Overview of Clinical Decision Support Systems in Healthcare

Medical Information ◽

Support Systems ◽

Clinical Decision ◽

Set Up ◽

Ongoing Support

Clinical Decision Support Systems (CDSS) are software designed to help clinicians to make decisions about patient diagnosis using technical devices such as desktops, laptops and iPads, and mobile devices, to obtain medical information and set up alert systems to monitor medication. A Clinical Decision Support System has been suggested by many as a key to a solution for improving patient safety together with Physician Based Computer Order Entry. This technology could prove to be very important in conditions such as chronic diseases where health outlay is high and where self-efficacy can affect health outcomes. However, the success of CDSS relies on technology, training and ongoing support. This chapter includes a historical overview and practical application of CDSS in medicine, and discusses challenges involved with implementation of such systems. It discusses new frontiers of CDSS and implications of self-management using social computing technologies, in particular in the management of chronic disease.

Methodologies of Legacy Clinical Decision Support System

International Journal of Computers in Clinical Practice ◽

10.4018/ijccp.2017070102 ◽

2017 ◽

Vol 2 (2) ◽

pp. 20-37

Author(s):

Meenakshi Sharmi ◽

Himanshu Aggarwal

Keyword(s):

Decision Support ◽

Decision Support System ◽

Support System ◽

Support Systems ◽

Clinical Decision ◽

Research Area

Information technology playing a prominent role in the field of medical by incorporating the clinical decision support system (CDSS) in their routine practices. CDSS is a computer based interactive program to assist the physician to make the right decision at right time. Nowadays, clinical decision support systems are a dynamic research area in the field of computers, but the lack of understanding, as well as functions of the system, make adoption slow by physicians and patients. The literature review of this article focuses on the overview of legacy CDSS, the kind of methodologies and classifiers employed to prepare such a decision support system using a non-technical approach to the physician and the strategy-makers. This article provides understanding of the clinical decision support along with the gateway to physician, and to policy-makers to develop and deploy decision support systems as a healthcare service to make the quick, agile and right decision. Future directions to handle the uncertainties along with the challenges of clinical decision support systems are also enlightened in this study.

The Effects of Computerized Clinical Decision Support Systems on Laboratory Test Ordering: A Systematic Review

Archives of Pathology & Laboratory Medicine ◽

10.5858/arpa.2016-0115-ra ◽

2017 ◽

Vol 141 (4) ◽

pp. 585-595 ◽

Cited By ~ 14

Author(s):

Nicolas Delvaux ◽

Katrien Van Thienen ◽

Annemie Heselmans ◽

Stijn Van de Velde ◽

Dirk Ramaekers ◽

...

Keyword(s):

Decision Support ◽

Laboratory Test ◽

Support System ◽

Support Systems ◽

Clinical Decision ◽

Test Ordering

Context.— Inappropriate laboratory test ordering has been shown to be as high as 30%. This can have an important impact on quality of care and costs because of downstream consequences such as additional diagnostics, repeat testing, imaging, prescriptions, surgeries, or hospital stays. Objective.— To evaluate the effect of computerized clinical decision support systems on appropriateness of laboratory test ordering. Data Sources.— We used MEDLINE, Embase, CINAHL, MEDLINE In-Process and Other Non-Indexed Citations, Clinicaltrials.gov, Cochrane Library, and Inspec through December 2015. Investigators independently screened articles to identify randomized trials that assessed a computerized clinical decision support system aimed at improving laboratory test ordering by providing patient-specific information, delivered in the form of an on-screen management option, reminder, or suggestion through a computerized physician order entry using a rule-based or algorithm-based system relying on an evidence-based knowledge resource. Investigators extracted data from 30 papers about study design, various study characteristics, study setting, various intervention characteristics, involvement of the software developers in the evaluation of the computerized clinical decision support system, outcome types, and various outcome characteristics. Conclusions.— Because of heterogeneity of systems and settings, pooled estimates of effect could not be made. Data showed that computerized clinical decision support systems had little or no effect on clinical outcomes but some effect on compliance. Computerized clinical decision support systems targeted at laboratory test ordering for multiple conditions appear to be more effective than those targeted at a single condition.

Methodologies of Legacy Clinical Decision Support System

Virtual and Mobile Healthcare ◽

10.4018/978-1-5225-9863-3.ch026 ◽

2020 ◽

pp. 553-568

Author(s):

Meenakshi Sharmi ◽

Himanshu Aggarwal

Keyword(s):

Decision Support ◽

Decision Support System ◽

Support System ◽

Support Systems ◽

Clinical Decision ◽

Research Area

Information technology playing a prominent role in the field of medical by incorporating the clinical decision support system (CDSS) in their routine practices. CDSS is a computer based interactive program to assist the physician to make the right decision at right time. Nowadays, clinical decision support systems are a dynamic research area in the field of computers, but the lack of understanding, as well as functions of the system, make adoption slow by physicians and patients. The literature review of this article focuses on the overview of legacy CDSS, the kind of methodologies and classifiers employed to prepare such a decision support system using a non-technical approach to the physician and the strategy-makers. This article provides understanding of the clinical decision support along with the gateway to physician, and to policy-makers to develop and deploy decision support systems as a healthcare service to make the quick, agile and right decision. Future directions to handle the uncertainties along with the challenges of clinical decision support systems are also enlightened in this study.

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 ◽

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.

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 ◽

Literature Search ◽

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.

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 ◽

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.

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 ◽

Support Systems ◽

Clinical Decision

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 ◽

General Practitioners ◽

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.