Behind the screens: Clinical decision support methodologies – A review

Fraccaro, Paolo; O’Sullivan, Dympna; Plastiras, Panagiotis; O׳Sullivan, Hugh; Dentone, Chiara; Biagio, Antonio Di; Weller, Peter

doi:10.1016/j.hlpt.2014.10.001

Cited by 31 publications

(23 citation statements)

References 44 publications

(33 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…During a training phase, machine learning systems learn to predict or classify a problem based on existing data, such as determining whether a certain disease exists or not. After the training phase, the system can make a prediction for a new dataset [29]. Information retrieval is defined as the extraction of information in a resource to find a required piece of information.…”

Section: Functionality: Machine Learning and Information Retrievalmentioning

confidence: 99%

Diagnosis of Rare Diseases: A scoping review of clinical decision support systems

Schaaf

Sedlmayr

Schaefer

et al. 2019

Preprint

View full text Add to dashboard Cite

Background Rare Diseases (RD), which are defined as diseases affecting not more than 5 out of 10,000 people, are often severe, chronic, degenerative and life-threating. A main problem is the delay in diagnosis of RD. Clinical Decision Support Systems (CDSS) for RD are software-systems to support physicians in the diagnosis of patients with RD. It would therefore be useful to get a comprehensive overview of which CDSS are available and can be used under what conditions. In this work we provide a review of current CDSS in RD and which functionality and data are used by the CDSS. Methods We searched Pubmed and Cochrane for CDSS in RD published between December 1, 2008 and December 16, 2018. Only English articles, original peer reviewed journals and conference paper describing a clinical prototype or a routine use of CDSS where included. A total of 2076 articles were found and following a screening step 16 articles (describing 13 different CDSS) were considered as relevant for the final analysis. We then described and compared the CDSS using the defined categories “functionality”, “development status”, “type of clinical data” and “system availability”. Results Three types of CDSS for RD were identified: “Machine Learning and Information retrieval”, “Web Search”, and “Phenotypic and genetic matching”. 8 of the 13 reviewed CDSS are publicly available and for use by physicians. The other remaining CDSS are clinical prototypes which have been applied in clinical studies but are not accessible to others. Only one clinical prototype online. The approaches of the CDSS differ depending on what type of clinical data is used. “Machine Learning and information retrieval” can show recommendations for a diagnosis, while Web “search CDSS” will retrieve articles from literature databases (e.g. case reports), which may provide hints for a possible Diagnosis. CDSS in “Phenotypic and genetic matching” can identify similar patients based on genetic or phenotypic data. Conclusions Different CDSS for different purposes have been established and physicians have to decide which CDSS is more accurate for a particular patient case. It remains to be seen which of the CDSS will be used and maintained in the future.

show abstract

Section: Functionality: Machine Learning and Information Retrievalmentioning

confidence: 99%

Diagnosis of Rare Diseases: A scoping review of clinical decision support systems

Schaaf

Sedlmayr

Schaefer

et al. 2019

Preprint

View full text Add to dashboard Cite

show abstract

“…According to Fraccaroa et al (2014), a clinical DSS can be implemented as a passive system, a semi-active system or an active system according to how it is being triggered. Depending on the clinical tasks to be achieved, typical technologies used to develop such a system include machine learning, knowledge representation and data mining.…”

Section: Technologiesmentioning

confidence: 99%

Decision support systems for adoption in dental clinics: A survey

Goh

Tao

et al. 2016

Knowledge-Based Systems

View full text Add to dashboard Cite

While most dental clinicians use some sort of information system, they are involved with administrative functions, despite the advisory potential of some of these systems. This paper outlines some current decision support systems (DSS) and the common barriers facing dentists in adopting them within their workflow. These barriers include lack of perceived usefulness, complicated social and economic factors, and the difficulty for users to interpret the advice given by the system. A survey of current systems found that although there are systems that suggest treatment options, there is no real-time integration with other knowledge bases. Additionally, advice on drug prescription at point-of-care is absent from such systems, which is a significant omission, in consideration of the fact that disease management and drug prescription are common in the workflow of a dentist. This paper also addresses future trends in the research and development of dental clinical DSS, with specific emphasis on big data, standards and privacy issues to fulfil the vision of a robust, user-friendly and scalable personalised DSS for dentists. The findings of this study will offer strategies in design, research and development of a DSS with sufficient perceived usefulness to attract adoption and integration by dentists within their routine clinical workflow, thus resulting in better health outcomes for patients and increased productivity for the clinic.

show abstract

“…A recommendation with low or no dependencies would benefit from automated, provider-input free action (e.g., flu vaccine order for appropriate individuals) whereas complex CDS such as those used in sepsis care would benefit from increased provider input at time of recommendation. CDS can be designed heterogeneously both in terms of target audience (provider vs. pharmacists vs. nurse) and trigger events such that alerts can be passive (pulled by users), semiactive (knowledge representation and call to action), to active (automatic action without user intervention or knowledge) [88].…”

Section: How Is Cds Effective?mentioning

confidence: 99%

Is there a “magic” hemoglobin number? Clinical decision support promoting restrictive blood transfusion practices

Goodnough

Shah

2015

American J Hematol

View full text Add to dashboard Cite

Blood transfusion has been identified as one of the most frequently performed therapeutic procedures, with a significant percentage of transfusions identified to be inappropriate. Recent key clinical trials in adults have provided Level 1 evidence to support restrictive red blood cell (RBC) transfusion practices. However, some advocates have attempted to identify a “correct” Hb threshold for RBC transfusion; whereas others assert that management of anemia, including transfusion decisions, must take into account clinical patient variables, rather than simply one diagnostic laboratory test. The heterogeneity of guidelines for blood transfusion by a number of medical societies reflects this controversy. Clinical decision support (CDS) uses a Hb threshold number in a smart Best Practices Alert (BPA) upon physician order, to trigger a concurrent utilization self‐review for whether blood transfusion therapy is appropriate. This review summarizes Level 1 evidence in seven key clinical trials in adults that support restrictive transfusion practices, along strategies made possible by CDS that have demonstrated value in improving blood utilization by promoting restrictive transfusion practices. Am. J. Hematol. 90:927–933, 2015. © 2015 Wiley Periodicals, Inc.

show abstract

Behind the screens: Clinical decision support methodologies – A review

Abstract: This is the accepted version of the paper.This version of the publication may differ from the final published version. Permanent

Cited by 31 publications

References 44 publications

Diagnosis of Rare Diseases: A scoping review of clinical decision support systems

Diagnosis of Rare Diseases: A scoping review of clinical decision support systems

Decision support systems for adoption in dental clinics: A survey

Is there a “magic” hemoglobin number? Clinical decision support promoting restrictive blood transfusion practices

Contact Info

Product

Resources

About