbarth S,
Vuilleumier N, Schrenzel J. Impact of total laboratory au-
tomation on turnaround times for urine cultures and
screening specimens for MRSA, ESBL, and VRE carriage:
retrospective comparison with manual workflow. Front
Cell InfectMicrobiol 2020;10:552122.
41. Dauwalder O, Landrieve L, Laurent F, de Montclos M,
VandeneschF, Lina G. Does bacteriologylaboratory auto-
mation reduce time to results and increase quality man-
agement?Clin Microbiol Infect2016;22:236–43.
42. Choi Q, Kim HJ, KimJW, Kwon GC, Koo SH. Manualver-
sus automated streaking system in clinical microbiology
laboratory: performance evaluation of Previ Isola for
blood culture and body fluid samples. J Clin Lab Anal
43. Burckhardt I, Last K, Zimmermann S. Shorter incubation
times for detecting multi-drug resistant bacteria in
Automation in Microbiology Review
Clinical Chemistry 68:1 (2022) 113
Downloaded from by ISREC Bibliotheque user on 03 January 2022
patient samples: defining early imaging time points us-
ing growthkinetics and total laboratory automation. Ann
Lab Med 2019;39:43–9.
44. Culbreath K, Piwonka H, Korver J, Noorbakhsh M.
Benefits derived from full laboratory automation in mi-
crobiology: a tale of four laboratories. J Clin Microbiol
45. Yu HE, Lanzoni H, Steffen T, Derr W, CannonK, Contreras
J, Olson JE. Improving laboratory processes with total
laboratory automation. Lab Med 2019;50:96–102.
46. Crabtree BF, Howard J, Miller WL, Cromp D, Hsu C,
Coleman K, et al. Leading innovative practice:leadership
attributes in LEAP practices. Milbank Q 2020;98:
47. Skodvin B, Aase K, Brekken AL, Charani E, Lindemann
PC, Smith I. Addressing the key communication barriers
between microbiology laboratories and clinical units: a
qualitative study. J Antimicrob Chemother 2017;72:
114 Clinical Chemistry 68:1 (2022)
Downloaded from by ISREC Bibliotheque user on 03 January 2022
... Financial and personnel capacities are given, to plan and integrate laboratory automation. However, operating the systems remains the responsibility of the scientists and significantly differs from manual laboratory processes [10]. ...
... The benefits of automated processes in life science laboratories have been widely described [1,7,10,12]. Especially in the field of bioanalytics, laboratory automation offers pertinent advantages. ...
... Others are not sure if an automation system is suitable for their laboratory -and they may be right in their expectations. The most important step before investing time and money into laboratory automation is to consider why the automation system is essential or at least meaningful for the specific laboratory situation [10,18]. There are certainly some good reasons for establishing automation, but there are also a various number of reasons that form the wrong foundation for an automation project [19]. ...
Full-text available
The large volumes of samples to be analysed every day would be impossible to manage without laboratory automation. As laboratory procedures have progressed, so have the tasks of laboratory personnel. With this feature article, we would like to provide (bio)chemical practitioners with little or no knowledge of laboratory automation with a guide to help them decide whether to implement laboratory automation and find a suitable system. Especially in small- and medium-sized laboratories, operating a laboratory system means having bioanalytical knowledge, but also being familiar with the technical aspects. However, time, budget and personnel limitations allow little opportunity for personnel to get into the depths of laboratory automation. This includes not only the operation, but also the decision to purchase an automation system. Hasty investments do not only result in slow or non-existent cost recovery, but also occupy valuable laboratory space. We have structured the article as a decision tree, so readers can selectively read chapters that apply to their individual situation. This flexible approach allows each reader to create a personal reading flow tailored to their specific needs. We tried to address a variety of perspectives on the topic, including people who are either supportive or sceptical of laboratory automation, personnel who want or need to automate specific processes, those who are unsure whether to automate and those who are interested in automation but do not know which areas to prioritize. We also help to make a decision whether to reactivate or discard already existing and unused laboratory equipment.
... With respect to technical capabilities, automation has been steadily gaining momentum for several years [1]. Along with digitization and robotization, these developments have both offset and perhaps also contributed to a reduced number of laboratory technicians, a trend anticipated to continue into the 2030s. ...
... Workflow automation provides an opportunity to overcome some of these barriers. General laboratory automation has been used in many different types of laboratories for years (6), and has only exploded more recently, resulting in a multi-billion dollar market (7). As demand for next-generation sequencing increases, it only makes sense to consider how automation could potentially be used to support this type of testing. ...
Full-text available
In the wake of COVID-19, the importance of next-generation sequencing (NGS) for diagnostic testing and surveillance-based screening has never been more evident. Considering this, continued investment is critical to ensure more public health laboratories can adopt these advanced molecular technologies. However, many facilities may face potential barriers such as limited staff available to routinely prepare, test, and analyze samples, lack of expertise or experience in sequencing, difficulties in assay standardization, and an inability to handle throughput within expected turnaround times. Workflow automation provides an opportunity to overcome many of these challenges. By identifying these types of sustainable solutions, laboratories can begin to utilize more advanced molecular-based approaches for routine testing. Nevertheless, the introduction of automation, while valuable, does not come without its own challenges. This perspective article aims to highlight the benefits and difficulties of implementing laboratory automation used for sequencing. We discuss strategies for implementation, including things to consider when selecting instrumentation, how to approach validations, staff training, and troubleshooting.
... A must-read is the reflections piece by Dr. Robin Patel describing her experience of developing and implementing novel genomic and proteomic assays over her esteemed 25-year career in clinical microbiology (17). Among the major innovative technologies are nucleic acid amplification, matrix-assisted laser desorption ionization, massively parallel (next-generation) sequencing (17)(18)(19), CRISPR (11), artificial intelligence (20), "total laboratory" automation (21), breath analyte analysis (5), and rapid antimicrobial susceptibility testing (22). Importantly, these tests have several strengths and limitations. ...
Full-text available
These are unprecedented times in medicine, and laboratory medicine is at the forefront. While advances in genomics and proteomics have dramatically improved our ability to diagnose and characterize infectious diseases, the world faces momentous challenges from increasing antimicrobial resistance, resurgence of ancient scourges, and outbreaks of emerging pathogens. There have been more than a dozen viral diseases outbreaks in the past 2 decades alone (Fig. 1), and the emergence of novel viral pathogens is predicted to accelerate in coming years (1). Most notable is the coronavirus disease 2019 (COVID-19) outbreak, which continues to upend daily lives around the planet and raise many medical, social, economic, and political issues. This special issue of Clinical Chemistry highlights the major challenges faced by the global laboratory medicine community, as well as the opportunities provided by the development and implementation of transformative technologies.
Full-text available
Objectives We aimed to evaluate the impact of an uninterrupted workflow regarding blood cultures on turnaround time and antibiotic prescription. Methods Monomicrobial episodes of bacteremia were retrospectively evaluated before and after a continuous 24/7 workflow was implemented in our clinical microbiology laboratory (pre- and post-intervention periods; PREIP and POSTIP). Primary outcome was the time from specimen collection to the first change in antibiotic therapy. Secondary outcomes included the time from specimen collection to effective antibiotic therapy and to antibiotic susceptibility testing results (or turnaround time), as well as hospital length of stay and all-cause mortality at 30 days. Results A total of 548 episodes of bacteremia were included in the final analysis. There was no difference in PREIP and POSTIP regarding patient characteristics and causative bacteria. In POSTIP, the mean time to the first change in antibiotic therapy was reduced by 10.4 h (p<0.001). The time to effective antibiotic therapy and the turnaround time were respectively reduced by 4.8 h (p<0.001) and 5.1 h (p=0.006) in POSTIP. There was no difference in mean hospital length of stay or mortality between the two groups. Conclusions Around the clock processing of blood cultures allows for a reduction in turnaround time, which in turn reduces the delay until effective antibiotic therapy prescription.
Full-text available
Automation in clinical microbiology is starting to become more common place and reportedly offers several advantages over the manual laboratory. Most studies have reported on the rapid turn-around-times for culture results including times for identification of pathogens and their respective antimicrobial susceptibilities, but few have studied the benefits from a laboratory efficiency point of view. This is the first large, multicenter study in North America to report on the benefits derived from automation measured in full-time equivalents (FTE), FTE reallocation, productivity, cost per specimen, and cost avoidance. Pre- and post-full automation audits were done at 4 laboratories that have vastly different culture volumes, and results show that regardless of size of the facility, improved efficiencies can be realized after implementation of full laboratory automation.
Full-text available
Using computerized time-stamps, we compared the turnaround-times (TAT) for urine samples and screening ESwabs of MRSA, VRE, and ESBL carriage in the bacteriology laboratory of Geneva University Hospitals between January and December 2017 (period preceding the implementation of the WASPLabTM) with the same specimen types analyzed between January and December 2019 (period after the implementation of the automation). During both 1-year periods, a total of 98'380 specimens were analyzed (48'158 in 2017 vs. 50'222 in 2019). On the WASPLabTM, all culture plates were imaged at defined intervals each day of incubation, but the processing of the cultures (i.e., pathogen identification and antimicrobial susceptibility testing) was only performed during day shift hours (~8:00 A.M. to 4:30 P.M.). The median TAT for negative reports decreased by almost half for urine samples from 52.1 (2017) to 28.3 h (2019) (p < 0.001), and for MRSA screening specimens from 50.7 to 26.3 h (p < 0.001). The difference in median TAT for negative reports was less pronounced for screening of ESBL (50.2 vs. 43.0 h) (p < 0.001) and VRE (50.6 vs. 45.7 h) (p < 0.001). Despite a trend toward shorter result delivery for positive samples, there was no significant change in the median TAT. These results suggest that TAT for negative samples immediately benefit from automation, whereas TAT for positive samples also depend on the laboratory hours of operation and daily human resource management. © Copyright © 2020 Cherkaoui, Renzi, Martischang, Harbarth, Vuilleumier and Schrenzel.
Full-text available
Group B Streptococcus (GBS) can be found to colonize about 25% of all healthy, adult women and is the leading infectious cause of early neonatal morbidity and mortality in the United States. This study evaluates the clinical performance of PhenoMatrix (PM) Chromogenic Detection Module (CDM) digital imaging software in detection of GBS from LIM broth plated on CHROMID® Strepto B Chromogenic media (ChromID) using the WASP automated processor. The performance of the PM CDM was compared to manual culture review of the digital images and molecular detection of GBS. ChromID alone had a sensitivity and specificity of 84.5% and 94.7%, respectively, after 48 hours as compared to nucleic acid amplification testing (NAAT). As compared to the composite reference for positivity, when PM CDM was used to detect GBS from ChromID the sensitivity was 100% with no true positive GBS isolates missed by 48 hours of incubation. Overall, evaluating all three methods for the detection of GBS, the sensitivities of NAAT, ChromID plus PM CDM at 48 hours, and ChromID alone at 48 hours were 96.8%, 95.5% and 90.3%, respectively. The specificities of NAAT, ChromID plus PM CDM and ChromID alone were 97.7%, 63.0%, and 95.4%, respectively. The use of ChromGBS in combination with the PM CDM was similar to the sensitivity of molecular detection. Further, the algorithm never called a culture negative that was determined to be positive by manual reading and identified an additional eight true positive specimens that were missed by manual digital image culture reading.
Full-text available
Objectives: Active screening is a crucial element for the prevention of carbapenemase-producing Enterobacteriaceae (CPE) transmission in healthcare settings. Here, we proposed a culture-based protocol for rectal swab CPE screening that combines the detection of CPE and the identification of carbapenamase type. Methods: The workflow integrates an automatic digital analysis of selective chromogenic media (WASPLab, Copan), with subsequent rapid tests for the confirmation of carbapenemase production (i.e. detection of Klebsiella pneumoniae KPC-specific peak by MALDI-TOF mass spectrometry; a multiplex immunochromatographic assay identifying the five commonest carbapenemase types). To in-depth evaluate the performance of this protocol, data about 21 162 rectal swabs submitted for CPE screening at the Microbiology of S. Orsola-Malpighi Hospital in Bologna were analyzed. Results: Considering its ability to correctly segregate plates with/without Enterobacteriaceae, WASPLab Image Analysis Software showed globally a sensitivity and a specificity of 100% and 79.4%, respectively. Out of the plates with a bacterial growth (n = 901), 76.9% were found positive for CPE by MALDI-TOF (specific KPC-peak for K. pneumoniae) or by the immunochromatographic assay. Only 2.8% of KPC-positive K. pneumoniae strains were missed by the specific MALDI-TOF MS algorithm, being detected by the immunochromatographic assay. The mean turn-around-time needed from the sample arrival to the final report ranged between 18 to 24 hours, with a significant time saving compared to a manual reading. Conclusions: This workflow proved to be fast and reliable, being particularly suitable for KPC-K. pneumoniae endemic areas and for high-throughput laboratories.
Full-text available
Colorex Strep A Agar (CHROMagar, Paris, France) was evaluated with the PhenoMATRIX™ Chromogenic Detection Module (CDM) (Copan Diagnostics Inc., Murrieta, CA) software to detect Group A Streptococcus (GAS) from throat specimens. The software results were compared to manual plate image reading. In addition, GAS PCR was performed on all specimens. A true positive was defined as culture positive (by either PhenoMATRIX™ CDM or manual) confirmed as GAS by MALDI-TOF MS plus any culture negative specimens that were positive by both initial and repeat PCR. Of 480 specimens, 96 were considered true positives. Software reading of the chromogenic agar for suspect colonies detected 110 orange colonies compared to technologist reading which interpreted only 93/110 (84.5%) as positive. None of the 361 cultures interpreted as negative by the PhenoMATRIX™ CDM software were positive by manual reading. When compared to true positive results, the percent sensitivity and specificity were 96.9% and 100% for PCR, 87.5% and 97.7% for technologist reading of chromogenic agar, 90.6% and 94.0% for software reading of chromogenic agar, 83.3% and 97.7% for technologist reading for beta-hemolysis on blood agar, and 39.5% and 83.1% for technologist reading for beta-hemolysis on blood agar, respectively. The software had the most accurate results of the non-molecular based testing, detecting all suspect colonies from the chromogenic agar, and identifying 3 additional true positives that were missed by manual reading. The PhenoMATRIX™ CDM software and the Colorex Strep A Agar can improve detection of GAS from throat specimens and compared favorably to molecular testing.
Full-text available
In order to realize the full potential of total laboratory automation (TLA) in the clinical microbiology laboratory, workflows must be optimized to match each laboratory’s capabilities, patient population, and staffing model. Using TLA-based digital photography to monitor urine cultures, we sought to improve culture result turn-around-time (TAT) by changing the time at which a culture is first photographed and thus available for analysis/work-up (Pre1) from 18 h (16,391 cultures) to 16 h (53,113 cultures) (with a total of 24-h culture incubation in both time periods); in both time periods, cultures were set up 24/7, and culture work-up occurred during the day shift only. With this change, we observed a significant decrease in time-to-final-result TAT for positive cultures (18 h-Pre1 median: 71.6 h; 16 h-Pre1 median: 61.0 h). This effect was most pronounced for Gram-negative organisms, with a median reduction in time-to-final-result for Escherichia coli cultures (51.8% of positive urine cultures) of 14.2 h (18 h-Pre1 median: 77.3 h; 16 h-Pre1 median: 63.1 h). This reduction in TAT was accompanied by a decrease in sensitivity at the Pre1 time point (18 h-Pre1 91.01%; 16 h-Pre1 88.06%), which we also found to vary by species: there was a reduction in sensitivity at the first culture reading of 1 to 2% for cultures with Gram-negative microorganisms, but for some Gram-positive microorganisms (e.g., Aerococcus urinae and non-aureus Staphylococcus species), there was a reduction in sensitivity at the Pre1 time-point of 3 to 7%. These results can guide workflow decisions for laboratories seeking to implement and/or optimize TLA systems, demonstrating a tradeoff between TAT and the sensitivity of preliminary urine culture results.
We assessed the performance of PhenoMatrix digital imaging software in detection of Group B Streptococcus from recto-vaginal swabs plated on a specific chromogenic medium, using the WASP automated processor. PhenoMatrix algorithm showed a sensitivity of 100% and a specificity of 64.5%. False-positive results were mainly due to commensal viridans streptococci.
Objectives: Urine sample is the most common material tested in clinical microbiology laboratories. Automated analysis is already performed allowing a decrease in time to obtain results and in laboratory technologist (LT) workload. Interestingly, these automatic systems introduced digital imaging concepts. PhenoMATRIX™ (PHM) is an artificial intelligence software that merges picture algorithms and user rules to provide presumptive results. This study aimed at designing a tailored workflow using PHM, performing its validation, and checking its performance in routine practice. Methods: Two data collections including 96 and 135 urines from nephrostomy/ureterostomy and artificial bladder (US), 948 and 1257 urines from catheter (UC), and 3251 and 2027 midstream urines (MSU) were used to compare LT results with those obtained using two versions of PHM. Another 19 US, 102 UC, and 508 MSU were used to monitor performance level three months after routine implementation. Results: Before and after revisions, agreement between the first version of PHM and LT results were 83% (95CI 74.3-90.2) and 83% (95CI 75.3-90.9) (US), 66.7% (95CI 63.5-69.5) and 71.7% (95CI 68.8-74.4) (UC), 65.4% (95CI 63.8-67.1) and 76% (95CI 74.1-77.1) (MSU). The second version improved results, demonstrating 96.2% (95CI 91.6-98.8) and 97% (95CI 92.6-99.2) (US), 87.5% (95CI 85.5-89.2) and 88.9% (95CI 87.0-90.5) (UC), 91% (95CI 89.7-92.1) and 92% (95CI 91.1-93.4) (MSU) of agreement with LT results, before and after revisions. The reliability of PHM results was confirmed by a routine study demonstrating 92% (95CI 90.0-94.2) of overall agreement. Conclusions: PHM showed high performance with > 90% of results in agreement with LT. PHM could help standardise and secure results, prioritise positive plates during analytical workflow, and likely save LT time.
Policy Points • An onslaught of policies from the federal government, states, the insurance industry, and professional organizations continually requires primary care practices to make substantial changes; however, ineffective leadership at the practice level can impede the dissemination and scale‐up of these policies. • The inability of primary care practice leadership to respond to ongoing policy demands has resulted in moral distress and clinician burnout. • Investments are needed to develop interventions and educational opportunities that target a broad array of leadership attributes. Context Over the past several decades, health care in the United States has undergone substantial and rapid change. At the heart of this change is an assumption that a more robust primary care infrastructure helps achieve the quadruple aim of improved care, better patient experience, reduced cost, and improved work life of health care providers. Practice‐level leadership is essential to succeed in this rapidly changing environment. Complex adaptive systems theory offers a lens for understanding important leadership attributes. Methods A review of the literature on leadership from a complex adaptive system perspective identified nine leadership attributes hypothesized to support practice change: motivating others to engage in change, managing abuse of power and social influence, assuring psychological safety, enhancing communication and information sharing, generating a learning organization, instilling a collective mind, cultivating teamwork, fostering emergent leaders, and encouraging boundary spanning. Through a secondary qualitative analysis, we applied these attributes to nine practices ranking high on both a practice learning and leadership scale from the Learning from Effective Ambulatory Practice (LEAP) project to see if and how these attributes manifest in high‐performing innovative practices. Findings We found all nine attributes identified from the literature were evident and seemed important during a time of change and innovation. We identified two additional attributes—anticipating the future and developing formal processes—that we found to be important. Complexity science suggests a hypothesized developmental model in which some attributes are foundational and necessary for the emergence of others. Conclusions Successful primary care practices exhibit a diversity of strong local leadership attributes. To meet the realities of a rapidly changing health care environment, training of current and future primary care leaders needs to be more comprehensive and move beyond motivating others and developing effective teams.
The first clinical microbiology laboratory in the United States adopted total automation for bacteriology processing in 2014. Since then, others have followed with installation of either the BD Kiestra TLA or the Copan WASPLab. This article discusses commercially available automated systems in the United States; why automation is needed; and quality improvements, efficiency, and cost savings associated with automation. After learning how these systems are used, gains and losses experienced, and how one can afford the most expensive equipment ever purchased for clinical microbiology laboratories, the question is, how can one afford not to purchase one of these microbiology automation systems?