MED INF 406 Decision Support Systems & Health Care
Course Description
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This course provides an introduction to clinical decision support systems in health information technology.
Instruction is given in formal decision analysis techniques as they apply to decisions in the medical domain. Clinical decision support systems are introduced and issues relating to their design and implementation discussed. The mathematical foundations upon which they are based will be examined. Evidence-based guidelines and performance measurement techniques will be presented. A framework for designing and implementing clinical decision support systems will be introduced. Principles learned from this framework will be applied in writing a final paper that describes a prototype decision support system, including justification for its use and a description of steps followed in its design, implementation and performance measurement. |
Instructor: Gerasimos Petratos, MD, MS and Imran Khan, MS
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Course Syllabus
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Learning Goals
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Text
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Hunink, H. & Glasziou, P. (2009). Decision making in health and medicine: Integrating evidence and
values (7th printing or later). Cambridge, England: Cambridge University Press. |
Decision making in health care means navigating through a complex and tangled web of diagnostic and therapeutic uncertainties, patient preferences and values, and costs. In addition, medical therapies may include side effects, surgery may lead to undesirable complications, and diagnostic technologies may produce inconclusive results. In many clinical and health policy decisions it is necessary to counterbalance benefits and risks, and to trade off competing objectives such as maximizing life expectancy vs optimizing quality of life vs minimizing the required resources. This textbook plots a clear course through these complex and conflicting variables. It clearly explains and illustrates tools for integrating quantitative evidence-based data and subjective outcome values in making clinical and health policy decisions.
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Berner, E. S. (ed.). (2007). Clinical decision support systems: Theory and practice (2nd ed. or later).
New York, New York: Springer, Health Informatics Series. |
This is a resource book on clinical decision support systems for informatics specialists, a textbook for teachers or students in health informatics and a comprehensive introduction for clinicians. It has become obvious that, in addition to physicians, other health professionals have need of decision support. Therefore, the issues raised in this book apply to a broad range of clinicians. The book includes chapters written by internationally recognized experts on the design, evaluation and application of these systems, who examine the impact of computer-based diagnostic tools both from the practitioner’s perspective and that of the patient.
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Osteroff, J. A., Pifer, E. A., Teich, J. M., Sittig, D. F., & Jenders, R. A. (2005). Improving outcomes with
clinical decision support: an implementer's guide. Chicago, IL: HIMSS. [ISBN 0-9761277-2-5] |
This implementer's guide provides a step-by-step roadmap on planning, implementing and monitoring a Clinical Decision Support (CDS) program for driving performance improvement.
Packed full of practical guidance, Improving Outcomes with Clinical Decision Support: An Implementer's Guide contains real world examples, worksheets, rich links to supportive materials, plus a robust glossary of terms and acronyms. |
Course Reflection:
A clinical decision support system (CDSS) is an application that analyzes data to help healthcare providers make clinical decisions. A CDSS is an adaptation of the decision support system commonly used to support business management.
Physicians, nurses and other health care professionals use a CDSS to prepare a diagnosis and to review the diagnosis as a means of improving the final result. Data mining may be conducted to examine the patient’s medical history in conjunction with relevant clinical research. Such analysis can help predict potential events, which can range from drug interactions to disease symptoms.
There are two main types of clinical decision support systems. One type of CDSS, which uses a knowledge base, applies rules to patient data using an inference engine and displays the results to the end user. Systems without a knowledge base, on the other hand, rely on machine learning to analyze clinical data.
There are several challenges impeding the adoption of clinical decision support systems. A CDSS must be integrated with a health care organization’s clinical workflow, which is often already complex. Most clinical decision support systems are standalone products that lack interoperability with reporting and electronic health record (EHR) software. The sheer number of clinical research and medical trials being published on an ongoing basis makes it difficult to incorporate the resulting data. Furthermore, incorporating large amounts of data into existing systems places significant strains on application and infrastructure maintenance.
Nevertheless, the use of clinical decision support systems is expected to increase in light of the Health Information Technology for Economic and Clinical Health (HITECH) Act, which stipulates that health care providers must demonstrate the meaningful use of health IT by 2015 or face reduced Medicare reimbursements beginning in 2016. Under meaningful use, providers must implement one clinical decision support rule, including diagnostic test ordering, as well as the ability to track compliance with that rule. That rule, furthermore, should apply to a specialty or high-priority condition.
A clinical decision support system (CDSS) is an application that analyzes data to help healthcare providers make clinical decisions. A CDSS is an adaptation of the decision support system commonly used to support business management.
Physicians, nurses and other health care professionals use a CDSS to prepare a diagnosis and to review the diagnosis as a means of improving the final result. Data mining may be conducted to examine the patient’s medical history in conjunction with relevant clinical research. Such analysis can help predict potential events, which can range from drug interactions to disease symptoms.
There are two main types of clinical decision support systems. One type of CDSS, which uses a knowledge base, applies rules to patient data using an inference engine and displays the results to the end user. Systems without a knowledge base, on the other hand, rely on machine learning to analyze clinical data.
There are several challenges impeding the adoption of clinical decision support systems. A CDSS must be integrated with a health care organization’s clinical workflow, which is often already complex. Most clinical decision support systems are standalone products that lack interoperability with reporting and electronic health record (EHR) software. The sheer number of clinical research and medical trials being published on an ongoing basis makes it difficult to incorporate the resulting data. Furthermore, incorporating large amounts of data into existing systems places significant strains on application and infrastructure maintenance.
Nevertheless, the use of clinical decision support systems is expected to increase in light of the Health Information Technology for Economic and Clinical Health (HITECH) Act, which stipulates that health care providers must demonstrate the meaningful use of health IT by 2015 or face reduced Medicare reimbursements beginning in 2016. Under meaningful use, providers must implement one clinical decision support rule, including diagnostic test ordering, as well as the ability to track compliance with that rule. That rule, furthermore, should apply to a specialty or high-priority condition.
Reflections Here