=Paper=
{{Paper
|id=None
|storemode=property
|title=Design and Prototypical Development of a Web Based Decision Support System for Early Detection of Sepsis in Hematology
|pdfUrl=https://ceur-ws.org/Vol-727/eics4med13.pdf
|volume=Vol-727
|dblpUrl=https://dblp.org/rec/conf/eics/WichtMK11
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==Design and Prototypical Development of a Web Based Decision Support System for Early Detection of Sepsis in Hematology==
https://ceur-ws.org/Vol-727/eics4med13.pdf
Design and Prototypical Development of a
Web Based Decision Support System for
Early Detection of Sepsis in Hematology
Andreas Wicht Gerrit Meixner Ulrike Klein
Heidelberg University Hospital, German Research Center for Heidelberg University Hospital,
Institute of Medical Biometry Artificial Intelligence (DFKI) Department of Hematology
and Informatics Trippstadter Strasse 122 and Oncology
Im Neuenheimer Feld 305 Kaiserslautern, 67663 Germany Im Neuenheimer Feld 410
Heidelberg, 69120 Germany Gerrit.Meixner@dfki.de Heidelberg, 69120 Germany
Andreas.Wicht@med. Ulrike.Klein2@med.
uni-heidelberg.de uni-heidelberg.de
ABSTRACT decades, the practical impact remains low for several
Physicians do not always make optimal decisions. reasons, e.g. [3]:
Computer based clinical support systems are intended to - Systems failed to cover an entire medical domain
provide clinicians with decision aids, but their practical - Poor practicability and integration into the clinical
impact remains low. We introduce a software architecture workflow
which might overcome key barriers and present the - Poor availability of digital patient data
prototypical implementation of a web based knowledge - Poor acceptance
module for early detection a life-threatening medical Within the ESGOAB1 project, which will be described in
condition, sepsis. more detail in the next section, we are trying to overcome
the weaknesses mentioned above. In this paper we will
Keywords
briefly describe the ESGOAB software architecture, which
Clinical decision support, Knowledge-based systems in
provides an electronic health record (EHR) and is designed
medicine, hematology, sepsis, fever, web-based application,
to provide the base to interact with knowledge modules. We
knowledge maintenance, ESGOAB.
focused on two specific clinical challenges: supporting the
INTRODUCTION physicians’ order entry process (CPOE) and supporting
The information overload physicians are confronted every early detection of sepsis (a serious and life-threatening
day with makes it impossible for them to keep up with all medical condition) on patients with hematological
the information and knowledge that would be potentially underlying diseases. In this paper we will describe the
useful in making optimal clinical judgments. Empirical second challenge. We introduce our conceptual design of
studies have shown that physicians do not always make the sepsis knowledge module and present the currently
optimal decisions [17] [6]. Clinical decisions are often implemented web-based prototype.
made under time pressure, without having all information
Project Background
and knowledge needed in the right place at the right time.
The ESGOAB project is a 2-year public funded joined
Computer-assisted clinical decision support systems
research project between two scientific partners
(CDSS) are intended to provide “clinicians, patients or
(Heidelberg University Hospital, DFKI) and two industrial
individuals with knowledge and person-specific or
partners (COPRA System GmbH, Dosing GmbH).
population information, intelligently filtered or presented
at appropriate times, to foster better health processes, A survey at the Department of Hematology and Oncology
better individual patient care, and better population health. of the Heidelberg University Hospital has shown a range of
CDS interventions include alerts, reminders, and order sets problems concerning various applications of Information
[…]” [11]. and Communications technologies (ICT) within the hospital
Although research has been done in the field of CDSS for (response rate = 70.5 %, 36 of 51 of the medical personnel)
1
Copyright © 2011 for the individual papers by the papers' ESGOAB = „Entwicklung einer Softwareumgebung zur Generierung
authors. Copying permitted only for private and academic von organisationsspezifischen Anwendungen zum Behandlungs-
prozessmanagement“; english: Development of a Software Environment
purposes. This volume is published and copyrighted by for Generation of Organization-Specific Applications for Treatment
the editors of EICS4Med 2011. Process Management
69
[20]. The study revealed problems concerning e.g., time stress, and optimization of clinical decisions, which
consuming searches, redundant data entries, use of various altogether may improve quality of care.
software applications to perform the various tasks, different
Related work
user interfaces, and only marginal decision support.
Research in the field of medical knowledge-based systems
However we found a promising openness towards CDSS
has been done since the early 1960s. Many different
[20]. In general the staff was open-minded towards new
research approaches have been explored, but yet the degree
Information Technology (IT) systems (88 % indicated to be
of impact of clinical decision support systems still remains
“rather open-minded” or “open-minded”), concerning
low [5]. Only few knowledge-based systems are widely
CDSS, the potential benefit was assessed by the majority
used day-to-day, such as automated electrocardiogram
(72 %) as “rather high” or “high”, as well as prospects of
(ECG) interpretation [18]. However, systems utilizing a
success (53 %). At least 47 % rated reliability as well as
broad variety of individual patient data had to fail due to
acceptance as “rather high” or “high”.
poor availability of digital data. Providing an EHR
The ESGOAB project aims at encapsulating various data eliminates this obstacle [3].
sources like e.g., hospital information system (HIS),
Research in the field of monitoring and analyzing vital
laboratory data or drug information systems (see Figure 1)
signs in Intensive Care Units (ICU) for early warning of
into one integrated software system which provides one
patient deterioration or sepsis were done by [15] and [12].
consistent user interface. The second main aim is the
However, including microbiological findings as well as the
development and integration of knowledge bases into the
accurate handling of the specifics of the hematological
ESGOAB system.
patients remained unconsidered.
The ESGOAB system is based on a 3-layer software
Clinical Background
architecture concept. A data collector (layer 1) is
The treatment of patients with hematological diseases has
responsible to capture and gather data from various existing
advanced enormously in the past years. Nevertheless such
sub systems which are illustrated below the three layers.
infections pose a serious life-threatening risk for these
Adapters for each of the sub systems are transforming data
immunocompromised patients. Beside various other clinical
into defined structures. The knowledge carrier (layer 2)
and laboratory parameters, fever is an essential factor,
analyzes incoming data from the data collector layer or
which indicates a manifest or beginning infection.
handles requests triggered by user interactions. The
Therefore a refined assessment of the body temperature is
knowledge carrier consists of various knowledge bases
needed. The responsible physician has to distinguish
(e.g., about drug information) which are connected
between innocent fever as immunologic reaction, fever of
following a modular concept. If there are e.g., new blood
unknown origin, fever caused by bacteremia or the onset of
values these will be evaluated by the knowledge carrier and
a severe sepsis. Hereby assists the combination of lab-
according hints or warnings will be immediately displayed
values, microbiological findings and vital signs. The
by the visualizer (layer 3) following a defined alerting
emphasis and valuation of the combination of single-values
concept, taking into account the importance, severity etc. of
and the experience of the doctor partly determine the
the alert [4].
treatment course and the outcome of the patient. Clinical
studies demonstrated that the survival probability of
Layer 3
Visualizer Patient X patients with sepsis depends most essentially on the period
…. Dynamic Desktop
…
of time between diagnosis and start of effective antibiotic
treatment [7]. Sepsis is not only a problem of hematological
Layer 2
patients. It’s rather a challenge for the population. Severe
Workflow Model
Knowledge Carrier sepsis is considered to be the most common cause of death
Decision Model in non-coronary critical care units. Approximately 150.000
persons die annually in Europe and more than 200.000 in
Layer 1 the United States [1]. The problematic nature of a timely
Data Collector Meta Data Model recognition is not that data is missing, but it is detached
Adapters
from one another, generated at various places and different
times. The responsible physician has to link the separated
information for the plurality of patients. Ward rounds,
HIS Lab PACS … Sub Systems
printouts with lab values, calls from the microbiologist and
signs from monitors serve as instruments for this
Figure 1: 3-layer architecture of the ESGOAB system connection. This work has to be done by the medical
At the end of the ESGOAB project we expect an increase of personnel even in the future, but the model we present in
efficiency of tasks and processes at the Department of this article offers the convincing advantage of automated
Hematology and Oncology at the Heidelberg University joining of relevant data and usable presentation, resulting in
Clinic (due to time-savings), a reduction of performance efficient and faster decisions.
70
APPROACH the flexibility to use the same implementation (objects) in
Conceptual Requirements two kinds of settings:
When deploying and operating knowledge-based systems, a 1. Interactive data retrieval with the user (module
weak point is often poor practicability, in particular in terms execution)
of maintenance. Either the knowledge model is
implemented statically, there is no way experts of the 2. Running in background through a web service
certain domain (in the present case hematologists) can retrieving data from the EHR
modify the model on their own. This leads to the fact that Knowledge Model
each adaptation has to be done by a software engineer. Or Sepsis accompanies with several symptoms, such as fever,
the user interfaces do not provide intuitive means to modify increased heart rate, low blood pressure etc. Further
the knowledge base; thus users have to be instructed and the important parameters are signs of infections such as specific
system becomes error-prone. Between designing and using blood values and microbiological findings. The sequence of
knowledge-based systems, a long-lasting cyclic process of appearance and the severity of these manifestations differ
modeling, testing, adapting, and retesting of the core engine from patient to patient. We have to deal with fuzzy and
has to be passed through. While operating knowledge-based uncertain information. However, some signs are more
systems the focus shifts towards maintenance issues. important than others and certain value ranges are
Maintaining the knowledge within the system is critical to supporting sepsis more than other diagnoses. So the idea
successful delivery of decision support [3]. In this context was to design a decision model, which balances between a
practicability plays an important role. Being aware of this set of differential diagnoses and specifies the one which can
issue, easy knowledge maintenance was an important goal. be explained best by the observed findings. The set
The clinical expert should be able to modify the underlying covering model is a potentially useful approach, which was
knowledge model without extensive training. It has to be introduced by [13], as well as the more abstract view on
simple and intuitive to use. Furthermore it should be multiple diagnose problems by [9]. Our approach is based
possible to test the constructed or adapted model right on the set covering model, extended by the possibility to
away. Beyond that, the knowledge model should be generic, define parameters which contradict certain diagnoses since
so it might be useful for other diagnostic problems. we experienced a further need for accuracy.
Knowledge Engineering Process Two sub modules based on a rule engine were required to
Knowledge Engineering is the systematic approach for the handle two specific problems:
development of knowledge based systems. The process may 1. Interpretation of microbiological findings: Presence of
be divided into two main phases (Figure 2): knowledge an infection or suspected contamination?
acquisition and knowledge operationalization [14]. It
should be noted, that typically the process of acquisition 2. Interpretation of white blood cell count (leukocytes):
and operationalization is not a linear process but rather a May we take this parameter into account?
cyclic process characterized by continuous, iterative Implementation
refinement. Initially, the basic idea was demonstrated using a prototype
realized in Microsoft Excel (Figure 3). Taking advantage of
Knowledge Acquisition Knowledge Operationalization quick implementation possibilities, this prototype helped us
to refine our knowledge model.
Capturing Design
Knowledge Model
Structuring Implementation
Formalization
Information Model
Figure 2: The Knowledge Engineering Process
Knowledge acquisition is the process of capturing,
structuring and formalizing knowledge. The result of the
acquisition phase is a knowledge model and an information
model which both serve as a base for the system design of
an implementation [5]. Sources of knowledge are typically Figure 3: Prototype developed with Microsoft Excel
domain experts, medical literature and patient data (result presentation in the upper left corner, value
repositories. Our approach was based on expert opinion and selection in the mid-area)
literature research. The decision to proceed with the development of a web-
Information Model based application was made due to the following main
A specification of the kinds of information that were reasons:
required was created, including the data format and the - Easy Accessibility from all clients via the clinic’s
taxonomy. The resulting information model – which will be intranet; no need to install software on clients
implemented as an object-oriented data model – provides us
- Quick deployment of new versions
71
We used a XAMPP [2] installation on Windows including
an Apache 2.2.14 web server and a MySQL database
system. We used the scripting language PHP and the Ajax
toolkit xajax [19].
The application is based on the Model-View-Controller
(MVC) design pattern; the PHP code architecture follows
an object-oriented approach. The application is
implemented using the open source relational database
management system MySQL, with use of the InnoDB
storage engine. An initial database model was designed
using the database-modeling tool MySQL Workbench [10].
The model was refined iteratively during the
implementation of the application. Figure 5: Screenshot of a weight table (columns:
diagnoses, rows: parameters and values)
WEB-BASED PROTOTYPE
The process of creating a new model facilitates the
Application Structure
following steps:
The application is composed of three modules: Sepmod,
Leukomod and Mibimod (Figure 4). Sepmod represents the 1. Add diagnoses
generic core model, which implements the weight model as 2. Add parameters
specified before and interacts with the sub modules
3. Add value ranges for each parameter
Leukomod and Mibimod. Leukomod is based on a rule
engine, which is responsible for the white blood cells’ 4. Create weight relations between value and diagnosis
assessment. Mibimod is also based on a rule engine and 5. Define Equivalencesets
performs the assessment of the microbiologic findings.
6. Define Minimalsets
These steps are described in more detail below.
Step 1: Firstly we need to add diagnoses, by providing its
name and optionally a description. For each diagnose
added, the table will be expanded by one column.
Example definition:
Name: SIRS
Range: Systemic Inflammatory Response Syndrome
Step 2: In the second step, symptoms or “parameter” can be
defined, specifying its name, type, unit, value range,
validity, importance. The type tells us, which kind of data
we deal with, e.g. integer, float or special medical
Figure 4: Structure of the application classifications like the anatomic therapeutic classification
The web-front-end provides the following two main (ATC) code. The value range defines the valid value range
sections: for the parameter and is used to perform plausibility checks.
- Maintenance: Provides editors for each module for Regarding the process of diagnosis, an important and
construction and modification of the knowledge base. relevant issue is always the time context. How long can I
rely on a measured value? The answer depends on each
- Module-Execution: Interactive tool, which requests for parameter. In the present model we therefore define for
data or selection of options by the user and presents each parameter a time frame (validity), entering a numerical
the results. value for the absolute time (minutes, hours or days) within
The interactive tool is basically designed for testing this parameter remains valid or in other words we can
purposes. The impact of adaptions of the knowledge base assume that the measured value may be used for the
can be explored right away. diagnostic assessment. If a parameter exceeds the defined
Knowledge Maintenance Section time frame, it will be ignored and treated, as it would be not
available. Alternatively we can define a relative time frame
Sepmod
In this section the user can create a new and edit existing such like “Valid until next measurement”. Some parameters
knowledge models. Created models can be loaded, saved, may have a more significant importance than other. Thus
and deleted. The core element is the weight table for each parameter the importance can be defined, choosing
(Figure 5). from given weights “very important”, “fairly important”,
“important”.
72
Example definition: least a certain set of parameters is available. In the section
minimalset the clinical experts can define, which
Name: bodytemperature parameters they consider as being essential for performing a
Type: float profound assessment. It is possible to define more then one
Unit: °C set.
Range: 30.0 to 43.0 Leukomod
Validity: 6 hours Leukomod is based on a rule engine. The rules were
Importance: very important defined by clinical experts. In the current version they are
implemented statically and can be activated or deactivated
Step 3: In the next step, for each parameter, several values through the web-front-end in the section leukomod/rules.
have to be defined. The values are specified by its name Further we can adapt various parameters (like thresholds) of
and its value range. the rules. For the assessment, if the white blood cells may
be included, the underlying disease is of high importance. A
Example definition:
dynamic list of diagnosis codes (ICD, International
Name: fever Statistical Classification of Diseases and Related Health
Problems) can be maintained in the section diagnoses.
Range: 38.0 to 43.0
Mibimod
Step 4: Adding diagnoses, parameter and its values results Mibimod is based on a rule engine. A dynamic list of germs
in a count(diagnoses) x count(values) matrix. For each pair which support the suspicion of a contamination can be
of a value and a diagnosis we can now define a weight maintained in this section. The rules of this module are
relation between a specific value and a diagnosis by currently implemented statically.
clicking on the corresponding button. A weight relation is
Module Execution Section
the symbol for the strength a specific value supports a
For testing purposes the web-front-end provides a section to
diagnosis. The currently implemented model supports five
run the modules. The current version has one section to run
different weights, ranging from H0 (value does not support
each of the modules separately and one, which encapsulates
the diagnosis or even contradicts) to H4 (value strongly
all three of them. These execution modules are
supports the diagnosis or is even essential) depending on
implemented as interactive tools, requesting each parameter
the currently used weight model which can be defined in a
step-by-step.
separate section. For each weight symbol the value can be
selected through a slide control. For each input field a check for plausibility is performed
while entering data, considering two main issues
Step 5: In the section called equivalencesets we define sets
of previously defined parameters, which are clinically - Valid characters (depending on the data type)
equivalent (Figure 6). In other words all of these parameters - Valid value range (as defined in the model).
support a specific context diagnosis, such as low blood
To keep track of entered values, we show breadcrumbs
pressure.
horizontally across the top of the input form (Figure 7).
Figure 7: Entering values using the interactive mode
Figure 6: Screenshot of the equivalenceset dialog The user can easily go back to previously entered values
and change them if necessary. Each breadcrumb item shows
In this case we would define that low blood pressure exists
the name of the parameters as well as the entered value and
if at least one of these blood pressure parameters takes a
unit. Once all values are entered, the system performs the
specific value (and a corresponding high weight) or in this
assessment and presents the results, giving explanations and
special case we also assume low blood pressure if
a visualization of the results (Figure 8). Presenting results in
vasopressors (substances which result in an increase in
an appropriate way means finding a balance between (1)
blood pressure) are given what we can specify through a list
simple, clear and aggregated representations, supporting
of ATC codes.
e.g. quick task completion and (2) comprehensive and
Step 6: A decision model might be sophisticated but its detailed representations, which are needed for making
accuracy highly depends on the available parameters. So it comprehensible decisions.
only makes sense to perform a diagnostic assessment if at
73
Elsevier Academic Press, Amsterdam; Boston, 2007.
6. Kohn, L.T. (Ed.). To err is human. National Academy
Press, Washington, DC, 2000.
7. Kumar, A., Roberts, D., Wood, K.E., et al. Duration of
hypotension before initiation of effective antimicrobial
therapy is the critical determinant of survival in human
Figure 8: Result presentation (diagram and textual septic shock. Critical care medicine 34, 6 (2006),
explanation) 1589-1596.
DISCUSSION & FUTURE WORK 8. Lo, H.G., Matheny, M.E., Seger, D.L., Bates, D.W.,
We identified two important and promising factors that will and Gandhi, T.K. Impact of non-interruptive
help overcome key barriers limiting more widespread use of medication laboratory monitoring alerts in ambulatory
CDSS: Firstly we did not experience the “physician care. Journal of the American Medical Informatics
resistance” using decision support systems; they are rather Association 16, 1 (2009), 66–71.
convinced of their usefulness as described in the
introduction. Secondly we can take advantage of the 9. Miller, R.A. A Heuristic Approach to the Multiple
software infrastructure we presented which has the potential Diagnoses Problem. Proceedings of the 6th
to improve clinical processes and to integrate and interact Conference on Artificial Intelligence in Medicine in
with sharable knowledge modules conceived to support the Europe, Springer-Verlag (1997), 187–198.
physicians in their decisions. We also introduced a web 10. MySQL Workbench visual tool. Available at
based prototype of a knowledge module for early detection http://www.mysql.com/products/workbench/.
of sepsis. We believe to contribute important elements to 11. Osheroff, J.A., Teich, J.M., Middleton, B., Steen, E.B.,
lift computer-aided decision support into widespread Wright, A., and Detmer, D.E. A roadmap for national
practice. action on clinical decision support. Journal of the
However, our approach still needs to be refined and American medical informatics association 14, 2
evaluated. It has to be shown that our knowledge modules (2007), 141-145.
are able to provide accurate and traceable support, 12. Pilz, U., and Engelmann, L. Früherkennung von
characterized by high sensitivity. A test concept as well as septischen Krankheitsverläufen durch Einsatz eines
test scenarios will be defined. Further alerting concepts will Expertensystems. Intensivmedizin und Notfallmedizin
be tested in practice since empirical studies have shown that 43, 7 (2006), 575-579.
too many generated alerts could lead to “alert fatigue”,
whereas “non-interruptive” alerts only have low impact and 13. Reggia, J.A., Nau, D.S., and Wang, P.Y. Diagnostic
are not effective [8]. Expert Systems Based on a Set Covering Model.
International Journal of Man-Machine Studies,
ACKNOWLEDGMENTS (1983), 437-460.
This work was funded by the German Federal Ministry of
14. Spreckelsen, C., and Spitzer, K. Wissensbasen und
Education and Research under grant number
Expertensysteme in der Medizin. Vieweg + Teubner,
01 IS 09027 C. The responsibility for the content of this
Wiesbaden, 2008.
publication lies with the authors.
15. Tarassenko, L., Hann, A., and Young, D. Integrated
REFERENCES
monitoring and analysis for early warning of patient
1. Angus, D.C., Linde-Zwirble, W.T., Lidicker, J.,
deterioration. British Journal of Anaesthesia 97, 1
Clermont, G., Carcillo, J., and Pinsky, M.R.
(2006), 64-68.
Epidemiology of severe sepsis in the United States:
analysis of incidence, outcome, and associated costs of 17. Warner, H.R., Sorenson, D.K., and Bouhaddou, O.
care. Critical care medicine 29, 7 (2001), 1303-1310. Knowledge engineering in health informatics.
Springer, New York; Berlin; Heidelberg, 1997.
2. Apache Friends. XAMPP distribution. Available at
http://www.apachefriends.org/xampp.html. 18. Willems, J.L., Abreu-Lima, C., Arnaud, P., et al.
Evaluation of ECG interpretation results obtained by
3. Bates, D.W., Kuperman, G.J., Wang, S., et al. Ten
computer and cardiologists. Methods of Information in
commandments for effective clinical decision support:
Medicine 29, 4 (1990), 308-316.
making the practice of evidence-based medicine a
reality. Journal of the American Medical Informatics 19. xajax Project. xajax framework. Available at
Association 10, 6 (2003), 523–530. http://www.xajax-project.org/.
4. Durieux, P. Electronic Medical Alerts – So Simple, So 20. Zuehlke, D., Meixner, G., and Klein, U. Smart Medical
Complex. New England Journal of Medicine 352, 10 Software Systems For Dummies? The Case For A
(2005), 1034-1036. User-Centered Systems Design. Proceedings of the 3rd
International Conference on Health Informatics 2010
5. Greenes, R.A. (Ed.). Clinical decision support.
(Valencia ESP, 2010), 350-354.
74