JMIR Medical Informatics
Clinical informatics, decision support for health professionals, electronic health records, and eHealth infrastructures
JMIR Medical Informatics (JMI, ISSN 2291-9694, Impact Factor: 3.2) (Editor-in-chief: Christian Lovis, MD, MPH, FACMI) is an open-access PubMed/SCIE-indexed journal that focuses on the challenges and impacts of clinical informatics, digitalization of care processes, clinical and health data pipelines from acquisition to reuse, including semantics, natural language processing, natural interactions, meaningful analytics and decision support, electronic health records, infrastructures, implementation, and evaluation (see Focus and Scope).
JMIR Medical Informatics adheres to rigorous quality standards, involving a rapid and thorough peer-review process, professional copyediting, and professional production of PDF, XHTML, and XML proofs. The journal is indexed in PubMed, PubMed Central, DOAJ, SCOPUS, and SCIE (Clarivate). In 2023, JMI received a Journal Impact Factor™ of 3.2 (5-Year Journal Impact Factor: 3.6) (Source: Journal Citation Reports™ from Clarivate, 2023).
Assessing patients’ suicide risk is challenging, especially among those who deny suicidal ideation. Primary care providers have a poor agreement in screening suicide risk. Patients’ speech may provide more objective language-based clues about their underlying suicidal ideation. Text analysis to detect suicide risk from depression is lacking in the literature.
In the context of a syphilis outbreak in neighboring states, a multifaceted systems change to increase testing for sexually transmitted infections (STIs) among young Aboriginal people aged 15 to 29 years was implemented at an Aboriginal Community Controlled Health Service (ACCHS) in New South Wales, Australia. The components included electronic medical record prompts and automated pathology test sets to increase STI testing in annual routine health assessments, the credentialing of nurses and Aboriginal health practitioners to conduct STI tests independently, pathology request forms presigned by a physician, and improved data reporting.
The medical teams in intensive care units (ICUs) spend increasing amounts of time at computer systems for data processing, input, and interpretation purposes. As each patient creates about 1000 data points per hour, the available information is abundant, making the interpretation difficult and time-consuming. This data flood leads to a decrease in time for evidence-based, patient-centered care. Information systems, such as patient data management systems (PDMSs), are increasingly used at ICUs. However, they often create new challenges arising from the increasing documentation burden.
Best Practice Alerts (BPAs) are alert messages to physicians in the electronic health record that are used to encourage appropriate use of health care resources. While these alerts are helpful in both improving care and reducing costs, BPAs are often broadly applied nonselectively across entire patient populations. The development of large language models (LLMs) provides an opportunity to selectively identify patients for BPAs.
Clinical trials are a crucial element in advancing medical knowledge and developing new treatments by establishing the evidence base for safety and therapeutic efficacy. However, the success of these trials depends on various factors, including trial design, project planning, research staff training, and adequate sample size. It is also crucial to recruit participants efficiently and retain them throughout the trial to ensure timely completion.
Investigating low-prevalence diseases such as multiple sclerosis is challenging because of the rather small number of individuals affected by this disease and the scattering of real-world data across numerous data sources. These obstacles impair data integration, standardization, and analysis, which negatively impact the generation of significant meaningful clinical evidence.