Keywords
clinical workflow - emergency medicine - electronic health records - complexity -
patient safety
Background and Significance
Background and Significance
Intensive work and time pressures in health care can put clinicians at risk for compromising
their own and patients' safety. This risk is exacerbated in high-intensity environments,
such as emergency departments (EDs), where additional pressures arise because of emergent
care requirements under severe time pressure. Increased stress arising from the work
environment can result in poor ED physician experience, potentially contributing to
errors and poor quality of care.[1] Using the vocabulary and concepts of complexity theory, ED—more so than other settings—can
be described as a complex system.[1] Domain complexity of health care practice is an important factor to consider for
patient safety and quality.[2]
ED clinicians have multiple simultaneous demands being made with competing priorities,
where the next step in the decision process for a patient's condition is not always
predictable. With new patients entering ED or changes in existing patients' conditions,
clinicians experience frequent interruptions that require them to continue the reprioritization
of tasks. These events typically occur under time pressures and incomplete patient
information, adding to the system complexity.[3]
Dealing with complexity appears to be a way of life in the ED, and emergency physicians
have developed various strategies for managing that complexity.[4]
[5] ED is situated within the hospital's sociocultural or economic boundaries, the local
community, or even within the greater health care system, all of which interact and
influence the behavior and work patterns in the ED.
In academic EDs, interruptions and quick task transitions are observed at twice the
rate of community settings.[6] Such demands on ED clinicians could be offset by technology, such as electronic
health records (EHRs), supporting task efficiency and accuracy. In addition to documentation
support, clinicians' EHR use is also known to aid in achieving other important goals,
such as communicating with team members and synthesizing large amounts of information.[7] However, literature suggests that the introduction of EHRs has not always considered
the nature of work in the ED, resulting in transformed and suboptimal workflow leading
to substantial frustrations with the systems.[8]
[9] In the United States, studies investigating the impact of EHRs on clinical workflows
suggest that the documentation has increased over the last decade, with 50 to 65%
time spent on EHR-related activities.[6]
[10]
[11]
[12] Unlike other settings, EDs are unable to regulate their volume and need to bolster
efficiency during peak patient loads.[6]
[13] Since many EHRs are optimized to support documentation as an uninterrupted process,
the mismatch between EHRs and ED environments can lead to clinical workflow fragmentation.
Workflow fragmentation describes the frequency of clinician task transitions, which
are associated with inefficiencies and workarounds that may lead to medical errors
that compromise safety.[10]
[14]
Our study provides insights into the organizational aspects affecting physician performance,
including the match between the task complexity and workflow, and potential for errors.
The nature of these tasks and the cognitive pressures physicians encounter within
two different organizational structures can provide insights about what pressures
push the physicians toward the boundary of compromising safe practices, generating
errors.[15]
[16]
Objectives
The goal of our study is to characterize and model physician workflow in two distinctive
urban EDs with different levels of complexity. Physician practices within these organizations,
mediated by two different EHRs, are explored within the context of organizational
complexity for the delivery of care.
Increased complexity in the environment is shown to be related to increased cognitive
load, where insufficient allocation of cognitive resources is available for completing
necessary tasks, adequately and, possibly, safety.[17]
Methods
Study Sites
This study was conducted at the two representative EDs of Icahn School of Medicine
at Mount Sinai, NY (site 1) and Mayo Clinic, AZ (site 2) ([Table 1] for site demographics). These EDs serve different patient populations and have distinct
models of care delivery.
Table 1
Emergency department demographics at two sites
Demographic
|
Site 1
|
Site 2
|
Type
|
Urban, academic setting with no trauma designation
|
Urban, nonacademic setting with no trauma designation
|
Staffing
|
Attendings, residents, PAs, nurses
|
Attendings, ED and non-ED residents, nurses
|
Consultation services
|
All surgery/medical subspecialities available
|
All surgical/medical subspecialities available except trauma surgery
|
Patient population
|
Underserved, inner city, and primarily low-income priority population
|
Existing patients seen at the hospital
|
Annual patients served
|
100,000
|
33,000
|
Hospitalizations originating from the ED
|
27%
|
50%
|
Abbreviations: ED, emergency department; PA, physician assistant.
Site 1 ED utilizes a split flow design such that patients are sent to specific areas
based on their acuity level upon entry.[18] After this distinction, physicians self-assign patients to themselves in the EHR.
Site 1 ED has two acute care areas with physician workstations centralized and surrounded
by nurses' workstations ([Supplementary Figure S1] [available in the online version]). Patient beds line the walls of these areas.
In each acute area, the medication room is located between the physician and nurse
workspace, and a supply closet is found next to the nurses' stations for convenient
access. Patients may enter the ED from the ambulance bay or the intake entrance area.
Two exits lead patients back through the intake entrance area.
Site 2 follows a linear model where most patient tasks are completed and updated before
proceeding to the next case. To increase efficiency and reduce the ED length of stay,
site 2 utilizes a rotational assignment system employing a predetermined algorithm
assigning patients to physicians or teams rather than self-assignment of patients.[19] At the start of a shift, each physician is assigned to the first four incoming patients.
From the fifth patient onward, patients are consecutively assigned among all on-call
physicians.
Site 2's ED is divided into three color-coded zones ([Supplementary Figure S2] [available in the online version]). Like site 1, physician and nurse workstations
are centralized and situated in proximity, with patient beds lining the ED walls.
Physician workstations are located with easy access to all three zones, whereas each
zone has its own nurses' station. Two medication rooms and supply closets are located
close to nurses' stations for convenient access. There is an exit near the hospital
radiology department to move patients for diagnostic tests. Like site 1, patients
may enter the ED from the ambulance bay or intake entrance area. After a bed is assigned
to a patient, a nurse is also assigned in the same zone (e.g., patient in zone “red”
is assigned nurse in zone “red”). Both sites utilize different commercial EHRs and
offer an 8-hour EHR training to clinicians.
Researcher Positionality
Our team approached the present study with multiple levels of expertise and connections
to the research settings. Four co-authors have the knowledge and experience in the
biomedical informatics and emergency medicine fields, and the two clinician co-authors
are senior attendings in the EDs under investigation. As such, we experienced an accelerated
establishment of trust between physician participants and researchers, as we worked
together as a team. To reduce potential biases related to our established connections,
only two co-authors were directly involved in recruitment and the primary data collection
at the EDs, whereas the remainder of the team engaged solely in data coding, analysis,
and interpretation of results. As such, our chosen position in relation to the study
settings and participants are both outsider and insider with substantial knowledge
of the study contexts.
Participants
All physicians received information about the study purpose, the nature of the proposed
observation, and were invited to participate in the studies during presentations at
bi-weekly faculty meetings. The New York Academy of Medicine, Icahn School of Medicine
at Mount Sinai, and Mayo Clinic institutional review boards approved this study. All
invited participants were board-certified by attending physicians, who agreed to participate
and provided written consent.
Data Collection: Observation and Shadowing
Two co-authors were trained to observe the workflow and shadow participating physicians
unobtrusively. The researchers noted the layout of the ED, including information on
general workflow patterns (e.g., patient pathways from entry to discharge). They recorded
notes on the nature of the tasks attempted during clinical activities and the role
of EHRs in completing them. The observers' notes included the specific time, location,
and type of clinical activity or interaction. A list of clinical activities with definitions
and illustrative examples are shown in [Table 2]. The researchers kept specific records on instances of multitasking when physicians
engaged in two or more distinct tasks (e.g., reviewing patient EKGs and listening
to outgoing physicians' description of other patients) and interruption when physicians'
activities were halted due to physical or verbal interruption from another clinician.
Table 2
Task classification codebook of emergency department physicians
Micro task category
|
Definition
|
Illustrative examples
|
Macro task category: EHR use
|
EHR documentation
|
Data entry into EHR, including details about patient encounters
|
P2 documents resident's description of patient 10's condition.
Incoming physician adds comment regarding ENT consult.
|
EHR review
|
Evaluation of patient history and details
|
P6 opens patient record to review vitals, but patient's temperature is missing (delay).
During call, incoming physician, referring to concerns, cause and consult with specialty
department.
|
EHR navigation
|
Movement within the EHR to locate information
|
P3 logs into EHR.
Incoming physician searches for ultrasound order in EHR but is unable to find order.
|
EHR orders
|
Submitting and accessing medication or test orders
|
P8 orders patient medications and tests in EHR.
Incoming attending physician places order for pain medication following speaking to
nurse.
|
Dictation (only at site 2)
|
Data entry via dictation where physicians call in information to later be recorded
by medical scribes in EHR
|
P10 has dictation call for patient in room 22.
|
Macro task category: paper system
|
Review
|
Review of paper charts
|
P8 looks up the paper chart for the new assigned patient.
P10 looks over paper charts of other assigned patients.
|
Documentation aid
|
Paper chart used as a memory aid for possible transfer of information into EHR/dictation
|
P8 reviews record printout with notes for patient medications.
P10 transfers patient 1 details to paper.
|
Macro task category: patient care
|
Direct care
|
Direct patient care
|
P4 speaks with patient 2.
P10 asks patient about current condition and home treatment.
|
Indirect care
|
Tasks related to patient care which may not occur in patient room
|
P2 signs EKGs provided by nurse.
P10 signs required charts for all patients assigned.
|
(delay)
|
Care is delayed due to external factor, such as unresolved laboratories or inaccurate
patient location listed in EHR
|
P2 unable to locate patient 5 or nurse to ask for help locating patient.
Secretary looking for P14 but not at station. Left message.
|
Macro task category: team communication
|
Handoff
|
Transfer of knowledge from outgoing attending to incoming attending physician
|
Outgoing attending describes patients from their shift to P6.
|
Consultation
|
Clinician calls specialist to discuss patient case
|
Resident approaches P6 for consultation.
P10 makes a phone call to consult with oncologist specialist regarding patient treatment.
|
Transfer of knowledge
|
Any patient-related discussion with a direct team member
|
Nurse asks P8 if they have extra pair of shoes for patient and for a social work referral.
Resident assigned to the ED enters the room and discusses the patient with incoming
attending physician.
|
Social
|
Any personal activity, such as nonpatient-related conversation
|
Three attending physicians talk about P8's birthday.
P10 chats with other doctors as their shift does not start until 10 am.
|
Administrative
|
Work-related communication, emails, or paperwork
|
P3 attends faculty meeting.
P14 asks questions about scheduling.
|
Macro task category: potential sources of error
|
Environmental distraction
|
Any noise or external disturbance from task
|
A loud stool falls over and all staff stop to identify the origin of the noise (P4).
P14 looks up emails, the environment is busy and there are a lot of disturbances in
the surroundings.
|
Interruption
|
Current activity is paused due to verbal or physical interruption
|
Nurse interrupts P1's her documentation to ask about deceased patient.
P14 has argument with physician on phone as specialty physicians cut him in between
and did not allow to finish his questions.
|
Correction of interruption
|
Clinician resumes activity which they were doing prior to interruption
|
After handover interruption, P6 and outgoing attending physician resume handoff.
|
Multitasking
|
Clinician undertaking multiple tasks simultaneously
|
P8 reviews stack of EKG's and EHR trackboard as outgoing attending physician describes
patient.
P10 has dictation call for patient in room 11.
|
Macro task category: other
|
In transit
|
Work-related movement between locations
|
P7 and resident walk to workstation after visiting patient.
P10 goes to see patient in room 11.
|
Abbreviations: ENT, ear, nose, and throat; EHR, electronic health record; P, physician.
A standardized MS Excel template was used to capture observation notes. For each session,
the shadowed physician was assigned a pseudonym (e.g., physician 1), and information
that could be traced back to that physician was de-identified, including interactions
with other providers. De-identified field notes were uploaded to an encrypted USB
drive to ensure security.
Data Analysis
A comprehensive coding scheme was developed for qualitative analysis. Descriptive
analyses were used to classify the quantitative elements of the observed workflow.
These quantitative analyses allowed us to visualize workflow using timeline belts
as reported in Zheng et al and Abraham et al.[14]
[20]
[21] Timeline belt is an analytical method for quantifying time and task patterns in
clinical workflow.
Coding Scheme Development
Two co-authors independently applied the thematic analysis approach for analyzing
qualitative data to two shadowing sessions.[22]
[23] Following Larcos et al, shadowing sessions were evaluated line by line for our initial
macro-codes of EHR use, team interactions, and patient care.[23] Preliminary coded data were reviewed by two co-authors (C.D.H. and H.C.S.) to develop
a provisional coding scheme, deductively with both macro- and micro-codes. There was
a 95% overlap across the two coders, showing a high degree of agreement. Disagreements
were resolved through discussion to achieve consensus. The final codebook included
22 codes ([Table 2]). The remaining 12 sessions were subsequently coded, inductively with this codebook.
Illustrative examples of coded excerpts of physician workflow at each study site are
given in [Supplementary Figures S3] and [S4] (available in the online version).
Calculations of Time Allocation
Using MS Excel software, descriptive analyses were performed to classify the session
time, the number of tasks, time spent on tasks, and time spent at various locations.
A general workflow model for each site was constructed, capturing the task order and
location using the Lucid Chart software.[24]
[25] The average rates of interruptions and multitasking per hour were calculated.
Temporal Order of Clinical Activities
During observations, identification of task interruptions from external agents, included
receiving a phone call, reacting to alarms and alerts, or interacting with a clinician
from another team. To visualize how multitasking and interruptions impact the observed
workflow, we created timeline belts representations using Lucid Chart software.[14]
[25]
Each physician observation is symbolized by a row (belt), depicting a specific clinical
task, and interruption or multitasking instances. Clinical and administrative tasks
were classified into three categories, and physical movements of the physicians were
depicted as “in transit,” any social interactions as “social,” and any administrative
tasks as “administrative.”
Results
Fourteen physicians (n = 14; site 1 = 9; site 2 = 5) were shadowed. The mean EHR experience of participants
at site 1 was 6.7 ± 1.4 years, and at site 2 was 7.3 ± 3.9 years. Between December
2014 and September 2016, a total of 62 hours of shadowing, lasting 60 to 324 (mean = 170)
minutes, were conducted on weekdays between 07:00 A.M. and 8:30 P.M. See [Fig. 1] for a breakdown of the sessions. At both sites, we spent approximately the same
time shadowing physicians during the afternoons and early evenings. At site 1, we
spent more time observing physicians during the morning shift, and at site 2 more
time was spent during the afternoon shift.
Fig. 1 Distribution of shadowing sessions at each study site.
Time Spent by Physicians on Clinical Activities, by Task
Data from the shadowing sessions were characterized by three main activities: updating
patient records, team communication, and direct and indirect patient care. [Fig. 2] shows the distribution of time spent per task.
Fig. 2 Percentage time spent on various clinical tasks in the two emergency departments.
Time Spent on Clinical Activities, by Location
Most clinical activities occurred at the workstation, nurse's station, and patient
bedside. Site 1 physicians spent 61% time at the workstation, followed by 26% at the
patient bedside. The remaining time was spent in hallway discussions (6%), administrative
office (2%), diagnostic laboratory (0.3%), or other locations for meetings (16%).
Site 2 physicians spent approximately 52% of time at the workstation, followed by
27% spent at the patient bedside. The remaining time was spent at the nurse's station
(4%), hallway (3%), or the diagnostic laboratory (0.3%). To better understand the
relationship between locations and tasks, we constructed a general workflow model
for each site.
We captured the physician workflows at the two sites. Similar steps in the workflows
are highlighted by using the same colors. The site 1 workflow ([Fig. 3]) comprised of these steps:
-
The attending physician self-assigns a patient.
-
Residents present the patient's case to the physician while the attending physician
reviews the patient record on the EHR and documents details of the presentation. This
step was unique to site 1 due to the lack of formal teaching activities at site 2
(highlighted in purple).
-
Next, the attending physician assesses the patient. If accompanied by a resident,
there may be some communication at the bedside.
-
From here, the physician may visit other patients before returning to the workstation
or directly to the workstation.
-
When the physician returns to the workstation, they document patient encounters or
submit orders for tests.
-
As information becomes available, the physician reviews patient records, communicates
with team members, and documents these communications if needed (6a). Steps 2 to 6a
may have happened many times before disposition occurred. The attending may have also
been assigned new patients in between.
-
Once disposition is set, the physician completes documentation for discharge, including
prescriptions.
-
Lastly, the attending hands over to the resident or assigned nurse for patient transfer/discharge.
Fig. 3 Schematic site 1 physician workflow by location: arrows and numbers denote temporal
order of activities and the dashed line box denotes iterative process. Solid line
rectangles specify the nature of the physician activities.
The site 2 workflow ([Fig. 4]) comprised of these steps:
-
Physician is assigned to a patient.
-
The attending physician reviews the patient record.
-
The physician places initial diagnostic and medication orders based on the review.
This step differs from site 1's practices as site 2 physicians preferred to place
initial orders before visiting the patient (step 4) compared with step 3 in site 1
workflow.
-
The physician visits the patient for assessment.
-
Optionally, the physician may check-in on other assigned patients before returning
to the workstation, although not a norm.
-
When the physician returns to the workstation, they may document or submit tests or
medications orders, as necessary. Attendings may modify orders placed in step 3. Also,
the attending physicians communicated (step 6a) with the care team and other consulting
physicians, as necessary.
-
Once results from the orders placed in step 6 are available, the physician reviews
the results and continues care.
-
The attending documents the patient encounter notes using the EHR system or phone
dictation service. As highlighted in green, this step is unique to site 2, as dictation
services are not available at site 1, and documentation is distributed between residents
and attending physicians. Steps 2 to 8 happened many times before disposition occurred.
Physicians may also have been assigned new patients in-between.
-
Once disposition is set, the attendings complete discharge documentation, including
prescriptions.
-
Finally, attending hands over to the nurse the patient transfers/discharge information.
Fig. 4 Schematic site 2 physician workflow by location. Attending physician is referred
to as a physician.
Interruptions and Multitasking
Physicians at site 1 had 2.6 interruptions/hour. These primarily occurred during team
communication when the physician was “interrupted” by another clinician working on
a different patient case or during the documentation process. At site 2, one interruption/hour
was observed, most of which occurred during communication with other clinicians, mostly
nurses. All interruptions were short (1–3 minutes).
Temporal Order of Clinical Activities at Two Sites
The frequency of task transitions is presented in [Fig. 5] as a visualized form of the temporal order of clinical events. At site 1, frequent
task transitions were observed throughout shadowing sessions. Team communication usually
occurred before and after other clinical activities, and frequently in combination
with EHR documentation. Multitasking primarily occurred at the beginning or about
two-thirds of the way through shadowing sessions. Instances of interruption were distributed
throughout the session.
Fig. 5 Timeline belt of physician time distribution as a clinical activity at two sites
for 170 minutes (average session length). The left-hand sides of the timeline belts
were aligned with the starting point of the observation sessions. Each colored section's
length is proportional to the amount of time spent on that task or activity.
At site 2, fewer transitions between tasks were observed. EHR use was frequently followed
by patient care and brief team communication. Instances of multitasking were observed
as confined to the first half of the shadowing sessions.
The distribution of time spent per patient and the temporal order of care is presented
in [Fig. 6]. Site 1 physicians were observed to switch back and forth between patients more
frequently than site 2 physicians. Physicians at site 2 most often completed all tasks
related to a patient (or as many tasks as possible) before moving on to the next patient
case. Generally, it was a model of one patient being seen at a time.
Fig. 6 Timeline belt of physician time distribution and patient care at two sites for 170 minutes
(average session length). The left-hand sides of the timeline belts were aligned with
the starting point of the observation sessions. Each colored section's length is proportional
to the amount of time spent on that task or activity.
Discussion
EDs face many challenges including overcrowding, long length of patient stays, multitasking,
distractions, and dealing with unexpected events. Given the complexity of such environments,
it is difficult to work optimally. How can we streamline the current workflow to make
it time efficient and at the same time provide effective delivery of care without
compromising safety? In our study, we describe and characterize physician workflow
in two distinctive urban EDs with different levels of workflow complexity.
Task Switching and Fragmentation
Clinical workflow at site 1 was characterized by team communication, where patient
cases were discussed with trainees, and these interactions were documented in the
EHRs. There was a parallel processing of multiple patients' data with multitasking
and interruptions interspersed between these clinical activities. Data visualization
illustrated fragmentation of clinical workflow at this site.
Studies have suggested that interruptions can compromise memory and attention by requiring
individuals to switch focus from one task to another.[26]
[27] Returning to a disrupted task requires completion of the interrupting task and then
regaining the context of the original task. Multiple variables, including the characteristics
of the primary task, the nature and length of interruptions themselves, and the environment
itself, may influence the impact of interruptions on clinical tasks and errors.[27]
[28] Interruptions could also disrupt complex cognitive tasks potentially, requiring
almost three times longer to resume effectively than simple tasks.[28]
[29] Studies also show that as tasks get more complex, people lose more time, showing
time costs to be greater when the participants switched to tasks that were relatively
unfamiliar.[30]
Thus, in busy interrupt-driven clinical environments, clinicians reduce the time they
spend on clinical tasks if they experience interruptions and may delay or fail to
return to a significant portion of interrupted tasks. Task shortening may occur because
interrupted tasks are truncated to “catch up” for lost time, which may have significant
implications for patient safety.[31]
[32]
Further studies that address a better understanding, the hidden costs of multitasking
may assist ED physicians in choosing strategies that boost their efficiency. For the
clinical informatics specialists, the challenge that lies ahead is to develop strategies
to mitigate the negative consequences of interruptions, while enhancing the positive
effects of delivering real-time clinical information.[33]
Team Communication and Error Checks
There is a close relationship between competency in the delivery of patient care and
the need to minimize errors. This is juxtaposed with the competing demand for learning
from errors, an essential part of the apprentice training process, such as at site
1. The challenge is to manage the balance between these two modes, professional practice,
and learning, for delivery of efficient and safe care in complex critical care settings.
At site 2, a unique mechanism of assigning patients to physicians is employed to ensure
that the work is completed efficiently. Physicians in this ED saw one or two patients
at a time and completed as much of the documentation about one patient with the help
of scribers, before moving to another in a linear manner, creating much less cognitive
load. This serial processing limits the need for multitasking and the prevalence of
interruptions. However, there is less of a chance of an error being caught, given
that there was no team error check observed. Good team communication allows for resiliency
and error checks, ensuring that safety is not compromised.[34]
A study of teamwork in an intensive care unit shows that teams perform better than
individuals, due to advantages conferred by the distribution of cognitive task across
multiple team members.[34] Data show that attending and trainee clinicians both generated errors and recover
from most of them.[34] Error detection and correction in a situation closer to complex real-world clinical
practice appear to induce certain urgency for quick action resulting in rapid detection
and correction.[16] Furthermore, clinicians working at the bedside, as in site 2, optimize performance
with little room for explicating any mistakes and thus little learning from errors.
Good teamwork is shown to be better than individuals working alone in detecting and
correcting errors.[35] Error generation and recovery lead to new learning.[16] In such events, the workflow will be slow and thus inefficient, but safer, as at
site 1. However, opportunities exist to enhance training so that team-based care is
better understood as a cognitive collaboration, one that requires joint discussion
and communication to ensure errors are recognized early.
Limitations
We investigated two clinical environments with two different EHR systems, which allowed
us to describe the specific features related to each, but with limited generalizability.
However, it did provide us with hypothesis-generating opportunities. We primarily
observed physicians' afternoon activities, which may not be representative of mornings
or the evenings. We focused primarily on the workflow patterns of physicians, although
their work is also affected by other clinicians (e.g., nurses), which may provide
somewhat skewed results. Although at each site we observed physicians for approximately
the same length of time, more physicians were observed at site 1 than site 2, which
may impact the variation in workflow patterns. Given that there was reasonable consistency
in the workflow pattern of all physicians observed at site 2, the impact was less
than anticipated. Finally, physicians' behavior may have changed due to being observed.
This type of bias is expected in human-involved observational studies.
Conclusion
The nature of the clinical practice and EHR-mediated workflow reflects the ED work
practices. Physicians in more complex organizations may be less efficient because
of the fragmented workflow due to multitasking and interruptions. However, these effects
can be mitigated by effort distribution through team communication, which affords
inherent safety checks. A better understanding of the hidden costs of multitasking
may assist ED physicians in choosing strategies that boost their efficiency.
Clinical Relevance Statement
Clinical Relevance Statement
Physicians' clinical practices in more complex EDs, with team interactions and multitasking,
are slow but have more effective safety checks. Practicing in real-world situations
with a high level of uncertainty and ambiguity, ED physicians learn to acknowledge
complexity, thereby allowing themselves to think of alternative solutions to problems
that match the complexity of any given patient condition. In this way, the physicians
would embrace challenges as opportunities for adaptation as situations evolve.
Multiple Choice Questions
Multiple Choice Questions
-
There are many specific characteristics of clinical workflow from structure of clinical
tasks, coordination for these tasks, and flow of information to support these tasks.
These are often interconnected and interdependent aspects, such that any intervention
in one aspect will impact the others. Which of the following is the least representative
characteristic of clinical workflow?
-
Time required to complete a task
-
The number of times a same task is repeated per day
-
Location of a task
-
Sequential order in which tasks are executed
Correct Answer: Clinical workflow is a complex phenomenon that has multiple facets. Research studies
focusing on one or few of these facets are bound to produce inconclusive even conflicting
findings. The number of times a same task is repeated per day, however, is driven
primarily by patient care needs, which is not a true characteristic of clinical workflow.
-
Qualitative studies (such as using ethnography) of clinical workflow and health IT
have reported that although clinicians spend more time working at computer workstations
than on patient care, these times are used in processing additional patient data and
for team interactions. On the other hand, many quantitative studies assessing health
IT's impact on time efficiency have generally shown no significant change in how much
time they spend on patient care or working on computers. What may account for this
discrepancy?
-
The institutional where these studies were conducted are different
-
Quantitative methods alone may not have captured the real impact of technology
-
Quantitative studies are more rigorous than qualitative studies
-
Participants in qualitative studies are behave differently than when reporting facts
Correct Answer: Quantitative studies assessing clinical workflow and the role of health IT's impact
on time have generally focused on the proportion time that clinicians allocate to
computer use versus direct patient care. This does not truly capture the impact of
health IT because (1) these studies do not consider the variations in activities spent
on computers (e.g., interacting with trainees and consultants) and (2) proportion
time is not an accurate measure for assessing clinical workflow, because it does not
capture disruptions to clinical work often associated with health IT use.