Critical Issues in Mobile Solution-Based Clinical Decision Support Systems

Support Systems ◽

Digital Health ◽

Clinical Decision ◽

Primary Objective ◽

Critical Issues

The use of mobile solutions for clinical decision support is still a rather nascent area within digital health. Shedding light on this important application of mobile technology, this chapter presents the initial findings of a scoping review. The review's primary objective is to identify the state of the art of mobile solution based clinical decision support systems and the persisting critical issues. The authors contribute by classifying identified critical issues into two matrices. Firstly, the issues are classified according to a matrix the authors developed, to be indicative of the stage (or timing) at which the issues occur along the timeline of mobile solution development. This classification includes the three classes: issues persisting at the (1) stage of developing mobile solutions, (2) stage of evaluating developed solutions, and (3) stage of adoption of developed solutions. Secondly, the authors present a classification of the same issues according to a standard socio-technical matrix containing the three classes: (1) technological, (2) process, and (3) people issues.

OP199 From Pilot Studies To System-Wide Innovation: Challenges And Opportunities For Clinical Decision Support Systems (CDSS) Implementation In Australia

International Journal of Technology Assessment in Health Care ◽

10.1017/s0266462321000775 ◽

2021 ◽

Vol 37 (S1) ◽

pp. 5-6

Author(s):

Mah Laka ◽

Adriana Milazzo ◽

Drew Carter ◽

Tracy Merlin

Keyword(s):

Decision Support ◽

Clinical Decision Making ◽

Support Systems ◽

Digital Health ◽

National Level ◽

Clinical Decision ◽

Policy Framework

IntroductionThe clinical data is increasing at a considerably higher rate than the capacity of the healthcare system and clinicians to manage this data. Digital tools such as clinical decision support systems (CDSS) provide opportunities for evidence-based patient care by intelligently filtering and presenting the information required for clinical decision making at the point of care. Despite the success of pilot projects, CDSS have had limited implementation in broader health systems. We aimed to identify challenges faced by policymakers for CDSS implementation and to provide policy recommendations.MethodsWe conducted eleven semi-structured interviews with Australian policymakers from state and national committees involved in digital health activities. The data were analyzed using reflexive thematic analysis to identify policy priorities.ResultsOur findings indicate that fragmentation of care processes and structures in the digital health ecosystem is one of the main impediments to delivering coordinated care using CDSS. Five themes for policy action were identified: (i) establishing a shared conceptual framework for user-centered design of CDSS that is aligned with stakeholders’ priorities, (ii) maintaining the right balance between the customization and standardization of systems, (iii) developing mutually agreed semantic interoperability standards at the local, state and national level, allowing generation and exchange of information across the health system without changing its context and meaning, (iv) reorienting organizational structures to build capacity to foster change, and (v) developing collaborative care models to avoid conflicting interests between stakeholders.ConclusionsFindings highlight the importance of developing system-wide guidance to establish a clear vision for CDSS implementation and alignment of organizational processes across all levels of health care. There is a need to build a shared policy framework for modelling the innovative activities such as CDSS implementation across the digital health landscape which minimizes the operational and strategic fragmentation of different organizations.

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.