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Clinical Decision Support as a Business Intelligence Application
Yu-Kai Lin
INTRODUCTION
Clinical Decision Support System (CDSS) is an information system (IS) which aims to provide clinical
recommendations, reduce clinical errors, and ultimately, improve patients’ health. A growing amount of
interests on CDSS has been seen since 2005 when American Medical Informatics Association (AMIA)
established a committee to advance Clinical Decision Support (CDS). The committee then developed a
roadmap for CDS (Osheroff et al., 2007), and highlighted three supporting pillars for better health care
through CDS—knowledge availability, effective IS use, and extensions on knowledge and methods. These
pillars can be closely fitted into IS problem domain, as they somehow connect to each other (Figure 1).
The linkage of medicare domain and IS domain may just like what Dr. Shortliffe, the President and CEO of
AMIA, says: “…biomedical research, are inherently information management tasks—and must accordingly
be tackled and supported as such.”
Medicare Perspectives
Best Knowledge
Available When Needed
High Adoption &
Effective Use
Continuous Improvement
of Knowledge & CDS
Methods
Knowledge Availability
Effective IS Use
Extensions on Knowledge
and Methods
Knowledge and
Semantic Data
Management
Technology Acceptance
& Adoption
Business Intelligence &
Data Mining
IS Perspectives
Figure 1. Mapping Medicare and IS Perspectives on CDS Issues
The benefits of CDSS are not only on better treatments for patients, but can potentially contribute to
more effective hospital administration as well as enhancing revenues (Erstad, 2003). Better treatments
are achieved by proper interventions in the clinical decision process, such as safety checks on drug-drug
interactions, dose calculation and treatment alerts based on patient history, etc. Hospital administration
becomes easier because of the effective means to create reports and manage care plans. Revenue will be
enhanced from improved medicare quality and more efficient workflows.
However, along with its substantial potential, the design and development of CDSS are of grand
challenges in terms of scope and complexity (Sittig et al., 2008). This study aims to utilize business
intelligence (BI) techniques in response to the top 10 challenges of CDS presented by Sittig and his
colleague (Table 1).
Table 1. Top 10 Challenges of CDS (Sittig et al., 2008) and the Responses from This Study
Top 10 challenges of CDS
Addressed in
this study?
Improve the human–computer interface
Yes
Disseminate best practices in CDS design, development, and implementation
No
Summarize patient-level information
Yes
Prioritize and filter recommendations to the user
Yes
Create an architecture for sharing executable CDS modules and services
Yes
Combine recommendations for patients with co-morbidities
Yes
Prioritize CDS content development and implementation
No
Create internet-accessible clinical decision support repositories
No
Use freetext information to drive clinical decision support
Yes
Mine large clinical databases to create new CDS
Yes
LITERATURE REVIEW
CDSS issues attract at least two academic disciplines: medical informatics and IS. Most medical informatics
researchers are motivated from their clinic experiences. Equipped with medical knowledge, medical
informatics researchers focus more on disease-specific treatments (Austin et al., 2010; Moskovitch &
Shahar, 2009a; Verplancke et al., 2008), clinical patterns (Bath et al., 2005; Moskovitch & Shahar, 2009b;
Toma et al., 2008), and risk assessment (M. Remm & K. Remm, 2008; Roukema et al., 2008; Silva et al.,
2008; Smith et al., 2009). On the other hand, IS research approaches CDSS issues from angles such as
knowledge management (Chen et al., 2005; Sarnikar et al., 2010) and decision support systems (Garg et
al., 2005), and business intelligence (Eggers et al., 2005).
METHODS
DATA COLLECTION
The data was collected between August 2007 and July 2010 from Min-Sheng General Hospital, a 600-bed
medium hospital with 6 campuses located in Northern Taiwan. The dataset contains a cohort of 894,061
patients with detailed demographic information, diagnose history, and treatment orders. The diagnoses
and treatments are all encoded in standard ICD-9-CM codes as they were created. Moreover, the dataset
is not only big, but also comprehensive, which covers outpatients, inpatients, emergency rooms, and
operations. Judged by unique ICD-9-CM codes, there are around 7000 different types of diseases from
outpatient records, and around 5000 from inpatient records.
EVALUATIONS
Recall, precision, and F-measure are widely adopted evaluation metrics in Data mining and text mining
studies. In the clinical domain, similar measures are used in the evaluation of performance. They are
sensitivity, specificity, positive prediction value, and negative predictive value. Figure 2 illustrates how
these metrics are calculated.
Figure 2. Illustration of Evaluation Metrics
SYSTEM FRAMEWORK
This study treats clinical decision support as an emerging BI application, and aims to advance traditional
CDSS with BI framework and BI associated techniques. The proposed system framework is demonstrated
in Figure 3. Four major functional areas in this framework are (a) pattern recognition, (b) risk analysis, (c)
recommendation rendering, and (d) presentation and interaction. Each of these will be briefly discussed
in the rest of this section.
Figure 3. BI-based CDSS Framework
Pattern recognition
The effectiveness of every BI system is bounded to the availability, selection, and representation of data.
Combining patient-independent hospital data and patient specific records, the first task is to find
appropriate features to represent patients’ disease. However, the nature and characteristics of diseases
vary from one to the other. To resolve the feature selection and representation issues, diseases will be
broadly assigned into three categories: chronic, acute, and infectious. Within each category, diseases are
grouped based on their semantic similarity on Unified Medical Language System1 (UMLS). UMLS is a
comprehensive medical metathesaurus that covers millions of biomedical concepts and names
(Bodenreider, 2004). UMLS represents a hierarchical structure that can be leveraged to calculate
distances/similarities of any two nodes on the hierarchy using the Generalized Cosine Similarity Measure
(Ganesan et al., 2003). Diseases in the same group will share the same features. The features will be
represented by Vector Space Model (VSM) which is widely employed in information retrieval and text
mining research. Once features are identified, we may use them to find similar patients in the database.
Those similar patients, along with the subject patient’s treatment history, could serve an important
source for the recognition of clinical patterns, including temporal disease developments, treatments,
demographic segmentation etc.
Risk analysis
Risk analysis techniques are benefit from the recognized patterns, including the identification of
comorbidities. Putting all the patterns together, the system would be able to identify risk factors that may
associate with the subject patient. Risk factors are identified by scanning the symptoms information the
subject patient and his/her reference patients, and then related the symptoms to diagnose results of the
reference patients. Risk modeling and assessment can also leverage survival analysis, which has been a
widely used statistical technique in the clinical environment (Arsene & Lisboa, 2007; Eleuteri et al., 2007;
Hakulinen & Dyba, 2007). Finally, a holistic risk assessment is given based on the statistical modeling of all
risk factors.
Recommendation rendering
Generating patient-centered recommendations means that the CDSS should automatically produce
recommendations based on the risk factors as well as the subject patient’s physical conditions, such as
weight, age, and allergies. A set of durg-durg and drug-disease interaction analyses will be invoked based
on the recommended treatments. The Metabolism & Transport Drug Interaction Database2 developed by
University of Washington is the target source of gathering drug interaction data. The recommendations
are then prioritized and filtered before they are rendered to present.
Presentation and interaction
While CDSS should focus on improving the process rather than aiming to provide answers (Henderson &
Schilling, 1985; Kuperman et al., 2007), the designs of intervention and interaction are crucial to a CDSS
adoption and implementation. Several factors could contribute to a successful intervention. First, patientlevel information summarization provides a gist overview of the clinical history of the subject patient. The
summarization is not only useful for the physician to quickly understand his/her patients, but also can be
used as a valuable reference as the physician use the summary to re-assure the CDSS generated clinical
1
2
http://www.nlm.nih.gov/research/umls/
http://www.druginteractioninfo.org/
recommendations. Second, the prioritized recommendations should be listed with proper explanations.
The explanations serve as mediums to alleviate distrust and suspicion of the system from the physician
(Bath, 2004). Third, when there is too many data to consume or the patterns are latent, proper
visualization tools can help surface the hidden relations, and help the physician to make informed
decisions. Lastly, the abilities to select recommendations, annotate options, and provide feedbacks bring
opportunities for continuous improvement for the CDSS. The learning could also happen on the physician
as he/she contemplates those recommendations and annotates his/her opinions.
CONCLUSION
Putting BI into the clinical context represents unique opportunities for IS researchers. As a design science
IT artifact (Hevner et al., 2004), the research on CDSS is both relevant and rigor. It is relevant as it
responses to a long-lasting need in the clinical environment. It is rigor as it adds new framework,
methods, and techniques to the knowledge base.
The contributions of this study are expected to be two fold. First, following a IS research framework, this
study analyzes the information needs in clinical decisions, and applies BI framework and techniques to
improve CDS. The BI-based CDSS will be equipped with functions and techniques to facilitate current
challenging clinical practice. Second, this exploratory study is potentially capable to uncover more
questions than answers. Each of the functional units in the BI-based CDSS framework represents
significant challenges and opportunities for more advanced techniques and methods—which are warrant
for my future continuous studies.
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