Aims and Scope
Aims & Scope
The Journal of Medical Informatics and Decision Making (JMID) is a peer-reviewed, open-access journal publishing research in medical and health informatics and the science of healthcare decision making.
ISSN 2641-5526Crossref DOI prefix 10.14302Published by Open Access PubCC BY 4.0
01 · What JMID publishes
JMID sits within medical informatics — the study and application of information science, data and computational methods to medicine and healthcare — and within health informatics more broadly, where the work concerns health information in clinical, population, consumer or health-system contexts. Clinical informatics, centered on care delivery, electronic health records and clinical decision support, is a major domain of the journal, but it is not the journal's boundary. JMID also publishes decision science: the formal and empirical study of how decisions in healthcare are supported, made and evaluated.
Biomedical informatics is a broader umbrella that spans biological, translational and clinical information sciences. Bioinformatics primarily concerns computational analysis of molecular and biological data. JMID considers work at this interface when its contribution concerns medical or health informatics, rather than molecular analysis alone.
The journal aims to:
Improve health information practice
Strengthens the systems, data, standards and workflows through which health information is created, exchanged and used.
Improve healthcare decisions
Advances decision support, decision analysis and decision quality for clinicians, patients, health systems and policy.
Connect methods to healthcare
Brings sound computational and data-science methods into genuine clinical or health contexts with appropriate evaluation.
Build reusable knowledge
Reports work completely enough — data, methods, limitations — for other groups to assess, reproduce and extend it.
02 · Core research domains
Research, reviews, technical reports and perspectives are considered across these core domains. Methodological, validation and implementation studies are research approaches, not additional manuscript categories.
Clinical informatics and health information systems
- Electronic health records and clinical documentation
- Clinical workflow analysis and redesign
- Health information exchange and clinical data quality
- Medication informatics and computerized provider order entry
- Health IT adoption, evaluation and safety
Clinical decision support and analytics
- Decision-support and knowledge-based systems
- Diagnostic, prognostic and risk-stratification models
- Treatment recommendation and alert/reminder systems
- Decision analytics and decision-support evaluation
Health data science and AI in healthcare
- Predictive analytics on real-world health data
- Clinical data mining and temporal health data
- Machine learning with clinically meaningful evaluation
- Algorithm validation, calibration, bias and fairness
Clinical NLP and imaging informatics
- Clinical natural language processing and information extraction
- Phenotyping, coding and clinical summarization
- Radiology and pathology informatics
- Computer-aided diagnosis and imaging workflow
Standards, terminologies and interoperability
- HL7 FHIR and data-exchange architectures
- Terminologies and ontologies (SNOMED CT, LOINC and others)
- Semantic interoperability and common data models
- Knowledge representation for health data
Human factors and implementation
- Usability and clinician–system interaction
- Alert fatigue and workflow fit
- Implementation, adoption and sustainability of digital systems
- Learning health systems and health-services informatics
03 · Decision science and decision support
JMID publishes formal, empirical and methodological research on how healthcare decisions are made, supported and evaluated. A substantive decision-science contribution can fit without new software or an information system. Areas include:
Clinical decision support and its evaluation in use
Diagnostic and prognostic reasoning, including reasoning under uncertainty
Decision analysis, decision theory and probabilistic or utility-based models in healthcare
Shared decision making, patient preferences and patient-facing decision aids, including non-digital approaches
Human–AI interaction in decisions: trust, over-reliance, automation bias and decision quality
Risk communication, risk-benefit modeling and decisions by health systems and policymakers
For example, a study of patient preferences that informs treatment choices can fit through decision science; a study of clinical information retrieval can fit through informatics. A predictive model should advance informatics knowledge or address a defined healthcare decision, rather than merely report accuracy on a medical dataset.
04 · Wider medical and health informatics
JMID also welcomes work across the wider medical and health informatics landscape, provided the manuscript makes a substantive medical or health informatics contribution or a contribution to healthcare decision science:
Consumer and patient-facing informatics
Patient portals, personal health records, patient engagement platforms and consumer health information.
Telehealth, remote monitoring and digital therapeutics
In scope where the contribution concerns information architecture, data, workflow, decision support, implementation or evaluation — not merely because a technology is digital.
Population and public-health informatics
Surveillance systems, population-health analytics, geospatial health information and outbreak informatics.
Clinical research and translational informatics
Research data platforms, cohort discovery, trial informatics and systems that integrate molecular or genomic data into clinical decision making.
Data governance, privacy and security
Health-data stewardship, consent architectures, de-identification and cybersecurity of health information systems.
Specialty and professional informatics
Pharmacy, nursing, dental, emergency-care, primary-care and other specialty informatics communities within medicine and health.
05 · Interdisciplinary submissions
JMID welcomes methods and perspectives from computer science, engineering, biostatistics, public health, economics and the behavioral sciences. The rule for fit is consistent: the contribution to medical or health informatics, or to healthcare decision science, must be central and substantive.
Artificial intelligence and machine learning fit when the healthcare context is genuine and the evaluation is appropriate to the claim — JMID is not a venue for generic AI benchmark papers.
Digital-health technologies (apps, wearables, sensors, telemedicine) fit when the paper contributes informatics, data, decision-support, workflow or implementation knowledge, not only a device or usage description.
Genomics and precision medicine fit where the work is informatics: systems or methods that integrate molecular data into clinical interpretation, decision support or care delivery. Pure sequence analysis, protein-structure or molecular-modeling studies without that integration belong in bioinformatics venues.
Public health, epidemiology and health economics fit when they advance health-information methods or systems, or the analysis and quality of healthcare decisions. Merely collecting health data or reporting associations is not sufficient.
06 · Methods and evidence expectations
JMID distinguishes between scope eligibility and evidence strength. Methodological research — new clinical NLP methods, health-data models, terminology and ontology methods, decision-support algorithms, interoperability methods, evaluation frameworks and decision-analysis methods — is eligible when the informatics or healthcare decision-science contribution is substantive, the healthcare context is genuine, and the evaluation is scientifically appropriate to the type of contribution. Real-world deployment, multi-site validation or demonstrated patient-outcome effects are strengths, not universal conditions of eligibility.
What a manuscript is expected to establish — as expectations of rigor, scaled to study type rather than as eligibility gates:
State the health problem and, where relevant, the decision or workflow the work supports and its intended users.
For empirical and computational studies, define data sources, participant or cohort selection, provenance and preprocessing. For formal or conceptual work, state assumptions, reasoning and the basis for evaluating the contribution.
Report performance against appropriate baselines or comparators, with clinically meaningful endpoints where outcomes are claimed.
For models estimating individual risks or probabilities, report calibration alongside appropriate discrimination measures. Use evaluation measures suited to other predictive tasks, and include external or multi-site validation where feasible.
Address bias, fairness and generalizability, and explain failure modes and limitations transparently.
Describe workflow integration and user impact where the work claims deployment relevance.
A clear data- and code-availability statement is expected for computational studies. Where data access is restricted, describe the access pathway and how results can be independently validated. Document software versions and model settings where applicable. Ethics, consent, de-identification and AI-disclosure requirements are set out in the Editorial Policies.
07 · Outside scope
The following generally fall outside JMID’s scope:
Bioinformatics or computational-biology studies of molecular systems (sequence analysis, omics pipelines, protein structure, molecular modeling) without clinical- or medical-informatics integration.
Generic machine-learning, NLP or computer-vision research that merely uses a medical dataset as a benchmark, without a substantive medical or health informatics or healthcare decision-science contribution.
Pure clinical studies — disease management, prognostic variables, intervention trials — without a substantive medical or health informatics or healthcare decision-science contribution.
Software, app or device descriptions without meaningful informatics contribution or evaluation.
Generic hospital-management or health-policy studies without a substantive health-informatics or healthcare decision-science contribution.
Opinion or commentary pieces with no analytic, informatics or decision-science content.
Hardware or device engineering without an informatics contribution.
08 · Manuscript categories
Four categories are accepted. Preparation requirements for each, including structure, formatting, reference style and reporting guidelines, are in the Instructions for Authors.
| Category | Length | Abstract, figures and references |
|---|---|---|
| Original Research | 3,000–6,000 words | 300-word structured abstract · up to 10 figures or tables · up to 50 references |
| Review Article | 4,000–8,000 words | 300-word structured abstract · up to 12 figures or tables · up to 100 references |
| Technical Report | 2,000–4,000 words | 200-word unstructured abstract · up to 8 figures or tables · up to 30 references |
| Perspectives | 1,500–3,000 words | 150-word unstructured abstract · up to 4 figures or tables · up to 25 references |
Where a manuscript exceeds these limits and reducing it would compromise the completeness of the work, contact the editorial office at [email protected] before submission to discuss an exception.
09 · Submission fit checklist
Use these questions to assess fit. Scope depends on the central contribution; evidence and reporting should support the claims and manuscript category. Final suitability is determined during editorial assessment.
Does the work make a substantive contribution to medical or health informatics, or to healthcare decision science?
Is the medical, health-information or healthcare-decision question central to the study, rather than incidental?
Are the methods, evaluation or scholarly argument appropriate to the contribution and claims? Real-world deployment is not required for every methodological paper.
Are data, software, assumptions and methods documented as applicable, with limitations explained and access restrictions addressed?
Does the manuscript meet the relevant category and reporting requirements without relying only on a medical dataset, a digital device or a clinical topic to establish fit?
Use keywords that accurately describe the informatics or decision-science contribution, the health context and the study methods.
How submissions are handled
All submissions undergo initial editorial screening. Manuscripts that meet the journal's scope and minimum requirements proceed to independent peer review. Manuscripts that proceed to external peer review are normally evaluated by at least two independent subject-matter experts.
- Peer review
- Single-blind by default; double-blind review is available on request.
- Decision
- Editorial decisions are based on scope, scientific quality, methodological rigor, ethical compliance, reporting quality, and relevance to the journal. Editorial decisions are independent of any fee, service, membership or role.
JMID · Editorial office
Not sure whether your work fits?
A short description of the study, the data it uses and the decision or information problem it addresses is enough for the editorial office to say whether it sits within scope. Write to [email protected] before preparing a full submission.
Journal of Medical Informatics and Decision Making · ISSN 2641-5526 · Crossref DOI prefix 10.14302 · published open access by Open Access Pub under CC BY 4.0. Editorial decisions are independent of any fee, service, membership or role.