The evolving landscape: Role of artificial intelligence in cancer detection by Praveen Kumar, Sakshi V. Izankar, Induni N. Weerarathna, David Raymond, Prateek Verma

“…Additionally, the discussion encompassed clinical decision support systems, explaining their utility in aiding healthcare professionals with evidence-based insights for more informed decision-making in cancer detection and management. …”
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Understanding integrative approach of translational bioinformatics on cardiovascular disease: Myocardial Ischemia by Yeswanth Ranganathan, Saayaa Nazar, Ravi Shankar Krishnan, Yuvaraj Dinakarkumar, Vijayalakshmi Varadarajan, Lenita Sebastian, Brindha Rethinam

“…Additionally, it examines the application of systems biology and network analysis to understand biological networks, and the use of clinical decision support systems to enhance patient care. …”
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Attitudes toward Management of Sickle Cell Disease and Its Complications: A National Survey of Academic Family Physicians by Arch G. Mainous, Rebecca J. Tanner, Christopher A. Harle, Richard Baker, Navkiran K. Shokar, Mary M. Hulihan

“…Physicians also felt that clinical decision support (CDS) tools would be useful for treatment (69.4%) and avoiding complications (72.6%) in managing SCD patients. …”
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Advancing hospital healthcare: achieving IoT-based secure health monitoring through multilayer machine learning by Ke Qi

“…Abstract Background Data based clinical decision support system is a boon for health care monitoring. …”
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Electronic health record and primary care physician self-reported quality of care: a multilevel study in China by Wenhua Wang, Mengyao Li, Katya Loban, Jinnan Zhang, Xiaolin Wei, Rebecca Mitchel

“…Each CHC director scored their CHC’s EHR functionality on the availability of ten typical features covering health information, data, results management, patient access, and clinical decision support. Data analysis utilised hierarchical linear modelling. …”
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Protocol for the impact of machine learning-based clinician decision support algorithims in perioperative care (IMAGINATIVE) in Singapore general hospital : a large prospective ran... by Marcus Ong, Hairil Rizal Abdullah, Ecosse Lamoureux, Elaine Lum, Gek Hsiang Lim, Tan Pei Yi Brenda, Celestine Loh

“…The primary outcome is to evaluate the effectiveness of the Machine Learning Clinical Decision Support (ML-CDS) algorithm in improving perioperative mortality rates when integrated into the clinical workflow.Ethics and dissemination The study has been approved by the SingHealth Centralised Institutional Review Board (CIRB Ref: 2023:2114) and is registered on ClinicalTrials.gov (trial number: NCT05809232). …”
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MOUD 2.0: a clinical algorithm and implementation evaluation protocol for sublingual and injectable buprenorphine treatment of opioid use disorder by Brandon L. Joa, Eric N. Fung, Michael S. Weinstein, Lara C. Weinstein, Lara C. Weinstein

“…We present the first clinical decision support algorithm incorporating long-acting buprenorphine (LAIB) in primary care. …”
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Performance of an Electronic Health Record–Based Automated Pulmonary Embolism Severity Index Score Calculator: Cohort Study in the Emergency Department by Elizabeth Joyce, James McMullen, Xiaowen Kong, Connor O'Hare, Valerie Gavrila, Anthony Cuttitta, Geoffrey D Barnes, Colin F Greineder

“…The accuracy of the ePESI was higher for low-risk scores (OR 2.96, PP ConclusionsIn this single-center study, the ePESI was highly accurate in discriminating between low- and high-risk scores. The clinical decision support should facilitate real-time identification of patients who may be candidates for outpatient PE management.…”
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Exploring digital health interventions for pregnant women at high risk for pre-eclampsia and eclampsia in low-income and-middle-income countries: a scoping review by Emily Seto, Anam Shahil Feroz, Noreen Afzal

“…The review identified key functions of interventions including data collection, prediction of adverse maternal outcomes, integrated diagnostic and clinical decision support, and personal health tracking. The review reported three major outcomes: maternal health outcomes including maternal and neonatal morbidity and mortality (n=4); usability and acceptability including ease-of-use, and perceived usefulness, (n=5); and intervention feasibility and fidelity including accuracy of device, and intervention implementation (n=7).Conclusion Although the current evidence base shows some potential for the use of digital health interventions for PE/E, more prospective experimental and longitudinal studies are needed prior to recommending the use of digital health interventions for PE/E.…”
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Electronic nudge tool technology used in the critical care and peri-anaesthetic setting: a scoping review protocol by Frank Kee, Martin Dempster, Bronagh Blackwood, Lisa McIlmurray, Lynne Lohfeld, Murali Shyamsundar, Charles Gillan, Rachael Hagan

“…Introduction Electronic clinical decision support (eCDS) tools are used to assist clinical decision making. …”
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Digital health technologies and innovation patterns in diabetes ecosystems by Odile-Florence Giger, Estelle Pfitzer, Wasu Mekniran, Hannes Gebhardt, Elgar Fleisch, Mia Jovanova, Tobias Kowatsch

“…Results Our analyses revealed the emergence of eight organization segments in digital diabetes ecosystems: real-world evidence analytics, healthcare management platforms, clinical decision support, diagnostic and monitoring, digital therapeutics, wellness, online community, and online pharmacy (RQ1). …”
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Antidepressant Treatment Response Prediction With Early Assessment of Functional Near-Infrared Spectroscopy and Micro-RNA by Lok Hua Lee, Cyrus Su Hui Ho, Yee Ling Chan, Gabrielle Wann Nii Tay, Cheng-Kai Lu, Tong Boon Tang

“…The performance of the proposed custom algorithm suggests the prediction of ATR can be improved with multiple features sources, provided that the inter-subject variability is properly addressed, and can be an effective tool for clinical decision support system in MDD ATR prediction. Clinical and Translational Impact Statement—The fusion of neuroimaging fNIRS features and miRNA profiles significantly enhances the prediction accuracy of MDD ATR. …”
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Effect of Artificial Intelligence Helpfulness and Uncertainty on Cognitive Interactions with Pharmacists: Randomized Controlled Trial by Chuan-Ching Tsai, Jin Yong Kim, Qiyuan Chen, Brigid Rowell, X Jessie Yang, Raed Kontar, Megan Whitaker, Corey Lester

“… BackgroundClinical decision support systems leveraging artificial intelligence (AI) are increasingly integrated into health care practices, including pharmacy medication verification. …”
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The Role of Clinician-Developed Applications in Promoting Adherence to Evidence-Based Guidelines: Pilot Study by Madhu Prita Prakash, Aravinda Thiagalingam

“… Abstract BackgroundComputerized clinical decision support systems (CDSS) are increasingly being used in clinical practice to improve health care delivery. …”
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Risk Estimation of Severe Primary Graft Dysfunction in Heart Transplant Recipients Using a Smartphone by Souhila Ait-Tigrine, Roger Hullin, Elsa Hoti, Matthias Kirsch, Piergiorgio Tozzi

“…Conclusions: The GREF-11 application should offer HTx teams several benefits, including standardized risk assessment and bedside clinical decision support, thereby helping minimize the risk of severe PGD post-HTx.…”
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Evaluation of an Interdisciplinary Educational Program to Foster Learning Health Systems: Education Evaluation by Sathana Dushyanthen, Nadia Izzati Zamri, Wendy Chapman, Daniel Capurro, Kayley Lyons

“…The course covered a number of topics including background on LHS, establishing learning communities, the design thinking process, data preparation and machine learning analysis, process modeling, clinical decision support, remote patient monitoring, evaluation, implementation, and digital transformation. …”
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Multi-Modal Fusion of Routine Care Electronic Health Records (EHR): A Scoping Review by Zina Ben-Miled, Jacob A. Shebesh, Jing Su, Paul R. Dexter, Randall W. Grout, Malaz A. Boustani

“…Recent advances in generative learning techniques were able to leverage the fusion of multiple routine care EHR data elements to enhance clinical decision support. <i>Objective</i>: A scoping review of the proposed techniques including fusion architectures, input data elements, and application areas is needed to synthesize variances and identify research gaps that can promote re-use of these techniques for new clinical outcomes. …”
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Investigating Smartphone-Based Sensing Features for Depression Severity Prediction: Observation Study by Yannik Terhorst, Eva-Maria Messner, Kennedy Opoku Asare, Christian Montag, Christopher Kannen, Harald Baumeister

“…Although these could become important parts of clinical decision support systems for depression diagnostics and treatment in the future, confirmatory studies are needed before they can be applied to routine care. …”
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Digital Health Technology Interventions for Improving Medication Safety: Systematic Review of Economic Evaluations by Widya Norma Insani, Neily Zakiyah, Irma Melyani Puspitasari, Muhammad Yorga Permana, Kankan Parmikanti, Endang Rusyaman, Auliya Abdurrohim Suwantika

“…Of the included studies, more than half (n=7, 53.9%) evaluated a clinical decision support system (CDSS)/computerized provider order entry (CPOE), 4 (30.8%) examined automated medication-dispensing systems, and 2 (15.4%) focused on pharmacist-led outreach programs targeting health care professionals. …”
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Predicting pediatric patient rehabilitation outcomes after spinal deformity surgery with artificial intelligence by Wenqi Shi, Felipe O. Giuste, Yuanda Zhu, Ben J. Tamo, Micky C. Nnamdi, Andrew Hornback, Ashley M. Carpenter, Coleman Hilton, Henry J. Iwinski, J. Michael Wattenbarger, May D. Wang

“…In the age of personalized medicine, the proposed responsible AI-enabled clinical decision-support system may facilitate pre-operative counseling and shared decision-making within real-world clinical settings.…”
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