Note: Descriptions are shown in the official language in which they were submitted.
1
Laboratory Device Monitoring
FIELD AND BACKGROUND OF THE INVENTION
The present invention relates to laboratory devices and, more particularly,
but
not exclusively to a system and method for monitoring and controlling
laboratory
devices.
Every day, millions of specimens such as blood samples, urine samples, etc.,
are analyzed in laboratories, using laboratory devices such as Extractors (say
ultrasonic homogenizers) and cyclers (say PCR machines).
An Extractor is a preparation device for patient tissue samples that turns a
tissue sample of a patient into a compound that can be evaluated, say by
centrifuging
solids, or by doing lysis on cell walls, as known in the art.
A cycler is a sample testing device, that produces machine readable results
from measurements taken during a reaction or other process that is performed
on a
sample - say a thermal cycler used to amplify DNA segments using PCR
(Polymerase
Chain Reaction) or to perform a restriction enzyme digestion, as known in the
art.
In many cases, when mechanic components of such devices malfunction, a
component inside the device gives a warning to that effect, alerting the user
to trouble
therein. In such cases, a user will resolve the trouble by following the
operating
manual, by calling a support center, etc.
However, in diagnostic testing, mechanical check of laboratory device is
insufficient for good quality control, particularly in as far as devices used
for biological
specimen testing is concerned.
To that end, many laboratories implement quality control routines.
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For example, in many laboratories, a same control substance is measured by a
specific laboratory device every day, and monitoring is performed, so as to
verify that
results obtained using the specific device, are stable.
Many laboratories do not even have such a routine, and rely on end users such
a researcher, a medical practitioner, a laboratory technician or another
worker, to
notice when results obtained from a specific test include suspicious results.
Further, currently used quality control routines are usually employed for a
specific combination (say a combination of a same test type, machine or
machines,
and laboratory technician). As a result, very often, such deviations are
detected in
to significant delay, letting malfunctioning devices run on many samples
(say for other
test types) and generate many inaccurate results, before being identified as a
malfunctioning.
SUMMARY OF THE INVENTION
According to one aspect of the present invention, there is provided a method
of monitoring laboratory devices, the method comprising computer executed
steps,
the steps comprising: receiving a plurality of results, each result pertaining
to a
respective one of a plurality of test types and to a respective one of a
plurality of
devices used for obtaining the results, determining a plurality of values for
at least one
reference parameter, each one of the plurality of values determined for the
reference
parameter, pertaining to a respective one of the test types, for each specific
one of the
test types, normalizing each one of the results pertaining to the specific
test type,
using the value of the reference parameter determined for the specific test
type, to
yield a respective unit-less result, and triggering a control operation upon
identifying
a deviating result among a group of the normalized results, the normalized
results of
the group pertaining to a same one of the devices.
According to a second aspect of the present invention, there is provided a non-
transitory computer readable medium storing computer processor executable
instructions for performing steps of monitoring laboratory devices, the steps
comprising: receiving a plurality of results, each result pertaining to a
respective one
of a plurality of test types and to a respective one of a plurality of devices
used for
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obtaining the results, determining a plurality of values for at least one
reference
parameter, each one of the plurality of values determined for the reference
parameter,
pertaining to a respective one of the test types, for each specific one of the
test types,
normalizing each one of the results pertaining to the specific test type,
using the value
of the reference parameter determined for the specific test type, to yield a
respective
unit-less result, and triggering a control operation upon identifying a
deviating result
among a group of the normalized results, the normalized results of the group
pertaining to a same one of the devices.
According to a third aspect of the present invention, there is provided a
system
for monitoring laboratory devices, the system comprising, a circuit comprising
a
computer processor and a computer memory storing instructions that are
executable
by the computer processor, for performing steps of monitoring laboratory
devices, the
steps comprising: receiving a plurality of results, each result pertaining to
a respective
one of a plurality of test types and to a respective one of a plurality of
devices used for
obtaining the results, determining a plurality of values for at least one
reference
parameter, each one of the plurality of values determined for the reference
parameter,
pertaining to a respective one of the test types, for each specific one of the
test types,
normalizing each one of the results pertaining to the specific test type,
using the value
of the reference parameter determined for the specific test type, to yield a
respective
unit-less result, and triggering a control operation upon identifying a
deviating result
among a group of the normalized results, the normalized results of the group
pertaining to a same one of the devices.
According to another aspect of the invention, there is provided a method of
monitoring laboratory devices, the method comprising computer executed steps,
the
steps comprising: receiving a plurality of results of tests, each one of the
tests
performed on at least one sample, each result pertaining to a respective one
of a
plurality of test types and to a respective one of a plurality of devices used
for
obtaining the results; determining a plurality of values for at least one
reference
parameter, each one of the plurality of values determined for the reference
parameter,
pertaining to a respective one of the test types; for each specific one of the
test types,
Date Recue/Date Received 2020-10-05
/40 \
3a
normalizing each one of the results pertaining to the specific test type,
using the value
of the reference parameter determined for the specific test type, to yield a
respective
unit-less result; and triggering a control operation upon identifying a result
deviating
from among a group of the normalized results, the normalized results of the
group
pertaining to a same one of the devices.
According to a further aspect of the invention, there is provided a non-
transitory
computer readable medium storing computer processor executable instructions
for
performing steps of monitoring laboratory devices, the steps comprising:
receiving a
plurality of results attests, each one of the tests performed on at least one
sample,
each result pertaining to a respective one of a plurality of test types and to
a
respective one of a plurality of devices used for obtaining the results;
determining a
plurality of values for at least one reference parameter, each one of the
plurality of
values determined for the reference parameter, pertaining to a respective one
of the
test types; for each specific one of the test types, normalizing each one of
the results
pertaining to the specific test type, using the value of the reference
parameter
determined for the specific test type, to yield a respective unit-less result;
and
triggering a control operation upon identifying a result deviating from among
a group
of the normalized results, the normalized results of the group pertaining to a
same
one of the devices.
According to another aspect of the invention, there is provided a system for
monitoring laboratory devices, the system comprising; a circuit comprising a
computer
processor and a computer memory storing instructions that are executable by
the
computer processor, for performing steps of monitoring laboratory devices, the
steps
comprising: receiving a plurality of results of tests, each one of the tests
performed on
at least one sample, each result pertaining to a respective one of a plurality
of test
types and to a respective one of a plurality of devices used for obtaining the
results;
determining a plurality of values for at least one reference parameter, each
one of the
plurality of values determined for the reference parameter, pertaining to a
respective
one of the test types; for each specific one of the test types, normalizing
each one of
the results pertaining to the specific test type, using the value
Date Recue/Date Received 2021-10-01
(4"is ,4==,\
3b
of the reference parameter determined for the specific test type, to yield a
respective
unit-less result; and triggering a control operation upon identifying a result
deviating
from among a group of the normalized results, the normalized results of the
group
pertaining to a same one of the devices.
Unless otherwise defined, all technical and scientific terms used herein have
the same meaning as commonly understood by one of ordinary skill in the art to
which
this invention belongs. The materials, methods, and examples provided herein
are
illustrative only and not intended to be limiting.
Implementation of the method and system of the present invention involves
performing or completing certain selected tasks or steps manually,
automatically, or a
combination thereof. Moreover, according to actual instrumentation and
equipment of
preferred embodiments of the method and system of the present invention,
several
Date Recue/Date Received 2021-10-01
4
selected steps could be implemented by hardware or by software on any
operating
system of any firmware or a combination thereof.
For example, as hardware, selected steps of the invention could be
implemented as a chip or a circuit. As software, selected steps of the
invention could
be implemented as a plurality of software instructions being executed by a
computer
using any suitable operating system. In any case, selected steps of the method
and
system of the invention could be described as being performed by a data
processor,
such as a computing platform for executing a plurality of instructions.
BRIEF DESCRIPTION OF THE DRAWINGS
The invention is herein described, by way of example only, with reference to
the accompanying drawings.
With specific reference made to the drawings in detail, it is stressed that
the
particulars shown are by way of example and for purposes of illustrative
discussion of
the preferred embodiments of the present invention only, and are presented in
order to
provide what is believed to be the most useful and readily understood
description of
the principles and conceptual aspects of the invention, the description taken
with the
drawings making apparent to those skilled in the art how several forms of the
invention may be embodied in practice.
In the drawings:
Fig. 1 is a simplified flowchart illustrating a first exemplary method of
monitoring laboratory devices, according to an exemplary embodiment of the
present
invention.
Fig. 2A is a first one of a series of simplified graphs illustrating an
exemplary
implementation scenario, according to an exemplary embodiment of the present
invention.
Fig. 2B is a second one of a series of simplified graphs illustrating an
exemplary implementation scenario, according to an exemplary embodiment of the
present invention.
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Fig. 2C is a third one of a series of simplified graphs illustrating an
exemplary
implementation scenario, according to an exemplary embodiment of the present
invention.
Fig. 3 is a simplified block diagram schematically illustrating a non-
transitory
computer readable medium storing computer executable instructions for
performing
steps of monitoring laboratory devices, according to an exemplary embodiment
of the
present invention.
Fig. 4 is a simplified block diagram schematically illustrating an exemplary
system for monitoring laboratory devices, according to an exemplary embodiment
of
the present invention.
DESCRIPTION OF THE PREFERRED EMBODIMENTS
The present embodiments comprise a system and method of monitoring
laboratory devices.
Laboratories have employed various methods to deal with quality control
aspects of using laboratory devices such Cyclers (for example, thermal cyclers
for
DNA processes such as PCR (Polymerase Chain Reaction) or for restriction
enzyme
to digestion, etc.), Extractors (for example, ultrasonic homogenizers),
etc.
For example, in many laboratories, a same control substance is measured by a
specific laboratory device (say thermal cycler) every day, so as to verify
that the
device produces stable measurement results, as described in further detail
he re inabove.
Many laboratories rather rely on researchers, medical practitioners,
laboratory
technicians, or other personnel, to notice when results obtained for a
specific test
appear to be suspicious, and then, alert on a problem to the laboratory's
manager. The
problem may be a potentially malfunctioning device that is used for obtaining
at least
some of the results, a technician who repeatedly fails to operate one of the
devices
properly, etc., as described in further detail hereinabove.
Thus, for example, an expert whose daily work includes using a computer to
plot results obtained (say using different extractors used by a laboratory
that provides
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the results) for a specific type of test, on the computer's screen, may
visually detect
results that deviate from other results obtained for the specific test type.
However, often, such a deviation is interpreted by the researcher as a one-
off,
erroneous result, and is discarded without even considering the option that a
device
used to obtain the discarded result malfunctions and may generate other
erroneous
results, even if for other test types, as described in further detail
hereinbelow.
Further, currently used quality control routines are usually employed for a
specific combination - say for tests of a same type, that are run using a same
machine
or combination of machines (say a specific extractor and a specific thermal
cycler),
and are monitored by a same specific technician, etc.
As a result, usually, such deviations are detected one at a time and are very
often discarded without further investigation.
Only after a large number of deviations are noticed for the specific
combination (say results obtained for tests performed using different cyclers,
but
using a same extractor, and by a same, specific technician), does one even
starts to
suspect that a device used to perform the tests malfunctions and generates
erroneous
results.
Meanwhile, until being identified as malfunctioning, the device (say
extractor)
may run on many other samples and cause erroneous results for other
combinations -
say when used for another type of test, together with another device, by
another
laboratory technician, etc.
Embodiments of the present embodiments recognize that such laboratory
practices may result in many erroneous results, and may lead to a variety of
mistakes,
due to inaccurate and potentially, too late diagnosis of medical conditions
(say life-
threatening diseases), food contaminations, etc.. or any combination thereof.
Indeed, many current laboratories process hundreds or even thousands of
samples every day. Accordingly, any delay in detection of a malfunctioning
device
may prove critical for many patients, consumers, environmental agencies, etc.
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Exemplary embodiments of the present invention may help detect which one
Of any) of a group of factors of a same type appears to malfunction, or
perform in a
way that generates erroneous results, as described in further detail
hereinbelow.
Specifically, exemplary embodiments of the present invention may help detect
which one (if any) of a group of laboratory devices of a specific type (say a
group of
PCR machines, homoganizers, etc.) malfunctions, and trigger a control
operation, as
described in further detail hereinabove.
According to an exemplary embodiment of the present invention, there are
received three or more results, say by one of the systems described in further
detail
hereinbelow.
Optionally, each one of the received results pertains to a respective one of
two
or more devices - say to a specific one of a number of PCR machines in use by
a
specific laboratory or to a specific one of a number of other thermal cyclers
in used by
the laboratory, and to a respective type of test - say to a specific type of
PCR test, etc.
The received results may include, for example, blood concentrations of a
specific pathogen (say in units/nil), fluorescence readings (say in RFU
(Relative
fluorescence units) taken during a specific type of PCR process, etc., as
described in
further detail hereinbelow.
Optionally, each on of the results is received with an attribute that
identifies
the specific device used for obtaining the result.
The specific device may be one of two or more devices (say PCR machines),
that are used (say by a specific laboratory) to measure the results, to take
measurements that the results are based on, or rather, one of two or more
devices used
to prepare samples for a chemical (say PCR) process used to measure the
results, as
described in further detail hereinbelow.
Next, there are determined two or more values for at least one reference
parameter, say for an average or a standard deviation, as described in further
detail
hereinbelow. Each one of the values determined for the reference parameter
pertains
to a respective, specific test type, as described in further detail
hereinbelow.
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Thus, in a first example, each one of the received results is a quantitative
result
obtained by a respective, specific device through fluorescence measurement,
say by a
specific one of a number of PCR machines in use by a specific laboratory, for
a
respective type of PCR based test, as described in further detail hereinbelow.
Thus, the received results may pertain to a variety of test types, performed
using different devices that are in use by the laboratory, using different
technicians,
etc.
In the example, each one of the results is received with an attribute (say
serial
number) that identifies the specific device (say a specific one of a
laboratory's PCR
machines) used to obtain the result, an attribute that indicates the type of
test that the
result pertains to, an attribute that indicates the technician in charge of
the result, etc.
Accordingly, in the first example, the received results may be divided into
several groups of results, according to one or more of the attributes, say
according to
device and result type.
Thus, a first group of the results received in the first example, includes
quantitative results obtained by a first one of the laboratory's PCR machines
when
running tests of a first type. A second group of the first example's results
includes
quantitative results obtained by a second one of the laboratory's PCR machines
when
running tests of the same, first type.
Further in the first example, a third group of the received results includes
quantitative results obtained by the first one of the laboratory's PCR
machines when
running tests of a second type, and a fourth group that includes quantitative
results
obtained by a third one of the laboratory's PCR machines when running tests of
the
same, second type.
In the first example, there is determined an average and a standard deviation
for each one of the two test types of the example.
Thus, in the first example, there is calculated an average and a standard
deviation over all quantitative results obtained for the first test type -
i.e. over the
results obtained using the laboratory's first PCR machine or second PCR
machine (i.e.
over the results included in the first group and second group). Further in the
example,
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there is calculated an average and a standard deviation over all quantitative
results
obtained for the second test type ¨ i.e. over the results obtained using the
first or third
PCR machines (i.e. over the results included in the third group and fourth
group).
Then, for each specific one of the test types, there is normalized each one of
the results that pertain to that specific test type, using the value of the
reference
parameter determined for that specific test type, to yield a respective unit-
less result.
Thus, in the first example, the average calculated over the quantitative
results
of the tests of the first type, is subtracted from each one of the
quantitative results
obtained for the first test type, to yield a respective difference. Then, each
one of the
differences is divided by the standard deviation calculated over the results
obtained
for the first test type, to yield a respective, unit-less value.
Each one of the results that pertain to the first test type is thus
normalized, by
replacing the result with the respective unit-less value calculated by the
subtraction of
the average from the received result, and the division of the difference by
the standard
deviation, as described in further detail hereinbelow.
Further in the first example, the results that pertain to the second test
type, are
similarly normalized using an average and a standard deviation that are
calculated
over the results obtained for tests of the second type.
Then, the normalized results are monitored, whether continuously or rather
26 periodically (say once an hour), as described in further detail
hereinbelow.
In one example, the monitoring includes presenting the normalized results to a
user (say to a laboratory manager or senior technician) using a GUI (Graphical
User
Interface), say as one or more machine-specific graphs, each of which graphs
presents together normalized results obtained using a same specific device,
but for
different test types, as described in further detail hereinbelow.
During that monitoring, a deviating result may be identified among the
normalized result (say a result that appears to deviate from the other
normalized
results that pertain to a specific one of the PCR machines of the first
example), say
automatically and according to a predefined parameter, or rather by a user of
the GUI.
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When the deviating result is identified, say by a user's clicking an element
of
the GUI (say a point on one of the device-specific graphs of the first
example) that
represents or is associated with the deviating result, or rather
automatically, there is
automatically triggered a control operation.
Optionally, the control operation suspends use of the device that the
deviating
result pertains to, as described in further detail hereinbelow.
The control operation may include, but is not limited to, for example, turning
off the device that the deviating result pertains to, locking a chamber of the
device
that the deviating result pertains to, diverting automatic loading of samples
from the
device that the deviating result pertains to, to another device, etc. as
described in
further detail hereinbelow.
Thus, with the exemplary embodiments, results obtained using different
extractor-technician, cycler-extractor, or any other combinations, may be used
together simultaneously and comparatively, so as to provide for a potentially
earlier,
and thus more efficient, detection of malfunctioning devices.
The principles and operation of a system and method according to the present
invention may be better understood with reference to the drawings and
accompanying
description.
Before explaining at least one embodiment of the invention in detail, it is to
be
understood that the invention is not limited in its application to the details
of
construction and the arrangement of the components set forth in the following
description or illustrated in the drawings.
The invention is capable of other embodiments or of being practiced or carried
out in various ways. Also, it is to be understood that the phraseology and
terminology
employed herein is for the purpose of description and should not be regarded
as
limiting.
Reference is now made to Fig. 1, which is a simplified flowchart illustrating
a
first exemplary method of monitoring laboratory devices, according to an
exemplary
embodiment of the present invention.
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A first exemplary method of monitoring laboratory devices may be
implemented using electric circuits, computer instructions, etc., as described
in further
detail hereinbelow.
Thus, in one example, the method is executed by a system that includes a
circuit (say an integrated electric circuit (IC)). The circuit includes one or
more
computer processors, one or more computer memories (say a DRAM (Dynamic
Random Access Memory) component, an SSD (Solid State Drive) component, etc.),
and one or more other components, as described in further detail hereinbelow.
The computer memory stores instructions, which instructions are executable
by the system's computer processor, for performing the steps of the method, as
described in further detail hereinbelow.
Optionally, the system is in communication with one or more devices that are
to used for performing one or more tests on samples (say biological
samples that are
taken from human beings or from farm animals), say with one or more
extractors,
cyclers, robots, or other machines, as described in further detail
hereinabove.
The communication between the system and the device(s) may be wireless
(say over a Wi-Fi Connection, a Bluetooth* connection, etc., or any
combination
thereof), wired (say a communication over a wired LAN (local Area network),
etc., or
any combination thereof, as described in further detail hereinbelow.
In the method, there are received 110 a plurality of results. Each one of the
=
received 110 results pertains to a respective one of a plurality of test types
and to a
respective, specific one of a plurality of devices used for obtaining the
results, as
described in further detail hereinbelow.
Optionally, each one of the results is received 110 through communication
between the system and a specific device used to obtain the result, say
between the
system and one of a number of PCR machines in use by a large laboratory that
operates the system, for obtaining the results, as described in further detail
hereinbelow.
The received 110 results may include, for example, blood concentrations of a
specific pathogen (say in units/ml), fluorescence readings (say in RF1J
(Relative
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fluorescence units) taken during a specific type of a PCR process or an ELBA
test,
etc., or any combination thereof.
Optionally, the results include or are calculated from, measurements taken
during a test (say a test based on a chemical process) - say of an electric
property of
the sample undergoing the process, of an optical property (say a one
indicative of
hemoglobin content of the sample), etc., as known in the art.
Optionally, each on of the results is received with an attribute that
identifies
the specific device used for obtaining the result (say a serial number of the
device, an
IP (Internet Protocol) address of the device, etc.), with an attribute that
identifies the
specific test type that the result pertains to, etc.
Optionally, the specific device is a device (say a specific one of a large
laboratory's PCR machines) used to measure the result, or to take measurements
that
the result is based on, as described in further detail hereinbelow.
Alternatively, the specific device is rather, a specific device (say a
specific
one of several homogenizers used by the large laboratory) used to prepare
samples for
a chemical (say PCR) process or another process, and the measurements are
rather
taken by another device (say by a cycler), as described in further detail
hereinbelow.
Next, there are determined 120 two or more values for at least one reference
parameter, say for an average or a standard deviation, say by the exemplary
system
described in further detail hereinbelow. Each one of the values determined 120
for the
reference parameter pertains to a respective, specific one of test types.
In a first example, each one of the received 110 results is a quantitative
result
(say copies per milliliter) obtained by a specific device (say by a specific
one of
several PCR machines in use by a large laboratory), and is a result of a
specific type
of tests (say a specific PCR test, a specific ELISA test, etc.).
The results received 110 in the first example, may thus be divided into a
number of groups.
A first group of the results received 110 in the first example, includes
quantitative results obtained by a first one of a large laboratory's PCR
machines when
CA 3062337 2019-11-22
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running tests of a first type. Further, a second group of the first example's
received
110 results includes quantitative results obtained by a second one of the
large
laboratory's PCR machines when running tests of the same, first type.
Further in the example, a third group of the received 110 results includes
quantitative results obtained by the first one of the laboratory's PCR
machines when
running tests of a second type. A fourth group of the example's received 110
results
includes quantitative results obtained by a third one of the laboratory's PCR
machines
when running tests of the same, second type.
In the first example, there is determined 120 an average and a standard
deviation for each one of the two test types of the example.
Thus, in the first example, there is calculated 120 an average and a standard
deviation over all quantitative results obtained for the first test type -
i.e. over the
results obtained using the laboratory's first PCR machine or second PCR
machine (i.e.
over the results included in the first group and second group). Further in the
example,
there is calculated 120 an average and a standard deviation over all
quantitative
results obtained for the second test type ¨ i.e. over the results obtained
using the first
or third PCR machines (i.e. over the results included in the third group and
fourth
group).
Next in the method, for each specific one of the test types, there is
normalized
130 each one of the results that pertain to that specific test type. using the
value of the
reference parameter determined 120 for that specific test type, to yield a
respective
unit-less result, say by the exemplary system described in further detail
hereinabove.
Thus, in the first example, the average calculated 120 over the quantitative
results of the tests of the first type, is subtracted from each one of the
quantitative
results obtained for the first test type, to yield a respective difference.
Then, each one
of the differences is divided by the standard deviation calculated 120 over
the results
obtained for the first test type, to yield 130 a respective, unit-less value.
Each one of the results that pertain to the first test type is thus normalized
130,
by replacing the result with the respective unit-less value calculated 130 by
the
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subtraction of the average from the received result, and the division of the
difference
by the standard deviation, as described in further detail hereinbelow.
Optionally, the results are rather normalized 130 using values determined 120
for other reference parameters. The other reference parameters may include,
for
example, parameters that are based on Westgard Rules, EWMA (Exponentially
Weighted Moving Average Chart), Root Mean Square Thresholds, CUSUM
(Cumulative Sum Control Chart), etc., as described in further detail
hereinbelow.
Further in the first example, the results that pertain to the second test
type, are
similarly normalized 130 using an average and a standard deviation that are
calculated
to 120 over the results obtained for tests of the second type, or rather
using one or more
of the other reference parameters mentioned hereinabove.
In the method, upon identifying a deviating result among a group of the
normalized 130 results, the normalized 130 results of the group pertaining to
a same,
specific one of the devices (say one of the PCR machines of the first
example), there
is automatically issued 140 a control operation, say by the exemplary system
described in further detail hereinbelow.
Optionally, for identifying the deviating result, the method further includes
a
step of monitoring, in which step, the normalized 130 results are monitored
continuously or rather periodically (say once an hour), as described in
further detail
hereinbelow.
Optionally, the step of monitoring includes presenting the normalized results
to a user (say to a laboratory manager or senior technician) using a GUI
(Graphical
User Interface), as described in further detail hereinbelow.
In one example, the GUI presents the results to the user using machine-
specific graphs, each of which graphs presents together normalized results
obtained
using a same specific device, but for different test types, as described in
further detail
for the exemplary implementation scenario illustrated using Fig. 2A-2C
hereinbelow.
During that monitoring, a deviating result may be identified 140 among the
normalized 130 results (say a result that appears to deviate from the other
normalized
130 results that pertain to a specific one of the PCR machines of the first
example),
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say automatically and according to a predefined parameter or rather by a user
of the
OW.
The parameter may be predefined, for example, by a user or programmer of
the system described in further detail and illustrated using Fig. 4
hereinbelow.
For example, the programmer may define that when more than one normalized
130 results of a same test type, that also pertain to a same specific device,
slip at least
two standard deviations (SD) away from the average determined 120 for the
specific
test type, on a same specific day, the results are to be identified 140 as
deviating.
Optionally, when the deviating result is identified 140 - say by a user's
to clicking an element of
the GUI (say a point on one of the device-specific graphs of the
first example) that represents, or is associated with the deviating result, or
rather
automatically - there is automatically triggered 140 a control operation.
Optionally, the triggered 140 control operation suspends use of the specific
device that the deviating result pertains to, as described in further detail
hereinbelow.
The triggered 140 control operation may include, but is not limited to,
turning
off the specific device that the deviating pertains to, locking a chamber of
the specific
device that the deviating result pertains to, instructing a robot to stop
loading samples
on the specific device, etc.
In one example, the control operation is triggered 140 as an instruction that
is
issued and sent by the system that implements the first exemplary method, to a
computer (say to an industrial controller or other computer, embedded or
otherwise
associated with the specific device), which computer controls the specific
device or a
part thereof.
The instruction may be sent to the computer that controls the device (or the
part thereof), for example, over an Intranet network connection and using the
received
110 attribute that identifies the specific device that the deviating result
pertains to, say
using the device's IP (Internet Protocol) address, as known in the art.
In a second example, there is additionally or alternatively, received 110 (say
as
one or more of the attributes) or generated circumstantial data that pertain
to a specific
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one of the received 110 results, or rather to a group that includes some or
all or the
received 110 results.
The circumstantial data may include, but is not limited, to: time of receipt
110
or of obtaining the result(s) using the specific device, data that identifies
one or more
other devices used for obtaining the result, data that identifies one or more
technicians
responsible for the result, etc.
Then, the circumstantial data may be used for identifying the specific device
that the result pertains to (when not received 110 with the result), say by
querying an
ERP (Enterprise Resources Planning) database, for retrieving data that
identifies the
to specific device used by the specific technician, etc., as known in
the art.
In the second example too, the control operation is issued 140 as an
instruction
that is sent using the retrieved data, by the system, say to the computer (say
an
industrial controller) that controls the specific device (or a part thereof),
as described
in further detail hereinabove.
Reference is now made to Fig. 2A-2C, which is series of simplified graphs
illustrating an exemplary implementation scenario, according to an exemplary
embodiment of the present invention.
In the exemplary scenario, a laboratory that is responsible for performing
various tests in a specific water reservoir, uses a different device (say
automatic
colony counter) every day, for carrying out different test using that device.
In the example, the GUI could present results received 110 for a specific test
type, say a specific pathogen's concentration given in CFU/ml (Colonies
Formed/
Milliliter) as measured daily in the water reservoir, using different
laboratory devices
(say automatic colony counters), together, as illustrated in Fig. 2A.
In the exemplary graph depicted in Fig. 2A, the result 214 obtained on the 14
day, appears to slip two standard deviations away. The standard deviation and
average
are calculated based on the results obtained for that specific test type, but
using
different devices. However, the following results (i.e. the ones obtained on
the next
three days) seem to indicate a return to normality.
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As a result, when a technician in charge of monitoring water reservoir is
presented the results using that graph shown in Fig. 2A, the technician
discards the
result 214 obtained on the 14th as a one-off, without even considering the
option that
one of the devices may be malfunctioning. The result 218 obtained for the 18'
day is
similarly discarded by the technician.
Similarly, the exemplary graph depicted in Fig. 2B, presents together results
received 110 for another specific test type, say a total count of another
pathogen as
measured daily on a specific surface of interest (say a specific filter used
in a pump
connected to the water reservoir), by the laboratory devices (i.e. using the
automatic
colony counters).
When a second technician, who is in charge of the pump's maintenance, is
presented the results using that graph shown in Fig. 2B, the second technician
too,
discards the result 224 obtained for the filter on the 14th as a one-off,
without even
considering the option that one of the devices may be malfunctioning. The
second
technician discards the filter's result 224 for the 14th day, because the
following
results (i.e. the ones obtained on the next three days) seem to indicate a
return to
normality, and similarly discards the result 228 obtained for the 18th day.
As a result, the device(s) used on the 14th and 1 8th day would keep
generating
erroneous results, say for different test types.
However, in the exemplary implementation scenario, the received results 110
are further normalized 130 as taught by the first exemplary method, and are
then,
presented to a senior user (say a laboratory manager of senior technician)
using a GUI
(Graphical User interface), say in a device-specific graph, as illustrated in
Fig. 2C.
Alternatively, the normalized 130 results are rather presented in a multi-
test, multi-
device graph, say a one that presents all normalized 130 results.
Using the graph illustrated in Fig. 2C. the GUI allows the senior user to
monitor the results that are normalized 130 using the average and standard
deviation
determined 120 per test type, and identify one or more deviating results
amongst the
normalized 130 results, as described in further detail hereinabove.
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Optionally, the GUI further allow the senior user to trigger an automatic
issuing of an instruction that triggers a control operation, say by clicking
on a point on
the graph, for identifying the deviating result(s), as described in further
detail
hereinabove.
More specifically, as shown in Fig. 2C, at least two 234 of the normalized
results appear to slip two standard deviations away on the 141h day, which
draws the
senior user's attention to the option that the device used on the 14'h day,
may be
malfunctioning.
Alternatively, in the scenario, one or more of the deviating normalized 130
results 234 of the 14'h day, is identified automatically, even without
presenting the
normalized 130 results in a GUI, arid the control operation (say turning off
the device)
is accordingly, issued automatically, as described in further detail
hereinabove.
Reference is now made to Fig. 3 which is a simplified block diagram
schematically illustrating a non-transitory computer readable medium storing
computer executable instructions for performing steps of monitoring laboratory
devices, according to an exemplary embodiment of the present invention.
According to an exemplary embodiment of the present invention, there is
provided a non-transitory computer readable medium 3000.
The medium 3000 may include, but is not limited to, a Micro SD (Secure
Digital) Card, a CD-ROM, a USB-Memory, a Hard Disk Drive (HDD), a Solid State
Drive (SSD), a computer's ROM chip, a DRAM (Dynamic Random Access Memory)
or other RAM (Random Access Memory) component, a cache memory component of
a computer processor, etc., or any combination thereof, as known in the art.
The computer readable medium 3000 stores computer executable instructions,
for performing steps of monitoring laboratory devices, say according to steps
of the
first exemplary method described in further detail hereinabove, and
illustrated using
Fig. 1.
The instructions may be executed on one or more computer, say by the first
system, as described in further detail hereinbelow and illustrated using Fig.
1.
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The computer executable instructions include a step of receiving 310 a
plurality of results. Each one of the received 310 results pertains to a
respective one of
a plurality of test types and to a respective, specific one of a plurality of
devices used
for obtaining the results, as described in further detail hereinabove.
Optionally, each on of the results is received 310 through communication with
a specific device used to obtain the result, say between the system and one of
a
number of PCR machines in use by a large laboratory that operates the system,
for
obtaining the results, as described in further detail hereinbelow.
The communication may be wireless (say over a Wi-Fl Connection, a
Bluetooth'g' connection, etc., or any combination thereof), wired (say a
communication
over a wired LAN (local Area network), etc., or any combination thereof, as
described
in further detail hereinabove.
The received 310 results may include, for example, blood concentrations of a
specific pathogen (say in units/ml), fluorescence readings (say in RFU
(Relative
fluorescence units) taken during a specific type of PCR (Polymerase Chain
Reaction),
etc., as described in further detail hereinabove.
Optionally, the results include, or are calculated from, measurements taken
during a test (say a test based on a chemical process) - say of an electric
property of
the sample undergoing the process. of an optical property of the sample (say a
one
indicative of hemoglobin content), etc., as described in further detail
hereinabove.
Optionally, each on of the results is received 310 with an attribute that
identifies the specific device used for obtaining the result (say a serial
number of the
device, an IP (Internet Protocol) address of the device, etc.). with an
attribute that
identifies the specific test type that the result pertains to, etc.
Optionally, the specific device is a device (say a specific one of a large
laboratory's PCR machines) used to measure the result, or to take
measurentents that
the result is based on, as described in further detail hereinabove.
Alternatively, the specific device is rather, a specific device (say a
specific one
of several homogenizers used by the large laboratory) used to prepare samples
for a
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chemical (say PCR) process or other process, and the measurements are rather
taken
by another device (say by a cycler), as described in further detail
hereinabove.
The computer executable instructions further include a step of determining
320 two or more values for at least one reference parameter, say for an
average or a
standard deviation, say by the exemplary system described in further detail
hereinbelow. Each one of the values determined 320 for the reference parameter
pertains to a respective, specific one of test types.
Inn first example, each one of the received 310 results is a quantitative
result
(say copies per milliliter) obtained by a specific device (say by a specific
one of
several PCR machines in use by a large laboratory), and is a result of a
specific type
of tests (say a specific PCR test, a specific ELISA test, etc.).
The results received 310 in the first example, may thus be divided into a
number of groups.
A first group of the results received 310 in the first example, includes
is quantitative results obtained by a first one of a large laboratory's
PCR machines when
running tests of a first type. Further, a second group of the results received
310 in the
first example, includes quantitative results obtained by a second one of the
large
laboratory's PCR machines when running tests of the same, first type.
Further in the first example, a third group of the received 310 results
includes
quantitative results obtained by the first one of the laboratory's PCR
machines when
running tests of a second type, and a fourth group of the received 310 results
includes
quantitative results obtained by a third one of the laboratory's PCR machines
when
running tests of the same, second type.
In the first example, there is determined 320 an average and a standard
deviation for each one of the two test types of the example.
Thus, in the first example, there is calculated 320 an average and a standard
deviation over all quantitative results obtained for the first test type -
i.e. over the
results obtained using the laboratory's first PCR machine or second PCR
machine (i.e.
over the results included in the first group and second group). Further in the
example,
there is calculated 320 an average and a standard deviation over all
quantitative
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results obtained for the second test type ¨ i.e. over the results obtained
using the first
or third PCR machines (i.e. over the results included in the third group and
fourth
group).
The computer executable instructions further include a step in which, for each
specific one of the test types, there is normalized 330 each one of the
results that
pertain to that specific test type. The result is normalized 330 using the
value of the
reference parameter determined 320 for that specific test type, to yield a
respective
unit-less result, say by the exemplary system described in further detail
hereinabelow.
Thus, in the first example, the average calculated 320 over the quantitative
results of the tests of the first type, is subtracted from each one of the
quantitative
results obtained for the first test type, to yield a respective difference.
Then, each one
of the differences is divided by the standard deviation calculated 320 over
the results
obtained for the first test type, to yield 330 a respective, unit-less value.
Each one of the results that pertain to the first test type is thus normalized
330,
IS by replacing the result with the respective unit-less value calculated
330 by the
subtraction of the average from the received result, and the division of the
difference
by the standard deviation, as described in further detail hereinbelow.
Further in the first example, the results that pertain to the second test
type, are
similarly normalized 330 using an average and a standard deviation that are
calculated
320 over the results obtained for tests of the second type.
The computer executable instructions further include a step of automatically
triggering 340 a control operation, say by the exemplary system described in
further
detail hereinabove.
In the step of triggering 340 the control operation. upon identifying 340 a
deviating result among a group of the normalized 330 results, the normalized
330
results of the group pertaining to a same, specific one of the devices (say to
one of the
PCR machines of the first example), there is automatically triggered 240 a
control
operation.
Optionally, for identifying the deviating 340 result, the computer executable
instructions further include a step of monitoring, in which step, the
normalized 330
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results are monitored continuously or rather periodically (say once an hour),
as
described in further detail hereinabove.
Optionally, the step of monitoring includes presenting the normalized results
to a user (say to a laboratory manager or senior technician) using a GUI
(Graphical
User Interface, as described in further detail hereinabove.
In one example, the GUI presents the results to the user using machine-
specific graphs, each of which graphs presents together normalized results
obtained
using a same specific device, but for different test types, as described in
further detail
for the exemplary implementation illustrated using Fig. 2A-2C hereinbelow.
During that monitoring, a deviating result may be identified 340 among the
normalized 330 results (say a result that appears to deviate from the other
normalized
330 results that pertain to a specific one of the first example's PCR
machines), say
automatically, or rather by a user of the GUI, as described in further detail
hereinabove.
For example, the programmer may define that when more than two normalized
330 results of a same test type, that also pertain to a same specific device,
slip at least
two standard deviations (SD) away from the average determined 320 for the
specific
test type, on a same specific day, the results are to be identified 340 as
deviating.
Optionally. the deviating result may be identified by a user's clicking an
element of the GUI (say a point on one of the device-specific graphs of the
first
example) that represents, or is associated with the deviating result.
Alternatively, or
additionally, the deviating result may be identified automatically.
With the computer executable instructions, upon the deviating result's being
identified 340, there is automatically triggered 340 the control operation, as
described
in further detail hereinabove.
Optionally, the triggered 340 control operation suspends use of the specific
device that the deviating result pertains to, as described in further detail
hereinabove
The triggered 340 control operation may include, but is not limited to,
turning
off the specific device that the deviating pertains to, locking a chamber of
the specific
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device that the deviating result pertains to, instructing a robot to stop
loading samples
on the specific device, etc.
In one example, the control operation is triggered 340 by issuing an
instruction
that is sent by the system that implements the first exemplary method, to a
computer
(say to an industrial controller or other computer, embedded or otherwise
associated
with the specific device) that controls the specific device or a part thereof.
Optionally, the instruction is sent to the computer that controls the device
(or a
part of the device), say over an intranet network connection and using the
received
310 attribute that identifies the specific device that the deviating result
pertains to, say
using the device's IP (Internet Protocol) address, as known in the art.
In a second example, there is additionally or alternatively, received 310 (say
as
one or more of the attributes) or generated circumstantial data that pertain
to a specific
one of the received 310 results, or rather to a group that includes some or
all or the
received 310 results.
The circumstantial data may include, but is not limited, to: time of receipt
310
or of obtaining the result(s) using the specific device, data that identifies
one or more
other devices used for obtaining the result, data that identifies one or more
technicians
responsible for the result, etc.
Then, the circumstantial data may be used for identifying the specific device
that the result pertains to (when not received 310 with the result), say by
querying an
ERP (Enterprise Resources Planning) database, for retrieving data that
identifies the
specific device used by the specific technician, etc., as known in the art.
" In the second example too, the control operation is issued as an instruction
that
is sent using the retrieved data, by the system, say to the computer (say an
industrial
controller) that controls the specific device or a part thereof, as described
in further
detail hereinabove.
Reference is now made to Fig. 4, which is a simplified block diagram
schematically illustrating an exemplary system for monitoring laboratory
devices,
according to an exemplary embodiment of the present invention.
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A system 4000 for monitoring laboratory devices, according to an exemplary
embodiment of the present invention may be implemented using electric
circuits,
computer software, computer hardware, etc.. or any combination thereof.
According to an exemplary embodiment, the system 4000 includes a circuit
that comprises a computer processor 401 and a computer memory 402.
The computer memory 402 may include, but is not limited to: a Hard Disk
Drive (HDD), a Solid State Drive (SSD), a computer's ROM chip, a DRAM
(Dynamic Random Access Memory) component or another RAM (Random Access
Memory) component, a cache memory component of the computer processor 401,
etc., or any combination thereof.
Optionally, the system 4000 further includes a communication card 403, that
is used for communicating over a remote (say a communication over the
Internet),
short-ranged (say a communication over a LAN (local Area network) or a Wi-Fi
Connection), or other connection, as described in further detail hereinabove.
The computer memory stores instructions that are executable by the computer
processor 401, for performing the steps of the method of monitoring laboratory
devices, as described in further detail and hereinabove and as Nostraled using
Fig. 1.
In the exemplary embodiment, the computer processor 401 is programmed to
perform the instructions, thereby implementing the system's 4000 one or more
additional parts, say the parts 410-440 shown in Fig. 5, as described in
further detail
hereinbelow.
Each one of the additional parts may be implemented as software - say by
programming the computer processor to execute the steps of the method
described in
further detail and illustrated using Fig. 1 hereinbelow, as hardware - say as
a hardware
part of the electric circuit that implements at least a part of that method,
etc., any
combination thereof.
The system 4000 may thus include a result receiver 410.
The result receiver 410 receives a plurality of results. Each one of the
received
results pertains to a respective one of a plurality of test types and to a
respective one
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of a plurality of devices used for obtaining the results, as described in
further detail
for the first exemplary method, hereinabove.
The system 4000 further includes a reference determiner 420, in
communication with the result receiver 410.
The reference determiner 420 determines a plurality of values for at least one
reference parameter, say an average, a standard deviation. etc.. or any
combination
thereof. Each one of the plurality of values determined for the reference
parameter,
pertains to a respective one of the test types, as described in further detail
for the first
exemplary method, hereinabove.
The system 4000 further includes a result normalizer 430, in communication
with the reference determiner 420.
For each specific one of the test types, the result normalizer 430 normalizes
each one of the results pertaining to the specific test type, using the value
of the
reference parameter determined for the specific test type, to yield a
respective unit-
less result, as described in further detail for the first exemplary method,
hereinabove.
The system 4000 further includes a control instruction issuer 440, in
communication with the result normalizer 430.
Upon identifying a deviating result among a group of the normalized results,
which group's results pertain to a same one of the devices, the control
instruction
issuer 540 triggers a control operation - say a one that is used to suspend
use of the
device that the deviating result pertains to, as described in further detail
for the first
exemplary method, hereinabove.
The control operation may include, for example, turning off the device that
the
deviating result pertains to, locking a chamber of the device that the
deviating result
pertains to, diverting automatic loading of samples from the device that the
deviating
result pertains to, to another device, etc., as described in further detail
for the first
exemplary method, hereinabove.
Optionally, the system 4000 further includes a result monitor (not shown), in
communication with the result normalizer 430.
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The result monitor monitors the results, whether continuously or periodically.
so as to identify the deviating result.
The result monitor may identify the deviating result automatically, or rather
using a Graphical User Interface (GUI) that presents the normalized results to
a user,
and allows the user to identify a normalized result as deviating from other
normalized
results that pertain to a same device, as described in further detail for the
first
exemplary method, hereinabove.
GENERAL FURTHER DISCUSSION
Some exemplary embodiments combine using knowledge of an originating
test, sample or device (e.g. sample preparation device, testing device, tissue
origin,
etc.) with a method to enable any results to be compared (setting parameters
to
determine standard deviation per subset, which delivers a normalized result).
By grouping and comparing normalized results, it becomes possible to
monitor a range of equipment and analyzers, and act accordingly with a
maintenance
procedure and disqualification of results. By evaluating statistical rules
across
multiple machines, technicians (or other users) and/or tests, it may be
possible to
identify the cause of variation.
Background and Terminology of the discussion below
Extractor: A preparation device for patient tissue samples. A device that
turns
a tissue sample of a patient into a compound that can be evaluated, say by
centrifuging solids, or by doing lysis on cell walls, etc.
Cycler: A sample testing device that produces machine readable output
Test: A composition used for evaluating the properties of a patient sample,
when tested using a testing device such as a Cycler. For example, qPCR
Influenza
Test, or Toxoplasmosis Antibodies ELYSA test.
Result: Numerical Output of a testing device as a function of a specific
device,
and test conducted.
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Comparison: Appropriate statistical tool for measuring acceptable variation
over multiple results. For example, cumulative sum, Standard deviation,
CUMSUM,
mean.
Normalized result: A result, scaled and adjusted according to a collection of
similar results, so that it can be placed in a universal scale to allow direct
comparison
with results of different origin.
Data Organization System: A database of users, tests and equipment combined
with appropriate user/auto-generated input, per analysis, to allow grouping of
results
according to various properties, such as specific Extractor, Specific
technician, shift
of the day, type of test, etc.
Statistical rules: A family of procedures that are used to detect variation of
results over time. For example, Westgard, EWMA, Root Mean Square Thresholds,
CUSUM, etc., as known in the art.
Discussion
An operational tests laboratory, say, in a hospital, is likely to run several
sample preparation devices, several testing devices and multiple tests from
various
sample sources eg blood, tissue, saliva, etc
Devices can add variations to quantitative and qualitative tests being done
Typically these variations are detected by evaluating an expected mean value
and
standard deviation of the results and then tracking how many standard
deviations a
given result (or set of consecutive results) deviate from the mean, or a Root
Mean
Squared threshold.
Mean/SD are in units of quantity (e.g. copies/m1), temperature (for HRM) an
equivalent (such as CT values), etc.
The causes for large variations in tests can be due to equipment failure, user
failure, consumable material failure, etc.
Current QC (Quality Control) rules also work only for very specific
combinations ¨ i.e. a same test, same machines. a same sample type. As a
result,
warnings can be late, thus bad results could slip in.
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Given a system that knows which machine, user, test executed, materials
batch, the system knows which machine is in use when a test is run.
By evaluating statistical rules across machines or users, as well as across
tests
and materials batches, it may be possible to identify the cause of variation.
It may
then be possible to take an automated action of disqualifying results given to
patients,
and invoke maintenance actions regarding machines and materials.
One example to an automated laboratory control process may include:
1. Test orders arrive.
2. Samples are marked and ordered.
3. Tests are prioritized and samples are queued up for the extraction
machines.
4. Samples are placed in Extraction Machines for plate preparation for
Testing.
5. Plates are moved to Thermal Cyclers
6. Thermal Cyders run tests.
7. Results are analyzed ¨ a QC monitoring module detects a problem on
Extraction Machine No. 1.
8. A STOP command is placed on Extraction Machine No. I, and all
samples are diverted to Extraction Machines No. 2, and 3.
9. Support Technician reviews the issue and resolves.
10. Extraction Machine No. 1 is brought back online.
As a preparation to direct comparison between results that originate from
different experiment criteria, the numerical results may be divided by the
standard
deviation, or by another unit size (such as the maximum value), to produce a
comparable pure (i.e. unit-less) number that is indicative of the offset from
mean of a
single result within the group of results.
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Alternatively, a relative quantity can be used, by finding the ratio between
the
result and a control numerical result. Statistical rules are the applied to
the unit-less
normalized results, in conjunction to the group being evaluated. This can
present
errors as they form, even across different tests and operators.
Multiple errors for a sample preparation device, or multiple errors for a
cycler,
or multiple errors for a specific batch, can be indicative of a system error
happening
for that component of a laboratory.
Being unit-less, it is possible to plot together group behavior over time, for
multiple groups simultaneously. The X axis can be time, and the Y axis can be
the
unit-less, normalized value, so that development of malfunctions can be
followed.
In one example, the process may include, for example: user-specified mean
(M) and standard deviation (SD) or preset calculation parameters, user-
specified sort
order (run date, input date, manual date, etc.), user-specified test result
grouping (e.g.
by extraction device, pipetting instrument, processing device, control type,
control
volume, test being run and/or any combination or alternative thereof), Run
control
measurements for a run being verified (X), and/or associated metadata required
for
grouping.
Then, the process is run on received test results, through the following
steps:
I. A Mean (M) and a Standard Deviation (SD) is calculated, based on past n
results or on all past results for test parameters, as configured by user (or
rather, pre-
set values as stored on a computer readable medium, are used as values of
reference
parameters, instead of M and SD values).
2. Each one (X) of the results is normalized into a unit-less value (rSD), say
using the formula rSD = (X-M) / SD.
3. Optionally, historic and new results are then plotted together on control
chart (e.g. Shewhart, Levey-Jennings or any other chart) as n vs AO where n is
order
of use, as specified by user.
4. Optionally, a user-selected automated control validation rules such as
Westgard, RMSD (Root Mean Standard Deviation), are applied to the normalized
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results, and optionally, the user is alerted when selected rules are triggered
and/or a
control operation is triggered, as described in further detail hereinabove.
It is expected that during the life of this patent many relevant devices and
systems will be developed and the scope of the terms herein, particularly of
the terms
"Laboratory", "Device", "Extractor", "Cycler", "PCR Machine", "Homogenizer",
"Compute r". "Computer Processor", "Circuit", "Micro SD", ='Card", "CD-ROM",
"USS-Memory", "Hard Disk Drive (HOD)", "Solid State Drive (SSD)", "ROM" "ROM
chip", "Cache Memory", and "DRAM (Dynamic Random Access Memory)", is intended
to include all such new technologies a priori.
It is appreciated that certain features of the invention, which are, for
clarity,
described in the context of separate embodiments, may also be provided in
combination in a single embodiment. Conversely, various features of the
invention,
which are, for brevity, described in the context of a single embodiment, may
also be
provided separately or in any suitable subcombination.
Although the invention has been described in conjunction with specific
embodiments thereof, it is evident that many alternatives, modifications and
variations
will be apparent to those skilled in the art.
Accordingly, it is intended to embrace all such alternatives , modifications
and
variations that fall within the spirit and broad scope of the appended claims.
Citation or identification of any reference in this application shall not be
construed as an admission that such reference is available as prior art to the
present
invention.
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