Keywords medication process - pediatrics - barcoding - medication administration systems -
infusion - smart pump
Background and Significance
Background and Significance
Medication errors are among the most serious patient events, causing significant physical
and psychological harm as well as a substantial burden to the health care system.[1 ]
[2 ] Medication errors are both more frequent and more likely to cause complications
in children compared with adults.[3 ]
[4 ] The most common medication errors include dose errors, drug preparation errors,
and incorrect rates of intravenous administration.[4 ]
[5 ]
In pediatric acute care medicine, prescription, administration, and evaluation of
drug doses are highly weight dependent, with patients' weights ranging from a few
hundred grams to more than 100 kg. Consequently, the required total dose rate (i.e.,
the amount of active substance to be administered per unit of time) can often differ
by several orders of magnitude in patients in immediately adjacent beds. At the same
time, an identical volume rate of the same medication in two patients can correspond
to an actual amount of active substance per unit of body weight that differs by three
orders of magnitude. Preparation of patient-specific drug concentrations for continuously
administered drugs is therefore still common in German pediatric care. These allow
for a fixed relationship between volume rate and dose rate per unit of body weight
and thus, when using pumps that display volume rates, facilitate bedside evaluation
as well as straightforward comparability of drug doses between patients with very
different body weights. However, preparing medication individually per patient induces
an increased risk of errors in the process of preparation as well as severe consequences
of patient misidentification when administering the prepared medication. The use of
standardized drug concentrations simplifies the process and reduces medication errors.[6 ]
[7 ] The Institute for Safe Medication Practices as well as the Vermont Oxford Network,
therefore, recommend patient-specific drug concentrations to be abolished, making
standard concentrations for medication infusions a requirement for hospital accreditation
in the United States and Canada.[8 ]
[9 ]
[10 ]
However, using standardized drug concentrations, fixed ratios between volume rate
and dose rate per body weight (by altering the drug concentration relative to the
patient's body weight) are no longer achievable. Thus, for the proper interpretation
of the actual amount of active substance per body weight, a calculation is required.
Nearly all severe dosing errors in pediatric patients have been found to be associated
with the incorrect use of equations or calculation errors.[11 ] Furthermore, when using infusion devices that display volume rates only, this calculation
is quite time-consuming and prone to potentially dangerous calculation errors, especially
when performed by mental calculation under stressful conditions in intensive care
units.[12 ]
To mitigate this error source, smart pumps can display weight-adapted dose rates (e.g.,
µg/kg/min) using a built-in dose-rate calculation tool. However, such a tool requires
manual input or selection from preconfigured choices of the drug concentration, the
patient's weight, and the prescribed dose rate at least. With critically ill patients
typically receiving up to 20 drug infusions simultaneously, the manual entry of these
data on each device is very time-consuming and error prone. A method is therefore
needed to transfer these data in a fully or partially automated way from the prescription
software system to the infusion device. Automatically generated machine-readable labels
(e.g., barcodes) applied to the medication preparation (e.g., syringe), combined with
appropriate adaptations of clinical workflows, are one approach to achieve this goal.[13 ] In addition, an identification step (e.g., by scanning patients' wristband barcode)
to ensure that the medication was prescribed for the patient at hand can greatly decrease
medication errors.[14 ] Unfortunately, integrating such systems and making them reliable and usable under
real-life clinical conditions is not trivial.[15 ]
Objectives
To prepare for the rollout of the above-mentioned approach across the University Hospital
Bonn pediatric intensive care units and anesthesiology workplaces, a new technology-supported
medication workflow was piloted on eight beds in the pediatric heart surgery intensive
care unit as well as in the pediatric anesthesia at the University Hospital Bonn.
As in many German pediatric acute care facilities, the prescription was exclusively
performed by medical doctors, while both the preparation and administration of prescribed
medications were usually performed by nursing staff. The infusion pumps and related
equipment were installed and maintained by the local medical device technology department,
while the patient data management system used for prescription and administration
documentation as well as the label printers were installed and maintained by the local
information technology (IT) department. Our current research investigates (1) challenges
and pitfalls in the process of workflow implementation, (2) technical problems and
solutions to these problems that enable high availability under real-life clinical
conditions, as well as (3) usability, user acceptance, and effects of the proposed
workflow on patient safety as perceived by the clinical end users.
Methods
Both system engineering tasks and clinical workflow adaptation planning were guided
and implemented by the interdisciplinary staff unit for Medical and Scientific Technology
Development & Coordination (MWTek), which also coordinated cross-departmental activities
and consensus processes. The MWTek team comprises experienced intensive care nurse
practitioners with additional academic qualifications as well as medical doctors with
clinical experience in pediatric acute care and additional training in medical informatics.
The same team also performed the technical acceptance tests.
Clinical and technical processes were subjected to stress tests, including specific
attempts to induce possible errors, as well as extensive clinical simulations before
being transferred to the clinical environment. Three separate systems (development,
testing/configuration, and staging) were used to develop and evaluate the implementation
before changes were applied to the clinical production system.
Continuous monitoring of technical availability and robustness was implemented. Redundancy
was provided for identified critical points of failure (e.g., the label printers).
The drugs were prescribed using the integrated order entry system of a patient data
management system (PDMS, Integrated Care Manager, Drägerwerk, Lübeck, Germany). The
data were exported from the electronic medication administration record of the PDMS
using the integrated proprietary script language. A PowerShell script (PowerShell
Core 2.0, Microsoft, Redmond, Washington, United States) was used to generate the
description language TSPL for the label printer (TC300, TSC, New Taipei City, Taiwan)
from these data while strictly avoiding any dose or unit calculation. All calculations
were made exclusively within the CE-labeled medical devices (Dräger ICM, risk class
IIa, and B. Braun Space [B. Braun, Melsungen, Germany], risk class IIb) in strict
accordance with the intended use of the medical devices. As a result, self-adhering,
disinfectant-resistant labels could be printed directly from the PDMS by the prescribing
physician. Drug name, concentration, and patient identification were printed on that
label in both plain text and stored in a 2D barcode that could be read by the scanners
supplied with the infusion devices. The same clinically established medication standards
and the same infusion devices (B. Braun Perfusor, Space, Infusomat Space, SpaceStation
with SpaceCom Dockingstation, Barcode Scanner Space) were used in all investigated
areas.
Clinical implementation was slowly ramped up starting with four intensive care unit
(ICU) beds and one pediatric cardiology/cardiac surgery operation room starting May
27, 2019 and extending to eight beds and two operation rooms starting October 14,
2019. The implementation was closely monitored by the implementation team. Users were
actively asked in regular intervals for possible improvements and problems experienced
when using the workflow. Initially, this was performed at least once per work shift
(i.e., three times per day), and to date a regular meeting with the users is held
at least once per month. The final workflow was analyzed using a failure modes and
effects analysis (FMEA).[16 ]
In February 2020, a self-report survey (see [Supplementary Appendix A ], available in the online version) was completed by nursing staff in the ICU who
routinely prepare and apply the medication according to the physicians' orders. The
questionnaire consisted of six closed questions (Likert-type scales graded from 1 = very
good to 6 = insufficient) and an open question asking for “other feedback, criticism,
and comments.”
Results
Workflow
The clinical and technical workflows were agilely developed in parallel. The aim was
to manage and mitigate as many risks as possible using medical informatics tools and
to use manual mitigation measures only if the former was currently not possible.
Technical Workflow
The medication configuration in the PDMS was optimized step by step and consented
across clinical teams using previously established hospital-wide change and configuration
release management processes for the PDMS. Similarly, for the corresponding medication
database and firmware configuration of the infusion devices, university hospital-wide
consensus, standardization, and change management process were established to ensure
uniform functionality and system behavior across all hospital departments using the
infusion pumps (with 23 involved departments significantly larger subset of the seven
clinical departments using the PDMS).
Risks and Mitigation Measures
Patient Misidentification
This common risk is addressed by the seamless transport of case IDs from PDMS to the
medication labels and a final technically supported cross-check of patient identification
by the infusion pump by scanning a patient identification label attached to the patient
or bed/incubator.
This built-in process identifies the patient to the smart pump and allows the device
to reject every medication that was not prescribed and compounded for this patient.
The smart pumps do allow the medical personnel to skip this identification step for
emergency situations (e.g., in case of an unknown emergency patient).
Labeling Based on Erroneous Orders
Support for and enforcement of guidelines and local standard operating procedures
(SOPs) was included in the configuration of PDMS and infusion device medication databases
to reduce prescription errors.
In particular, in both clinical simulations and practical application, the respective
prescription and administration units were found to be critical sources of errors
and possible misunderstandings (e.g., confusion between mg/kg/min and mcg/kg/min or
misinterpretations of mg/kg/h as mg/kg/d). Test scripts were developed for the PDMS
and infusion device configuration to detect possible configuration inconsistencies
and errors prior to each configuration release. Test coverage included the detection
of unit inconsistencies both within and across PDMS and infusion device configurations.
Data extraction based on the proprietary scripting language integrated into the PDMS
was designed in such a way that obviously inconsistent orders cannot be printed. The
limited functionality of the integrated scripting language does not allow “smart”
detection of prescription errors, but can, for example, exclude prescriptions with
prescription modes that are not permitted or do not make sense for infusion devices
(e.g., enteral prescriptions).
Technical Failure of Label Printing Procedure
The printing process is realized via a RAW driver in the TSPL description language
of the printer to avoid formatting errors and to improve performance to under 1 second
per label. The bedside PDMS fat clients use a print server, whereas the three independent
PDMS terminal servers permitting remote access to the PDMS user interface from arbitrary
computers on the hospital network each use local printer queues to create redundancies
for a failure of individual printer management components. Two independent thermal
printers per ward can be individually selected by the users.
Failure of Labels to be Readable or Scannable
The physical properties of the drug labels have been optimized in size and texture
to fit the syringe types and environmental conditions in which they are clinically
used while still offering good readability for the human eye and the barcode scanners
([Fig. 1 ]). We finally chose 75 × 45 mm self-adhering disinfectant stable thermal printer
labels with a detachable 75 × 5 mm part for labeling the infusion line ([Fig. 1A ]).
Fig. 1 Medication label. (A ) Detachable part for labeling the infusion line. (B ) 2D barcode with a cell width of three dots (using a 300 dots per inch printer) and
an error correction recovery level of 15%. (C ) Marker to help align the label with the scaling of the syringe for optimal readability
even when the syringe is already placed into the infusion device. (D ) Space for initials to be signed by the respective specialist for preparation and
final verification of the prescription. Line three contains the prescribed volume
rate, as well as the default dose rate per kilogram of body weight and the child's
body weight used for the calculation.
The cell size of the two-dimensional (2D) barcode has been maximized to allow for
the easiest and most stable readability through the scanner of the infusion device
while still being small enough to not be obscured by the surface curvature of the
smallest syringes used, which was found to prevent successful scanning of 2D barcodes
above a certain size. In several clinical simulations, we found a cell width of three
dots (using a 300 dots per inch printer) with an error correction recovery level of
15% to give the most reliable results under real-life clinical conditions ([Fig. 1B ]). A marker has been added to help align the label with the scaling of the syringe
for optimal readability even when the syringe is already placed in the infusion device
([Fig. 1C ]).
Failure of Label Content to Agree with Current Prescription at Time of Initiation
of Drug Administration
The most prominent process risk for which no direct technical or automated mitigation
measures could be found under the given conditions is the possibility of divergence
of the current prescription from the prepared and labeled drug when initiating administration
even though the drug was prepared correctly as prescribed. We concluded that here,
given our current technological capabilities, a final manual verification step before
initiating drug administration is indispensable for two reasons: First, new incoming
information or very recent clinical developments of the patient's condition (e.g.,
unexpected onset of an acute hemorrhagic shock) may enforce a change of the prescription
after the label has been printed. Second, possible technical errors in the transfer
of data from the PDMS to the label and from the label to the infusion device cannot
be excluded with certainty and thus must be detected before administration.
Integrated Clinical and Technical Workflow
The clinical process including all risk mitigation measures described above starts
with the prescription of the medication in the PDMS based on preconfigured standards
which include the concentration of active substance(s), base diluent where applicable,
and route of administration. Body weight-based medication calculations are based on
a medication weight which is part of the prescription (explicitly verified and ordered
by the responsible physician) and can—under special clinical circumstances (e.g.,
fluid accumulation)—differ from the actual physical weight of the patient. On the
day the medication is administered or just in time, the medication labels are printed.
The labels are then used by the physician, a pharmaceutical technician, or a specialist
nurse to prepare the prescribed medication according to the individual choice of standardized
drug concentrations. Verification of correct preparation, including active substance(s),
concentration, and base diluent, is documented by the respective specialist by signing
the label in a designated space ([Fig. 1D ]). Just in time for the beginning of the administration, the nurse practitioner or
physician compares the prepared medication with the current prescription in the PDMS.
The syringe is then inserted into the infusion device. The infusion device prompts
the input of a patient and stay-specific case number, which is attached to each patient's
bed or present on the patient's wristband and has to be entered using a handheld scanner
attached to the infusion pump docking station (B. Braun SpaceCom). The 2D barcode
on the medication label is then scanned. The infusion device prompts the confirmation
of each data element transferred by the 2D barcode, including the name of the medication,
drug concentration, and patient's weight. The infusion device displays an error notification
if the first scanned patient and stay-specific case number does not match with the
one transferred by the 2D barcode on the medication label, thus preventing administration
to the wrong patient.
The weight-adapted dose rate is set on the pump as prescribed (e.g., “0.05 micrograms
per kg body weight per minute”). Additionally, the volume rate is displayed by the
infusion device and is crosschecked with the prescription.
The final workflow is shown in [Fig. 2 ].
Fig. 2 Workflow. Schematic illustration of the final workflow.
We also conducted a FMEA for the final process shown in [Table 1 ]. Each potential failure mode was listed with its causes, effects, and mitigation
measures and ranked for severity (S), likelihood of occurrence (O), and likelihood
of detection (D). A risk priority number (RPN) was calculated by multiplying S, O,
and D.[16 ]
Table 1
Failure mode effect analysis
Process step
Potential failure mode
Cause of failure
Potential effects of failure
S[a ]
O[b ]
D[c ]
RPN[d ]
Applied process mitigation measures
Estimated effects of mitigation measures
Prescription
Prescription error by physician
Lack of knowledge, lack of time, lack of information, etc.
Severe patient harm or death
10
4
6
240
Medication database with preconfigured standards and warnings
Reduction of O and D
Configuration errors favoring incorrect prescriptions
Lack of knowledge, lack of time, lack of information etc.
Severe patient harm or death
10
1
2
20
Four eyes principle, manual and (partly) automated testing in development and Staging
before release
Reduction of O
Data extraction and Label Printing
Technical failure leading to dangerous change of label information
Unknown cause
Severe patient harm or death
10
1
3
30
Manual crosscheck of the setup infusion device with the prescription on the beside
PDMS-monitor right before the start of administration
Reduction of D
Technical failure leading to inability to print labels
Printer failure, Printer queues management failure, etc.
Use of more time consuming backup process with higher risks of error
4
3
5
60
Independent redundancy and surveillance of printing infrastructure (2 Printer per
ward, double redundant printer queues management)
Substantial reduction of O and D
Drug preparation
Failure in drug preparation/drug not matching the label
Lack of knowledge, lack of time, lack of information etc.
Severe patient harm or death
10
2
9
180
Highly standardized medication preparation, correct preparation is documented by the
respective specialist by signing the label ([Fig. 1D ]).
Reduction of O
Drug administration
Patient misidentification
Lack of knowledge, lack of time, lack of information, etc.
Severe patient harm or death
10
2
4
80
Clearly labeled medication and cross check by the infusion device by scanning a patient
identification label attached to the patient or bed/incubator
Substantial reduction of O and D
Failure of label content to agree with current prescription
Change of prescription after printing and before administration
Severe patient harm or death
7
3
4
84
Final manual cross-checking of the current Prescription with the label
Reduction of D
Abbreviation: D, detection; O, occurrence; RPN, risk priority number; S, severity.
Notes: Each potential failure mode listed with its causes, effects, and mitigation
measures ranked for severity (S = 1: slight annoyance to 10: severe patient harm or
death), likelihood of occurrence (O = 1: no known occurrence to 10: almost certain),
and likelihood of detection (D = 1: always to 10: detection nearly impossible).
An RPN was calculated by multiplying S, O, and D.[16 ]
a Severity (S = 1: slight annoyance to 10: severe patient harm or death).
b Likelihood of occurrence (O = 1: no known occurrence to 10: almost certain).
c Likelihood of detection (D = 1: always to 10: detection nearly impossible).
d RPN calculated by multiplying S, O, and D.
Technical Functionality
Between May 2019 and February 2020, the workflow was used 44,111 times. The technical
and procedural security mechanisms led to the identification of several software errors
in the medical devices involved. These were immediately communicated to the respective
manufacturers and have since been addressed. During the pilot phase, a total of 114
known failures in the technical infrastructure occurred, namely printer defects (109
errors), server malfunction (two errors), and medical technology electromechanical
malfunctions (scanner/scanner interface, three errors). All these errors were successfully
mitigated by redundant technical infrastructure (two printers per site, three-terminal
servers, and replacement scanners) resulting in no known effective downtime of the
new standard process. Only a fraction of the technical failures was actively reported
by the users before the implementation team specifically asked for problems. As part
of a planned maintenance procedure of the PDMS servers, the process was interrupted
once for 7 hours without technical redundancy requiring a fallback to paper-based
backup processes.
After the successful completion of the pilot phase and further rollout to the remaining
pediatric intensive care units, failures of individual print management components,
in particular the printer queues on the terminal servers, occurred with increasing
frequency (e.g., due to accidental attempts to print a discharge report on the medication
label printer). We therefore also had to establish a monitoring and failure alerting
infrastructure for these components to minimize response times to technical failures.
User Evaluation
We received 19 completed questionnaires from 33 caregivers working on the pilot ward.
The caregivers gave good ratings for usability and safety (median “school grade” 2
or B for patient safety, understandability, patient identification, and handling).
Most of the users were indifferent regarding the perception of the medication process
by the patients' parents or other visitors (median grade 3 [C]). In total, 74% of
the participating users preferred to continue using the process (grade 1 [A] and 2
[B]), 21% were indifferent to further use (grade 3 [C]), and one caregiver (5%) no
longer wanted to use the process (grade 6 [F]). The qualitative feedback contained
nine feature requests which were subsequently introduced in the change management
process and addressed. [Fig. 3 ] shows box plots of questionnaire responses. The medical management of the acute
care facilities involved rated the process as clearly beneficial regarding patient
safety, resulting in the rollout of the proposed workflow to all pediatric intensive
care areas.
Fig. 3 Box plots of questionnaire answers (19 completed questionnaires from 33 caregivers,
answer graded from 1 = very good, to 6 = insufficient).
Discussion
Development and Implementation
Coordination of process and technology development, consensus processes, and risk
management by an interdisciplinary team of physicians and nurses with additional technical
qualifications proved key to the successful implementation of these complex procedural
and technical changes with deep clinical impacts.
We believe that extensive testing of reliability and usability by team members experienced
in clinical practice as well as at the interfaces between informatics, medical technology,
and clinical routine has provided us with essential insights to optimize the processes
prior to clinical piloting. Nevertheless, the implementation team took great care
to maintain close contact with users and to actively gather feedback on technology
and processes on a regular basis. We are convinced that this is fundamentally beneficial
for the acceptance of a new process and especially helps to initiate and maintain
a constructive and agile improvement cycle with the users at the bedside which is
paramount for ensuring usability, staff satisfaction, and patient safety.[17 ] After all, analogous to Moltke's insight that no battle plan survives first contact
with the enemy, we typically find that the highly dynamic demands on technology, material,
and personnel in acute care require rapid agile adaptation cycles based on real-life
clinical experience generated with sufficient safety and risk mitigation measures
in place.
Another important factor for the acceptance and practicability of a clinical process
is its reliable availability. We tried to achieve high availability primarily through
technical redundancy. However, we had to learn that redundancy alone is often not
sufficient in clinical practice, as the stressed clinical staff does not always find
the time to adequately address and report a failure. This especially holds true in
those cases where one component fails but the process still works because of the implemented
redundancy (e.g., one of two printers fails). Therefore, we urgently recommend establishing
a monitoring system allowing active and immediate response by the technical support
team to the failure of any critical component supporting safety-critical systems and
processes in acute medicine and to practice predictive maintenance where possible.
Workarounds
Using barcode medication administration systems, even when these work reliably, cannot
totally prevent errors. Koppel et al have described several workarounds for barcode
medication administration systems.[18 ] These workarounds would often be performed either to save time and/or to compensate
for dysfunctions of the underlying technology. The main “workarounds” we observed
were skipping the identification process or not scanning the new barcode label when
changing the syringe (both should only be done in unforeseeable emergency situations
where established SOPs cannot be applied). According to our impression, workflows
or “workarounds” lacking technical support were more error-prone after the introduction
of the new process than before. This may be due to the fact that the staff is less
trained and experienced in carrying out the unsupported process than before the introduction
of the new process. In particular, new staff may not have had sufficient relevant
experience in performing the tasks without the additive technical and procedural safety
measures. The clinical management of one intensive care unit has therefore recently
instructed its staff that if, for whatever reason, the technically supported process
including the scanning of the label cannot be used, the parameterization of an infusion
device may only be performed by two independent, trained operators cross-checking
each other (“four-eyes principle”).[19 ]
Survey
The survey data of the nursing staff provide an idea of a perceived increase in patient
safety as well as the usability of the proposed process. However, it is not a very
powerful surrogate for its actual effects on patient safety or process efficacy. Although
highly desirable for almost any new process, there was no structured measurement tool
for process-related patient safety available in the areas concerned at the time of
the piloting. Furthermore, the pilot phase coincided with a relocation of the relevant
wards as well as the introduction of electronic documentation in the PDMS (coming
from pencil and paper), new standards for drug preparation, and a significant increase
in the size of the relevant areas with a corresponding increase in new staff, making
any attempt to quantify independent effects on hard outcome measures of the new medication
process futile.
Remaining Challenges and Future Work
In our opinion, the most important remaining problem suggesting further workflow optimization
potential is the final manual reconciliation with the PDMS. Some of our health care
professionals have now gone through the process tens of thousands of times without
finding a problem in the final reconciliation. This may increase the probability that
a rare error will not be noticed. Ideally, technical support would be available to
allow the infusion devices to securely communicate with the PDMS to detect a change
in the order or a technical error in the process. We are currently working with the
manufacturers of our medical products to establish a practical way of transmitting
the dose rate from the infusion device back to the PDMS, which should at least make
it somewhat easier to detect an incorrect dose rate. However, even this can only work
with a timely manual check and may only detect a subset of the possible errors that
could occur in principle. Secure and reliable communication between the medical devices
in both directions is not yet commonly available in Germany. This holds especially
true for devices from different vendors. Feasible approaches have already been demonstrated
and will hopefully soon find their way into broad clinical applications.[20 ] In addition, we plan to evaluate the archival and analysis of infusion device log
files to obtain a more objective measurement of usage and faults in the daily clinical
routine.
As a limitation, the processes described in this manuscript do not address approaches
to reduce errors in preparing a correctly prescribed medication beyond the contribution
of establishing standard concentrations and detailed SOPs to reducing such risks.
Although several approaches to explicitly addressing such problems exist, most of
them require a central pharmacy supply of medication preparations.[21 ]
[22 ] Such centralized supply processes are currently only available for select medications
like chemotherapeutics and custom parenteral nutrition at our hospital and have yet
to be established for general intravenous medications. Additionally, such centralized
supply processes may run into fundamental limitations for the high-urgency individualized
preparation and application scenarios commonly found in pediatric ICU and anesthesia
settings.
Conclusion
Closing the communication gap between PDMS prescription and smart pumps with 2D barcode-equipped
medication labels can increase safety and user satisfaction in pediatric acute care.
The implementation of such a process reveals a multitude of technical, organizational,
and usability challenges that can be adequately addressed with a team that is experienced
in both clinical practice and medical informatics and maintains close user contact
with the bedside practitioners. Secure and reliable communication between medical
devices in both directions, ideally based on open standards, could provide even more
safety and contribute to further improving usability and practicability.
Multiple-Choice Questions
Multiple-Choice Questions
When implementing a new clinical medication workflow, which of the following helps
to adequately address technical, organizational, and usability challenges?
Maintaining close contact with the bedside practitioners/users
Choosing an implementation team that consists only of technicians
Focusing only on technical risks to avoid getting sidetracked
Avoiding continuous monitoring of critical components to minimize risk of alarm fatigue.
Correct Answer: The correct answer is option a. In our experience, significant parts of the challenges
in establishing a safe new workflow only become apparent in the clinical reality.
Consequently, maintaining close contact with the bedside practitioners/users is very
crucial to address these. Furthermore, it is helpful to have not only technical but
also clinical expertise in the implementation team and to assess and address the technical
risks in their clinical environment. Continuous monitoring of critical components
should generally be considered especially for critical clinical processes.
Which of the following statements is true regarding adequate risk management of a
new technology-supported clinical workflow?
When it comes to risk management, certified medical devices/products can be ignored,
as they and their handling are generally error-free.
Basic IT infrastructure, such as printers, is irrelevant to clinical risk management.
Clinical risk management should take into account technical as well as human actors.
Human factors should be disregarded for risk management purposes.
Correct Answer: The correct answer is option c. Clinical work often requires sufficient risk management.
This also applies to the use of certified medical devices/products, especially when
they are used as part of a new workflow. Technology-supported clinical processes in
particular can heavily depend on basic IT infrastructure. The respective IT infrastructure
should therefore be included in the risk management, as should the people involved
and the clinical environment.
Clinical Relevance Statement
Clinical Relevance Statement
Intravenous medication errors are among the most serious error events, especially
in pediatric care, causing great physical and psychological harm as well as a substantial
burden to the health care system. We present and discuss challenges, solutions, and
lessons learned in the implementation of a medical information-technology-supported
medication workflow in pediatric acute care.
