ArticleBMC medical research methodology2018
CDSS-RM: a clinical decision support system reference model.
Article in BMC medical research methodology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers, 1 of them a synthesis that pooled it.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
30 citing papers in PubMed, 1 synthesis or guideline pooled it.
- The Role of Clinical Decision Support Systems in Preventing Stroke in Primary Care: A Systematic Review.Perspectives in health information managemen · 2023Pooled it
- Design, implementation, and evaluation of neonatal abstinence syndrome care management web application: a multimethod study.Scientific reports · 2026Trial
- Randomized controlled trial evaluating the benefit of the app-based clinical decision support system for the management of venous thromboembolism.BMC medical education · 2025Trial
- Article
- Toward Safer Diagnoses: A SEIPS-Based Narrative Review of Diagnostic Errors.Diagnostics (Basel, Switzerland) · 2026Review
- Factors Influencing Stroke Severity Based on Collateral Circulation, Clinical Markers and Machine Learning.Diagnostics (Basel, Switzerland) · 2025Article
- Expectations of Intensive Care Physicians Regarding an AI-Based Decision Support System for Weaning From Continuous Renal Replacement Therapy: Predevelopment Survey Study.JMIR medical informatics · 2025Article
- Exploring the need for a clinical decision support system for deprescribing - A qualitative interview study.Exploratory research in clinical and social pharmacy · 2025Article
- Healthcare AI, explainability, and the human-machine relationship: a (not so) novel practical challenge.Frontiers in medicine · 2025Article
- Machine Learning Model in Obesity to Predict Weight Loss One Year after Bariatric Surgery: A Pilot Study.Biomedicines · 2024Article
- A decision support system based on recurrent neural networks to predict medication dosage for patients with Parkinson's disease.Scientific reports · 2024Article
- Harnessing the power of clinical decision support systems: challenges and opportunities.Open heart · 2023Review
- Development, implementation, and evaluation of neonatal thermoregulation decision support web application.BMC medical informatics and decision making · 2023Article
- Design and evaluation of a Mobile-Based decision support system to enhance lung transplant candidate assessment and management: knowledge translation integrated with clinical workflow.BMC medical informatics and decision making · 2023Article
- Article
- Applying cognitive task analysis to health services research.Health services research · 2023Article
- Assessing the decision quality of artificial intelligence and oncologists of different experience in different regions in breast cancer treatment.Frontiers in oncology · 2023Article
- Clinical decision support system for quality of life among the elderly: an approach using artificial neural network.BMC medical informatics and decision making · 2022Article
- Delphi consensus on the American Society of Anesthesiologists' physical status classification in an Asian tertiary women's hospital.Korean journal of anesthesiology · 2022Article
- Study of patients' attitude to automatic interpretation of laboratory test results and its influence on follow-up rate.BMC medical informatics and decision making · 2022Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Clinical Decision Support Systems (CDSS) provide aid in clinical decision making and therefore need to take into consideration human, data interactions, and cognitive functions of clinical decision makers. The objective of this paper is to introduce a high level reference model that is intended to be used as a foundation to design successful and contextually relevant CDSS systems. The paper begins by introducing the information flow, use, and sharing characteristics in a hospital setting, and then it outlines the referential context for the model, which are clinical decisions in a hospital setting. Important characteristics of the Clinical decision making process include: (i) Temporally ordered steps, each leading to new data, which in turn becomes useful for a new decision, (ii) Feedback loops where acquisition of new data improves certainty and generates new questions to examine, (iii) Combining different kinds of clinical data for decision making, (iv) Reusing the same data in two or more different decisions, and (v) Clinical decisions requiring human cognitive skills and knowledge, to process the available information. These characteristics form the foundation to delineate important considerations of Clinical Decision Support Systems design. The model includes six interacting and interconnected elements, which formulate the high-level reference model (CDSS-RM). These elements are introduced in the form of questions, as considerations, and are examined with the use of illustrated scenario-based and data-driven examples. The six elements /considerations of the reference model are: (i) Do CDSS mimic the cognitive process of clinical decision makers? (ii) Do CDSS provide recommendations with longitudinal insight? (iii) Is the model performance contextually realistic? (iv) Is the 'Historical Decision' bias taken into consideration in CDSS design? (v) Do CDSS integrate established clinical standards and protocols? (vi) Do CDSS utilize unstructured data? The CDSS-RM reference model can contribute to optimized design of modeling methodologies, in order to improve response of health systems to clinical decision-making challenges.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.