A taxonomic description of computer-based clinical decision support systems

Berlin, Amy; Sorani, Marco D.; Sim, Ida

doi:10.1016/j.jbi.2005.12.003

Cited by 103 publications

(73 citation statements)

References 57 publications

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“…Existing taxonomies would not suffice because they do not readily map to a measurement framework. [4][5][6][7][8] We sought to generate metrics in sets rather than one by one to reduce the time and expense of developing and maintaining performance metrics for alerts.…”

Section: Objectivementioning

confidence: 99%

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A Metrics Taxonomy and Reporting Strategy for Rule-Based Alerts

Krall¹,

Gerace²

2015

TPJ

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Section: Objectivementioning

confidence: 99%

“…There have been a number of clinical decision support and alert taxonomies developed for different purposes. [4][5][6][7][8] …”

Section: Original Research and Contributionsmentioning

confidence: 99%

A Metrics Taxonomy and Reporting Strategy for Rule-Based Alerts

Krall¹,

Gerace²

2015

TPJ

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“…ESS tools are known to be highly heterogeneous and standard taxonomy systems have been used for their classification, like the one suggested from Berlin et al [10]. Studies were not stratified by formal quality scores, as current evidence suggests against employing quality scores in the analysis of diagnostic accuracy studies in systematic reviews [11].…”

Section: Study Selection and Data Analysismentioning

confidence: 99%

Diagnostic Performance of Electronic Syndromic Surveillance Systems in Acute Care

Kashiouris

O’Horo

Pickering³

et al. 2013

Appl Clin Inform

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SummaryContext: Healthcare Electronic Syndromic Surveillance (ESS) is the systematic collection, analysis and interpretation of ongoing clinical data with subsequent dissemination of results, which aid clinical decision-making. Objective:To evaluate, classify and analyze the diagnostic performance, strengths and limitations of existing acute care ESS systems. Data Sources: All available to us studies in Ovid MEDLINE, Ovid EMBASE, CINAHL and Scopus databases, from as early as January 1972 through the first week of September 2012. Study Selection: Prospective and retrospective trials, examining the diagnostic performance of inpatient ESS and providing objective diagnostic data including sensitivity, specificity, positive and negative predictive values. Data Extraction: Two independent reviewers extracted diagnostic performance data on ESS systems, including clinical area, number of decision points, sensitivity and specificity. Positive and negative likelihood ratios were calculated for each healthcare ESS system. A likelihood matrix summarizing the various ESS systems performance was created. Results: The described search strategy yielded 1639 articles. Of these, 1497 were excluded on abstract information. After full text review, abstraction and arbitration with a third reviewer, 33 studies met inclusion criteria, reporting 102,611 ESS decision points. The yielded I2 was high (98.8%), precluding meta-analysis. Performance was variable, with sensitivities ranging from 21% -100% and specificities ranging from 5%-100%. Conclusions: There is significant heterogeneity in the diagnostic performance of the available ESS implements in acute care, stemming from the wide spectrum of different clinical entities and ESS systems. Based on the results, we introduce a conceptual framework using a likelihood ratio matrix for evaluation and meaningful application of future, frontline clinical decision support systems.

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“…[13].There are several taxonomies for CDSSs in the literature (E.g. [5,14,15]) but in each of them Diagnosis Decision Support Systems are present.…”

Section: Introductionmentioning

confidence: 99%

SeDeLo: Using Semantics and Description Logics to Support Aided Clinical Diagnosis

et al. 2011

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Automated medical diagnosis systems based on knowledge-oriented descriptions have gained momentum with the emergence of semantic descriptions. The objective of this paper is to propose a normalized design that solves some of the problems which have been detected by authors in previous tools. The authors bring together two different technologies to develop a new clinical decision support system: description logics aimed at developing inference systems to improve decision support for the prevention, treatment and management of illness and semantic technologies. Because of its new design, the system is capable of obtaining improved diagnostics compared with previous efforts. However, this evaluation is more focused in the computational performance, giving as result that description logics is a good solution with small data sets. In this paper, we provide a well-structured ontology for automated diagnosis in the medical field and a three-fold formalization based on Description Logics with the use of Semantic Web technologies.

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A taxonomic description of computer-based clinical decision support systems

Abstract: Reported CDSSs are heterogeneous along many dimensions. Caution should be taken in generalizing the results of CDSS RCTs to different clinical or workflow settings.

Cited by 103 publications

References 57 publications

A Metrics Taxonomy and Reporting Strategy for Rule-Based Alerts

A Metrics Taxonomy and Reporting Strategy for Rule-Based Alerts

Diagnostic Performance of Electronic Syndromic Surveillance Systems in Acute Care

SeDeLo: Using Semantics and Description Logics to Support Aided Clinical Diagnosis

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