=Paper=
{{Paper
|id=Vol-3216/paper_197
|storemode=property
|title=WAM! - WorkAround Mining in Healthcare
|pdfUrl=https://ceur-ws.org/Vol-3216/paper_197.pdf
|volume=Vol-3216
|authors=Wouter van der Waal
|dblpUrl=https://dblp.org/rec/conf/bpm/Waal22
}}
==WAM! - WorkAround Mining in Healthcare==
https://ceur-ws.org/Vol-3216/paper_197.pdf
WAM! - WorkAround Mining in Healthcare
Wouter van der Waal
Utrecht University, Heidelberglaan 8, 3584 CS Utrecht, the Netherlands
Keywords
Workarounds, Automated Detection, Event data, Healthcare, Process Mining, Business Process Analysis
1. Problem Statement
Many organizations use standard operating procedures to streamline their work. When proce-
dures are clear, people know what to do. Still, it often happens that work is performed in a way
that is different from the prescribed procedure. When confronted with unexpected situations,
limited time, or a lack of resources, workers may be unable to follow a procedure and may feel
compelled to perform a workaround to solve a problem [1].
Some workarounds are beneficial and can be leveraged to improve organizational proce-
dures [2, 3]. In other cases, not following a procedure may be harmful or outright dangerous.
Workarounds can result in noncompliance, privacy issues, or negative effects in the process
downstream [4, 5]. Whatever the effect, it is important that process owners are provided with
insights into the occurrence of workarounds. These insights can help to prevent workarounds
from happening again or to improve the concerned procedure [3]. In addition, being able to
structurally and comprehensively identify workarounds would allow for the monitoring of their
emergence, diffusion, and evolution, potentially enabling process analysts to detect and respond
to new workarounds faster than with current techniques.
To date, most studies in which workarounds were identified and analyzed relied on qualitative
methods, primarily through interviewing and observing users during their work [3]. This
approach has led to valuable insights related to the mechanics and effects of workarounds,
as well as the motivations of the people using them [6, 4, 7]. However, the use of qualitative
methods is labor-intensive; furthermore, users may not disclose their normal behavior when
they are aware of being observed [8]. To address these drawbacks, my focus is on the use
of event logs, process mining, and other quantitative analysis techniques. The state of the
art shows that workarounds can be detected and monitored with this data [9]. However, it is
unclear to what extent this information can be used to discover new workarounds when starting
from the data. Note that if both the workaround and the normative process occur outside of the
system, there will not be any information available to detect this. While these workarounds
may be discovered using interviews, they cannot be found using event logs.
BPM’22: International Conference on Business Process Management, September 11–16, 2022, Münster, Germany
†
Dissertation Supervisors: Hajo A. Reijers & Inge van de Weerd
$ w.g.vanderwaal@uu.nl (W. v. d. Waal)
0000-0001-6514-7570 (W. v. d. Waal)
© 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR
Workshop
Proceedings
http://ceur-ws.org
ISSN 1613-0073
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51
Earlier work shows that event logs from a Health Information System (HIS) can be used to
automatically detect and monitor known workarounds [9]. I stay in line with this focus on the
healthcare domain. Studies have shown that in hospital settings specifically workarounds are a
widespread phenomenon [10]: Nurses share each other’s passwords to save time, physicians
send each other X-rays via WhatsApp to get quick second opinions, and secretaries use shadow
systems on paper to track the department’s occupation. The principles behind my work are
nonetheless transparent and I expect that many of the patterns can be transferred to other
domains.
This leads to the following research question:
How can we use event data stored in HISs to detect workarounds?
2. Related Work
As illustrated in Figure 1, workarounds occur when users intend to reach a goal but perceive
a block to do so using the official procedure [1]. Similar to the conformance-checking field
of process mining, we can look for differences between process variants to detect them [11].
However, we see two important differences. First of all, a workaround requires an intent to
reach a business goal. So, fraud, deception, and errors are not in scope. Secondly, the user is
unable to achieve this goal by using the intended procedure [1]. So, accidentally following a
different route is not a workaround.
In addition, while non-conformance usually supposes strict rules [12, 13] or a known process
model to conform to [14], workarounds can occur without these. Deviance mining also uses
process mining but looks at smaller deviations between processes [15], which may also be
useful to mine for workarounds. Quite different from a deviance, a workaround can be very
common. If a workaround is sufficiently effective, it may be shared throughout the organization,
potentially becoming more common than the official procedure [16].
One approach specifically used to automatically detect workarounds is to use deep learning [8].
Neural networks are trained to recognize different workaround types from event logs. These
methods can be difficult to use in practice because they require a large amount of labeled training
Workaround
Intent Designed Path
Block Goal
Figure 1: An illustration of the definition of a workaround. A worker has an intent to reach a goal, but
is prevented to do so by a block on the designed path. The worker can then deviate from the path to
work around that block to reach the goal.
52
and testing data. In addition, even if neural networks reach a high classification accuracy, it is
difficult to explain why this is happening [17], which is often required if you want to use the
results in a healthcare environment.
Alternatively, Outmazgin and Soffer define six workaround patterns, four of which can be
used to detect workarounds in event logs [18]. These patterns require very strict definitions of
what is considered a workaround, such as exact thresholds for activity duration or a full process
model. While these patterns can be used to detect known workarounds, we cannot discover
new workarounds with them.
Outside of the control-flow, the time, resource, and data perspectives are valuable for confor-
mance checking [11]. By investigating workarounds discovered using qualitative methods, such
as interviews and observations, previous studies show that these perspectives can also be used to
recognize different types of workarounds [9]. We will continue this multi-perspective approach
to investigate if, in addition to recognizing workarounds, we can discover new workarounds
using event logs.
3. Intended Approach
As described in the previous section, detecting workarounds has similarities with several
approaches in process mining, such as conformance checking. Both focus on differences
between the intended and actual process. I will investigate which differences that are used in
process mining fields other than workaround mining can also be used to discover workarounds.
Many workarounds were discovered through interviews in previous studies in five different
healthcare organizations [9]; a general hospital, two district hospitals, and two specialized
centers. They are documented as ‘workaround snapshots’ and include a description of (1) the
setting in which the workaround was found, (2) the workaround compared to the normative
process, (3) a motivation, and (4) the expected effect of the workaround on cost, time, quality,
and flexibility. Using the comparison between the workaround and the normative process, I can
determine if and how each workaround could be monitored using (event log) data, resulting in
more patterns in addition to the ones extracted from literature.
While the detection of potential workarounds can be performed in a highly automated fashion,
the actual confirmation of the occurrence of workarounds still needs to be done by domain
experts. In other words, while the defined patterns can show potentially interesting differences
between process variants, only domain experts can determine if a difference is a workaround.
To test the patterns and techniques for workaround mining, I intend to use case studies.
Depending on the research question, I can extract data from different wards in a hospital from
the HIS over select periods. This data can then be transformed into event logs in which I look for
the patterns. Finally, I further evaluate results that indicate a workaround in a semi-structured
interview with an expert of the corresponding ward or process. The expert can determine if the
observed behavior is normative, a workaround, or something else.
53
4. Preliminary Findings
By investigating the literature and snapshots, I created a Semi-automated WORkaround De-
tection (SWORD) framework containing 22 patterns [19]. Table 1 shows an overview of the
patterns. I created this framework in the two steps as described in the previous section: 1) a
literature study to determine how process mining topics other than workarounds mining define
differences between process instances and 2) finding differences between the workarounds and
normative processes discovered in earlier studies. For more details on this method, I refer to the
full paper.
Note that the framework is extensible, meaning that new patterns can be added. In addition,
while the focus of the framework is on workarounds, many of the patterns can be applied to
other process mining fields that focus on differences between process variants.
In the same study, I tested three of the patterns on real hospital data. While the results were
not verified by experts, they were nonetheless promising; I discovered the occurrence of logging
measurements in batches (a previously known workaround), unexpected differences in the
delay between taking measurements, and their logging for different types of measurements.
In my current (unpublished) work, I have applied the patterns to different wards in a hospital
and held interviews with process experts about the observed outliers. The experts were able to
verify multiple outliers as being workarounds, including previously unknown workarounds.
For example, in the emergency room (ER), a physician needs to place a mark in the file if (s)he
sees a patient for the first time. This activity can be seen in event logs as ’Seen by physician’.
The ’Delay between the start of trace and activity is out of bounds’ pattern shows that there are
a few cases where this happens a few days after admission to the ER. The expert could explain
this behavior as a workaround. If a physician does not perform the required task, administrative
personnel update the logs to make sure they reflect that the patient was seen by a physician.
5. Open points
5.1. Better Detection
There are two major points to improve workarounds detection using the patterns.
Firstly, how do we apply the patterns? Currently, I intentionally keep this simple by looking
for the largest deviation(s) from the mean. For example, in the confirmed workaround in the
previous section, I looked for the events that occurred the most standard deviations away from
their meantime. This approach is obviously quite simple and could be improved with more
advanced statistics.
Secondly, we have seen in previous work that many workarounds can be detected with
multiple patterns. The example workaround was detected by looking for an activity that occurs
at an unusual moment, but we can also see that the same trace takes longer than usual. If we
combine the information of multiple patterns, this could also improve the workaround detection.
While both points may enhance the quality of workaround detection, it is difficult to exactly
quantify this improvement. The usual approach for this is to compare the results to established
labels in an extensive dataset using measures such as precision and recall. However, this is not
possible for workaround detection, since there is no ground truth available. While experts can
54
manually determine if a single trace is a workaround or normative, it is unfeasible to do so for
large event logs containing thousands of traces.
Regardless, it is important to determine if and to what extent potential improvements enhance
the detection rate. It may be sufficient to measure this in a smaller event log. In addition, instead
of having an experienced high-level expert evaluate traces, the (initial) evaluation may be
performed by a less-involved expert, such as a medical student. Both options would decrease
the quality of the comparison, but they make the evaluation more feasible to perform.
5.2. Future Work
Being able to detect more workarounds using event logs allows us to further investigate them.
From a sociological point of view, there are many questions about where, when, and how
workarounds emerge and evolve. If we can monitor them using event logs, we can give more
insight into this. We may potentially even pinpoint when and where a workaround begins and
see how it spreads through the organization.
We can also take a more practical approach and investigate the effects of workarounds. If we
can reliably distinguish between the normative process and a workaround (or different types of
workarounds), we can link this to other available data, such as costs, quality of care, or patient
satisfaction using statistical measures such as regression. This would allow organizations to
better decide what to do with specific workarounds.
A. Appendix
Table 1: Workaround detection patterns
Detection pattern Explanation
Occurrence of an activity A specific activity occurs
Occurrence of recurrent activity A recurrent activity sequence occurs within a trace
sequence
Frequent occurrence of activity An activity frequently occurs within a trace
Occurrence of activities in an order The order of activities in a trace is other than in a predefined
Control-flow
different from process model process model
Occurrence of mutually exclusive Specific activities occur that are mutually exclusive within a
activities trace
Occurrence of unusual neighboring An activity is directly followed by an activity other than
activities usual
Occurrence of directly repeating activity An activity is immediately repeated within a trace
Missing occurrence of activity A specific activity is missing in the trace
Continued on next page
55
Table 1: Workaround detection patterns (Continued)
Detection pattern Explanation
Data object with value outside boundary The value of a data object deviates from the usual values
Change in value between events Data values change unexpectedly between events
Data
Specific information in free-text fields Information is logged in free-text fields instead of dedicated
fields
Activity executed by unauthorized An activity is executed by a resource other than those
resource authorized
Activities executed by multiple Activities within the same trace are executed by multiple
resources resources
Resource
Activities executed by a single resource Activities within the same trace are all executed by the same
resource
Frequent occurrence of activity for a An activity occurs more frequently for one resource
resource compared to other resources
Frequent occurrence of value for a A data value occurs more frequently for one resource
resource compared to other resources
Occurrence of activity outside of time An activity occurs outside of the usual time period
period
Delay between start of trace and activity There is a deviation in the delay between the start of the
is out of bounds trace and the time of an activity
Time
Time between activities out of bounds There is a deviation in the time between activities
Duration of activity out of bounds There is deviation in the duration of an activity
Duration of trace out of bounds There is a deviation in the duration of a trace
Delay between event and logging is out There is a deviation in the delay between time of event and
of bounds time of logging
Acknowledgments
This publication is part of the WorkAround Mining (WAM!) project (with project
number 18490) which is (partly) financed by the Dutch Research Council (NWO).
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