Aim and Scope

Clinical informatics and decision science research with clinical relevance and validated outcomes.

JMID advances the science and practice of medical informatics by publishing research that improves clinical decision making and digital health delivery. We prioritize studies that demonstrate real-world relevance, measurable impact, and strong methodological rigor.

• Advance evidence-based decision support across care settings
• Translate informatics innovation into clinical workflows
• Promote interoperability and data quality standards
• Encourage reproducible AI and analytics in healthcare
• Support implementation research and adoption at scale

JMID welcomes submissions that advance clinical informatics with demonstrated relevance to healthcare practice. Evaluation with patient/clinical outcomes is welcome when it validates the informatics method/tool:

• Substantive informatics contribution with clinical relevance
• EHR/clinical data research with workflow or outcomes implications
• Clinical decision support systems with validation data
• Interoperability solutions with implementation evidence
• AI/ML in clinical settings with appropriate evaluation

JMID also welcomes work that connects informatics methods to real-world healthcare operations and patient engagement. Cross-disciplinary studies are welcome when they remain anchored in clinical informatics.

• Consumer health informatics, patient portals, and engagement platforms
• Remote monitoring, telehealth workflows, and digital therapeutics
• Clinical quality measurement, safety, and adverse event detection
• Implementation of clinical AI with human factors evaluation
• Population health informatics and care coordination analytics
• Data governance, stewardship, and interoperability policy studies

We prioritize evidence that demonstrates feasibility, validity, and clinical usefulness. Methods papers should include evaluation in real or representative clinical data.

• Prospective or retrospective validation of clinical decision support tools
• Implementation studies with adoption, usability, or workflow outcomes
• Comparative evaluations of algorithms or data models
• Multi-site studies showing generalizability across health systems
• Mixed-methods research linking technical results to clinician experience

• Implementation Science: System deployment, adoption, and sustainability studies
• Usability Research: User interface evaluation, workflow integration assessment
• Clinical Outcomes: Validation of methods/tools against patient outcomes (when appropriate)
• Health Services: Informatics in healthcare delivery and quality improvement

JMID values rigorous study design and transparent reporting. Manuscripts should clearly describe data sources, preprocessing steps, evaluation methods, and limitations so that results can be reproduced and trusted.

• Define patient cohorts, inclusion criteria, and data provenance
• Report model performance with appropriate baselines and confidence intervals
• Address bias, fairness, and generalizability across care settings
• Describe clinical workflow integration and user impact where applicable
• Provide code or methodological details sufficient for replication

JMID prioritizes work that clarifies how informatics innovations influence clinical decisions. Manuscripts should explain the decision point, the user (clinician, nurse, pharmacist), and the expected change in care quality or safety.

• Define the clinical decision or workflow that the system supports
• Describe how recommendations are delivered and acted upon
• Report measurable improvements such as reduced errors or faster time to treatment
• Include user feedback or usability metrics when available

Comparative evaluation strengthens evidence of utility. We encourage authors to benchmark against standard baselines and report statistical significance for key performance outcomes.

• Use clinically meaningful endpoints and reference standards
• Report sensitivity, specificity, and calibration for predictive models
• Include external validation or multi-site testing where feasible
• Explain failure modes and limitations transparently

We encourage submissions that address emerging challenges in digital health and clinical decision making:

• Explainable AI and human centered decision support
• Interoperability at scale using FHIR and real world data models
• Clinical NLP for note summarization and cohort discovery
• Remote monitoring, virtual care, and digital therapeutics
• Algorithmic safety, calibration, and post-deployment monitoring

JMID expects a clear data and code availability statement for computational studies. Where data access is restricted, authors should describe the access pathway, governance constraints, and how results can be validated independently.

We encourage documentation of software versions, model configuration, and environment details. When datasets cannot be shared, provide synthetic examples or detailed feature descriptions that support verification.

Include dataset identifiers, repository links, or access steps whenever possible for clarity.

Examples of out-of-scope submissions include studies that lack a clinical informatics contribution or omit validation in healthcare data or workflows.

• Purely molecular or omics studies without clinical decision relevance
• General computer vision or NLP papers without healthcare application
• Software tool descriptions without evaluation or deployment evidence
• Clinical trials that do not involve informatics methods or systems
• Policy or commentary pieces without analytic or decision-support content

• Does the manuscript present a novel informatics contribution tied to healthcare delivery?
• Is there validation using clinical data, workflow evidence, or real-world deployment?
• Are methods reproducible with clear documentation of data and software?
• Does the work improve decision making, interoperability, or care quality?
• Is the study positioned within clinical informatics rather than basic bioinformatics?

Authors are encouraged to align abstracts, keywords, and methods with clinical informatics terminology to improve reviewer matching and reader discovery. Clear scope alignment helps the editorial team reach a timely decision. Use specific keywords to reflect data sources, clinical settings, and evaluation methods.