Review on barriers and considerations of Clinical Decision Support System for medication prescribing

Rahim, Nur Raidah; Nordin, Sharifalillah; Dom, Rosma Mohd

doi:10.1109/scored.2015.7449384

Cited by 6 publications

(7 citation statements)

References 23 publications

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“…Despite the fast‐growing research on AI in combination with RWD to support CDM in China, the complexity of AI has considerably impeded its clinical acceptability and applicability. The effectiveness, interpretability/explainability, and transparency were indicated to be key contributors to the expectations and confidence of clinicians, which could positively impact the adoption of AI‐based CDSSs 92–95 . In the era of medical big data, the training of ML/DL based on aggregated clinical source data dramatically increases the precision, but not internal validity 19,96 .…”

Section: Discussionmentioning

confidence: 99%

“…The effectiveness, interpretability/explainability, and transparency were indicated to be key contributors to the expectations and confidence of clinicians, which could positively impact the adoption of AI-based CDSSs. [92][93][94][95] In the era of medical big data, the training of ML/DL based on aggregated clinical source data dramatically increases the precision, but not internal validity. 19,96 The gained value of ML/DL algorithms would be maximized under the appropriate conditions of high-quality RWD and methodologically rigorous RWE design.…”

Section: Technology-based Disease Management Toolsmentioning

confidence: 99%

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Recent advancement in integrating artificial intelligence and information technology with real‐world data for clinical decision‐making in China: A scoping review

Liao,

Yao,

Zhang

et al. 2023

J Evidence Based Medicine

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ObjectiveStriking innovations and advancements have been achieved with the use of artificial intelligence and healthcare information technology being integrated into clinical real‐world data. The current scoping review aimed to provide an overview of the current status of artificial intelligence‐/information technology‐based clinical decision support tools in China.MethodsPubMed/MEDLINE, Embase, China National Knowledge Internet, and Wanfang data were searched for both English and Chinese literature. The gray literature search was conducted for commercially available tools. Original studies that focused on clinical decision support tools driven by artificial intelligence or information technology in China and were published between 2010 and February 2022 were included. Information extracted from each article was further synthesized by themes based on three types of clinical decision‐making.ResultsA total of 37 peer‐reviewed publications and 13 commercially available tools were included in the final analysis. Among them, 32.0% were developed for disease diagnosis, 54.0% for risk prediction and classification, and 14.0% for disease management. Chronic diseases were the most popular therapeutic areas of exploration, with particular emphasis on cardiovascular and cerebrovascular diseases. Single‐center electronic medical records were the mainstream data sources leveraged to inform clinical decision‐making, with internal validation being predominately used for model evaluation.ConclusionsTo effectively promote the extensive use of real‐world data and drive a paradigm shift in clinical decision‐making in China, multidisciplinary collaboration of key stakeholders is urgently needed.

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Section: Discussionmentioning

confidence: 99%

Section: Technology-based Disease Management Toolsmentioning

confidence: 99%

Recent advancement in integrating artificial intelligence and information technology with real‐world data for clinical decision‐making in China: A scoping review

Liao,

Yao,

Zhang

et al. 2023

J Evidence Based Medicine

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“…The main issue that healthcare facilities are facing is the adoption and implementation of CPOE and CDSS [ 10 ]. Since the physician’s task during medication prescribing is highly complex, the physician must be aware of the patient’s biomedical status, history, and medication interactions and contradictions [ 11 ], making prescribing medications a hazardous process.…”

Section: Introductionmentioning

confidence: 99%

The Reasons for Physicians and Pharmacists’ Acceptance of Clinical Support Systems in Saudi Arabia

Seliaman

Albahly

2023

IJERPH

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This research aims to identify the technological and non-technological factors influencing user acceptance of the CDSS in a group of healthcare facilities in Saudi Arabia. The study proposes an integrated model that indicates the factors to be considered when designing and evaluating CDSS. This model is developed by integrating factors from the “Fit between Individuals, Task, and Technology” (FITT) framework into the three domains of the human, organization, and technology-fit (HOT-fit) model. The resulting FITT-HOT-fit integrated model was tested using a quantitative approach to evaluate the currently implemented CDSS as a part of Hospital Information System BESTCare 2.0 in the Saudi Ministry of National Guard Health Affairs. For data collection, a survey questionnaire was conducted at all Ministry of National Guard Health Affairs hospitals. Then, the collected survey data were analyzed using Structural Equation Modeling (SEM). This analysis included measurement instrument reliability, discriminant validity, convergent validity, and hypothesis testing. Moreover, a CDSS usage data sample was extracted from the data warehouse to be analyzed as an additional data source. The results of the hypotheses test show that usability, availability, and medical history accessibility are critical factors influencing user acceptance of CDSS. This study provides prudence about healthcare facilities and their higher management to adopt CDSS.

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“…In general, it incorporates concepts that are derived from scientific literature and expert knowledge and should be constantly updated to keep up with new evidence generated in clinical practice. 7 Traditional CDSSs can offer clinicians patient-specific advice based on globally recognised recommendations, as well as increase physician adherence to medical guidelines.…”

Section: Introductionmentioning

confidence: 99%

“…Traditional CDSSs consist of a clinical knowledge base, which is the inference engine that combines information from the knowledge base with input data, and of the user interface. In general, it incorporates concepts that are derived from scientific literature and expert knowledge and should be constantly updated to keep up with new evidence generated in clinical practice 7. Traditional CDSSs can offer clinicians patient-specific advice based on globally recognised recommendations, as well as increase physician adherence to medical guidelines.…”

Section: Introductionmentioning

confidence: 99%

Clinical decision support systems to improve drug prescription and therapy optimisation in clinical practice: a scoping review

Armando

Miglio

Cosmo³

et al. 2023

BMJ Health Care Inform

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ObjectiveClinical decision support systems (CDSSs) can reduce medical errors increasing drug prescription appropriateness. Deepening knowledge of existing CDSSs could increase their use by healthcare professionals in different settings (ie, hospitals, pharmacies, health research centres) of clinical practice. This review aims to identify the characteristics common to effective studies conducted with CDSSs.Materials and methodsThe article sources were Scopus, PubMed, Ovid MEDLINE and Web of Science, queried between January 2017 and January 2022. Inclusion criteria were prospective and retrospective studies that reported original research on CDSSs for clinical practice support; studies should describe a measurable comparison of the intervention or observation conducted with and without the CDSS; article language Italian or English. Reviews and studies with CDSSs used exclusively by patients were excluded. A Microsoft Excel spreadsheet was prepared to extract and summarise data from the included articles.ResultsThe search resulted in the identification of 2424 articles. After title and abstract screening, 136 studies remained, 42 of which were included for final evaluation. Most of the studies included rule-based CDSSs that are integrated into existing databases with the main purpose of managing disease-related problems. The majority of the selected studies (25 studies; 59.5%) were successful in supporting clinical practice, with most being pre–post intervention studies and involving the presence of a pharmacist.Discussion and conclusionA number of characteristics have been identified that may help the design of studies feasible to demonstrate the effectiveness of CDSSs. Further studies are needed to encourage CDSS use.

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Review on barriers and considerations of Clinical Decision Support System for medication prescribing

Cited by 6 publications

References 23 publications

Recent advancement in integrating artificial intelligence and information technology with real‐world data for clinical decision‐making in China: A scoping review

Recent advancement in integrating artificial intelligence and information technology with real‐world data for clinical decision‐making in China: A scoping review

The Reasons for Physicians and Pharmacists’ Acceptance of Clinical Support Systems in Saudi Arabia

Clinical decision support systems to improve drug prescription and therapy optimisation in clinical practice: a scoping review

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