This is the official journal for multidisciplinary clinical and health sciences and a leading platform that bridges multidisciplinary clinical science with evidence-based practice to advance healthcare quality, safety, and patient outcomes.
Nexus of Integrated Clinical Science (NICS) is a peer-reviewed, open-access journal dedicated to publishing high-quality, multidisciplinary research that advances clinical decision-making, improves patient outcomes, and strengthens healthcare systems. The journal prioritizes studies that bridge the gap between robust scientific evidence and practical, patient-centered clinical interventions.
NICS specifically encourages submissions that:
Advance integrated and multidisciplinary approaches to complex clinical care.
Promote evidence-based medicine and real-world clinical research.
Enhance healthcare quality, patient safety, and institutional system performance.
Develop, validate, and implement practical tools that support real-time clinical decision-making.
Demonstrate a clear, measurable, and positive impact on patient outcomes.
The journal welcomes clinically relevant research spanning diverse healthcare disciplines. Primary emphasis is placed on studies that directly inform patient care, optimize healthcare delivery, and refine clinical decision-making.
NICS invites submissions under the following 16 Key Areas of Interest:
Evaluation of multidisciplinary treatment strategies and clinical care pathways.
Patient-centered care models and shared decision-making frameworks.
Optimization of diagnostic and therapeutic strategies in acute and chronic clinical settings.
Observational studies, pragmatic clinical trials, and longitudinal cohorts.
Real-world evidence (RWE) and comparative effectiveness research.
Translational studies that clearly bridge laboratory discoveries with immediate clinical application.
Research directly informing the development of clinical guidelines and decision-support tools.
Clinical pharmacokinetics, pharmacodynamics, and post-marketing drug safety/pharmacovigilance.
Personalized medicine, pharmacogenomics, and targeted biologic therapies.
Therapeutic optimization and rational prescribing strategies in complex patient populations.
Advances in radiology, pathology, dermatology, cardiology, and imaging-based diagnostics.
Diagnostic accuracy, predictive performance, and validation studies of novel biomarkers.
Development and clinical evaluation of image-guided decision-support tools.
Innovative physical and occupational therapy interventions.
Neurorehabilitation, interdisciplinary chronic pain management, and cardiopulmonary rehabilitation.
Functional recovery outcomes and evidence-based disability management strategies.
Clinical studies on psychiatric disorders, psychotherapeutic interventions, and psychopharmacology.
Addiction medicine, suicide prevention, clinical risk assessment, and crisis intervention.
Strategies for the seamless integration of mental health care into general clinical practice.
Evidence-based chronic disease prevention, screening, and long-term management strategies.
Population-based interventions, health promotion, and early prevention programs.
Epidemiological studies that carry direct, actionable clinical implications.
Health behavior models, lifestyle medicine, and sustainable behavior change interventions.
Social determinants of health (SDOH), mitigating healthcare disparities, and promoting equity.
Optimization of patient–clinician communication and metrics enhancing treatment adherence.
Medical nutrition therapy (MNT) in chronic diseases, metabolic disorders, and critical care settings.
Clinical assessment and interventional management of malnutrition and wasting syndromes.
Evidence-based dietary strategies for obesity and metabolic syndrome interventions.
Novel health service delivery models and institutional system performance evaluations.
Implementation science regarding clinical guidelines and clinical care pathways.
Patient safety protocols, clinical governance, and systematic quality improvement (QI) initiatives.
Evaluation of policy and regulatory impacts on routine clinical practice.
Implementation methodologies for scaling up evidence-based clinical interventions.
Health system design, organizational models, and population-level clinical evaluations.
Telemedicine, remote patient monitoring, and connected health ecosystems.
Clinical Artificial Intelligence (AI) applications and digital health tools with measurable outcomes.
Health informatics infrastructure, electronic health record (EHR) optimization, and data interoperability.
Development, validation, and utilization of Patient-Reported Outcome Measures (PROMs) and Patient-Reported Experience Measures (PREMs).
Quality of life (QoL) assessments in chronic illness and palliative care.
Practical integration of patient perspectives into formal clinical outcome evaluations.
Competency-based medical education (CBME) and objective clinical skills assessment.
Simulation-based learning, virtual environments, and interprofessional training models.
Curriculum development, innovation, and outcome tracking in residency and fellowship programs.
Cost-effectiveness, cost-utility, and cost-benefit analyses of clinical interventions.
Resource utilization, healthcare efficiency studies, and budget impact modeling.
Economic evaluation of novel clinical technologies, therapeutics, and healthcare delivery models.
High-quality systematic reviews, meta-analyses, network meta-analyses, and scoping reviews.
Evidence-based clinical guidelines, consensus statements, and expert commentaries.
Professional perspectives and debates on emerging clinical challenges and paradigm shifts.
Important Screening Criteria for Authors:
Basic Biomedical Research: Basic laboratory or bench science (e.g., pure physiology, pharmacology, or biochemistry) is considered for review only when it is directly, explicitly, and clearly translatable to clinical practice.
Preclinical Studies: Preclinical or mechanistic in vivo/in vitro studies must contain a dedicated discussion that clearly demonstrates immediate clinical relevance.
AI and Digital Health: Submissions focusing on artificial intelligence, machine learning, or digital tools must demonstrate a measurable, interpretable, and validated clinical impact rather than purely theoretical or technical performance.