JMIR Medical Informatics

The COVID-19 pandemic has significantly altered the global health and medical landscape. In response to the outbreak, Chinese hospitals have established 24-hour fever clinics to serve patients with COVID-19. The emergence of these clinics and the impact of successive epidemics have led to a surge in visits, placing pressure on hospital resource allocation and scheduling. Therefore, accurate prediction of outpatient visits is essential for informed decision-making in hospital management.

The creation of computer-supported collaborative clinical cases is an area of research in teaching that has been widely studied. However, the reuse of cases and their sharing with other platforms is a problem that encapsulates knowledge in isolated platforms without interoperability. This paper proposes a workflow ecosystem for the collaborative design and distribution of clinical cases through online computing platforms that (i) allow medical students to create clinical cases collaboratively in a dedicated environment; (ii) make it possible to export these clinical cases in terms of the HL7 FHIR interoperability standard; (iii) provide support to transform imported cases into learning object repositories, and (iv) use e-learning standards (e.g., IMS CP, SCORM) to incorporate this content into widely-used learning management systems, letting medical students democratize a valuable knowledge that would otherwise be confined within proprietary platforms.

Large language models have had a huge impact on natural language processing (NLP) in recent years. However, their application in epidemiological research is still limited to analysis of electronic health records and social media data.

Recent advances in natural language processing (NLP) have heightened the interest of the medical community in its application to health care in general, in particular to stroke, a medical emergency of great impact. In this rapidly evolving context, it is necessary to learn and understand the experience already accumulated by the medical and scientific community.

Computerized decision support systems (CDSS) are increasingly adopted in healthcare to optimize resources and streamline patient flow. However, they often lack scientific validation against standard medical care.

Machine Learning-Enabled Clinical Information Systems (ML-CIS) have the potential to drive healthcare delivery and research. The Fast Healthcare Interoperability Resources (FHIR) data standard is increasingly applied in developing these systems. However, methods for applying FHIR to ML-CIS are variable.

It remains a challenge to predict which treatment will work for which patient in mental healthcare.  

Throughout the COVID-19 pandemic, many hospitals conducted routine testing of hospitalized patients for SARS-CoV-2 infection upon admission. Some of these patients are admitted for reasons unrelated to COVID-19, and incidentally test positive for the virus. Because COVID-19-related hospitalizations have become a critical public health indicator, it is important to identify patients who are hospitalized because of COVID-19 as opposed to those who were admitted for other indications.

Electronic health records (EHRs) have struggled to fully capture social determinants of health (SDOH) due to challenges such as nonexistent or inconsistent data capture tools across clinics, lack of time, and the burden of extra steps for the clinician. However, patient clinical notes (unstructured data) may be a better source of patient-related SDOH information.

In the Medical Informatics in Research and Care in University Medicine (MIRACUM) consortium, an IT-based clinical trial recruitment support system (CTRSS) was developed based on the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM). Currently, OMOP CDM is populated with German Fast Healthcare Interoperability Resources (FHIR) using an Extract-Transform-Load (ETL)-process, which was designed as bulk load. However, the computational effort that comes with an everyday full load is not efficient for daily recruitment.

With the advent of the digital economy and the aging population, the demand for diversified health care services and innovative care delivery models has been overwhelming. This trend has accelerated the urgency to implement effective and efficient data exchange and service interoperability, which underpins coordinated care services among tiered health care institutions, improves the quality of oversight of regulators, and provides vast and comprehensive data collection to support clinical medicine and health economics research, thus improving the overall service quality and patient satisfaction. To meet this demand and facilitate the interoperability of IT systems of stakeholders, after years of preparation, Health Level 7 formally introduced, in 2014, the Fast Healthcare Interoperability Resources (FHIR) standard. It has since continued to evolve. FHIR depends on the Implementation Guide (IG) to ensure feasibility and consistency while developing an interoperable health care service. The IG defines rules with associated documentation on how FHIR resources are used to tackle a particular problem. However, a gap remains between IGs and the process of building actual services because IGs are rules without specifying concrete methods, procedures, or tools. Thus, stakeholders may feel it nontrivial to participate in the ecosystem, giving rise to the need for a more actionable practice guideline (PG) for promoting FHIR’s fast adoption.

Lower back pain is a common weakening condition that affects a large population. It is a leading cause of disability and lost productivity, and the associated medical costs and lost wages place a significant burden on individuals and society. Recent advances in artificial intelligence (AI) and natural language processing (NLP) have opened new opportunities for the identification and management of risk factors for lower back pain. In this paper, we propose and train a deep learning model on a dataset of clinical notes that have been annotated with relevant risk factors, and we evaluate the model's performance in identifying risk factors in new clinical notes.