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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. 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