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      Requirements for guidelines systems: implementation challenges and lessons from existing software-engineering efforts

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          Abstract

          Background

          A large body of work in the clinical guidelines field has identified requirements for guideline systems, but there are formidable challenges in translating such requirements into production-quality systems that can be used in routine patient care. Detailed analysis of requirements from an implementation perspective can be useful in helping define sub-requirements to the point where they are implementable. Further, additional requirements emerge as a result of such analysis. During such an analysis, study of examples of existing, software-engineering efforts in non-biomedical fields can provide useful signposts to the implementer of a clinical guideline system.

          Methods

          In addition to requirements described by guideline-system authors, comparative reviews of such systems, and publications discussing information needs for guideline systems and clinical decision support systems in general, we have incorporated additional requirements related to production-system robustness and functionality from publications in the business workflow domain, in addition to drawing on our own experience in the development of the Proteus guideline system ( http://proteme.org).

          Results

          The sub-requirements are discussed by conveniently grouping them into the categories used by the review of Isern and Moreno 2008. We cite previous work under each category and then provide sub-requirements under each category, and provide example of similar work in software-engineering efforts that have addressed a similar problem in a non-biomedical context.

          Conclusions

          When analyzing requirements from the implementation viewpoint, knowledge of successes and failures in related software-engineering efforts can guide implementers in the choice of effective design and development strategies.

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          Most cited references30

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          Decision trees: an overview and their use in medicine.

          In medical decision making (classification, diagnosing, etc.) there are many situations where decision must be made effectively and reliably. Conceptual simple decision making models with the possibility of automatic learning are the most appropriate for performing such tasks. Decision trees are a reliable and effective decision making technique that provide high classification accuracy with a simple representation of gathered knowledge and they have been used in different areas of medical decision making. In the paper we present the basic characteristics of decision trees and the successful alternatives to the traditional induction approach with the emphasis on existing and possible future applications in medicine.
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            • Record: found
            • Abstract: not found
            • Article: not found

            Application of artificial neural networks to clinical medicine.

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              • Record: found
              • Abstract: found
              • Article: not found

              Bayesian statistics in medicine: a 25 year review.

              This review examines the state of Bayesian thinking as Statistics in Medicine was launched in 1982, reflecting particularly on its applicability and uses in medical research. It then looks at each subsequent five-year epoch, with a focus on papers appearing in Statistics in Medicine, putting these in the context of major developments in Bayesian thinking and computation with reference to important books, landmark meetings and seminal papers. It charts the growth of Bayesian statistics as it is applied to medicine and makes predictions for the future. From sparse beginnings, where Bayesian statistics was barely mentioned, Bayesian statistics has now permeated all the major areas of medical statistics, including clinical trials, epidemiology, meta-analyses and evidence synthesis, spatial modelling, longitudinal modelling, survival modelling, molecular genetics and decision-making in respect of new technologies.
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                Author and article information

                Journal
                BMC Med Inform Decis Mak
                BMC Med Inform Decis Mak
                BMC Medical Informatics and Decision Making
                BioMed Central
                1472-6947
                2012
                9 March 2012
                : 12
                : 16
                Affiliations
                [1 ]Henry Ford Health System, Detroit, USA
                [2 ]Center for Medical Informatics, Yale University Medical School, New Haven, USA
                Article
                1472-6947-12-16
                10.1186/1472-6947-12-16
                3342141
                22405400
                c060edeb-a040-41e7-91c3-e1d7656d51ed
                Copyright ©2012 Shah et al; licensee BioMed Central Ltd.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 4 August 2011
                : 9 March 2012
                Categories
                Correspondence

                Bioinformatics & Computational biology
                Bioinformatics & Computational biology

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