Opening Access To Practice-based Evidence in Clinical Decision Support Systems with Natural Query Language

<p>Evidence-based medicine can be effective only if constantly tested against errors in medical practice. Clinical record database summarization supported by a machine allows allow to detect anomalies and therefore help detect the errors in early phases of care. Summarization system is a part of Clinical Decision Support Systems however it cannot be used directly by the stakeholder as long as s/he is not able to query the clinical record database. Natural Query Languages allow opening access to data for clinical practitioners, that usually do not have knowledge about articial query languages. Results: We have developed general purpose reporting system called Ask Data Anything (ADA) that we applied to a particular CDSS implementation. As a result, we obtained summarization system that opens the access for these of clinical researchers that were excluded from the meaningful summary of clinical records stored in a given clinical database. The most significant part of the component - NQL parser - is a hybrid of Controlled Natural Language (CNL) and pattern matching with a prior error repair phase. Equipped with reasoning capabilities due to the intensive use of semantic technologies, our hybrid approach allows one to use very simple, keyword-based (even erroneous) queries as well as complex CNL ones with the support of a predictive editor. By using ADA sophisticated summarizations of clinical data are produced as a result of NQL query execution. In this paper, we will present the main ideas underlying ADA component in the context of CDSS.</p>

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Architectures for Architects

Methods of Information in Medicine ◽

10.1055/s-0038-1634899 ◽

1993 ◽

Vol 32 (01) ◽

pp. 12-13 ◽

Cited By ~ 7

Author(s):

M. A. Musen

Keyword(s):

Decision Support ◽

Decision Support Systems ◽

Clinical Decision Support ◽

Support Systems ◽

Clinical Decision Support Systems ◽

Clinical Decision ◽

Design And Development

Abstract:Response to Heathfield HA, Wyatt J. Philosophies for the design and development of clinical decision-support systems. Meth Inform Med 1993; 32: 1-8.

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Characterizing Decision Support Telemedicine Systems

Methods of Information in Medicine ◽

10.1055/s-0038-1634113 ◽

2006 ◽

Vol 45 (05) ◽

pp. 523-527 ◽

Cited By ~ 3

Author(s):

A. Abu-Hanna ◽

B. Nannings

Keyword(s):

Decision Support ◽

Decision Support Systems ◽

Clinical Decision Support ◽

Literature Search ◽

Support Systems ◽

Clinical Decision Support Systems ◽

Clinical Decision ◽

Problem Dimension ◽

Process Dimension

Summary Objectives: Decision Support Telemedicine Systems (DSTS) are at the intersection of two disciplines: telemedicine and clinical decision support systems (CDSS). The objective of this paper is to provide a set of characterizing properties for DSTSs. This characterizing property set (CPS) can be used for typing, classifying and clustering DSTSs. Methods: We performed a systematic keyword-based literature search to identify candidate-characterizing properties. We selected a subset of candidates and refined them by assessing their potential in order to obtain the CPS. Results: The CPS consists of 14 properties, which can be used for the uniform description and typing of applications of DSTSs. The properties are grouped in three categories that we refer to as the problem dimension, process dimension, and system dimension. We provide CPS instantiations for three prototypical applications. Conclusions: The CPS includes important properties for typing DSTSs, focusing on aspects of communication for the telemedicine part and on aspects of decisionmaking for the CDSS part. The CPS provides users with tools for uniformly describing DSTSs.

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The acceptance of clinical decision support systems among clinicians in the treatment of neck and/or back pain

European Journal of Public Health ◽

10.1093/eurpub/ckaa165.1234 ◽

2020 ◽

Vol 30 (Supplement_5) ◽

Author(s):

S M Jansen-Kosterink ◽

M Cabrita ◽

I Flierman

Keyword(s):

Back Pain ◽

Decision Support ◽

Focus Groups ◽

Decision Support Systems ◽

Clinical Decision Support ◽

Support Systems ◽

Clinical Decision Support Systems ◽

Clinical Decision ◽

Case Based Reasoning ◽

Online Questionnaire

Abstract Background Clinical Decision Support Systems (CDSSs) are computerized systems using case-based reasoning to assist clinicians in making clinical decisions. Despite the proven added value to public health, the implementation of CDSS clinical practice is scarce. Particularly, little is known about the acceptance of CDSS among clinicians. Within the Back-UP project (Project Number: H2020-SC1-2017-CNECT-2-777090) a CDSS is developed with prognostic models to improve the management of Neck and/or Low Back Pain (NLBP). Therefore, the aim of this study is to present the factors involved in the acceptance of CDSSs among clinicians. Methods To assess the acceptance of CDSSs among clinicians we conducted a mixed method analysis of questionnaires and focus groups. An online questionnaire with a low-fidelity prototype of a CDSS (TRL3) was sent to Dutch clinicians aimed to identify the factors influencing the acceptance of CDSSs (intention to use, perceived threat to professional autonomy, trusting believes and perceived usefulness). Next to this, two focus groups were conducted with clinicians addressing the general attitudes towards CDSSs, the factors determining the level of acceptance, and the conditions to facilitate use of CDSSs. Results A pilot-study of the online questionnaire is completed and the results of the large evaluation are expected spring 2020. Eight clinicians participated in two focus groups. After being introduced to various types of CDSSs, participants were positive about the value of CDSS in the care of NLBP. The clinicians agreed that the human touch in NLBP care must be preserved and that CDSSs must remain a supporting tool, and not a replacement of their role as professionals. Conclusions By identifying the factors hindering the acceptance of CDSSs we can draw implications for implementation of CDSSs in the treatment of NLBP.

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Enhancing trust in clinical decision support systems: a framework for developers

BMJ Health & Care Informatics ◽

10.1136/bmjhci-2020-100247 ◽

2021 ◽

Vol 28 (1) ◽

pp. e100247

Author(s):

Caroline Jones ◽

James Thornton ◽

Jeremy C Wyatt

Keyword(s):

Decision Support ◽

Decision Support Systems ◽

Clinical Decision Support ◽

Support Systems ◽

Clinical Decision Support Systems ◽

Clinical Decision

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Barriers and facilitators to the adoption of electronic clinical decision support systems: a qualitative interview study with UK general practitioners

BMC Medical Informatics and Decision Making ◽

10.1186/s12911-021-01557-z ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Elizabeth Ford ◽

Natalie Edelman ◽

Laura Somers ◽

Duncan Shrewsbury ◽

Marcela Lopez Levy ◽

...

Keyword(s):

Decision Support ◽

Decision Support Systems ◽

General Practitioners ◽

Clinical Decision Support ◽

Support Systems ◽

Clinical Decision Support Systems ◽

Clinical Decision ◽

Structured Interview ◽

Qualitative Interview Study ◽

High Level

Abstract Background Well-established electronic data capture in UK general practice means that algorithms, developed on patient data, can be used for automated clinical decision support systems (CDSSs). These can predict patient risk, help with prescribing safety, improve diagnosis and prompt clinicians to record extra data. However, there is persistent evidence of low uptake of CDSSs in the clinic. We interviewed UK General Practitioners (GPs) to understand what features of CDSSs, and the contexts of their use, facilitate or present barriers to their use. Methods We interviewed 11 practicing GPs in London and South England using a semi-structured interview schedule and discussed a hypothetical CDSS that could detect early signs of dementia. We applied thematic analysis to the anonymised interview transcripts. Results We identified three overarching themes: trust in individual CDSSs; usability of individual CDSSs; and usability of CDSSs in the broader practice context, to which nine subthemes contributed. Trust was affected by CDSS provenance, perceived threat to autonomy and clear management guidance. Usability was influenced by sensitivity to the patient context, CDSS flexibility, ease of control, and non-intrusiveness. CDSSs were more likely to be used by GPs if they did not contribute to alert proliferation and subsequent fatigue, or if GPs were provided with training in their use. Conclusions Building on these findings we make a number of recommendations for CDSS developers to consider when bringing a new CDSS into GP patient records systems. These include co-producing CDSS with GPs to improve fit within clinic workflow and wider practice systems, ensuring a high level of accuracy and a clear clinical pathway, and providing CDSS training for practice staff. These recommendations may reduce the proliferation of unhelpful alerts that can result in important decision-support being ignored.

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