JMIR Formative Research
Process evaluations, early results, and feasibility/pilot studies of digital and non-digital interventions
Editor-in-Chief:
Amaryllis Mavragani, PhD, Scientific Editor at JMIR Publications, Canada
Impact Factor 2.4 More information about Impact Factor CiteScore 4.2 More information about CiteScore
Recent Articles

Natural language processing and large language model systems are increasingly used to support mental health documentation, screening, and follow-up planning. In counseling contexts, model outputs may influence diagnostic framing, risk recognition, and clinical record content. Static performance metrics and fluent generated summaries are not sufficient to support safe implementation without governance, safety gating, human review, and monitoring.

Cesarean section (C-section) is the most common surgical procedure in the United States, yet its use varies widely across regions and institutions. Although clinical risk factors are central to delivery decisions, geographic context, health system capacity, and local practice patterns may also influence C-section use. Understanding both the determinants and predictability of C-section delivery is important for improving obstetric quality and equity.



Hospital IT outages severely disrupt clinical workflows and use of electronic medical records, threatening patient safety and operational continuity. Traditional disaster response training faces limitations, including high resource requirements, restricted repeatability, and inability to be conducted without interrupting 24/7 hospital operations. Digital twin technology enables realistic, repeatable simulation training in virtual environments, avoiding operational disruption.

Literature reviews rely on rigorous title and abstract screening by researchers, which is time-consuming. AI-assisted literature screening tools have been proposed to improve efficiency by prioritizing titles and abstracts with the highest likelihood of meeting the inclusion criteria, thereby reducing the need to screen all records.

Cohort selection criteria play a critical role in shaping machine learning (ML) model performance and the equity of clinical outcome predictions across demographic groups. In practice, cohort definitions are often influenced by variable and sometimes inconsistent data processing decisions, which may introduce bias and limit the generalizability of ML models. During the COVID-19 pandemic, rapid cohort construction further increased concerns about transparency and fairness in ML-based analyses.

Neighborhood disinvestment, characterized by built environment disrepair and deterioration, has been linked to health behaviors and outcomes, including cancer survival. However, disinvestment temporal dynamics, including time-lagged exposure estimates among colorectal cancer (CRC) cases, remain underexplored.

Parent management training (PMT) is an evidence-based intervention for addressing child behavioral difficulties; however, caregivers often need additional guidance when implementing skills in daily life. Pat is an AI conversational agent designed to augment a therapist-led PMT program by providing caregivers with real-time guidance, reinforcement, and answers to parenting questions between sessions.

Rapid advances in large language models (LLMs) have expanded interest in patient-facing health applications that support symptom assessment and care-seeking decisions. Although AI-enabled symptom-guidance tools could improve patient navigation and recognition of clinically serious conditions, inappropriate recommendations may result in missed needed care or unnecessary escalation. Structured predeployment evaluation is therefore needed before prospective clinical use.

Dual use of combustible cigarettes and nicotine vapes is disproportionately high among lesbian, gay, bisexual, transgender, and queer (LGBTQ+) young adults. Mass-reach health communications may be effective at curbing dual use. Current research is exploring whether comparative risk messaging, which presents nicotine vapes as less harmful than cigarettes, reduces dual use.







