Process mining techniques analyze processes based on event data. We analyzed the trajectories of patients in a Dutch hospital from their registration in the emergency room until their discharge. We considered a sample of 1050 patients with symptoms of a sepsis condition, which is a life-threatening condition. We extracted an event log that includes events on activities in the emergency room, admission to hospital wards, and discharge. The event log was enriched with data from laboratory tests and triage checklists. We try to automatically discover a process model of the patient trajectories, we check conformance to medical guidelines for sepsis patients, and visualize the flow of patients on a de-jure process model. The lessons-learned from this analysis are: (1) process mining can be used to clarify the patient flow in a hospital; (2) process mining can be used to check the daily clinical practice against medical guidelines; (3) process discovery methods may return unsuitable models that are difficult to understand for stakeholders; and (4) process mining is an iterative process, e.g., data quality issues are often discovered and need to be addressed.
Process models are used by organizations to document, specify, and analyze their processes. A process model describes the expected behavior of a process. Any set of related activities that are executed in a repeatable manner and with a defined goal can be seen as process. For example, in a health care context the trajectory of a patient from arrival in the emergency ward to the admission to a hospital ward and up to the discharge can be seen as a process. Often the execution of such a processes is supported by information systems. For example, the hospital may record medical information such as symptoms, the condition upon arrival of the patient, and the results of blood tests. Moreover also logistical information are recorded such as the movement of patients between wards and different types of discharge.
Process mining uses data recorded by such systems to analyze the actual
execution of processes. Common goals of process mining are: process discovery,
the discovery of accurate process models that help in understanding the process
execution and conformance checking, the diagnosis of problems in the process
execution by comparing the real execution (as-is process ) to a process model
that defines the desired process execution (to-be process ). Process mining has
been previously used in the health care context [
We used state-of-the-art process mining methods to analyze the trajectories
of 1050 patient that are admitted to the emergency ward of a Dutch hospital1
because they display symptoms of a sepsis. Sepsis is a life-threatening condition
that requires immediate treatment [
We conducted the process mining project in a regional hospital in The Netherlands.
The hospital has about 700 beds at several locations and is visited by about
50,000 patients per year. The scope of our project was on the patient trajectories
of patients that are admitted to the emergency ward. We focused on a specific
sub group of patients toTotaalEvRPoatiiendts a common problem in process mining for health
care processes: the inherent complexity and flexibility of health care processes [
Which SIRS criteria? IV therapy: Yes/No
Antibiotics: Yes/No
Type of visit?
Which diagnostics
are ordered? Filter: Time, Delay, ..
Discharge Home
Is there organ dysfunction?
Continuation Admission Normal Ward
Admission ICU
Admission from ER to Normal Ward + Admission to ICU up to 72 hours after ER visit 1 For privacy reasons we do not disclose the name of the hospital. 1. are particular medical guidelines for the treatment of sepsis patients followed: – patients should be administered antibiotics within one hour, – lactic acid measurements should be done within three hours; 2. visualize and investigate the following specific trajectories: – discharge without admission, – admission to the normal care, – admission to the intensive care, – admission to the normal care and transfer to intensive care; 3. investigate the trajectory of patients that return within 28 days. Moreover, the document in Figure 1 facilitated the collection of event data. Based on the document, we could identify several sources for events: – the triage document filled in for sepsis patients with information on the time the triage was conducted, the symptoms present (SIRS criteria for sepsis), the diagnostics ordered, the time infusions of liquid and antibiotics were administered; – documents created by the laboratory for several blood tests, – information about the further trajectory of the patient recorded by financial systems.
All documents and records are stored in different databases of the ERP system that is used by the hospital.
emergency ward
triage documents record record record laboratory lab documents consolidate (SQL, Text mining)
extract data warehouse event log other wards
financial records Fig. 2. Consolidation of data from several source systems to a single event log Based on the identified sources, we collected data about several activities that are executed for this group of patients from three different systems of the hospital. The activities can be coarsely categorized into medical activities and logistical activities.
Figure 2 shows how we collected the event data and consolidated it into a single
anonymized event log covering the traces that were recorded for 1050 patients
over the course of 1.5 years in the hospital information systems. The resulting
event log is made available for further process mining research purposes [
We applied several process mining methods on the event log. We started by
applying the Inductive Miner (IM) [
Therefore, the process model returned by IM is difficult to be used for the communication with doctors and nurses. At a first glance it appears that administering antibiotics (IV Antibiotics) and filling in the triage document (ER Sepsis Triage) is in an exclusive choice. Yet, it is possible to repeat the entire middle part of the process, thus, both activities can occur in the same process instance. Using different parameter settings for the IM did not help. Even though we could obtained a more precise process model, unfortunately, that process model does not allow the laboratory tests to be repeated. Moreover, it does not allow to visualize the patient trajectory: first admission to normal care and, then, a transfer to intensive care. This trajectory is important to address Question 2. Since we use process discovery to address concrete questions, we did not rely on measures such as fitness and precision to estimate the quality of the model. Instead, we determined the model quality based on whether it is suited to answer our process questions and whether it is helpful in communicating with doctors and nurses.
As an alternative to automatic process discovery, we iteratively designed a suitable process model based on domain knowledge obtained from doctors and nurses working in the emergency ward. One example for such domain knowledge are the descriptions shown in Figure 1. In each iteration, we used alignment-based conformance checking techniques to validate whether the hand-drawn process model reflects the observations in the event log. Since the iteratively designed model follows the expectations of doctors and nurses working in the emergency ward, it is can be used (1) to communicate with the stakeholders, and (2) to answer the process questions.
Figure 4 shows the iteratively designed model. It fits 98.3% of the event log,
i.e., almost all of the events can be correctly replayed on the model. We used the
ProM plug-in Multi-perspective Process Explorer (MPE) [
Question 1: medical guidelines There are two time rules specified by the sepsis guideline: 1. between ER Sepsis Triage and IV Antibiotics should be less than 1 hour, 2. between ER Sepsis Triage and LacticAcid should be less than 3 hours.
We used Data Petri nets (DPN) to encode both time-perspective rules using
three variables timeTriage, timeAntibiotics, and timeLacticAcid. We added guard
expressions to both activities IV Antibiotics (timeAntibiotics’ timeTriage +
60) and LacticAcid (timeLacticAcid’ timeTriage + 180). Then, we used the
multi-perspective conformance checking technique that is described in work [
Question 2: patient trajectories Using the MPE, we could visualize the four trajectories of interest. In Figure 4 they are represented as an exclusive choice in the main branch of the process on the right side. The choice regarding the admission of patients is made after filling in the general triage document (ER Triage), the triage document for patients with suspicion for a sepsis (ER Sepsis Triage), and, possibly, giving infusions of antibiotics and liquid (IV Liquid and IV Antibiotics ). It can be seen that 18.1% of the patients leave the emergency ward without admission to the hospital. Most patients (70.6%) are admitted to the normal care ward (Admission NC ), less patients (6.8%) are admitted to the intensive care ward (Admission IC ), and a small group of patients (3.6%) is first admitted to the normal care ward and, directly afterwards, admitted to the intensive care ward. The latter group of patients is of interest since their condition got worse. Therefore, it would be beneficial to avoid patients following this trajectory.
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Question 3: returning patients We tried decision mining techniques such as
described in [
We now report on the lessons learned from analyzing an event log recorded for sepsis patients in a regional hospital with process mining technology. The project was successful in visualizing the trajectories of patients with a suspicion for sepsis. In fact, a doctor found the process mining analysis to be "[a] magnificent way to clarify patient flow".
Unfortunately, little actionable results were obtained. We attribute this to
the lack of a set of initial hypotheses that could lead to actionable result. We
started the project with some process questions such as the compliance to the
medical guidelines and patients returning within 28 days. Still, it turned out to
be difficult to follow-up on the findings due to lack of data quality regarding the
time when antibiotics were given and the general lack of data that could explain
the returning patients. In conclusion, we found that:
1. Process mining can be used to clarify and visualize the flow of patients
in a hospital. This confirms previously reported results in [
Acknowledgments We would like to thank the hospital and the doctor for the support with the process mining project and the provided insights.