Skip to main content
WoMWeb of Medicine
SearchGraphHistory
Family Medicine›Condition·Updated Jul 11, 2026·v1

Clinical Decision Support Systems

Clinical Decision Support Systems (CDSS) are computer-based tools that integrate patient data with knowledge bases to deliver evidence-based alerts, risk estimates, and recommendations at the point of care. They improve diagnostic accuracy (96% with structured input of key findings), reduce medication errors (reduction), and enhance guideline adherence across acute and chronic settings. However, CDSS do not replace clinical judgment; their success depends on tailored design, high alert specificity, workflow integration, and clinician engagement. Process improvements are reproducible, but effects on mortality remain inconsistent. Optimal use requires matching CDSS type to clinical task, entering focused findings, and addressing special population needs (pregnancy, elderly, pediatrics, immunocompromised). Deploy with multidisciplinary implementation teams and monitor for alert fatigue.

Moderate Evidence108 references·9,668 words·39 min read·v1
Clinical Decision Support SystemsCDSSHealth InformaticsPatient SafetyDiagnostic Decision SupportAlert FatigueArtificial Intelligence in Medicine

Quick Reference

RxDrug of choiceStructured diagnostic CDSS with manual entry of 3-6 key clinical findings (e.g., Isabel web-based system, sensitivity 96%)
AltAlternativesRule-based alert systems for drug-lab interactions; machine learning models for non-linear risk prediction (e.g., XGBoost for necrotizing fasciitis); LLM-based tools for differential generation (e.g., ChatGPT-4o with clinician supervision)
⊘AvoidDo not use CDSS as a substitute for clinical reasoning; avoid relying on it in atypical presentations where the system may provide no additional insight; do not deploy systems with low PPV (<1%) for population screening without restricting to high-risk subgroups
DxTest of choiceDiagnostic CDSS with structured input of 3-6 key findings, sensitivity 96%, specificity variable by tool; for imaging, AI-assisted CDSS achieves median sensitivity 93.6% and specificity 90.6% across modalities
ScKey scoreCHA2DS2-VASc for atrial fibrillation anticoagulation decision support (increased appropriate anticoagulation from 52.6% to 59.8% when embedded in CDSS)
→When to referWhen CDSS suggests an unexpected diagnosis that conflicts with clinical assessment, or when risk stratification exceeds local threshold (e.g., PPV <1% in screening), refer to specialist for confirmatory testing or population health management
CDSS consistently improve process measures (prescribing, monitoring, follow-up) but have uncertain effects on mortality; deploy with attention to alert specificity, workflow integration, and clinician engagement to maximize benefit and minimize alert fatigue
Clinical Decision Support Systems (CDSS) are evidence-based tools that integrate patient data with curated knowledge to deliver recommendations, alerts, and risk estimates at the point of care. They have evolved from simple rule-based alerts to sophisticated AI-driven systems capable of improving diagnostic accuracy (up to 96% with structured input), reducing medication errors (reduction in one trial), and enhancing guideline adherence across acute and chronic settings. However, CDSS do not replace clinical judgment; their effectiveness depends on tailored design, workflow integration, and clinician engagement. Process improvements are reproducible, but effects on hard outcomes like mortality remain inconsistent. This reference provides a practical guide to CDSS selection, implementation, and optimization for the family medicine clinician.

Overview and Recommendations

Background

  • •Clinical Decision Support Systems (CDSS) are computer-based tools that integrate patient-specific clinical data with curated knowledge bases to deliver evidence-based recommendations, alerts, or risk estimates at the point of care, augmenting, not replacing, clinician judgment. Baseline medication error rates in hospitalized adults reach 6.83 per 1,000 patient-days, and adverse drug events occur in 8.9 per 100 admissions, framing the clinical opportunity for CDSS to improve safety and guideline adherence.
  • •CDSS are broadly classified into knowledge-based (rule-driven, e.g., drug-lab interaction alerts generated by ATC drug class and lab thresholds) and non-knowledge-based (machine learning or AI-driven, e.g., gradient boosting models for sepsis prediction or videolaryngoscopy planning). A third emerging category uses large language models prompted with clinical inputs, though their outputs show prompt-sensitive threshold behavior and must be interpreted cautiously as quantitative risk estimates.
  • •The clinical impact of CDSS is mediated by its interaction with human cognition and workflow. Unhelpful AI predictions increase clinician dwell time on original data by 19%-26%, and displaying AI uncertainty paradoxically lengthens cognitive processing time, meaning poorly calibrated systems can negate the efficiency gains CDSS is meant to provide.
  • •The effectiveness of CDSS depends critically on the match between knowledge architecture and clinical task: rule-based alerts excel for predictable drug-lab interactions, while machine learning models capture complex non-linear risk. A Clinician Turing Test paradigm has been proposed as a Phase 1b validation step, testing whether clinicians can distinguish AI-generated from human-generated treatment recommendations, a measure not only of predictive performance but of clinical plausibility.
  • •CDSS have evolved from generic rule-based systems to context-aware AI tools. Early systems suffered from poor workflow fit and excessive alerts, but tailored alerts (suppressing low-risk drug interactions) reduced administered high-risk combinations by 12% in ICU settings. The field now recognizes that CDSS consistently improve process measures (preventive services OR 1.42, clinical study ordering OR 1.72, prescribing OR 1.57) but have inconsistent effects on hard outcomes like mortality, which remains an active area of investigation.

Evaluation

  • •Suspect that a CDSS may be useful when managing an undifferentiated patient with diagnostic uncertainty. Controlled testing shows that entering 3 to 6 key clinical findings into a diagnostic CDSS (e.g., Isabel) yields the correct diagnosis in 96% of complex cases, compared to 74% when the full history is pasted, a difference larger than most single diagnostic tests.
  • •Examine the clinical context: CDSS are most helpful when the clinician already has a provisional differential and needs to broaden or confirm it, rather than when the presentation is genuinely obscure. In atypical presentations, CDSS may provide no additional insight and should not be relied upon.
  • •Choose the right type of CDSS for the clinical task: use rule-based alerts for predictable drug-lab interactions (e.g., vitamin K antagonist plus elevated INR), machine learning models for non-linear risk prediction (e.g., necrotizing fasciitis prediction with AUC 0.809, pediatric sepsis), and LLM-based systems for differential generation, each has specific strengths but also limitations such as low positive predictive value in low-prevalence conditions.
  • •Assess the quality of the underlying data before acting on a CDSS recommendation. Stale vital signs, incomplete medication lists, or inaccurate lab values generate misleading alerts, always verify that the CDSS is connected to a current, structured electronic health record.
  • •Order a diagnostic CDSS proactively by entering a focused set of discriminative features extracted from the history and physical. For internal medicine cases, this process takes less than a minute with results in 2-3 seconds; the key is to enter discriminating positive and negative findings, not the full narrative.
  • •Consider the cognitive phase of decision-making when selecting CDSS delivery mode. During early heuristic triage (System 1), passive, user-initiated tools (risk calculators, diagnostic checklists) are more effective than active interruptive alerts. During later analytical phases (System 2) when diagnostic uncertainty decreases and information quantity increases, active alerts (e.g., IV-to-oral switch prompts, medication reconciliation alerts) become appropriate.
  • •Evaluate alert specificity: low-specificity alerts (e.g., pancreatic cancer screening models with positive predictive value below 1%) cause alert fatigue and erode clinician trust, leading to high override rates. The most effective CDSS reduce false positive rates by tailoring alerts to clinical context, for example, suppressing low-risk drug-drug interaction alerts in ICU settings.
  • •Monitor the impact on workflow and cognitive load. CDSS should not increase burden; the TraumaFlow system for polytrauma improved documentation completeness without increasing NASA-RTLX workload scores. If a CDSS increases dwell time on original data by 19%-26% (as seen with unhelpful AI), it may hinder rather than help clinical decision-making.
  • •Assess clinician acceptance and engagement before full deployment. In a large German trial, practices with lower CDSS usage intensity but higher change commitment showed larger intervention effects on hospitalization and mortality, indicating that implementation success depends more on attitudinal factors than on usage volume alone.
  • •Use CDSS to support medication reconciliation at care transitions. Medication reconciliation using EHR-integrated CDSS reduces adverse drug events (OR 0.38) and improves follow-up adherence, in an HIV clinic, interactive alerts reduced 6-month suboptimal follow-up from 30.1 to 20.6 events per 100 patient-years.
  • •For special populations, adjust CDSS expectations accordingly: in pregnancy, ensure CDSS includes teratogenicity alerts and pregnancy-adjusted vital sign thresholds (the PANDA system more than doubled quality of antenatal care); in the elderly, integrate STOPP/START criteria and fall-risk alerts; in pediatrics, weight-based dosing algorithms are essential; in immunocompromised patients, incorporate drug-drug interaction databases and adjusted infection marker thresholds.
  • •Recognize that diagnostic CDSS achieves its highest sensitivity (96%) when clinicians enter 3 to 6 key findings rather than the full history; always cross-check the CDSS differential with local disease prevalence and patient-specific risk factors before ordering confirmatory tests.
  • •When using CDSS for risk stratification (e.g., CHA2DS2-VASc for atrial fibrillation anticoagulation), ensure that all necessary data fields are complete and accurate, missing entries can misclassify patients and erode clinical trust.
  • •For acute settings, deploy CDSS that provide real-time, time-critical recommendations. The Cerebri smartphone app for acute ischemic stroke reduced imaging-to-decision time from 22 to 6 minutes and improved overall guideline adherence from 73.9% to 96%.

Management

  • •Implement CDSS as a process-improvement tool, not as a substitute for clinical judgment. Set realistic expectations: CDSS consistently improve prescribing, monitoring, and follow-up processes but have not shown consistent reductions in mortality or length of stay in meta-analyses.
  • •Tailor drug-drug interaction alerts to high-risk combinations only. In a stepped-wedge trial across 9 Dutch ICUs, this approach reduced the number of administered high-risk drug combinations from 35.6 to 26.2 per 1000 drug administrations, a 12% reduction, without compromising safety.
  • •Require clinicians to supply a reason when overriding CDSS advice. This practice increases the likelihood of the CDSS achieving its intended effect by an odds ratio of 11.23, as it forces cognitive engagement and accountability.
  • •Integrate CDSS with complementary interventions such as point-of-care testing or pharmacist review. Bundled strategies achieve higher guideline adherence success rates (85%) than single-component CDSS alone.
  • •For diagnostic CDSS, train clinicians to enter 3 to 6 key clinical findings rather than pasting full clinical notes, this simple technique raises diagnostic accuracy from 74% to 96%.
  • •For acute time-critical conditions (stroke, polytrauma, sepsis), deploy CDSS that deliver real-time recommendations at the point of care. The Cerebri smartphone app reduced imaging-to-decision time by 16 minutes (from 22 to 6 minutes) and improved overall guideline adherence from 74% to 96%.
  • •For chronic disease management, use CDSS to flag patients with suboptimal follow-up or therapy. In HIV care, interactive alerts increased the monthly CD4 count slope (0.0053 vs 0.0032 ×10⁹ cells/L) and reduced 6-month suboptimal follow-up events by 31.5%.
  • •Implement deprescribing CDSS in older adults with polypharmacy. Use tools that integrate STOPP/START criteria, renal function-based dosing, and anticholinergic burden scoring to reduce potentially inappropriate medication initiation by up to 18%.
  • •For antimicrobial stewardship, deploy CDSS as part of a digital intervention, 58% of studied programs use CDSS. CDSS consistently improves therapeutic appropriateness and reduces antibiotic consumption, but pair it with pharmacist review for maximal effect on clinical outcomes.
  • •Avoid non-interruptive alerts for urgent safety issues that require immediate attention (e.g., severe drug allergy, critical lab value). Reserve non-interruptive modes for low-urgency preventive reminders (e.g., statin initiation in eligible patients) to minimize alert fatigue.
  • •Monitor alert override rates as a quality metric. If override rates exceed 70%, reevaluate alert specificity and consider suppressing low-yield alerts or adjusting thresholds.
  • •For pregnancy, ensure CDSS includes teratogenicity alerts with safer alternative suggestions, pregnancy-adjusted vital sign thresholds, and breastfeeding safety recommendations. The PANDA system in Burkina Faso increased excellent antenatal care quality more than twofold (RR 2.71).
  • •For pediatric populations, implement weight-based dosing algorithms and age-adjusted normal ranges. Flag off-label prescribing and provide evidence-based dose adjustments when pediatric data exist.
  • •Ensure data quality by implementing validation checks before CDSS inference. Stale or inaccurate EHR data produce misleading recommendations that undermine trust and may lead to clinical errors.
  • •When CDSS is unavailable or fails (e.g., technical downtime), revert to standard institutional protocols and paper-based checklists. Have a contingency plan for acute settings where time pressure is highest.
  • •Deploy CDSS with a multidisciplinary implementation team that includes clinicians, informaticians, and change management leaders. User-centered design, early stakeholder engagement, and workflow integration are the strongest predictors of long-term sustainability.
  • •For risk stratification, use CDSS that embed validated clinical scores (e.g., CHA2DS2-VASc for atrial fibrillation, PI-RADS for prostate MRI) alongside machine learning models. Hybrid approaches combining traditional scores with ML offer the best balance of interpretability and predictive performance.
  • •Consider cost-effectiveness: CDSS may reduce unnecessary medical visits (LabTest Checker reduced potential visits by 41.6%) and improve resource utilization, but economic evidence is still limited, incorporate local cost data before large-scale deployment.

Board Review — High Yield

  • •3 to 6 key findings, Entering 3-6 discriminative clinical features into a diagnostic CDSS yields 96% accuracy vs 74% with full history paste.
  • •Cognitive phase matching, Passive, user-initiated CDSS (risk calculators) are effective in early heuristic triage (System 1); active interruptive alerts suit later analytical phases (System 2).
  • •Alert tailoring, Suppressing low-risk drug-drug interaction alerts reduces administered high-risk combinations by 12% in ICU settings.
  • •Override justification, Requiring a reason for overriding CDSS advice increases success odds ratio to 11.23.
  • •Process vs outcome, CDSS improves process measures (OR 1.42-1.72) but meta-analyses show no reduction in mortality (RR 0.97).
  • •Deprescribing in elderly, CDSS reduces potentially inappropriate medication initiation by up to 18% in older adults.
  • •Pregnancy CDSS, The PANDA system more than doubled excellent antenatal care quality (RR 2.71).
  • •PPV limitation, AI pancreatic cancer screening has pooled AUC 0.785 but PPV <1%, limiting use to high-risk groups.
  • •Data quality, Stale or incomplete EHR data undermine CDSS recommendations; always verify input currency.
  • •Implementation science, Higher CDSS usage intensity does not guarantee better outcomes; attitudinal engagement and change commitment are stronger predictors.

Deep Dive — Evidence Details

References

  1. [1]

    Gres E, Brigadoi G, Zamperetti E et al.. “Antibiotic stewardship and point-of-care testing for children in 25 low-income and lower-middle-income countries: a systematic review and meta-analysis.” EClinicalMedicine (2025). PMID: 41377907 ↗

    L1SR_OBSCited in: Definition, Classification and Nomenclature
  2. [2]

    Rabinowitz EJ, Ouyang A, Guerriero R et al.. “Impaired Oxygen Delivery Risk Analytics in the 6 Hours Before Extracorporeal Membrane Oxygenation: Single-Center Retrospective Cohort Study in Infants, 2013-2017.” Pediatric critical care medicine : a journal of the Society of Critical Care Medicine and the World Federation of Pediatric Intensive and Critical Care Societies (2026). PMID: 42042630 ↗

    L3COHORTCited in: Definition, Classification and Nomenclature, Diagnosis and Workup
  3. [3]

    Kuo JR, Chen GY, Yap XV et al.. “Prompt-Sensitive Decision Behavior of Large Language Models in Intensive Care Unit Mortality Prediction for Spontaneous Intracerebral Hemorrhage: Comparative Benchmarking Study.” Journal of medical Internet research (2026). PMID: 42422967 ↗

    L4OTHERCited in: Definition, Classification and Nomenclature, Diagnosis and Workup
  4. [4]

    Boltaboyeva A, Amangeldy B, Baigarayeva Z et al.. “Frailty-Driven Prediction of Inpatient Obstructive Sleep Apnea and Related Sleep Disorder Diagnoses Using Explainable AI.” Biomedicines (2026). PMID: 42351732 ↗

    L4OTHERCited in: Definition, Classification and Nomenclature, Pathophysiology and Mechanism, Clinical Presentation, Complications
  5. [5]

    Dintilhac A, Lohan L, Laureau M et al.. “From bedside observations to clinical decision support system (CDSS) rules: using real-world adverse drug events (ADEs) data to identify high-risk iatrogenic situations.” International journal of medical informatics (2026). PMID: 42322885 ↗

    L2OTHERCited in: Definition, Classification and Nomenclature
  6. [6]

    Fernández-Vaquero MA, Oyarzun-Silva R, Hernández-Hernández P et al.. “Airway coach project: development of a machine learning-based model using clinical and ultrasound parameters to support videolaryngoscopy strategy.” BMC anesthesiology (2026). PMID: 42316024 ↗

    L2OTHERCited in: Definition, Classification and Nomenclature
  7. [7]

    Tsai CC, Kim JY, Chen Q et al.. “Effect of Artificial Intelligence Helpfulness and Uncertainty on Cognitive Interactions with Pharmacists: Randomized Controlled Trial.” Journal of medical Internet research (2025). PMID: 39888668 ↗

    L1RCTCited in: Pathophysiology and Mechanism, Generalist Reasoning under Diagnostic Uncertainty, Point-of-Care Scores & Referral Thresholds
  8. [8]

    Angeli Gazola A, Bishop NS, Schmid BE et al.. “Evaluating AI-based comprehensive clinical decision support for sepsis and ARDS: protocol for a Clinician Turing Test.” BMJ open (2025). PMID: 41448698 ↗

    L5TRIAL_NONRANDOMCited in: Pathophysiology and Mechanism, Epidemiology, Etiology and Risk Factors, Acute Management
  9. [9]

    Li Z, Zhang X, Ding L et al.. “Rationale and design of the GOLDEN BRIDGE II: a cluster-randomised multifaceted intervention trial of an artificial intelligence-based cerebrovascular disease clinical decision support system to improve stroke outcomes and care quality in China.” Stroke and vascular neurology (2024). PMID: 37699726 ↗

    L5TRIAL_NONRANDOMCited in: Pathophysiology and Mechanism
  10. [10]

    Strachna O, Asan O, Stetson PD. “Managing Critical Patient-Reported Outcome Measures in Oncology Settings: System Development and Retrospective Study.” JMIR medical informatics (2022). PMID: 36326801 ↗

    L4COHORTCited in: Pathophysiology and Mechanism
  11. [11]

    Zheng W, Wang Y, Lu J et al.. “From empirical hemostasis to precision reversal: clinical challenges, technological innovations, and individualized strategies in anticoagulant-associated bleeding.” Frontiers in cardiovascular medicine (2026). PMID: 42428488 ↗

    L5REVIEW_NARRATIVECited in: Pathophysiology and Mechanism, Long-term and Definitive Management
  12. [12]

    Jeon H, Jang HR. “Emerging therapeutic strategies for acute kidney injury: a new dawn in renal medicine.” Kidney research and clinical practice (2025). PMID: 42298994 ↗

    L5OTHERCited in: Pathophysiology and Mechanism
  13. [13]

    Sri AV, Pusapati B, Puli RS et al.. “Adaptive aggregation in federated learning for retinal vein occlusion detection: A dynamic approach based on data distribution.” Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society (2026). PMID: 42066515 ↗

    L5OTHERCited in: Pathophysiology and Mechanism
  14. [14]

    Shakarbaev N. “Ai-enhanced clinical decision support reduces medication errors and adverse drug events in a multicenter teaching hospital network: A prospective randomized controlled trial.” International journal of medical informatics (2026). PMID: 42054932 ↗

    L1RCTCited in: Epidemiology, Etiology and Risk Factors, History and Evolution of Treatment
  15. [15]

    Dijk SW, Mommersteeg M, Steltenpool S et al.. “Inappropriate imaging requests in abdominal imaging: observations from the MIDAS study, a multicenter randomized controlled trial in Germany.” Abdominal radiology (New York) (2025). PMID: 41417083 ↗

    L2RCTCited in: Epidemiology, Etiology and Risk Factors, History and Evolution of Treatment
  16. [16]

    Ito T, Hirosawa T, Hayashi A et al.. “Utility of a Smartphone-Based Clinical Decision Support System for Pressure Ulcer Management by Physicians: Randomized Crossover Pilot Study.” JMIR formative research (2026). PMID: 41871340 ↗

    L1RCTCited in: Epidemiology, Etiology and Risk Factors, Prognosis and Natural History
  17. [17]

    Correa EL, Strobel R, Canciglieri Junior O et al.. “Prioritizing Clinically Relevant Criteria for Longitudinal Obesity Management: A Systemic Framework to Support Decision-Making.” Obesity surgery (2026). PMID: 41989748 ↗

    L5SR_OBSCited in: Epidemiology, Etiology and Risk Factors
  18. [18]

    Ferede Z, Patterson S, Perera J et al.. “Risk Factors for Long-Term Health-Related Quality-of-Life and Mental Health Outcomes in Traumatic Brain Injury: A Systematic Review and Meta-Analysis.” Journal of neurotrauma (2026). PMID: 41873521 ↗

    L2SR_OBSCited in: Epidemiology, Etiology and Risk Factors, Clinical Presentation
  19. [19]

    Makiev GG, Samoylenko IV, Nazarova VV et al.. “The Efficacy of Electronic Health Record-Based Artificial Intelligence Models for Early Detection of Pancreatic Cancer: A Systematic Review and Meta-Analysis.” Cancers (2026). PMID: 41595234 ↗

    L2SR_OBSCited in: Epidemiology, Etiology and Risk Factors, Severity, Staging and Risk Stratification, Prevention, Screening and Health Maintenance
  20. [20]

    Holbrook AM, Silva JM, Faruque JAY et al.. “Effect of electronic drug-drug interaction alerts on patient and clinician outcomes: a systematic review.” Journal of the American Medical Informatics Association : JAMIA (2025). PMID: 40853269 ↗

    L2SR_OBSCited in: Epidemiology, Etiology and Risk Factors, Multimorbidity, Polypharmacy & Deprescribing
  21. [21]

    Kafke SD, Kuhlmey A, Schuster J et al.. “Can clinical decision support systems be an asset in medical education? An experimental approach.” BMC medical education (2023). PMID: 37568144 ↗

    L4RCTCited in: Clinical Presentation
  22. [22]

    Coulibaly A, Sogo AE, Chikvaidze N et al.. “Assessing the effectiveness of the Pregnancy And Newborn Diagnostic Assessment system on the quality of antenatal care in Burkina Faso: A cluster-randomised controlled trial.” Digital health (2024). PMID: 39664758 ↗

    L1RCTCited in: Clinical Presentation, Special Populations and Pregnancy
  23. [23]

    Fu S, Li F, Qian SY. “Artificial intelligence in pediatric intensive care units: current applications in sepsis management.” World journal of pediatrics : WJP (2026). PMID: 42265523 ↗

    L2SR_OBSCited in: Clinical Presentation, Diagnosis and Workup, Severity, Staging and Risk Stratification, Long-term and Definitive Management
  24. [24]

    Clement J, Maldonado AQ. “Augmenting the Transplant Team With Artificial Intelligence: Toward Meaningful AI Use in Solid Organ Transplant.” Frontiers in immunology (2021). PMID: 34177958 ↗

    L5SR_OBSCited in: Clinical Presentation
  25. [25]

    Graber ML, Mathew A. “Performance of a web-based clinical diagnosis support system for internists.” Journal of general internal medicine (2008). PMID: 18095042 ↗

    L4OTHERCited in: Clinical Presentation, Diagnosis and Workup, History and Evolution of Treatment
  26. [26]

    Szumilas D, Ochmann A, Zięba K et al.. “Evaluation of AI-Driven LabTest Checker for Diagnostic Accuracy and Safety: Prospective Cohort Study.” JMIR medical informatics (2024). PMID: 39149851 ↗

    L2COHORTCited in: Clinical Presentation
  27. [27]

    Salama V, Youssef S, Xu T et al.. “Temporal characterization of acute pain and toxicity kinetics during radiation therapy for head and neck cancer. A retrospective study.” Oral oncology reports (2023). PMID: 38638130 ↗

    L3COHORTCited in: Clinical Presentation
  28. [28]

    Robbins GK, Lester W, Johnson KL et al.. “Efficacy of a clinical decision-support system in an HIV practice: a randomized trial.” Annals of internal medicine (2012). PMID: 23208165 ↗

    L1RCTCited in: Diagnosis and Workup, Long-term and Definitive Management, History and Evolution of Treatment
  29. [29]

    Roshanov PS, Fernandes N, Wilczynski JM et al.. “Features of effective computerised clinical decision support systems: meta-regression of 162 randomised trials.” BMJ (Clinical research ed.) (2013). PMID: 23412440 ↗

    L1SR_OBSCited in: Diagnosis and Workup, Prognosis and Natural History
  30. [30]

    Kostopoulou O, Rosen A, Round T et al.. “Early diagnostic suggestions improve accuracy of GPs: a randomised controlled trial using computer-simulated patients.” The British journal of general practice : the journal of the Royal College of General Practitioners (2015). PMID: 25548316 ↗

    L1RCTCited in: Diagnosis and Workup, Long-term and Definitive Management, History and Evolution of Treatment
  31. [31]

    Song J, Liu Y, Shang Q et al.. “Clinical Decision Support System, Antihypertensive Treatment Intensification, and Blood Pressure Control: A Post Hoc Secondary Analysis of a Cluster Randomized Trial.” JAMA network open (2026). PMID: 42201734 ↗

    L2RCTCited in: Diagnosis and Workup, Long-term and Definitive Management, History and Evolution of Treatment
  32. [32]

    Agarwal S, Chin WY, Vasudevan L et al.. “Digital tracking, provider decision support systems, and targeted client communication via mobile devices to improve primary health care.” The Cochrane database of systematic reviews (2025). PMID: 40193137 ↗

    L1SR_OBSCited in: Diagnosis and Workup, Long-term and Definitive Management, Prognosis and Natural History, Special Populations and Pregnancy, Prevention, Screening and Health Maintenance
  33. [33]

    Boudra T, Idrissou A, Barakat O et al.. “Machine Learning in HIV Care and Antiretroviral Therapy: Systematic Review.” Journal of medical Internet research (2026). PMID: 42048495 ↗

    L2SR_OBSCited in: Diagnosis and Workup, Prevention, Screening and Health Maintenance
  34. [34]

    Wadie P, Zakher B, Elgazzar K et al.. “AI in Point-of-Care Imaging for Clinical Decision Support: Systematic Review of Diagnostic Accuracy, Task-Shifting, and Explainability.” JMIR AI (2026). PMID: 42044298 ↗

    L2SR_OBSCited in: Diagnosis and Workup, Prognosis and Natural History
  35. [35]

    Robson J, Dostal I, Mathur R et al.. “Improving anticoagulation in atrial fibrillation: observational study in three primary care trusts.” The British journal of general practice : the journal of the Royal College of General Practitioners (2014). PMID: 24771841 ↗

    L4OTHERCited in: Diagnosis and Workup, Severity, Staging and Risk Stratification, Long-term and Definitive Management, Prognosis and Natural History
  36. [36]

    Laiteerapong N, Ham SA, Ari M et al.. “A Quasi-Experimental Evaluation of a Primary Care Behavioral Health Integration Program Based on the Chronic Care Model.” Journal of general internal medicine (2025). PMID: 40481385 ↗

    L2OTHERCited in: Diagnosis and Workup, Long-term and Definitive Management
  37. [37]

    Hu X, Su X, Chen S et al.. “Predictors of 30-day readmission in young and middle-aged T2DM patients: a retrospective cohort study.” Endocrine (2026). PMID: 42068438 ↗

    L3COHORTCited in: Diagnosis and Workup, Long-term and Definitive Management, Prevention, Screening and Health Maintenance
  38. [38]

    Yang M, Jin J, Liu YL et al.. “Advancements of artificial intelligence in Chinese herbal medicine recommendation: A comprehensive review of data-driven approaches and clinical applications form 2016 to 2025.” Medicine (2026). PMID: 42065178 ↗

    L2SR_OBSCited in: Diagnosis and Workup, Long-term and Definitive Management
  39. [39]

    Usiskin IM, Danila MI, Cai T et al.. “Toward Artificial Intelligence-driven Clinical Decision Support Tools in Rheumatology.” Rheumatic diseases clinics of North America (2026). PMID: 42409432 ↗

    L5REVIEW_NARRATIVECited in: Diagnosis and Workup
  40. [40]

    Chen Z, Tang Z, Ewing RM et al.. “Artificial intelligence in respiratory medicine: From diagnosis to treatment and future directions.” Chinese medical journal pulmonary and critical care medicine (2026). PMID: 42396189 ↗

    L5REVIEW_NARRATIVECited in: Diagnosis and Workup
  41. [41]

    McCarthy DM, VanWagner LB, Rafferty MR et al.. “Improving diagnostic safety through STeatosis Identification, Risk stratification, and Referral pathway in the ED (STIRRED): Protocol for an effectiveness implementation trial.” Contemporary clinical trials (2025). PMID: 41407098 ↗

    L5TRIAL_NONRANDOMCited in: Severity, Staging and Risk Stratification, Acute Management, Generalist Reasoning under Diagnostic Uncertainty, Point-of-Care Scores & Referral Thresholds
  42. [42]

    Zhang F, Yun W, Qin L. “Development and Application of a Nurse-Led Clinical Decision Support System for Safe Intravenous Medication Administration: A Nonrandomized Controlled Trial.” Applied clinical informatics (2026). PMID: 42091056 ↗

    L2RCTCited in: Severity, Staging and Risk Stratification
  43. [43]

    Rucco A, Shumba AT, Montanaro T et al.. “Enhancing Chronic Heart Failure Monitoring, Prevention, and Management With IoT and AI: A Systematic Literature Review.” IEEE journal of biomedical and health informatics (2026). PMID: 41182927 ↗

    L2SR_OBSCited in: Severity, Staging and Risk Stratification
  44. [44]

    Jang J, Kim WJ, Park SW et al.. “Development of explainable machine learning models to predict side effects in patients with rheumatoid arthritis taking methotrexate treatment: a nationwide multicentre cohort study.” BMJ open (2025). PMID: 41320203 ↗

    L2COHORTCited in: Severity, Staging and Risk Stratification
  45. [45]

    Naderian S, Soleimanzadeh F, Nikniaz L et al.. “A Systematic Review of Artificial Intelligence-Based Clinical Decision Support Systems in Prostate Cancer Management.” Healthcare technology letters (2025). PMID: 41262209 ↗

    L2SR_OBSCited in: Severity, Staging and Risk Stratification
  46. [46]

    Onwuharine EN, Clark AJ, McIntyre A et al.. “Clinical and MRI variables in decision support systems for prostate MRI: A systematic review of decision support tools, nomograms, and risk models.” Radiography (London, England : 1995) (2025). PMID: 41202669 ↗

    L2SR_OBSCited in: Severity, Staging and Risk Stratification
  47. [47]

    Bonura A, Musotto G, Rossi SS et al.. “Cerebri: A Web-App to Reduce Door-to-Treatment Decision Time and Improve Guideline Adherence in Acute Ischemic Stroke.” Stroke (2025). PMID: 41208703 ↗

    L1RCTCited in: Acute Management, History and Evolution of Treatment
  48. [48]

    Ciapponi A, Fernandez Nievas SE, Seijo M et al.. “Reducing medication errors for adults in hospital settings.” The Cochrane database of systematic reviews (2021). PMID: 34822165 ↗

    L1SR_OBSCited in: Acute Management, Multimorbidity, Polypharmacy & Deprescribing, Prognosis and Natural History
  49. [49]

    Rangraz Jeddi F, Nabovati E, Sharif R et al.. “The effects of information technology interventions for optimizing antibiotic prescribing in urinary tract infections: a systematic review.” BMC infectious diseases (2025). PMID: 41462120 ↗

    L2SR_OBSCited in: Acute Management
  50. [50]

    Beauchamp FO, Thériault J, Emeriaud G et al.. “Lung Recruitment Maneuvers During Invasive Mechanical Ventilation: Single-Center Retrospective PICU Cohort Study, 2016-2023.” Pediatric critical care medicine : a journal of the Society of Critical Care Medicine and the World Federation of Pediatric Intensive and Critical Care Societies (2026). PMID: 42383786 ↗

    L3COHORTCited in: Acute Management
  51. [51]

    Skryabin V, Masyakin A, Pozdniakov S et al.. “Personalizing Treatment for Acute Alcoholic Hallucinosis: Clinical Utility of Integrated Pharmacogenetic Testing, Metabolic Phenotyping, and microRNA Biomarkers.” Psychopharmacology bulletin (2026). PMID: 41532000 ↗

    L4RCTCited in: Acute Management
  52. [52]

    Xu Z, Zhang R, Han Q et al.. “Explainable Machine Learning Models Using Routine Clinical Data for Early Prediction of Necrotizing Fasciitis: A Single-Center Retrospective Study.” ANZ journal of surgery (2025). PMID: 41277728 ↗

    L3COHORTCited in: Acute Management
  53. [53]

    Bruckelt L, Neumann J, Keß A et al.. “Clinical evaluation of a computer-assisted decision support and documentation system for the primary care of polytrauma patients.” Frontiers in digital health (2026). PMID: 42434373 ↗

    L4OTHERCited in: Acute Management, Long-term and Definitive Management
  54. [54]

    Bright TJ, Wong A, Dhurjati R et al.. “Effect of clinical decision-support systems: a systematic review.” Annals of internal medicine (2012). PMID: 22751758 ↗

    L1SR_OBSCited in: Long-term and Definitive Management, Prognosis and Natural History
  55. [55]

    Roumeliotis N, Sniderman J, Adams-Webber T et al.. “Effect of Electronic Prescribing Strategies on Medication Error and Harm in Hospital: a Systematic Review and Meta-analysis.” Journal of general internal medicine (2019). PMID: 31396810 ↗

    L1SR_OBSCited in: Long-term and Definitive Management, Prognosis and Natural History
  56. [56]

    Fan E, Laupacis A, Pronovost PJ et al.. “How to use an article about quality improvement.” JAMA (2010). PMID: 21098772 ↗

    L5OTHERCited in: Long-term and Definitive Management
  57. [57]

    Basten J, Köberlein-Neu J, Ihle P et al.. “Patterns of CDSS adoption in primary care: a cluster analysis and predictive modelling study from a stepped wedge trial.” Implementation science : IS (2026). PMID: 42316212 ↗

    L1RCTCited in: Long-term and Definitive Management, History and Evolution of Treatment, Prognosis and Natural History
  58. [58]

    Agarwal S, Glenton C, Tamrat T et al.. “Decision-support tools via mobile devices to improve quality of care in primary healthcare settings.” The Cochrane database of systematic reviews (2021). PMID: 34314020 ↗

    L1SR_OBSCited in: Long-term and Definitive Management
  59. [59]

    Mandrina M, Gevorkyan T, Zvezda S et al.. “Gastric cancer survival prediction using artificial intelligence models based on electronic health records: a systematic review and meta-analysis.” Frontiers in digital health (2026). PMID: 42416807 ↗

    L2SR_OBSCited in: Long-term and Definitive Management, Prognosis and Natural History
  60. [60]

    Cortés Sánchez CJ, Salazar González F, Gómez Portolés JM et al.. “The role of digital tools and artificial intelligence in supporting antimicrobial stewardship: a systematic review.” The Journal of antimicrobial chemotherapy (2026). PMID: 42313419 ↗

    L2SR_OBSCited in: Long-term and Definitive Management
  61. [61]

    Gerber F, Gupta R, Sanchez-Samaniego G et al.. “Community health worker-led versus facility-based type 2 diabetes care in rural Lesotho: a cluster-randomized trial within the ComBaCaL cohort study.” BMC medicine (2026). PMID: 42174613 ↗

    L1COHORTCited in: Long-term and Definitive Management, Prevention, Screening and Health Maintenance
  62. [62]

    Robson J, Hull S, Mathur R et al.. “Improving cardiovascular disease using managed networks in general practice: an observational study in inner London.” The British journal of general practice : the journal of the Royal College of General Practitioners (2014). PMID: 24771840 ↗

    L2OTHERCited in: Long-term and Definitive Management, Prognosis and Natural History
  63. [63]

    Aksu BN, Gashi B, Schiefenhövel F et al.. “Effectiveness of Guideline-Based Clinical Decision Support Systems: Protocol for a Systematic Review.” JMIR research protocols (2026). PMID: 42275446 ↗

    L5SR_OBSCited in: Long-term and Definitive Management
  64. [64]

    Dillen H, Dankaerts A, Snijders D et al.. “Physicians' preferences for the use of clinical decision support systems in the context of acutely ill children presenting to ambulatory care: a focus group study.” BMC medical informatics and decision making (2026). PMID: 42410436 ↗

    L5OTHERCited in: Long-term and Definitive Management
  65. [65]

    Vasey B, Nagendran M, Campbell B et al.. “Reporting guideline for the early stage clinical evaluation of decision support systems driven by artificial intelligence: DECIDE-AI.” BMJ (Clinical research ed.) (2022). PMID: 35584845 ↗

    L1GUIDELINECited in: History and Evolution of Treatment
  66. [66]

    Bakker T, Klopotowska JE, Dongelmans DA et al.. “The effect of computerised decision support alerts tailored to intensive care on the administration of high-risk drug combinations, and their monitoring: a cluster randomised stepped-wedge trial.” Lancet (London, England) (2024). PMID: 38262430 ↗

    L1RCTCited in: History and Evolution of Treatment, Multimorbidity, Polypharmacy & Deprescribing, Prognosis and Natural History
  67. [67]

    Samore MH, Bateman K, Alder SC et al.. “Clinical decision support and appropriateness of antimicrobial prescribing: a randomized trial.” JAMA (2005). PMID: 16278358 ↗

    L1RCTCited in: History and Evolution of Treatment
  68. [68]

    Rousseau N, McColl E, Newton J et al.. “Practice based, longitudinal, qualitative interview study of computerised evidence based guidelines in primary care.” BMJ (Clinical research ed.) (2003). PMID: 12574046 ↗

    L5RCTCited in: History and Evolution of Treatment
  69. [69]

    ShojaeiBaghini M, Ghaemi MM, Ahmadipour A. “Artificial intelligence in the identification and prediction of adverse transfusion reactions(ATRs) and implications for clinical management: a systematic review of models and applications.” BMC medical informatics and decision making (2025). PMID: 41152861 ↗

    L2SR_OBSCited in: History and Evolution of Treatment, Complications
  70. [70]

    Tun HM, Rahman HA, Naing L et al.. “Trust in Artificial Intelligence-Based Clinical Decision Support Systems Among Health Care Workers: Systematic Review.” Journal of medical Internet research (2025). PMID: 40772775 ↗

    L2SR_OBSCited in: History and Evolution of Treatment
  71. [71]

    Rezende VMLR, Borges IN, Ravetti CG et al.. “Efficacy and safety of a digital clinical decision support system using a C-reactive protein-based algorithm and evidence-based stopping rules to guide antibiotic therapy duration: A single-center, open-label randomized controlled trial in a tertiary care hospital.” International journal of infectious diseases : IJID : official publication of the International Society for Infectious Diseases (2026). PMID: 41990873 ↗

    L1RCTCited in: History and Evolution of Treatment, Prognosis and Natural History
  72. [72]

    Roemer A, Schlicker N, Kernder A et al.. “Large language models enhance diagnostic reasoning of medical students in rheumatology: a randomized controlled trial.” BMC medical education (2026). PMID: 41877128 ↗

    L1RCTCited in: History and Evolution of Treatment, Prognosis and Natural History
  73. [73]

    Bolton WJ, Wilson R, Gilchrist M et al.. “The impact of artificial intelligence-driven decision support on uncertain antimicrobial prescribing: a randomised, multimethod study.” The Lancet. Digital health (2025). PMID: 41372053 ↗

    L1RCTCited in: History and Evolution of Treatment, Generalist Reasoning under Diagnostic Uncertainty, Point-of-Care Scores & Referral Thresholds
  74. [74]

    Dijk SW, Wollny C, Kroencke T et al.. “Sex differences in inappropriate imaging requests: insights from the Medical Imaging Decision And Support (MIDAS) study.” European radiology (2025). PMID: 41196364 ↗

    L4RCTCited in: History and Evolution of Treatment
  75. [75]

    Atlas SJ, Burdick TE, Wright A et al.. “Comparing clinical decision support systems for improving follow-up of abnormal cervical cancer screening test results.” Journal of biomedical informatics (2025). PMID: 40935221 ↗

    L2RCTCited in: History and Evolution of Treatment
  76. [76]

    Gimbel RW, Pirrallo RG, Lowe SC et al.. “Effect of clinical decision rules, patient cost and malpractice information on clinician brain CT image ordering: a randomized controlled trial.” BMC medical informatics and decision making (2018). PMID: 29530029 ↗

    L1RCTCited in: Generalist Reasoning under Diagnostic Uncertainty, Point-of-Care Scores & Referral Thresholds
  77. [77]

    Carroll AE, Biondich P, Anand V et al.. “A randomized controlled trial of screening for maternal depression with a clinical decision support system.” Journal of the American Medical Informatics Association : JAMIA (2012). PMID: 22744960 ↗

    L1RCTCited in: Generalist Reasoning under Diagnostic Uncertainty, Point-of-Care Scores & Referral Thresholds
  78. [78]

    Lester C, Rowell B, Zheng Y et al.. “Effect of Uncertainty-Aware AI Models on Pharmacists' Reaction Time and Decision-Making in a Web-Based Mock Medication Verification Task: Randomized Controlled Trial.” JMIR medical informatics (2025). PMID: 40249341 ↗

    L1RCTCited in: Generalist Reasoning under Diagnostic Uncertainty, Point-of-Care Scores & Referral Thresholds
  79. [79]

    Born C, Schwarz R, Böttcher TP et al.. “The role of information systems in emergency department decision-making-a literature review.” Journal of the American Medical Informatics Association : JAMIA (2024). PMID: 38781289 ↗

    L2SR_OBSCited in: Generalist Reasoning under Diagnostic Uncertainty, Point-of-Care Scores & Referral Thresholds
  80. [80]

    Rosati A, Criscione M, Lilli L et al.. “Natural language processing as consultation service platform or clinical decision support system in gynecologic oncology: a systematic review.” International journal of gynecological cancer : official journal of the International Gynecological Cancer Society (2026). PMID: 41819636 ↗

    L2SR_OBSCited in: Generalist Reasoning under Diagnostic Uncertainty, Point-of-Care Scores & Referral Thresholds
  81. [81]

    Alldred DP, Raynor DK, Hughes C et al.. “Interventions to optimise prescribing for older people in care homes.” The Cochrane database of systematic reviews (2013). PMID: 23450597 ↗

    L1SR_OBSCited in: Multimorbidity, Polypharmacy & Deprescribing, Prognosis and Natural History
  82. [82]

    Alldred DP, Kennedy MC, Hughes C et al.. “Interventions to optimise prescribing for older people in care homes.” The Cochrane database of systematic reviews (2016). PMID: 26866421 ↗

    L1SR_OBSCited in: Multimorbidity, Polypharmacy & Deprescribing, Prognosis and Natural History
  83. [83]

    van Hylckama Vlieg MAM, Pot IE, Visser HPJ et al.. “Appropriate medication use in Dutch terminal care: study protocol of a multicentre stepped-wedge cluster randomized controlled trial (the AMUSE study).” BMC palliative care (2024). PMID: 38172930 ↗

    L5TRIAL_NONRANDOMCited in: Multimorbidity, Polypharmacy & Deprescribing
  84. [84]

    Vamadevan A, Vijayan V, Cole C et al.. “A NASSS framework-guided systematic review and exploratory modelling of digital health interventions for polypharmacy management in older adults.” BMC geriatrics (2025). PMID: 41272510 ↗

    L2SR_OBSCited in: Multimorbidity, Polypharmacy & Deprescribing
  85. [85]

    Ng Y, Hsu JTY, Ng NNE et al.. “Evaluating the role of clinical decision support systems in medication safety for older people: a systematic review.” Age and ageing (2025). PMID: 40716041 ↗

    L1SR_OBSCited in: Multimorbidity, Polypharmacy & Deprescribing
  86. [86]

    Skains RM, Hayes JM, Selman K et al.. “Emergency Department Programs to Support Medication Safety in Older Adults: A Systematic Review and Meta-Analysis.” JAMA network open (2025). PMID: 40067297 ↗

    L1SR_OBSCited in: Multimorbidity, Polypharmacy & Deprescribing
  87. [87]

    Tlili NE, Robert L, Gerard E et al.. “A systematic review of the value of clinical decision support systems in the prescription of antidiabetic drugs.” International journal of medical informatics (2024). PMID: 39106772 ↗

    L2SR_OBSCited in: Multimorbidity, Polypharmacy & Deprescribing, Complications
  88. [88]

    Chen W, O'Bryan CM, Gorham G et al.. “Barriers and enablers to implementing and using clinical decision support systems for chronic diseases: a qualitative systematic review and meta-aggregation.” Implementation science communications (2022). PMID: 35902894 ↗

    L5SR_OBSCited in: Multimorbidity, Polypharmacy & Deprescribing
  89. [89]

    Bhatt M, Benterud E, Palechuk T et al.. “Advancing Community Care and Access to Follow-up After Acute Kidney Injury Hospitalization: Design of the AFTER AKI Randomized Controlled Trial.” Canadian journal of kidney health and disease (2024). PMID: 38495365 ↗

    L5RCTCited in: Complications
  90. [90]

    Cai J, Li P, Li W et al.. “Outcomes of clinical decision support systems in real-world perioperative care: a systematic review and meta-analysis.” International journal of surgery (London, England) (2024). PMID: 39037722 ↗

    L1SR_OBSCited in: Complications
  91. [91]

    Murzabekov M, Lidströmer N, Borg N et al.. “The association between vital signs at hospital admission and adverse outcomes in patients with COVID-19: a retrospective cohort study.” Frontiers in medicine (2025). PMID: 40678139 ↗

    L2COHORTCited in: Complications
  92. [92]

    Palomares SM, Ferrara G, Sguanci M et al.. “The Impact of Artificial Intelligence Technologies on Nutritional Care in Patients With Chronic Kidney Disease: A Systematic Review.” Journal of renal nutrition : the official journal of the Council on Renal Nutrition of the National Kidney Foundation (2025). PMID: 40588047 ↗

    L2SR_OBSCited in: Complications
  93. [93]

    Wang J, Wu Y, Huang Y et al.. “Comparative effectiveness of delirium recognition with and without a clinical decision assessment system on outcomes of hospitalized older adults: Cluster randomized controlled trial.” International journal of nursing studies (2024). PMID: 39700738 ↗

    L1RCTCited in: Complications
  94. [94]

    Patel H, Crusco S, Hansen D et al.. “A Systematic Review and Meta-analysis of Externally Validated Epic Clinical Decision Support Tools.” Journal of general internal medicine (2026). PMID: 41917292 ↗

    L1SR_OBSCited in: Prognosis and Natural History
  95. [95]

    Muñoz J, Fernández-Araujo NJ, Ruíz-Cacho R et al.. “Artificial intelligence and computerized decision support in adult intensive care: A systematic review of randomized controlled trials.” Journal of critical care (2026). PMID: 42048766 ↗

    L1SR_MA_RCTCited in: Prognosis and Natural History
  96. [96]

    Zhang T, Shin N. “From interface to outcome: a 4I framework for AI-linked functionality in electronic health records.” BMC medical informatics and decision making (2026). PMID: 42174575 ↗

    L5SR_OBSCited in: Prognosis and Natural History
  97. [97]

    Almutairi H, Stafford A, Etherton-Beer C et al.. “Optimisation of medications used in residential aged care facilities: a systematic review and meta-analysis of randomised controlled trials.” BMC geriatrics (2020). PMID: 32641005 ↗

    L1SR_MA_RCTCited in: Special Populations and Pregnancy
  98. [98]

    Marko B, Palmowski L, Nowak H et al.. “Employing artificial intelligence for optimising antibiotic dosages in sepsis on intensive care unit: a study protocol for a prospective observational study (KI.SEP).” BMJ open (2024). PMID: 39672586 ↗

    L5TRIAL_NONRANDOMCited in: Special Populations and Pregnancy
  99. [99]

    Crowley EK, Sallevelt BTGM, Huibers CJA et al.. “Intervention protocol: OPtimising thERapy to prevent avoidable hospital Admission in the Multi-morbid elderly (OPERAM): a structured medication review with support of a computerised decision support system.” BMC health services research (2020). PMID: 32183810 ↗

    L5TRIAL_NONRANDOMCited in: Special Populations and Pregnancy
  100. [100]

    Carter J, Anumba D, Burden C et al.. “Tommy's Clinical Decision Support Tool: an intervention development and feasibility study to inform a future randomised controlled trial.” Pilot and feasibility studies (2026). PMID: 41742257 ↗

    L4RCTCited in: Special Populations and Pregnancy
  101. [101]

    Drenth-van Maanen AC, Leendertse AJ, Jansen PAF et al.. “The Systematic Tool to Reduce Inappropriate Prescribing (STRIP): Combining implicit and explicit prescribing tools to improve appropriate prescribing.” Journal of evaluation in clinical practice (2017). PMID: 28776873 ↗

    L5RCTCited in: Special Populations and Pregnancy
  102. [102]

    Kamaruzaman LH, Abdul Rahman H, Mohd Nor N'U et al.. “Integrating aqli (rationale) and naqli (revealed) knowledge in AI-driven clinical decision support systems to enhance healthcare delivery for guiding termination of pregnancy among Muslim patients: a systematic review (1).” BMC medical informatics and decision making (2026). PMID: 41742117 ↗

    L5SR_OBSCited in: Special Populations and Pregnancy
  103. [103]

    Badisy IE, Assarag B, Belrhiti Z. “Interpretable clinical decision support systems in high-risk pregnancy: a scoping review of models, methods, and implementation.” BMC pregnancy and childbirth (2026). PMID: 41495684 ↗

    L5SR_OBSCited in: Special Populations and Pregnancy, Prevention, Screening and Health Maintenance
  104. [104]

    Derksen C, Le Noury KA, Akbar AB et al.. “Why aren't they used? Systematic review of barriers to implementation of clinical decision support systems for early cancer detection in primary care.” The British journal of general practice : the journal of the Royal College of General Practitioners (2026). PMID: 41494775 ↗

    L5SR_OBSCited in: Prevention, Screening and Health Maintenance
  105. [105]

    Wright AP, Choi L, Nairon KG et al.. “Interruptive versus Non-Interruptive Reminders for Statin tHerApy in Primary Care (INIRSHA-PC): protocol and statistical analysis plan for a randomised clinical trial.” BMJ open (2026). PMID: 41605591 ↗

    L5TRIAL_NONRANDOMCited in: Prevention, Screening and Health Maintenance
  106. [106]

    Giordano L, Durand C, Ahmad R et al.. “Strategies and outcomes of CDSS implementation for antimicrobial stewardship in hospital settings: a systematic review.” Antimicrobial resistance and infection control (2026). PMID: 41992388 ↗

    L5SR_OBSCited in: Prevention, Screening and Health Maintenance
  107. [107]

    Jain G, Bodade A, Pati S. “Impact of clinical decision support systems (CDSS) on clinical outcomes and healthcare delivery in low- and middle-income countries: protocol for a systematic review and meta-analysis.” BMJ open (2025). PMID: 41360443 ↗

    L5SR_OBSCited in: Prevention, Screening and Health Maintenance
  108. [108]

    Heo Y, Kim M, Han SS et al.. “AI-Driven Predictions of Readmission and Mortality for Improved Discharge Decisions in Critical Care: A Retrospective Study.” Diagnostics (Basel, Switzerland) (2026). PMID: 41897607 ↗

    L3COHORTCited in: Prevention, Screening and Health Maintenance

Revision History

All updates applied to this page

Loading revisions…