understanding, a feasibility study, requirements engineering,
developmental design and compliance with
established laboratory standards issued by certification
or accreditation agencies (26). The lesson learned
from improvement methodologies such as Six Sigma
and Lean Management, which are successfully
applied to industrial production and are focused on
reducing time and errors required to complete an
operation or production, may be valuable options to
assist in this process (27). For almost all systems,
there is more than one possible solution to meet
accreditation requirements and standards, covering a
broad range of combinations with different integration
of hardware, software and human operations.
Although the apparent best solution will be very close
to fulfilling the basic requirements for a particular
laboratory environment, additional organizational,
political and economic issues may influence the definitive
arrangement. Therefore, laboratory managers
will not always be able to develop an ideal system,
rather, they may be constrained to suit the model to
local or objective constraints.
Regardless of these boundaries, comprehensive
knowledge and a description of system specifications,
even at the sub-system level, are essential to rationalize
the workflow and optimize activities. This can be
accomplished by disseminating detailed organizational
charts, work instructions, and a description of
responsibilities and standard operating procedures in
the form of recommendations or operating guidelines
to all operators actively involved in developing each
specific activity of the process. For the preanalytical
phase, detailed quality manuals should be provided
to all operators with responsibilities in specimen collection
and handling, encompassing clear notions on
common sources of preanalytical variability, such as
time of sampling, circadian rhythm of some analytes,
posture, tourniquet application, collection tools, order
of draw, procedures for handling, transportation and
storage of specimens, and indications on the effect of
at least commonly encountered influence and interference
factors (28). Although the most manually
intensive areas in traditional laboratory activities (collection,
handling and processing of specimens) present
the greatest potential for errors and flaws, they
have not been the focus of quality improvement strategies
until recently, slowing down the weighting
process of quality improvement throughout the total
testing process. Ongoing research into human factors
is being focused on tools for limiting unsafe manual
actions. Thus, health systems that have adopted
error-reducing technologies have cut medication mistakes,
reduced the rate of patient readmission due to
drug interactions, and improved outcomes for
patients, especially among those with chronic diseases.
In the laboratory environment, an increase in
the automation of multiple steps of the total testing
process has the potential to rationalize the workflow
and to reduce stress and the burden of manual errors,
leading to a greater degree of safety for operators and
limiting the risks for patient care associated with incidents
and fatalities (13).
The second crucial step is the implementation of a
comprehensive risk management strategy, focused
on what, why, where and when problems may arise
and what can be done to avoid, tolerate or reduce
their adverse outcomes (4). This strategy requires a
critical analysis of the process through implementation
of specific, detailed and reliable performance
indicators that identify critical steps, reducing risks
and preventing undesirable conditions. In processes
characterized by high complexity and involving several
sequential activities that are ultimately prone to
errors, an objective criterion for risk acceptability is
necessary to optimize resources, depending on what
is threatened by the risk, the probability of the causes
of the risk arising, and the probability that these causes
will result in an incident. A continuous monitoring
process, by introducing reliable error-tracking systems
based on accredited performance indicators
specifically suited to the local environment, would
help to identify vulnerabilities, allowing system redesign
or reorganization in a less hazardous model, possibly
with decreased complexity and less error-prone
activities (27, 28). Moreover, the opportunity to relate
errors to economic and clinical outcomes represents
an ideal basis for efficient audit and feedback in clinical
departments and decentralized ambulatory facilities,
improving the entire healthcare process. Powerfully
supported by innovative information technology,
this approach should not entail extraordinary expenditure,
which is always attractive for healthcare
managers.
Strengthen defenses
If defensive layers (protection) in a system are intended
to intercept accident trajectories, it is conceivable
that an increase in the number of controls in the process
should achieve this aim. This consideration is
guided by the evidence that accidents or critical incidents
rarely have a single cause, but generally arise
because several events (human errors, hardware or
software failure, inappropriate system behavior as a
consequence of a wrong requirement) occur simultaneously.
Over the past century, the identification of
drawbacks and errors within the total testing process
was highly dependent on the individual skills and
expertise of the operators. Remarkable advances in
technology and informatics have now made available
additional and more objective defensive systems that
can also operate in series rather than in parallel. Interference
evaluation is challenging, as it may variably
influence the concentration of measurable analytes,
and the interferent may interact with the analyte or
alter the assay result irrespective of the analyte concentration
(11). Modern laboratories contain highly
automated systems for sample transport, preparation,
analysis and storage, which may be supplied with efficient
means for detecting unsuitable specimens,
including insufficient volume, endogenous or exogenous
interfering substances and partial clots (29).
Therefore, if the laboratory professional does not
promptly identify an unsuitable sample by visual
inspection, there will be one or more further checks
724 Lippi and Guidi: Risk management in the preanalytical phase of laboratory testing
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