A Roadmap for National Action on Clinical Decision Support

Osheroff, Jerome A.; Teich, Jonathan M.; Middleton, Blackford; Steen, Elaine B.; Wright, Adam; Detmer, Don E.

doi:10.1197/jamia.m2334

Cited by 529 publications

(367 citation statements)

References 36 publications

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“…The work of many authors supports the evidence that decision support systems can be extremely effective in improving the quality and safety of healthcare (e.g. [3,[7][8][9][10]). The most famous expert systems were built as diagnosis assistants and therapy advisors in different medical areas (MYCIN [11], ONCOCIN [12], etc.)…”

Section: Introductionmentioning

confidence: 57%

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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Section: Introductionmentioning

confidence: 57%

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

et al. 2011

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“…(11,12) Finally, there are significant limitations in retrieving data to make inferences: implementing decision support in existing EHR platforms is challenging. (11,12,14) In order to surmount these challenges, better guidelines (15,16) and an improved understanding of EHR workflow and usability are needed. (17) However, another key advance needed to enable wider use of effective CDS is the ability to share actionable CDS content across sites.…”

Section: Challenges To Adoption Of Clinical Decision Supportmentioning

confidence: 99%

“…(17) However, another key advance needed to enable wider use of effective CDS is the ability to share actionable CDS content across sites. (11)(12)(13)(14) Past efforts have focused on sharing CDS among institutions with the same EHR vendor using internet-based libraries (18), which limits the scalability of CDS, or moving decision support artifacts from an institution with one EHR to another with a different EHR as structured logic representations. (19)(20)(21) Though numerous standards have been developed to facilitate guideline-sharing, the benefits have been counterbalanced by timeintensive efforts to resolve local terminology issues (e.g., Arden's "curly braces problem") (22), and a lack of consensus about which of the many standards to use.…”

Section: Challenges To Adoption Of Clinical Decision Supportmentioning

confidence: 99%

Lessons learned from implementing service-oriented clinical decision support at four sites: A qualitative study

Wright

Sittig

Ash

et al. 2015

International Journal of Medical Informatics

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Objective: To identify challenges, lessons learned and best practices for service-oriented clinical decision support, based on the results of the Clinical Decision Support Consortium, a multi-site study which developed, implemented and evaluated clinical decision support services in a diverse range of electronic health records.Methods: Ethnographic investigation using the rapid assessment process, a procedure for agile qualitative data collection and analysis, including clinical observation, system demonstrations and analysis and 91 interviews. Results:We identified challenges and lessons learned in eight dimensions: (1) hardware and software computing infrastructure, (2) clinical content, (3) human-computer interface, (4) people, (5) workflow and communication, (6) internal organizational policies, procedures, environment and culture, (7) external rules, regulations, and pressures and (8) system measurement and monitoring. Key challenges included performance issues (particularly related to data retrieval), differences in terminologies used across sites, workflow variability and the need for a legal framework.Discussion: Based on the challenges and lessons learned, we identified eight best practices for developers and implementers of service-oriented clinical decision support: (1) optimize performance, or make asynchronous calls, (2) be liberal in what you accept (particularly for terminology), (3) foster clinical transparency, (4) develop a legal framework, (5) support a flexible front-end, (6) dedicate human resources, (7) support peer-to-peer communication, (8) improve standards. Conclusion:The Clinical Decision Support Consortium successfully developed a clinical decision support service and implemented it in four different electronic health records and four diverse clinical sites; however, the process was arduous. The lessons identified by the Consortium may be useful for other developers and implementers of clinical decision support services.

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“…A clinical DSS is an IS which has the ability to provide knowledge and personalised information to users, intelligently filtered to enhance health and healthcare outcomes (Osheroff et al, 2007). They are not intended to replace the dentist's judgment and responsibility for decision-making, but to provide assistance in diagnosis and treatment planning (Vikram and Karjodkar, 2009).…”

Section: Understanding Clinical Decision Support Systemsmentioning

confidence: 99%

“…Awareness of the barriers against adoption of an IS in a dental clinic, the expectations and requirements of dentists for such a system, and important factors such as perceived usefulness, sociocultural and economic factors, and ease of interpretation, will aid vendors in customising a more relevant and efficient system. Providing comprehensive and consistent knowledge through a DSS will result in increased demand for such systems (Osheroff et al, 2007).…”

Section: Introductionmentioning

confidence: 99%

Decision support systems for adoption in dental clinics: A survey

Goh

Tao

et al. 2016

Knowledge-Based Systems

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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.

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A Roadmap for National Action on Clinical Decision Support

Cited by 529 publications

References 36 publications

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

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

Lessons learned from implementing service-oriented clinical decision support at four sites: A qualitative study

Decision support systems for adoption in dental clinics: A survey

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