#clinicaldecisionmaking

JMIR Publications

@jmirpub.bsky.social

1 week ago

Digitally Assisted Clinical Decision-Making in Traditional Chinese Medicine: Comparative Study of 5 Large Language Models Background: Traditional Chinese medicine (TCM) clinical decision-making involves complex integration of syndrome differentiation, constitutional assessment, and individualized treatment selection, creating challenges for standardization and quality assurance. While large language models (LLMs) demonstrate capabilities in medical knowledge integration and clinical reasoning, their application to TCM remains largely unexplored, particularly regarding syndrome differentiation principles and prescription formulation. Objective: This study evaluated 5 contemporary LLMs in TCM clinical decision-making and assessed human–artificial intelligence (#AI) (AI) collaboration compared with independent approaches. Specific objectives were to benchmark LLM performance in TCM knowledge assessment, evaluate clinical case analysis capabilities, identify the optimal model, and assess the quality, efficiency, and acceptability of human-AI collaboration. Methods: In total, 5 mainstream LLMs were evaluated—Claude 3.7 Sonnet-Extended (Anthropic), ChatGPT 4.5 (OpenAI), Grok3-DeepSearch (xAI), Gemini 2.0 Flash Thinking Experimental (Google), and DeepSeek-R1 (DeepSeek). The evaluation consisted of four phases, (1) TCM knowledge assessment using 160 standardized questions, (2) clinical case analysis of 30 cases representing different disease systems and complexity levels, (3) optimal model selection using weighted scoring (40% knowledge and 60% clinical analysis), and (4) clinical application assessment involving 10 TCM practitioners and 2 experts comparing physician-only, AI-only, and human-AI collaboration across 5 clinical cases. Statistical analysis included descriptive statistics, reliability analysis, comparative testing, and regression analysis. Results: DeepSeek-R1 demonstrated superior performance across both evaluation domains, achieving 96.7% accuracy in knowledge assessment and 17.31/20 (SD 2.65) in clinical case analysis, significantly outperforming other models (

JMIR Formative Res: Digitally Assisted Clinical Decision-Making in Traditional Chinese Medicine: Comparative Study of 5 Large Language Models #TraditionalChineseMedicine #TCM #HealthcareInnovation #AIinHealthcare #ClinicalDecisionMaking

Inovine Conferences

@inovineconferences.bsky.social

3 weeks ago

🎓 Advance Your Nursing Expertise with Global Leaders! 🌍
Join us at the 25ᵗʰ Global Summit on Nursing Education & Practice
📍 Los Angeles, USA
🗓 October 19–20, 2026
🔗 Website: shorturl.at/NMnae

#nursingeducation #nursingpractice #nursingresearch #clinicaldecisionmaking

Inovine Conferences

@inovineconferences.bsky.social

3 weeks ago

🎓 25th Global Summit on Nursing Education & Practice
📍 Los Angeles, USA |
🗓 October 19–20, 2026
🔗 Website: shorturl.at/NMnae
📄 Abstract submission open for speaker participation!

#nursingresearch #clinicaldecisionmaking #advancednursing #adulthealthnursing

Inovine Conferences

@inovineconferences.bsky.social

1 month ago

🎓 Exciting Scientific Session Announcement!
Join us at the 25th Global Summit on Nursing Education & Practice
📅 October 19–20, 2026
📍 Los Angeles, USA
🔗 Website: shorturl.at/NMnae

#clinicaldecisionmaking #advancednursing #adulthealthnursing #complexcare #healthcare

Citizen Portal News Vermont

@citizenptnewsvt.bsky.social

1 month ago

Experts tell committee H.583 would curb private‑equity control of Vermont physician practices Scholars and policy experts testified to a legislative committee on Jan. 30 that H.583’s corporate practice of medicine and transparency provisions would limit management‑service‑organization tactics used by private equity to influence clinical care while preserving lawful investment, citing national evidence on price increases, staffing impacts and concentrated ownership of methadone clinics.

Experts warn that unchecked private equity control over healthcare could lead to financial crises and compromised patient care in Vermont.

Read the full story

#VT #PrivateEquityOversight #VermontHealthcare #HealthcareTransparency #CitizenPortal #ClinicalDecisionMaking

JMIR Publications

@jmirpub.bsky.social

3 months ago

Interpretation of Health-Smart Home Data and Implications for Clinical Decision-Making: Inductive Content Analysis Background: Health-smart home technologies offer real-time sensor-based monitoring of older adults, allowing for early detection of health changes. How clinicians interpret and utilize this data, particularly in visualized formats such as bar, line, and pie graphs, remains underexplored. Objective: This study aimed to examine how #nurses interpret health smart home (HSH)-generated data visualisations and their clinical implications. Specifically, it investigated how #nurses engage with bar, line, and pie graphs displaying HSH sensor data, identifying key patterns in their analysis of patient activity, sleep, and mobility, as well as challenges that may impact clinical decision-making. Methods: Using a qualitative descriptive methodology and inductive content analysis approach with a quantitative component, we analysed #nurses’ qualitative interpretations of existing health-smart home data from 3 older adults living with ambient whole-home sensing. #nurses provided structured written feedback on visualised trends in activity, sleep, and mobility patterns. Results: The findings highlight both opportunities and challenges of using sensor-derived health data in older adults’ care. #nurses identified key patterns in sleep, mobility, and home engagement, but interpretation difficulties, such as unclear sleep metrics and lack of clinical context, hindered decision-making. #nurses preferred bar and line graphs over pie charts for interpreting these data. Survey results show a statistically significant difference in how #nurses rated different graph types (χ²(2) = 17.11, p = 0.00019), with pie charts rated significantly lower than both bar and line graphs (p < 0.001 and p = 0.0082, respectively). These findings underscore the need for improved data visualisation and integration to enhance clinical utility. Conclusions: Findings indicate that #nurses were able to provide accurate interpretations of the sensor-based data. However, there is a need for improved visualisation techniques and clinician training to optimize health-smart home data for early intervention. Standardized approaches to data representation could enhance #nurses' ability to detect and act on subtle yet important information about older adults’ health changes occurring in home settings. Clinical Trial: N/A

New in JMIR Nursing: Interpretation of Health-Smart Home Data and Implications for Clinical Decision-Making: Inductive Content Analysis #HealthTech #SmartHome #Nursing #DataVisualization #ClinicalDecisionMaking

Prkash Mishra

@iprkashmishra.bsky.social

3 months ago

#Prioritization45
#NursingPriorities #PatientCare #CriticalThinking #StableVsUnstable
#ExpectedVsUnexpected #ActualVsPotential #AcuteVsChronic #MaslowsHierarchy #PhysiologicalNeeds #ABCs #AirwayBreathingCirculation #NursingAssessment #ClinicalDecisionMaking #UrgentCare #NurseEducation #NCLEXTips

Citizen Portal News New Hampshire

@citizenptnewsnh.bsky.social

4 months ago

Health Department Defends September Guidance, Says It Generally Aligns With CDC Department of Health and Human Services officials told the Fiscal Committee that the department's September health alert for respiratory season used peer‑reviewed data and largely aligns with CDC guidance; officials said some CDC guidance was not yet available when the department published its annual guidance.

New Hampshire's Health Department defends its September guidance on respiratory viruses, claiming it aligns with CDC recommendations despite some missing data at the time.

Learn more here!

#NH #CitizenPortal #NewHampshirePublicHealth #PublicSafety #ClinicalDecisionMaking #RespiratoryHealth

40:40 Recruitment - celia@4040recruitment.co.uk

@4040rec.bsky.social

5 months ago

Roche gets EU nod for kidney disease algorithm Roche and Klinrisk have picked up a CE-mark for an AI tool that can be used to predict renal function decline in patients with chronic kidney disease.

#Roche #kidneydisease #kidneydiseasealgorithm #AI #Klinrisk #DigitalHealth #clinicaldecisionmaking #chronickidneydisease #CKD #digitalhealthtechnologies #CKDpatients #digitalhealthalgorithms #KidneyKlinriskAlgorithm #AIbasedriskstratificationtool #KidneyKFREAlgorithm
zurl.co/qFtM9

Acta Médica Portuguesa

@actamedport.bsky.social

6 months ago

AMP just published!
"Awareness and Barriers to Guideline Adherence: Slovenian Family Physicians Survey and Qualitative Feedback"

Full paper here:
www.actamedicaportuguesa.com/revista/inde...

#clinicaldecisionmaking #familypractice #guidelineadherence #Slovenia #actamedicaportuguesa

40:40 Recruitment - celia@4040recruitment.co.uk

@4040rec.bsky.social

7 months ago

Medical chatbot firm OpenEvidence raises $210m OpenEvidence has raised $210m to expand the capabilities of its physician decision-making tool and launch a new service offering 'PhD-level' research.

#medtech #Medicalchatbot #OpenEvidence #SequoiaCapital #Coatue #Conviction #Thrive #clinicaldecisionmaking #GoogleVentures #KleinerPerkins #NEJM #JAMA #DeepConsult #AI #artificialintelligenceagent #artificialintelligence #AIagent #AIcopilot
pharmaphorum.com/news/medical...

MSK Library

@msklibrary.bsky.social

7 months ago

Synapse: Your Connection to our MSK Authors
Meet: Hiram S Cody III
Research Focus: Surgery; NE Emeritus

synapse.mskcc.org/synapse/work...

#BreastCancer #TumorRecurrence #BreastSurgery #OncologyResearch #CancerCare #EvidenceGap #ClinicalDecisionMaking #CancerRecurrence #BreastConservation

MSK Library

@msklibrary.bsky.social

7 months ago

Synapse: Your Connection to our MSK Authors
Meet: Mary Susan Brady, MD
Research Focus: Surgery; Attending

synapse.mskcc.org/synapse/work...

#SentinelLymphNode #BreastCancerResearch
#CancerSurgery #OncologyTrends
#ClinicalDecisionMaking #SurgicalOncology #PrecisionMedicine #BreastCancerCare

pharmaphorum

@pharmaphorum.bsky.social

7 months ago

Medical chatbot firm OpenEvidence raises $210m OpenEvidence has raised $210m to expand the capabilities of its physician decision-making tool and launch a new service offering 'PhD-level' research.

The company behind a #searchagent to assist #clinicaldecisionmaking – #OpenEvidence – has raised an impressive $210 million in a second-round #financing that will support the launch of a tool that can provide more in-depth responses to #doctors.

pharmaphorum.com/news/medical...

Nursing Reports MDPI

@nursrep.bsky.social

8 months ago

🧠 Just published in Nursing Reports:
A systematic review + meta-analysis on how RNs apply critical thinking and clinical decision making in practice.
📊 Views: 1485
🔗 www.mdpi.com/2039-4403/15...
#NursingResearch #ClinicalDecisionMaking #CriticalThinking #OpenAccess

OncoDaily

@oncodaily.bsky.social

9 months ago

Advancing Autonomous AI for Clinical Decision-Making in Oncology - The Babak Lab - OncoDaily Advancing Autonomous AI for Clinical Decision-Making in Oncology - The Babak Lab / artificial intelligence, cancer, Daniel Truhn, Dirk Jäger, Dyke Ferber,

The Babak Lab - Advancing Autonomous AI for Clinical Decision-Making in Oncology
@thebabaklab.bsky.social

oncodaily.com/science/auto...

#OncoDaily #Oncology #Cancer #Health #Medicine #MedEd #MedOnc #MedNews #AI #ClinicalDecisionMaking

Yasser ElDershaby, MD. MSc. NC.

@yassereldershaby.bsky.social

1 year ago

Critical Appraisal of research evidence - Tools and Methods - Tutorial 3 Welcome to the Critical Appraisal of Research Evidence - Tutorial About Tools and Methods This tutorial covers essential tools and methods used to critically appraise research evidence, ensuring you can effectively evaluate the quality and relevance of scientific studies. Learn about various appraisal tools like CONSORT, PRISMA, CASP, and more. Gain insights into their application in evidence-based medicine to enhance your clinical decision-making and research evaluation skills. #CriticalAppraisal #ResearchEvidence #EvidenceBasedMedicine #CONSORT #PRISMA #CASP #MedicalResearch #HealthcareEducation #ResearchMethods #ClinicalDecisionMaking #AppraisalTools #MedicalTutorial #ResearchEvaluation

Critical Appraisal of research evidence - Tools and Methods
Watch here: youtu.be/mfKao3Gjhn8

#CriticalAppraisal #ResearchEvidence #EvidenceBasedMedicine #CONSORT #PRISMA #CASP #MedicalResearch #HealthcareEducation #ResearchMethods #ClinicalDecisionMaking #AppraisalTools #MedicalTutorial #ResearchE

Mind Ready Tools

@mindreadytools.bsky.social

1 year ago

By adding software agents to the diagnostic workflow, the clinical decision making process changes. Whether it is better or worse depends on how the joint decision making team is structured.

#AI, #JointCognitiveSystem, #ClinicalDecisionMaking, #DecisionSupportTools, #SkillLoss, #MentalModels

Leonard Van Gelder

@leonardvangelder.bsky.social

1 year ago

Current #AI in healthcare struggles with #psychosocial factors. We continue to need uptraining in #medicine #physio #physicaltherapy to bridge #clinicaldecisionmaking gaps.

#painscience #behavior&evolution

bmjopen.bmj.com/content/14/1...

Jack Barrett

@respara-jb.bsky.social

1 year ago

💊 Anticoagulants, frailty, and injury mechanisms are big red flags for clinicians. BUT assessing asymptomatic patients remains tricky—making "to transport or not" decisions more nuanced and challenging. #TBI #ClinicalDecisionMaking 3/5

Anisa Jafar

@emergemedglobal.bsky.social

6 years ago

Turnberg cup winner (@Medicine_UoM) @GovindOliver presenting for the Rod Little Award at #RCEMasc talking all things #clinicaldecisionmaking in ACS