Clinical Decision Support Systems

Teufel, Andreas; Binder, Harald

doi:10.1159/000519420

Cited by 14 publications

(7 citation statements)

References 66 publications

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“…Our clinical stakeholders had no concerns about linking data for research, suggesting they are more familiar with the process than the public and therefore may have better comprehension of the ethical considerations and data management systems in place to protect health data. Primary care digital knowledge support systems [ 48 , 49 ] are poised to be particularly effective in managing postnatal care, given the individual variations in women’s experiences during pregnancy and labour, considering specific medical events that may influence both immediate and future postnatal treatment.…”

Section: Discussionmentioning

confidence: 99%

Acceptability of data linkage to identify women at risk of postnatal complication for the development of digital risk prediction tools and interventions to better optimise postnatal care, a qualitative descriptive study design

O’Connor,

Tilston,

Jones

et al. 2024

BMC Med

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Background Pregnancy acts as a cardiovascular stress test. Although many complications resolve following birth, women with hypertensive disorder of pregnancy have an increased risk of developing cardiovascular disease (CVD) long-term. Monitoring postnatal health can reduce this risk but requires better methods to identity high-risk women for timely interventions. Methods Employing a qualitative descriptive study design, focus groups and/or interviews were conducted, separately engaging public contributors and clinical professionals. Diverse participants were recruited through social media convenience sampling. Semi-structured, facilitator-led discussions explored perspectives of current postnatal assessment and attitudes towards linking patient electronic healthcare data to develop digital tools for identifying postpartum women at risk of CVD. Participant perspectives were gathered using post-it notes or a facilitator scribe and analysed thematically. Results From 27 public and seven clinical contributors, five themes regarding postnatal check expectations versus reality were developed, including ‘limited resources’, ‘low maternal health priority’, ‘lack of knowledge’, ‘ineffective systems’ and ‘new mum syndrome’. Despite some concerns, all supported data linkage to identify women postnatally, targeting intervention to those at greater risk of CVD. Participants outlined potential benefits of digitalisation and risk prediction, highlighting design and communication needs for diverse communities. Conclusions Current health system constraints in England contribute to suboptimal postnatal care. Integrating data linkage and improving education on data and digital tools for maternal healthcare shows promise for enhanced monitoring and improved future health. Recognised for streamlining processes and risk prediction, digital tools may enable more person-centred care plans, addressing the gaps in current postnatal care practice.

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Section: Discussionmentioning

confidence: 99%

Acceptability of data linkage to identify women at risk of postnatal complication for the development of digital risk prediction tools and interventions to better optimise postnatal care, a qualitative descriptive study design

O’Connor,

Tilston,

Jones

et al. 2024

BMC Med

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“…Evaluations of AI-based CDSSs mainly focus on the performance of algorithms [22,27]. Thus, this highlights the need for dedicated verification and validation tools for practical implementation [27,33]. Lastly, current interoperability standards vary across different programs, leading to complexities in data exchanges between different systems-a fundamental challenge that must be addressed to enable proper implementation of CDSSs.…”

Section: Rules and Regulationsmentioning

confidence: 99%

The Facilitators and Barriers of the Implementation of a Clinical Decision Support System for Breast Cancer Multidisciplinary Team Meetings—An Interview Study

Kočo,

Siebers,

Schlooz

et al. 2024

Cancers

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Background: AI-driven clinical decision support systems (CDSSs) hold promise for multidisciplinary team meetings (MDTMs). This study aimed to uncover the hurdles and aids in implementing CDSSs during breast cancer MDTMs. Methods: Twenty-four core team members from three hospitals engaged in semi-structured interviews, revealing a collective interest in experiencing CDSS workflows in clinical practice. All interviews were audio recorded, transcribed verbatim and analyzed anonymously. A standardized approach, ‘the framework method’, was used to create an analytical framework for data analysis, which was performed by two independent researchers. Results: Positive aspects included improved data visualization, time-saving features, automated trial matching, and enhanced documentation transparency. However, challenges emerged, primarily concerning data connectivity, guideline updates, the accuracy of AI-driven suggestions, and the risk of losing human involvement in decision making. Despite the complexities involved in CDSS development and integration, clinicians demonstrated enthusiasm to explore its potential benefits. Conclusions: Acknowledging the multifaceted nature of this challenge, insights into the barriers and facilitators identified in this study offer a potential roadmap for smoother future implementations. Understanding these factors could pave the way for more effective utilization of CDSSs in breast cancer MDTMs, enhancing patient care through informed decision making.

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“…Electronic Prescribing Systems (EPSs) can reduce the incidents of medication errors (Graf et al, 2023). Clinical Decision Support Systems (CDSSs) can provide essential support to clinicians in patient care (Sutton et al, 2020;Teufel and Binder, 2021). Mobile Health (mHealth) applications can support self-management of chronic diseases (World Health Organization, 2011).…”

Section: Introductionmentioning

confidence: 99%

Validation of a novel framework to assess clinical information in digital health technologies: structural equation modelling (Preprint)

Fadahunsi,

Wark,

Mastellos

et al. 2024

Preprint

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BACKGROUND Digital health is a critical driver of quality, safety, and efficiency in healthcare. However, poor quality of clinical information in Digital Health Technologies (DHTs) can compromise the quality and safety of care. The Clinical Information Quality (CLIQ) framework was developed as a pragmatic tool to assess the quality of clinical information in DHTs. OBJECTIVE The aim of this study is to assesses the applicability, internal consistency, and construct validity of the CLIQ framework. METHODS This study was conducted as a cross-sectional survey of healthcare professionals across the UK who regularly use SystmOne Electronic Health Records (EHRs). Participants were invited through emails and social media platforms. The CLIQ questionnaire, developed based on systemic review and international eDelphi study, was administered as an online survey. Spearman’s correlation coefficients were computed to investigate the linear relationship between the dimensions in the CLIQ framework. The Cronbach’s alpha coefficients were computed to assess the internal consistency of the global scale (i.e., CLIQ framework) and the sub-scales (i.e., the informativeness, availability and usability categories). Confirmatory factor analysis was used to assess the extent to which the survey data supported the construct validity of the CLIQ framework. RESULTS A total of 109 healthcare professionals completed the survey, of which two-third (n = 67; 61.5%) were doctors and a quarter (n = 26; 23.9%) were nurses or advance nurse practitioner. Overall, the CLIQ dimensions had good quality scores except for portability which had a modest score. The inter-item correlations were all positive and not likely due to chance. The Cronbach’s alpha coefficient for the overall CLIQ framework was 0.89 (95 CI%: 0.85 – 0.92). The confirmatory factor analysis provided a modest support for the construct validity of the CLIQ framework with the Comparative Fit Index (CFI) of 0.86 and Standardised Root Mean Square Residual (SRMR) of 0.08. CONCLUSIONS The CLIQ framework demonstrated a high reliability and a modest construct validity. The CLIQ framework offers a pragmatic approach to assessing the quality of clinical information in DHTs and could be applied as part of information quality assurance systems in healthcare settings to improve quality of health information.

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Clinical Decision Support Systems

Cited by 14 publications

References 66 publications

Acceptability of data linkage to identify women at risk of postnatal complication for the development of digital risk prediction tools and interventions to better optimise postnatal care, a qualitative descriptive study design

Acceptability of data linkage to identify women at risk of postnatal complication for the development of digital risk prediction tools and interventions to better optimise postnatal care, a qualitative descriptive study design

The Facilitators and Barriers of the Implementation of a Clinical Decision Support System for Breast Cancer Multidisciplinary Team Meetings—An Interview Study

Validation of a novel framework to assess clinical information in digital health technologies: structural equation modelling (Preprint)

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