A.A.T. Bui et al a patient complains of knee pain, then traditionally as a first step an x-ray is obtained But if the patient's symptoms are suggestive of pain when going up stairs, then a knee magnetic resonance(MR) imaging study is warranted over an x-ray(this symptom being suggestive of a meniscal tear). When asked whether going up stairs aggravates e knee pain, the patient indicated that she was unsure. Thus, her PCP must now make a decision as to what imaging test should be ordered. Furthermore, the selection of the imaging exam must be tempered by the availability of the imaging equipment, the needed expertise to interpret the imaging study, and other potential constraints (e.g, cost, speed of interpretation, etc. First, supporting the practice of evidence-based medicine(EBM) is a guiding principle of biomedical informatics, and hence medical imaging informatics. The development and deployment of practice guidelines in diagnosis and treatment has been an enduring effort of the discipline, suggesting and reminding physicians on courses of action to improve care. For instance, if the patient,'s clinician was unaware of the sign of a meniscal tear, the system should automatically inform him that an MR may be indicated if she has knee pain when climbing stairs; and supporting literature can be automatic- ally suggested for review. Second, formal methods for medical decision-making are central to informatics, as are the representation of medical knowledge needed to inform the algorithms [10]. Techniques from computer science, ranging from rudimentary rule- bases to statistical methods(e.g, decision trees); through to more complex probabilistic hidden Markov models(HMMs) and Bayesian belief networks(BBNs) are finding applications in medicine(see Chapter 8). For example, the evidence of the patients medical history, her response to the physician,s inquiries, the availability of imaging, and the relative urgency of the request can be used in an influence diagram to choose between the x-ray and MR (see Chapter 9). Such formalizations are providing new tools to model disease and to reason with partial evidence. Essential to the construction of many of these models is the compilation of large amounts of(observational)data from which data mining and other computational methods are applied to generate new knowledge. In this example, these disease models can be used: to identify further questions that can be asked to further elucidate the patients condition(improving the likelihood of choosing an optimal imaging exam); and to select the type of imaging udy, and even its acquisition parameters, to best rule in/out elements of the differential diagnosis Ultimately, an electronic imaging infrastructure that expedites accurate diagnosis can improve the quality of healthcare; and even within this simple example of choosing an imaging protocol, the role of informatics is apparent in enhancing the process of care When used appropriately, medical imaging is effective at objectifying the initial diagnostic hypothesis(differential diagnosis) and guiding the subsequent work-up Given a chief complaint and initial assessment data, one can envision that specialists or software algorithms would select an imaging protocol for an appropriate medical condition even before a visit to the PCP. The PCP can then access both objective imaging and clinical data prior to the patients visit. Medical imaging informatics research looks to improve the fundamental technical methods, with ensuing translation Cost Considerations Some have targeted the cost of imaging as a major problem in healthcare within the United States: one 2005 estimate by the American College of Radiology (ACR) was that $100 billion is spent annually on diagnostic imaging, including computed8 A.A.T. Bui et al. a patient complains of knee pain, then traditionally as a first step an x-ray is obtained. But if the patient’s symptoms are suggestive of pain when going up stairs, then a knee magnetic resonance (MR) imaging study is warranted over an x-ray (this symptom being suggestive of a meniscal tear). When asked whether going up stairs aggravates the knee pain, the patient indicated that she was unsure. Thus, her PCP must now make a decision as to what imaging test should be ordered. Furthermore, the selection of the imaging exam must be tempered by the availability of the imaging equipment, the needed expertise to interpret the imaging study, and other potential constraints (e.g., cost, speed of interpretation, etc.). First, supporting the practice of evidence-based medicine (EBM) is a guiding principle of biomedical informatics, and hence medical imaging informatics. The development and deployment of practice guidelines in diagnosis and treatment has been an enduring effort of the discipline, suggesting and reminding physicians on courses of action to improve care. For instance, if the patient’s clinician was unaware of the sign of a meniscal tear, the system should automatically inform him that an MR may be indicated if she has knee pain when climbing stairs; and supporting literature can be automatic￾ally suggested for review. Second, formal methods for medical decision-making are central to informatics, as are the representation of medical knowledge needed to inform the algorithms [10]. Techniques from computer science, ranging from rudimentary rule￾bases to statistical methods (e.g., decision trees); through to more complex probabilistic hidden Markov models (HMMs) and Bayesian belief networks (BBNs) are finding applications in medicine (see Chapter 8). For example, the evidence of the patient’s medical history, her response to the physician’s inquiries, the availability of imaging, and the relative urgency of the request can be used in an influence diagram to choose between the x-ray and MR (see Chapter 9). Such formalizations are providing new tools to model disease and to reason with partial evidence. Essential to the construction of many of these models is the compilation of large amounts of (observational) data from which data mining and other computational methods are applied to generate new knowledge. In this example, these disease models can be used: to identify further questions that can be asked to further elucidate the patient’s condition (improving the likelihood of choosing an optimal imaging exam); and to select the type of imaging study, and even its acquisition parameters, to best rule in/out elements of the differential diagnosis. Ultimately, an electronic imaging infrastructure that expedites accurate diagnosis can improve the quality of healthcare; and even within this simple example of choosing an imaging protocol, the role of informatics is apparent in enhancing the process of care. When used appropriately, medical imaging is effective at objectifying the initial diagnostic hypothesis (differential diagnosis) and guiding the subsequent work-up. Given a chief complaint and initial assessment data, one can envision that specialists or software algorithms would select an imaging protocol for an appropriate medical condition even before a visit to the PCP. The PCP can then access both objective imaging and clinical data prior to the patient’s visit. Medical imaging informatics research looks to improve the fundamental technical methods, with ensuing translation to clinical applications. Cost Considerations Some have targeted the cost of imaging as a major problem in healthcare within the United States: one 2005 estimate by the American College of Radiology (ACR) was that $100 billion is spent annually on diagnostic imaging, including computed