Linked Data for Clinical
Decision Support
Use Case Description
Rinke Hoekstra and Richard Vdovjak
Data, version 0.3
Summary
Goal
Significantly improve the speed and accuracy of bringing
clinical research information to clinical practice.
Target
Development of infrastructure to support a prototype clinical
decision support system (CDS) for risk stratification, early
detection and management of serious adverse events (SAEs) in
the domain of Oncology
Users
Clinicians involved in Oncology patient management
Implementation
The use case will be implemented as a fully working proof of
concept infrastructure. The resulting system will be provided
with a user interface that is sufficient for feature
demonstration, but not for use as an actual clinical decision
support system.
User Involvement
This is a proof of concept project that mainly focuses on
infrastructure aspects of the use case. No formal user study is
envisaged, but clinicians participating in other projects (at
Philips) will be asked to provide input.1 This use case has been
chosen to match the needs and interests of these clinicians.
Content
The use of content is driven by this use case. We will use both
publicly available data sources, as well as proprietary data
from Elsevier (and Philips?). We are making an inventory of
data, information and knowledge that can be used.
A proviso list of content:
Content related to SAEs
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1
AERS (US)
Eudra Vigilance (EU, EudraCT)
Philips is involved in a number of projects involving clinicians.
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Clinical Trials reporting SAEs (ClinicalTrials.gov,
EudraCT)
Publications reporting SAEs
Content related to Febrile Neutropenia
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SAEs involving FN
Publications about FN
CTs involving FN (ClinicalTrials.gov, EudraCT)
Descriptions of FN (Diseasome)
Content related to drugs (LODD)
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Active ingredients
Drug interaction (DIKB, …)
Drug side effects (SIDER, …)
Links to CTs
SAEs involving drugs
Patient data (aggregated)
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Access to Content
EMRs of patients with low to high risk of FN
…
Content accessibility is subject to the COMMIT consortium
agreement. Additional usage agreements may need to be
signed. Proprietary content will only be made available to the
project partners, and not distributed to third parties unless
explicitly permitted by the data provider. The proof of concept
system itself is not restricted, except for features that would
expose closed content, or proprietary components.
Content will be made available to the Data2Semantics project
partners
Content Hosting
Content will be hosted by one of the partners in
Data2Semantics, unless (conveniently) available elsewhere.
Product
The use of foreground results of the project (applications,
annotations, data etc.) is governed by the COMMIT consortium
agreement.
This project will explore/develop necessary technologies to
build a proof of concept demo. There is no commitment to
directly commercialize the proof of concept.
Philips is interested in commercializing a system derived from
the proof of concept, in compliance with the rules of the CA.
Introduction
This use case concerns the development of infrastructure to support a
prototype clinical decision support system (CDS) for risk stratification, early
detection and management of serious adverse events (SAEs) in the domain of
Oncology. SAEs are often associated with the aggressive treatment options
that are associated with oncological diseases. In some cases, the adverse
effects of the harsh treatment become even more dangerous than the treated
disease itself. A CDS solution which combines the established guidelines, the
new research results, as well as the actual patient data/context, would greatly
help the clinicians in 1) minimizing the probability of the occurrence of SAEs
(by devising a treatment and preventive measures which will take into
account the individual risk factors based on SAE risk stratification); 2) early
detection of SAEs (by proper analysis of the available patient data ); 3) proper
management of SAEs according to the latest validated clinical research and
guidelines.
In this use case we plan to investigate, among others, how the use of external
knowledge sources and the linked data technology may improve current
practice in CDS with a broader goal to speed up the process of making
important recent research results available to clinical practice .
While our platform should be extendable to other SAEs, the content selection
for this use case is primarily driven by the SAE of Febrile Neutropenia, a
frequently occurring adverse event in Oncology that has a high risk of
mortality: Febrile Neutropenia makes Cancer an emergency case.
Clinical Decision Support
Proper implementation and use of CDS systems is regarded as an important
recommendation for reducing the frequency and consequences of errors in
medical care. Additionally, clinical decision support has the potential to bring
research results faster to the front-line clinician and significantly improve
patient outcome. To that end, such systems need to be able to answer complex
questions and aggregate data from multiple sources. Any such question will
combine complex patient specific data with information from external
sources.
Following Greenes (2007), clinical decision support is defined as “the use of
the computer to bring relevant knowledge to bear on the health care and well
being of a patient”, here “a primary task of the computer is to select
knowledge that is pertinent, and/or to process data to create the pertinent
knowledge”.
The task for CDS systems is to:
1.
2.
3.
4.
5.
6.
bring the right information
at the right time
customized to the clinician in the
given clinical context to
improve efficiency and
patient outcomes
All our searches for information have a context and relate to some
information or data. For example, clinicians want to extract targeted
information that fits the context of a specific patient case (e.g. similar cases
reported in literature or stored in a reference database, outcomes for that
specific disease, best treatment options, etc.). Current solutions are only able
to provide support for very simple questions and decisions, and are not able
to fully address the increased complexity of clinical decision in the context of
oncology.
The new generation of clinical decision support systems that are both
semantically aware and up-to-date with the latest validated research results
will depend on the availability of flexible frameworks supporting the ease of
development of such applications and on the existence and seamless selection
of relevant sources providing high quality content and of tools enabling the
extraction and aggregation of such content.
An important goal of a clinical decision support system is to provide to the
front-line clinician relevant targeted information for a specific disease and
patient case. As CDS are bound to specific clinical domains, often such
applications need to duplicate the work required for the selection of the
relevant data sources and for accessing the data itself. It is a challenging task
to select out of the existing information sources those that are indeed relevant
in the specific domain. It is even more difficult to evaluate the quality of the
returned content. Current keyword search returns content ordered by page
rank without taking into account the relevance or the quality of the sources in
a specific domain. In the clinical domain this may overload the clinician with
information that is irrelevant or low quality, while missing out on very
relevant information from highly specialized sources (as their rank based on
the site popularity may be low and not match the relevance of the
information).
Severe Adverse Events: Febrile Neutropenia
A severe adverse event (SAE) is described as a medical occurrence due to
treatment that:
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results in death
is life-threatening
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requires inpatient hospitalization or prolongation of existing
hospitalization
results in persistent of significant disability/incapacity
is a congenital anomaly/birth defect
requires intervention to prevent permanent impairment or damage
The occurrence of a SAE has to be reported to the health authorities:
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In the US, this is to the FDA through the Adverse Event Reporting
System (AERS).2
In the EU, this is through Eudra Vigilance.3
SAEs are a major cause for hospitalization: known chemotherapy-related
SAEs in breast cancer (US only) were linked to 22% of hospitalizations.
Febrile Neutropenia is a frequently occurring SAE associated with
chemotherapy, a common systemic treatment in Oncology:
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Febrile
o temperature of >38.5˚ C or a
o sustained temperature of >= 38.0˚C for over one hour
Neutropenia is a haematological condition with an abnormally low
number of neutrophils. Neutrophils usually make up 50-70% of
circulating white blood cells and serve as the primary defence against
infections. Neutrophils contain enzymes that help the cell kill and
digest microorganisms. The absolute neutrophil count (ANC) is
measured in cells per micro litre of blood.
Neutropenia occurs when:
o ANC is < 500/mm3, or < 1000/mm3 with predicted decline to <
500/mm3 over the next 48 hours.4
FN is an important SAE in oncology
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Neutropenia is a very strong predisposing factor to infection
Infection is a common cause of death for cancer patients
Without prompt medical attention, the condition may become lifethreatening
Intravenous antibiotics should be administered within 30 minutes of
presentation to the emergency department.
See
http://www.fda.gov/Drugs/GuidanceComplianceRegulatoryInformation/Surv
eillance/AdverseDrugEffects/
3 See http://eudravigilance.ema.europa.eu/
4 The actual numbers vary depending on the guidelines used
2
Decision Support in Febrile Neutropenia
Clinical decision support systems can contribute to stratification of patients,
early detection and decision support.
Patient stratification concerns determining the risk that a patient has FN.
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Given the patient context (their history and current status) and
available data about prior cases, what is the risk of FN?
What are adequate prevention measures for a patient of this type?
Can we distinguish different approaches for high and low risk
patients?
The task of patient stratification combines the results of existing research, i.e.
explicit knowledge of risk factors in guidelines around FN, with
opportunities for mining and extracting knowledge from existing data of
prior cases, or publications.
Because of the time pressure involved in FN (30 minutes after presentation to
the emergency department) it is highly important that FN cases are detected
as early as possible. This is complicated by the fact that patients undergoing
chemotherapy are typically at home during the treatment.
Based on data in the Electronic Medical Record (EMR) or Patient Health
Record (PHR), the CDS can provide an early alert when a patient is febrile
while having a low ANC. Additionally, the CDS can automate reporting of
the Severe Adverse Event.
Example Queries
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Retrieve all research papers describing clinical trials for disease X with
drug Y sorted by the overall survival. (What treatment protocol works
best?)
Given the patient context, compute the risk estimation of FN based on
published clinical trials (matching the type of cancer and the chosen
therapy) and reported SAEs.
….
Scenarios
1 Simple stratification and management
This scenario supports the clinician with a simple albeit often used monogram
(or risk model) called the MASCC (Multinational Association of Supportive
Care in Cancer) index. This model helps asses the risk that a patient might
suffer from FN in the near future. Based on the results of this model, the
clinician is pointed to the appropriate section in the relevant guideline that
recommends treatment for the particular patient.
Carmona-Bayonas et al. Prognostic evaluation of febrile
neutropenia in apparently stable adult cancer patients
Preparation
 Formalize the MASCC index
 Identify and annotate available FN guidelines
 Define the patient meta-model for the patient profiles
Execution
 Based on the patient data/profile compute the MASCC index.
 Based on the resulting MASCC score, link to the appropriate guideline
for FN
 Provide evidence by linking in databases, research papers etc. to the
guidelines, when available
2 Advanced stratification and management
This scenario supports the clinician in selecting a risk model out of a library of
models. To refine this recommendation, the tool also takes external (linked)
data into account such as drug interaction databases (AERS or
PharmaPendium, the Side effect databases, etc.
Preparation
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Formalize more advanced risk model(s), taking into account more
clinical variables such as those depicted below.
Carmona-Bayonas et al. Prognostic evaluation of febrile
neutropenia in apparently stable adult cancer patients
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Create a library of risk models and the dependencies among them to
allow a more refined stratification, e.g. sub-stratify the low risk
patients from the MASCC score by a more advanced model such as the
one in Carmona-Bayonas et al.
Classify published models into retrospective studies and prospectively
validated studies. Use the former only as an “indication”.
Create a repository of established guidelines with proposed
management in validated research literature – formalize them into an
executable model and interlink them with the stored risk models
Establish a link to external (Linked) data sources that can provide
additional information, e.g. Drug interaction (DIKB), Drug side effects
(SIDER), Pharmapendium, and others
Execution
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Based on the patient data/profile, compute the selected risk model
Based on the resulting, link to the appropriate guideline for FN
Provide evidence (research papers etc.) to the guidelines when
available
3 Combining known risk models as well as all available external data for
the purpose of meta-analysis and derivation of an improved risk estimation
This scenario aims to improve the status-quo wrt current risk stratification.
Here our tool will basically support the clinical user with a meta analysis
which takes into account known risk models, published results of clinical
trials, reported Side or Adverse Effects, as well as other relevant data. The
model(s) we propose can be provided to the clinical researchers as hypotheses
that will be further validated and applied.
Given the low semantic description of the published results (e.g. from a
clinical trial), this is the most challenging scenario. We will demonstrate in
our proof of concept how a clinical trial should be semantically annotated so
that CDS tools like these are able to “execute” a computational task over the
published results. We will investigate how to tailor approaches like OCRei in
order to achieve the right balance between the ease of annotation (lowering
the entry barrier for the prospective authors) and the required expressivity.
Other external data sources such as AERS, Eudra Vigilance, etc. will be
examined and interlinked in order to provide an increase statistical sample on
the incidence of chosen SAEs for particular patient profiles.
Required Advances
An infrastructure that provides a next generation clinical decision support
tool will advance the current state of the art significantly. This moves beyond
the development of “just” a linked data-based clinical decision support
system, and requires the design and implementation of a repeatable or even
continuous update strategy for integrating and publishing new clinical
information to the system.
A. Information Availability
Making necessary information available to the clinician requires bringing
together and aggregating existing information from a wide variety of sources.
Concretely, we need to:
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Identify information and datasets relevant for clinical decision support
in the domain.
Convert these information sources to linked data (in as far as needed,
possible or required)
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Interlink these information sources with one another (alignment,
identity reconciliation)
Enrich existing (non data) information sources (such as documents)
with annotations.
B. Information Presentation
The decision support system can only present a selection of available
information to the clinician. This requires:
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Comparing information of a current case (PHR or EMR) with prior
experience
Selecting relevant guidelines and (additional) non-formalised
knowledge of potential treatments
Ranking information according to relevance and urgency for the
current case. We will investigate both heuristics-based ranking as well
as ranking techniques based on expert knowledge.
Organising this information in a user interface
C. Performance
Clinicians have to make critical decisions under (sometimes extreme) time
pressures. A clinical decision support system will have conform to the speed
of decision making required by the clinical setting. Also, clinical knowledge
develops very rapidly. The system will need to ingest new information on a
continuous basis. This requires:
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Pro-active information provision based on patient data
Responsive query performance
Continuous information ingestion (see A.)
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