Nova Medica Informatica (NOVEM) is a peer-reviewed, open-access quarterly journal that pioneers the Medicine-Driven Informatics paradigm in global health technology research. The journal serves as an international scholarly platform for publishing high-quality, clinically grounded research where medical needs, evidence-based practice, and healthcare challenges drive digital innovation.

Nova Medica Informatica (NOVEM) bridges the critical gap between clinical medicine and information technology, focusing on translational research that demonstrates tangible impact on patient outcomes, healthcare delivery, and health systems optimization. The journal prioritizes studies with rigorous methodological frameworks, clinical validation, and practical implementation insights.

Nova Medica Informatica (NOVEM) invites original research, systematic reviews, meta-analyses, and case studies in the following domains:

Core Research Areas

a. Clinical Artificial Intelligence & Machine Learning
Development and validation of AI/ML algorithms for diagnosis, prognosis, and treatment optimization with clinical trial evidence

b. Digital Health Implementation Science
Real-world implementation, scalability, and sustainability studies of digital health interventions across diverse healthcare settings

c. Clinical Decision Support Systems (CDSS)
Design, development, and outcome evaluation of CDSS with demonstrated clinical efficacy and workflow integration

d. Health Data Science & Analytics
Advanced analytics, predictive modeling, and big data applications using clinical datasets with ethical and regulatory considerations

e. Telemedicine & mHealth Innovations
Evidence-based telemedicine frameworks, mobile health applications, and remote monitoring technologies with clinical validation

f. Medical Internet of Things (MIoT)
Wearable medical devices, sensor technologies, and connected health ecosystems with clinical safety and efficacy data

g. Health Information Systems & Interoperability
EHR/EMR systems, health information exchange, and standardized data architectures supporting clinical workflows

h. Clinical Bioinformatics & Precision Medicine
Genomic data integration, biomarker discovery, and personalized treatment algorithms with clinical correlation

Methodological Focus
a. Randomized Controlled Trials (RCTs) of digital health interventions
b. Longitudinal cohort studies with digital endpoints
c. Health economic evaluations of health technologies
d. Mixed-methods studies combining quantitative and qualitative approaches
e. Implementation research using established frameworks (RE-AIM, CFIR, etc.)
f. Regulatory science and health technology assessment
g. Editorial Standards & Quality Assurance

Current Issue

Vol. 1 No. 01 (2026): Foundations of Medicine-Driven Informatics: Clinical Necessity as the Catalyst for Technological Innovation [In Press]

This inaugural issue of NOVA MEDICA INFORMATICA (NOVEM) establishes the empirical baseline for the Medicine-Driven Informatics paradigm. Founded upon a formal consortium between the School of Medicine and the School of Information Technology, this volume presents research that subjects technological innovation to clinical validation rather than the reverse. Contributions herein demonstrate that valid health informatics requires interdisciplinary fidelity: clinical constraints, ethical boundaries, and ground truth must guide computational frameworks to ensure tangible healthcare improvement.   The issue is organized around three integrated themes. First, Clinical Artificial Intelligence and Validation features studies that extend beyond retrospective accuracy to present prospective validation, external testing across heterogeneous populations, and rigorous error analysis, with particular emphasis on Explainable AI mechanisms that enable clinical audit of algorithmic inference. Second, Implementation Science and Workflow Integration addresses socio-technical barriers to adoption, evaluating how digital health tools affect clinician cognitive load, patient engagement, and operational efficiency, treating technical performance as a necessary precondition rather than a primary endpoint. Third, Ethical Frameworks and Data Integrity examines clinical data provenance, bias, privacy, and equity, proposing frameworks for ethical deployment that prevent innovation from exacerbating health disparities.   All manuscripts underwent dual-track peer review involving both clinical practitioners and information scientists. Selection prioritized methodological transparency, reproducibility, and explicit acknowledgment of limitations. Negative findings and failure analyses were considered equally valuable as positive outcomes, provided they yielded actionable insights into technological constraints. This issue asserts that health informatics is not an engineering problem alone, but a complex scientific challenge requiring sustained interdisciplinary collaboration.   NOVA MEDICA INFORMATICA operates on a triannual cycle. Following this inaugural release, subsequent issues are scheduled for March, July and November of each year. We invite the scientific community to contribute to this ongoing iterative process.  

Editor-in-Chief

Professor Dr.med. Paul L Tahalele, MD, PhD, FICS, FCTS
NOVA MEDICA INFORMATICA (NOVEM)
(On behalf of the School of Medicine and the School of Information Technology)

Published: 2026-03-04

Articles

A Deep Learning Approach Focusing on Diagnostic Sensitivity to Enhance Clinical Differentiation of Brain Tumors via Sequential ADC-MRI Analysis

Muhammad Fuad Alharis, Heni Fatmawati, MD

6-17

Differential Diagnosis of Infectious Respiratory Diseases via Hybrid Deep Learning: Clinical Evaluation of COVID-19, Pneumonia, and Tuberculosis on Chest Radiography

Ageng Salmanarrizqie, R.M. Yusuf Irzan

18-32

Enhancing Clinical Governance: A Multi-Criteria Decision Support Approach for Physician Performance Evaluation

MY Teguh Sulistyono, Anggrek Bulan Purnama, Dewangga Putra Kalenaya, Stalina Anggraeny Dewi Amodia

33-45

Deep Learning-Based Diagnostic Support for Complex Autoimmune Syndromes: A Multi-label Classification Study

Jacky Tuamelly, MD , Joestiantho Laurenz Kilmanun, MD

46-56

A Computational Framework for Nutrient Density Assessment and Food Categorization

Rafael Suseno; Kevin Matthew Siregar; Devi Dwi Purwanto

57-69

Literature Review

Optimizing Prostate Cancer Management: A Review on the Diagnostic Accuracy and Health Disparities using Machine Learning

Franklin Vincentius Malonda, MD, Alvin Julian, MD

70-84

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