Download patient identification errors are not uncommon

Efficiency, Thoroughness, and the Patient Identification Process***
Jenna L. Marquard, PhDa ([email protected])
Philip L. Henneman, MDb ([email protected])
Tuan A. Phama ([email protected])
Megan M. Campbellc ([email protected])
Donald L. Fisher, PhDa ([email protected])
a. Department of Mechanical and Industrial Engineering
University Massachusetts Amherst
219 Engineering Laboratory
160 Governors Drive
Amherst, MA 01003
b. Baystate Medical Center; Tufts University School of Medicine
759 Chestnut Street
Springfield, MA 01199
c. School of Nursing
University Massachusetts Amherst
Skinner Hall
651 North Pleasant Street
Amherst, MA 01003
Corresponding author:
Jenna L. Marquard
University of Massachusetts Amherst
219 Engineering Laboratory
160 Governors Drive
Amherst, MA 01002
Tel: (413) 545-0646
Fax: (413) 545-1027
[email protected]
***Preliminary and Incomplete Draft
Abstract
The main objective of this study was to develop and test a methodology, based on the efficiencythoroughness trade-off (ETTO) principle, to evaluate whether and how health care workers balance
efficiency and thoroughness while completing patient care tasks. Health care workers’ abilities to
complete patient care tasks in a timely manner is essential for the provision of safe care, as untimeliness
can result in care provided too late or not at all. Additionally, health care workers’ thoroughness in
completing tasks is paramount to providing safe care, as workers play a key role in preventing and
intercepting medical errors.
This paper details the proposed methodology and tests its effectiveness by attending to one vital
health care process (verifying a patient’s identity) in a complex yet critical subsystem of the health care
system (the Emergency Department). Patient identification errors – a topic of national interest – are a root
cause of medication, surgical, charting, dietary, and other medical errors. Research shows patient
identification errors are relatively common, providers are not good at noticing identification errors, and
the failure to correctly identify a patient can result in serious subsequent errors.
Study researchers observed emergency department (ED) employees (N=61) as they completed three
common patient-related tasks in a simulated patient care space while wearing an eye tracking device.
Each HCW performed a common patient care task on three researchers acting as patients, with one patient
having an incorrect identity. The results of the study show that workers who detected the identification
error were less efficient in completing the process than workers who did not detect the error. Additionally,
this paper describes why some workers fail while following well-defined standard processes, where others
succeed while following non-standard processes.
Relevance to industry: The ETTO principle is a useful mechanism to understand why some health
care workers detect patient identification errors and why others do not. The approach described in this
paper can be extended to empirically test how individuals detect and mediate other types of medical
errors. Based on the findings of this study, and coupled with existing literature, identifying within-group
differences related to individuals’ efficiency levels and ability to detect identification errors will guide
more thoughtful job design interventions.
Keywords
Efficiency; Thoroughness; Patient Identification; Medical Errors; Eye Movements
1. Introduction
Heath care processes are complex – or ‘messy’ – for various reasons: demand is largely stochastic,
resources are constrained, and health care workers (e.g. physicians, RNs, etc.) with imperfect information
must act autonomously while functioning seamlessly in teams, with all workers focused on improving
patient outcomes [1-3]. Additionally, health care workers are expected to make judgments and complete
tasks in an attentive, thorough manner while conducting their work with a high level of efficiency.
The nature of the health care system and related processes can be described using Perrow’s [4]
system dimensions: coupling and interactiveness. Coupling means the degree to which processes are
connected to or closely rely on one another, usually in a time-dependant manner [4]. While hospitals
exhibit tight coupling, much of the health care system currently exhibits loose coupling, evidenced by
siloed information systems and repeated tests and scans, for instance [5, 6]. Interactiveness, whether
linear or complex, means the degree to which events are expected or unexpected, familiar or unfamiliar,
and visible or invisible [4]. The health care system exhibits complex as opposed to linear interactiveness,
because events tend to be unexpected and unfamiliar, and individuals largely cannot foresee the
downstream consequences of their actions [4]. Finally, the health care system can be considered
intractable. That is, the principles of the system’s functioning are largely unknown, and the system
descriptions are extremely detailed. Further, the system changes as these descriptions are produced [7, 8].
Intractable systems, as opposed to tractable systems, are difficult (and occasionally impossible) to control
[7].
This paper aims to develop and test a methodology to evaluate how individuals embedded in the
aforementioned complex sociotechnical system balance efficiency and thoroughness, while completing
patient care tasks. The paper details the proposed methodology and tests its effectiveness by using the
methodology to evaluate one complex, human-in-the-loop process – emergency department workers
verifying a patient’s identity. This paper also outlines how the methodology can be used to identify
appropriate job design interventions that improve patient safety, given how health care workers currently
balance efficiency and thoroughness while completing patient care tasks. Health care workers’ abilities to
complete patient care tasks in a timely manner is essential for the provision of safe care, as untimeliness
can result in care provided too late or not at all. Additionally, health care workers’ thoroughness in
completing tasks is paramount to providing safe care, as workers must still prevent or intercept errors.
1.1. Health Care System Safety
Health care researchers and practitioners, referred to here as domain experts are accounting for the
aforementioned system components – coupling, interactiveness, and intractability – when designing
interventions to improve patient safety. They are focused on reducing medical errors, adverse events, and
near misses, by designing interventions that improve patient safety. For instance, domain experts are
addressing the tightly-coupled inpatient setting by focusing on team communication strategies, shift handoffs, and supporting technologies, such as electronic medical records and barcode medication
administration systems, so as to ensure timely coordination of patient care [9-12]. Individuals are
addressing the loosely-coupled outpatient setting through health information exchange and personal
health record initiatives, in an attempt to strengthen coordination across disparate organizations and
patient populations [5, 6]. Individuals are addressing health care’s complex interactiveness by instituting
standard procedures and methods such as quality reporting measures and error reporting systems [13] in
order to provide feedback to individuals and organizations. Finally, individuals are addressing health
care’s intractability through approaches such as root cause analysis and failure modes and effects
analysis, both of which aim to understand and evaluate health care’s underlying structure and processes
[14-16]. In general, domain experts are making progress in their work to improve health care processes,
tools and technologies, and physical and organizational working environments, so as to prevent or reduce
errors, or lessen their consequences. [12, 17-20]. Their approaches align with the Resilience Engineering
domain’s concept of safety, where safety is not a state variable (i.e. a system’s current level of safety or
quality) but a process variable (i.e. a system’s set of attributes and practices that promote safety and
quality) [21].
According to Hollnagel, system safety means “freedom from unacceptable risks” [7, 22]. Because
risk in the health care system cannot be eliminated entirely, decision-makers at the strategic and policy
levels, tactical and managerial levels, and at the operational level must acknowledge and define what
degree(s) of risk are acceptable [7, 22]. Once acceptable and unacceptable risks are defined, domain
experts can then redesign the health care system and its numerous subsystems to prevent unexpected
events and protect against unwanted outcomes – thus controlling the system to the best of their abilities.
1.2. Patient Identification Errors
This paper seeks to improve system safety by attending to one vital health care process (verifying a
patient’s identity) in a complex yet critical subsystem of the health care system (the Emergency
Department). Health care system domain experts, and many members of the general public, are aware of
the pervasiveness and costs of medical errors [23-27]. Given the need to reduce errors and costs, leaders
in the patient safety domain identify process and task design flaws as contributors to medical errors. In
2005 the Joint Commission, which accredits all hospitals, established the following National Patient
Safety Goal to reduce medical errors: “improve the accuracy of identifying patients” [28].
Patient identification errors – a topic of national interest – are a root cause of medication, surgical,
charting, dietary, and other medical errors. The failure to correctly identify a patient can result in serious
subsequent errors. In a study of blood transfusion errors, the most frequent error leading to a fatal
outcome was “administration to other than the intended recipient;” this specific error accounted for 49%
of fatal cases [29]. While this process may seem simple, evidence shows it is indeed complex and that
workers perform the task poorly [30]. In a recent study, physicians performed a computerized physician
order entry task, where the patient had the same name but a different date of birth than the one listed in
the system [30]. Twenty-three of the 25 physicians ordered tests on an incorrect patient [30]. The
consequences of misidentifying a patient are significant, as this task has high risk consequences including death. Additionally, the instances where misidentification can occur are wide spread, as this
task is typically performed before every health care intervention (e.g. selecting a medical record,
performing a test or a procedure, or giving a medication).
Identification errors are not rare; mislabeled laboratory specimens are a commonly reported medical
error [31, 32]. These findings are especially disturbing if coupled with a study in which phlebotomists
looked at 2.46 million patient wrist bands at 712 hospitals [33]. Of these bands, 9% (approx 216,000) had
erroneous data, 8% (approx 192,000) of the patients had multiple bands with different information and
0.5% (approx 12,000) of the patients were wearing wrist bands with another patient’s identity information
[33]. The 2006 National Hospital Discharge Survey [34] states that the total number of discharges from
short-stay hospitals in 2006 was 34,854,000. Scaling the rates shown above using this discharge data,
these figures would translate to 3,136,860 bands with erroneous data, 2,788,320 patients wearing multiple
bands, and 174,270 bands with another patient’s information.
1.3. The Efficiency-Thoroughness Trade-Off Principle
A range of methods exist to aid domain experts in delineating how particular chains of events lead to
health care system failures or malfunctions – namely medical errors, adverse events, and near misses.
This paper aims to evaluate a cognitive approach to understanding system errors and malfunctions,
specifically the patient identification process, as opposed to the commonly used systems analysis
methods: fault tree, event tree, FMEA, and root cause analyses.
Fault tree analysis details “how combinations of individual faults can lead to failure” of a system [15,
35-37]. For instance, a patient may be wearing a wrong ID band, and a provider may not notice – two
faults that may result in the patient being given an incorrect medication. Whereas a fault tree focuses on
combinations of faults leading to a failure, an event tree is a “graphical representation of the paths to
success and failure” which determines all possible outcomes that could result from an event [15, 35-37].
An example of an event tree might start with the event of a pharmacy mislabeling an IV bag. The tree
then outlines possible outcomes resulting from that initial event. Failure modes and effects analysis
(FMEA), popularly used to evaluate high-risk health care processes, provides a structure to identify what
can go wrong in a process (failure modes), and the consequences of those failure modes (failure effects)
[14, 16, 37]. FMEA is typically used to analyze a system before a given failure takes place, and includes
additional assessments such as the frequency of the failure mode, the likelihood of the effect, and the
criticality of the failure mode. A team could, for example, undertake an FMEA analysis to determine all
combinations of failure modes and effects leading to the incorrect administration of a medication. Finally,
root cause analysis (RCA) identifies the causes of a failure or malfunction that has already taken place –
taking place after a wrong medication has been administered, for instance [38, 39].
Due to the dynamic nature of the health care system, the aforementioned methods may fail to account
for how underlying attributes of individuals affect system safety. For instance, the same action (e.g.
verifying an order with an MD), may be appropriate in one situation and inappropriate in another
situation. In one situation, verifying an order with an MD would mitigate an error; in another case without
an error, this behavior would make the ordering process less efficient. Thus, an individual largely cannot
foresee the downstream consequences of his/her actions. Therefore, it is unclear how individuals will
determine whether an action is appropriate in a given situation. This paper therefore focuses on
identifying how one underlying characteristic of individuals causes those individuals to determine which
actions they choose in completing patient care tasks [7].
The efficiency-thoroughness trade-off (ETTO) principle, as described by Hollnagel [7], states that:
In their daily activities, at work or at leisure, people routinely make a choice between being efficient
and being thorough, since it rarely possibly to be both at the same time. If demands for productivity
or performance are high, thoroughness is reduced until the productivity goals are met. If demands for
safety are high, efficiency is reduced until safety goals are met.
Knowing that health care workers are not good at catching identification errors [30], the aim of this
paper is to empirically test whether the ETTO principle can help explain whether and why some health
care workers catch patient identification errors and why others do not. Given this knowledge, the paper
also aims to outline how job design interventions can account for the rules individuals use to balance
efficiency and thoroughness while verifying a patient’s identity.
While the meanings of ‘thoroughness’ and ‘efficiency’ change depending on context, we focus on
definitions as they relate to frontline health care workers. By efficiency, we mean a health care worker’s
ability to achieve a desired outcome with as few resources as possible [7, 41]. By thoroughness, we mean
a health care worker’s knowledge of what outcomes are to be achieved, the current status of the system,
and the actions needing to be taken to ensure desired outcomes are met [7].
Efficiency and thoroughness must be balanced to help ensure patient safety – no matter which health
care process is of interest. Health care workers’ ability to complete patient care tasks in a timely manner
is essential for the provision of safe care. Untimeliness can result in care provided too late or not at all,
and can cause serious implications for patient safety (e.g. delayed medication administration) and patient
satisfaction [42]. Additionally, health care workers’ thoroughness in completing tasks is paramount to
providing safe care. Health care workers play a key role in preventing and intercepting medical errors,
and are now often supported in their tasks by technology [25]. Yet, they must still be thorough in
conducting their work, to prevent or intercept latent errors that can occur because of the technology [11,
43, 44]. This change in the nature of health care workers’ jobs, which now includes understanding, using,
and trusting technology, can lead to new errors often rooted in the misunderstanding, misuse, and/or
mistrust of the technologies [12, 45].
2. Methods
2.1. Setting and Participants
We observed emergency department (ED) employees (N=61) as they completed three common
patient-related tasks in a simulated patient care space while wearing an eye tracking device. The
participants were ED employees at a 600 bed, urban, level 1 trauma, pediatric and tertiary referral center
in Western Massachusetts with an annual ED census > 100,000. Of the 61 employees, 28 were RNs
(RNs), 17 were clerks (ESAs), and 16 were technicians (TAs).
Participants volunteered to participate in the study during one of their day or evening shifts.
Participants were told that the purpose of the study was to evaluate how expert health care workers
(HCWs) use visual cues to perform common, patient care tasks. Study participants were told that they
would wear an eye tracking device that would capture a video of the field in front of them and place cross
hairs on the video showing precisely where they were looking throughout the scenarios. HCWs were not
aware that the purpose of the study was to investigate the process of verifying a patient’s identity (ID).
The study was approved by the hospital’s institutional review board (IRB) and all participants read and
signed an informed consent form, while having any questions answered by a study researcher.
2.2. Study Overview
Each HCW performed a common patient care task on three researchers acting as patients. RNs
completed a scenario in which they acted as if they were to give intravenous medications to the patients.
The ESAs’ scenario involved placing an ID band on the patients. TAs completed a scenario in which they
acted as if they drew, labeled, and sent a blood specimen to the laboratory. Each HCW was asked to
perform the task the same way he or she does every day in the ED except for giving an actual medication
or drawing blood.
All participants performed their tasks on the same three patients. The third patient seen by the RN and
TA participants had the same name but different date of birth (DOB) and medical record number (MRN)
on the ID band than the artifacts (e.g., medication or blood labels) they brought into the room. The third
patient seen by the ESAs had the same name but a different DOB than the ID band the ESAs brought into
the room.
2.3. Study Procedure
Participants wore an eye-tracking device while completing the scenarios. The ASL Mobile Eye
(Applied Science Laboratories, Bedford, MA) is a tetherless eye tracking system that allows freedom of
movement and can be worn by participants who must move freely through a study environment. The eye
tracker shown in Figure 1, weighing 76 grams, includes a scene camera, optics, and reflecting mirror all
mounted on safety glasses. The device’s scene camera records a video of the area in front of the wearer
and uses pupil–corneal reflection to measure the position of the eye – sampled at 25 Hz. With the head
stable, the device is accurate to within 0.5 degrees of visual angle, with a resolution of 0.10 degrees of
visual angle; the visual range of the eye-tracking device is 50 degrees horizontally and 40 degrees
vertically with respect to the head.
To calibrate the eye-tracking device for each participant, participants looked at twelve specific
reference points in their field of view, with marks of their fixation adjusted to correspond to the reference
points. The Mobile Eye software program, after calibration, overlays cross hairs (plus signs) at the exact
locations in a scene where the individual is gazing throughout the scenario.
A researcher then led the HCW to a series of numbered patient rooms, and gave the participant a list
of patients in rooms six through eleven. The simulated patients were located in the first three rooms. For
each patient, a researcher gave the HCW a clipboard with patient specific materials. RNs received an
order sheet, a documentation page and an intravenous medication. ESAs received an ID band and a
documentation page, and the TAs received a blood specimen label and a documentation page.
<<Insert Figure 1 about here>>
Figure 1: ASL Mobile Eye Tracker
All materials were labeled with patient specific information in exactly the same way. Labels were
specifically designed to look realistic, yet allow the eye tracking device to differentiate the specific ID
information at which the HCW was looking. The patient’s name, DOB and MRN were each placed on a
different vertical and/or horizontal axis on the labels.
The patients wore an ID band during scenarios completed by the RNs and TAs. The patients did not
wear an ID band during scenarios completed by the ESAs, as the task involved placing an ID band on the
patient. All patients, including the third patient with the ID error, would state their correct name and DOB
if asked. For the RNs and TAs, the patient’s stated name and DOB matched the name and DOB on the ID
band the patient was wearing but the DOB and MRN on the ID band did not match the labels on the
artifacts they brought into the room. For the ESAs, the patient’s stated name and the name on the ID band
matched, but the stated DOB did not match the DOB on the ID band.
HCWs were asked to not discuss the study with any colleagues over the 4 days the study was
conducted.
2.4. Data Collection
One member of the research team observed the HCW interacting with each patient while completing
a standardized data sheet. The data sheet documented whether the HCW introduced himself or herself;
whether they asked for the patient’s name, or if the patient’s name was “X”; whether they asked for the
patient’s DOB, or if the patient’s DOB was “Y”; and whether they identified the ID error on the third
simulated patient and stopped their assigned task. The research team defined “identifying the patient ID
error” as stopping the assigned task with or without voicing that the patient had a different identification
than the artifact labels or ID band they brought into the room. The observer also recorded additional notes
on the data sheet as necessary. A second observer completed the same data sheet for ten percent of the
HCW to ensure accuracy of data collection. There was 100% agreement between the two independent
observers.
Two researchers independently reviewed all eye-tracker videos to assess whether the HCW looked or
did not look at each of the patient identifiers on the artifact labels and ID bands. The researchers reviewed
the videos in 0.4 second intervals to determine precisely which patient identifiers the participant looked
at. There was 91% agreement between the two independent observers reviewing the eye tracking data,
and a third researcher resolved disagreements between the two researchers. The researchers determined
that a HCW looked at a specific patient identifier if the cross hairs on the video were within a box that
covered the patient identifier information on the calibrated video during a 0.4 second interval.
The researchers used the observational and eye tracking data to determine if a HCW verified the
patient to their ID band and/or verified the artifact to the patient or their ID band. Forty-nine of the 183
patient scenarios (27%) did not have eye tracking data. The reasons for the eye tracking failures included:
no cross hairs on the video image (31/49), glare or poor focus obscuring the ID information (15/49), and
inability to wear the eye tracking device over participants’ glasses (3/49). For the purpose of this study,
only those participants with eye tracking data were included when determining if the RN and ESA
verified the artifact to the patient or their ID band and the TA verified the ID band to the patient.
Using the eye tracker videos, the researchers were able to detail all steps completed by the participant,
in the order of completion. This analysis was important, as the observation sheets included only a few key
steps and did not signify the order in which participants completed the steps. The researchers also
separated the process steps for each participant into three time frames: steps completed before entering
the patient room, steps completed while in the patient room, and steps completed after the participant
caught the error. For this analysis, the research team disregarded the steps after the participant caught the
error, as these steps were only present for the subgroup that caught the error.
3. Results
While the steps completed by ESAs, RNs, and TAs were similar, ESAs completed a one step
identification process (i.e. patient to ID band), while RNs and TAs completed a two step identification
process (i.e. patient to ID band and patient or ID band to artifact). In addition, RNs and TAs used
different artifacts – a medication label or blood label, respectively – while completing their task. For this
reason, the research team analyzed the process of verifying a patient’s identity separately for each roletype, but addressed whether there were consistent findings across the groups.
3.1. The Standard Patient Identification Process
To verify the patient’s identity, participants had to 1) verify the information on the patient’s ID band
and 2) verify an artifact to the patient or patient’s ID band - using two available patient identifiers (i.e.,
name, DOB or MRN). For the purpose of this study, a participant could verify a patient’s name by only
asking for his/her last name, but not by only asking for his/her first name. As the only artifact used by the
ESAs was the patient’s ID band, steps one and two of the verification process were the same for the
ESAs. ESAs could therefore only do this task in one way - asking the patient’s name and DOB and
matching them to the artifact – as patients would not yet know their MRN. RNs and TAs could complete
this task in 8 different ways, shown in <<Insert Table 1 about here>>
Table 1.
<<Insert Table 1 about here>>
Table 1: The Standard Process for Verifying a Patient’s Identity (ID)
We used the standard process outlined in <<Insert Table 1 about here>>
Table 1 and data about whether each participant caught the identification error in the third scenario to
separate participants into four categories. Participants either followed the standard process outlined in
<<Insert Table 1 about here>>
Table 1, or they did not follow the standard process. Additionally, participants either caught the
identification error or did not catch the identification error. These participant subgroups are shown in
Figure 2.
<<Insert Figure 2 about here>>
Figure 2: Participant Subgroups
Expected Success participants completed the process as outlined in <<Insert Table 1 about here>>
Table 1, and caught the identification error. Likewise, Expected Failure participants did not complete
the process as outlined in <<Insert Table 1 about here>>
Table 1, and did not catch the error. Non-Vigilant participants completed the process as outlined in
<<Insert Table 1 about here>>
Table 1, but did not catch the error – presumably because they looked at the correct identifiers and/or
asked the patient for the correct information, but failed to perceive or retain the identification information
adequately. Finally, Process Variant participants did not complete the process as outlined in <<Insert
Table 1 about here>>
Table 1, but caught the error – presumably because they caught the error in an unexpected way.
<<Insert Table 2 about here>>
Table 2 outlines a sample process for one part from each subgroup. In particular, the Process Variant
participant caught the error using an unexpected artifact – the patient’s chart and as shown, the NonVigilant participant completed all expected steps but did not catch the error.
<<Insert Table 2 about here>>
Table 2: Sample Process for Each Subgroup
Each role-type (RN, ESA, TA) varied as to whether participants fell into the four subgroups. As
shown in Figure 3, ESA participants fell into three groups: Expected Success, Non-Vigilant, or Expected
Failure. Thus, some ESAs followed the standard process but failed to catch the error. Furthermore, all
ESAs who did not follow the standard process failed to catch the error. RN participants fell into three
groups: Expected Success, Process Variant, and Expected Failure. Thus, no RNs who followed the
standard process failed to catch the error. Surprisingly, some RNs who did not follow the standard
process did catch the error. Finally, TAs fell into two groups: Expected Successes and Process Variants.
Thus, all TAs caught the identification error.
<<Insert Figure 3 about here>>
Figure 3: Participant Subgroups by Role-Type
3.2. Efficiency, Thoroughness, and the Patient Identification Process
For the purpose of this study, efficiency is defined as the number of steps it took a participant to
complete the process of verifying the patient’s identity – specifically in the third scenario with the
embedded patient identification error. This proxy for efficiency aligns with Hollnagel’s definition of
efficiency, which is a worker’s “ability to achieve the desired outcome with as few resources as possible”,
with the desired outcome in this case being completion of the task given to the participants and the
resource being time.[7] In this study, thoroughness meant the participant completed the task in a way that
led to his/her catching the identification error.
We measured the efficiency of each subgroup in Figures 4-6, using the average number of steps it
took participants to complete the assigned task.
<<Insert Figure 4 about here>>
Figure 4: Average Number of Steps Completed by ESA Participants
<<Insert Figure 5 about here>>
Figure 5: Average Number of Steps Completed by RN Participants
<<Insert Figure 6 about here>>
Figure 6: Average Number of Steps Completed by TA Participants
<<Insert Table 3 about here>>
Table 3 shows the relationships between groups within each role-type. For this exploratory study, we
used a high p-value of 0.1 to test for significant difference for two reasons. First, as participants were
divided into four subgroups within each role-type, all subgroups consisted of sample sizes less than ten
participants. Knowing that further study with a larger sample size is required to confirm the findings
presented here, we are therefore more willing to accept a false positive finding (Type I error) than false
negative finding (Type II error). In other words, we would rather conclude that there is a difference and
explore this issue further, rather than concluding there is no difference and not proceed accordingly.
Across the two role-types, ESAs and RNs, where Expected Success and Expected Failure participants
could be compared, Expected Success participants completed significantly more process steps than
Expected Failure participants. Additionally, across RNs, and TAs, the two role-types where Expected
Success and Process Variant participants could be compared, Expected Success participants completed
significantly more process steps than Process Variant participants. The only Non-Vigilant participants
were ESAs, and there was no statistically significant difference in the number of steps completed by NonVigilant participants and either Expected Success or Expected Failure participants.
<<Insert Table 3 about here>>
Table 3: Subgroup T-tests for Total Number of Steps
Based on the above analysis, the subgroups of health care workers can be plotted according to their
relative levels of thoroughness and efficiency, shown in Figure 7. Expected success and Process Variant
participants are considered more thorough than Non-Vigilant and Expected Failure participants because
they caught the identity error. The Non-Vigilant participants are outlined with a dashed line to indicate
that while the research team suspects the Non-Vigilant participants to fall in the less-efficient, lessthorough quadrant, this study did not provide sufficient evident to suggest where on the efficiency
continuum Non-Vigilant participants fall.
<<Insert Figure 7 about here>>
Figure 7: Subgroup Locations on Efficiency-Thoroughness Plot
In practice, health care workers will likely remain unaware as to whether they miss an identification
error – unless the implications of that error are significant enough to warrant inspection into the cause of
the incident. If health care workers were to have feedback about whether they missed an identification
error, these subgroups may have different feelings about the efficiency-thoroughness trade-off they made.
According to Hollnagel [7], Expected Success participants are likely to feel vindicated - that they wisely
used their efforts. Process variant participants may feel relief that they succeeded while being relatively
efficient. Expected failure participants may regret not having been thorough in carrying out the task.
Finally, Non-Vigilant participants may feel that their efforts were in vain.
4. Discussion
There is a wealth of historical research to help explain why health care workers differ in their
efficiency-thoroughness trade-offs. Klein’s recognition-primed decision-making (RPD) model, stemming
from the field of Naturalistic Decision Making, posits that individuals use their experience to match a
current situation with previous situations, to generate a plausible course of action [46]. Because patient
identification errors are high-risk, low-probability events, health care workers’ experiences in identifying
and mitigating identification errors may be lacking. Some health care workers may rely on a body of
experience void of encounters with identification errors, or they may have missed identification errors in
the past that were never discovered, a fact that suggests efficiency was weighted more heavily than
thoroughness. Likewise, health care workers who have encountered or experienced identification errors
may weigh thoroughness more heavily.
Research on schema or schemata describes the cognitive structures used by individuals to understand
a situation [47, 48]. Schemas are the mental representations used in the RPD model – to which an
individual matches his/her past experiences. In addition to having insufficient experiences to match with
their schema, an individuals’ schema can itself be flawed. Individuals whose schema is simple and
encompasses only the primary task (i.e. giving a medication, putting a wrist band on a patient, drawing
and labeling blood) may weight efficiency over thoroughness. Those whose schema is more complex,
including both the primary task and the secondary task of verifying the patient’s identity, may weigh
thoroughness over efficiency.
The body of research addressing speed-accuracy trade-offs, from physical movements to complex
work, shows that individuals must balance the speed and accuracy of tasks to be successful; otherwise,
individuals complete tasks very quickly but commit a lot of errors, or commit few errors, but complete
tasks very slowly [7, 49]. For a health care worker verifying a patient’s identity, (s)he will be penalized
for being at either extreme of the spectrum. Therefore, the individual must find an appropriate balance
between efficiency and accuracy, a similar concept to thoroughness. Yet, individuals still determine this
balance on their own.
The ETTO principle, which guided this research, provides direction for understanding how a health
care organizations’ focus on provider efficiency may impact health care workers’ perception of time
shortage and health care workers’ resulting efficiency-thoroughness choices. If providers feel short on
time, they are susceptible to several rules of thinking based on the ETTO principle and the
aforementioned bodies of literature. An individual may quickly refer to an insufficient body of past
experiences and decide the situation is “normally OK”, so there is “no need to check” [7]. The worker
may also weigh efficiency over accuracy when short on time by assuming the patient’s identity “will be
checked by someone else” or “has been checked by someone else” [7]. Finally, a worker may use too
simple a schema and incorrectly assess the situation, reasoning that “It looks like Y, so probably is Y” [7].
These insights can aid job design interventions. If catching errors is of paramount importance to
patient safety, organizations must be aware that job design interventions may cause a subgroup of workers
to become less efficient, particularly the expected error subgroup (those who did not complete the
standard process and did not catch the error). For the Non-Vigilant subgroup (those who completed the
standard process but did not catch the error), job design interventions must increase workers’ perception
and information processing capabilities, as opposed to correcting process-related problems. Finally, job
design interventions aimed at the Process Variant subgroup (those who did not complete the standard
process but did catch the error) must be thoughtfully constructed. This group may inform the redesign of
the standard process, as they maintained accuracy while increasing efficiency, compared to the Expected
Success group who conducted standard processes. Yet, this subgroup may also be taking shortcuts, such
as not checking specific identifiers, and thus be considered ‘near misses’ – health care workers at risk to
fall into the Expected Failure group.
In summary, the ETTO principle is a useful mechanism to understand why some health care workers
detect patient identification errors and why others do not. The approach described in this paper can be
extended to empirically test how individuals detect and mediate other types of medical errors. Based on
the findings of this study, and coupled with existing literature, identifying within-group differences
related to individuals’ efficiency levels and ability to detect identification errors will guide more
thoughtful job design interventions.
Acknowledgements
This research was supported in part by the National Science Foundation under awards CCF-0427071,
CCF-0829901, 0552548, and 0313747, and the Summer Scholars Program for the University
Massachusetts Amherst and Baystate Health. The authors acknowledge Elizabeth A. Henneman, Amy
Brady, and Qi Ming Lin for their contributions to the work outlined here.
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Table 1
Table 1: The Standard Process for Verifying a Patient’s Identity (ID)
1. Verify patient to ID band
a. Ask patient their first and last name.
b. Confirm stated first and last name are an exact match to listed first and last name on ID band.
c. Ask patient their date of birth.
d. Confirm stated date of birth is an exact match to listed date of birth on ID band
2. Verify artifact to the patient
a. Confirm two identifiers from the artifact match the patient or the patient’s ID band
i. Stated name and DOB match artifact
ii. Stated name and listed MRN on artifact match ID band
iii. Stated name and listed DOB on artifact match ID band
iv. Stated DOB and listed MRN on artifact match ID band
v. Stated DOB and listed name on artifact match patient
vi. Listed name and DOB on artifact match ID band
vii. Listed name and MRN on artifact match ID band
viii. Listed DOB and MRN on artifact match ID band
Table 2
Table 1: Sample Process for Each Subgroup
Expected Success (TA 8)
Intended Artifact: Specimen Label
1. Looked at Name on Log Book
Label
2. Check Test on Log Book Label
3. Looked at Name on Log Book
Label
4. Check Test on Log Book Label
5. Looked at Name on Log Book
Label
6. Looked at Age on Specimen Label
7. Looked at Name on Specimen
Label
8. Looked at Age on Specimen Label
9. Entered Room
10. Asked for the patient's name
11. Q & A
12. Looked at Name on Log Book
Label
13. Ask patient to spell last name
14. Ask patient to spell first name
15. Looked at MRN on ID Band
16. Asked patient to state DOB
17. Looked at DOB on ID Band
18. Looked at MRN on ID Band
19. Looked at Name on ID Band
20. Looked at Name on Specimen
Label
21. Asked for the patient's name
22. Ask patient to spell last name
23. Ask patient to spell first name
24. Looked at Name on ID Band
25. Looked at DOB on ID Band
26. Asked patient to state DOB
27. Looked at MRN on ID Band
28. Looked at MRN on Specimen
Label
29. Noticed Error
Process Variant (RN 28)
1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
11.
12.
13.
14.
Intended Artifact:
Medication Label
Before Entering Room:
Looked at Name on chart
Entered Room
Looked at DOB on chart
Looked at Age on chart
Asked for the patient's
name
Introduced self
Looked at Age on chart
Looked at DOB on chart
Q&A
Looked at DOB on chart
Asked patient to state
DOB
Looked at MRN on ID
Band
Looked at DOB on ID
Band
Noticed Error (Caught
error by asking for DOB &
looking at DOB on chart,
instead of DOB on
medication label)
Non-Vigilant (ESA 11)
Expected Failure (RN 11)
1. Looked at MRN on
ID band
2. Looked at DOB on
ID band
3. Looked at Age on ID
band
4. Looked at Name on
ID band
5. Entered Room
6. Asked
for
the
patient's name
7. Asked patient to
state DOB
8. Looked at DOB on
ID band
9. Q & A
10. Put ID band on
patient
(completed
process for ESAs, but
did not catch the
error)
11. Filled out and signed
chart
Intended Artifact:
Medication Label
1. Looked at MRN on chart
2. Looked at Name on
Medication Label
3. Entered Room
4. Asked for the patient's
name
5. Looked at Name on ID
Band
6. Q & A
7. Looked at DOB on chart
8. Looked at MRN on chart
9. Looked at Name on chart
10. Filled out and signed
chart
11. Gave
medication
to
patient (did not verify
DOB on patient, ID band,
or medication label)
Table 3
Table 1: Subgroup T-tests for Total Number of Steps
ESAs
RNs
TAs
p-value
T
df
SE
Sig (p<0.1)
ES > EF
0.0623
2.2854
6
2.407
Yes
NV > EF
0.2097
1.4389
5
4.575
No
NV > ES
0.8431
0.2085
5
5.196
No
ES > EF
0.0649
2.0732
10
9.707
Yes
ES > PV
0.0415
2.3074
11
9.246
Yes
EF > PV
0.7813
0.2827
15
4.275
No
ES > PV
0.0344
2.4903
9
5.983
Yes
Figure 1
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Figure 2
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Figure 3
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Figure 4
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Figure 5
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Figure 6
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Figure 7
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