JMIR Medical Informatics

Over the last decade, natural language processing (NLP) has provided various solutions for information extraction (IE) from textual clinical data. In recent years, the use of NLP in cancer research has gained considerable attention, with numerous studies exploring the effectiveness of various NLP techniques for identifying and extracting cancer-related entities from clinical text data.

Millions of people affected with complex medical conditions with diverse symptoms often turn to online discourse to share their experiences. While some studies have explored natural language processing methods and medical information extraction tools, these typically focus on generic symptoms in clinical notes and struggle to identify patient-reported, disease-specific, subtle symptoms from online health discourse.

Diseases of the oral cavity, including oral squamous cell carcinoma (OSCC) pose major challenges to healthcare worldwide due to its late diagnosis and complicated differentiation of oral tissues. The combination of endoscopic Hyperspectral Imaging (eHSI) and Deep Learning (DL) models offer a promising approach to the demand for modern, non-invasive tissue diagnostics. This study presents a large-scale in vivo dataset designed to support DL–based segmentation and classification of healthy oral tissues.

Total knee and hip arthroplasty (TKA and THA) are among the most performed elective procedures. Rising demand and the resources intensive nature of these procedures has contributed to longer wait times despite significant healthcare investment. Current scheduling methods often rely on average surgical durations, overlooking patient-specific variability.

Despite the Coronary Artery Reporting and Data System (CAD-RADS) providing a standardized approach, radiologists continue to favor free-text reports. This preference creates significant challenges for data extraction and analysis in longitudinal studies, potentially limiting large-scale research and quality assessment initiatives.

Cirrhosis is a leading cause of non-cancer deaths in gastrointestinal diseases, resulting in high hospitalization and readmission rates. Early identification of high-risk patients is vital for proactive interventions and improve healthcare outcomes. However, the quality and integrity of real-world Electronic Health Records (EHRs) limited their utility in developing risk assessment tools.

Artificial intelligence (AI) algorithms offer an effective solution to alleviate the burden of diabetic retinopathy (DR) screening in public health settings. However, there are challenges in translating diagnostic performance and its application when deployed in real-world conditions.

Primary liver cancer, particularly hepatocellular carcinoma (HCC), poses significant clinical challenges due to late-stage diagnosis, tumor heterogeneity, and rapidly evolving therapeutic strategies. While systematic reviews and meta-analyses are essential for updating clinical guidelines, their labor-intensive nature limits timely evidence synthesis.

Transportation insecurity is a known barrier to accessing eye care and is associated with worse visual outcomes. However, it is infrequently captured in structured electronic health record (EHR) fields, limiting efforts to identify and support affected patients. Free-text clinical documentation may contain valuable information about transportation-related challenges that structured data miss.

Deep learning has demonstrated significant potential in advancing computer-aided diagnosis for neuropsychiatric disorders, such as migraine, enabling patient-specific diagnosis at an individual level. However, despite the superior accuracy of deep learning models, the interpretability of image classification models remains limited. Their black-box nature continues to pose a major obstacle in clinical applications, hindering biomarker discovery and personalized treatment.

The primary healthcare service in Indonesia consists of 10,260 Public Health Centres (Puskesmas), which play a major role in providing health care in the community, recording and reporting health data, using digital Health Information Systems (HIS) and/or manual reports. The utilisation of HIS across Puskesmas is crucial to capture the dynamic evolution of health problems and monitor interventions, thus providing effective primary healthcare services for the community. The primary healthcare service in Indonesia consists of 10,260 Public Health Centres (Puskesmas), which play a major role in providing health care in the community, recording and reporting health data, using digital Health Information Systems (HIS) and/or manual reports. The utilisation of HIS across Puskesmas is crucial to capture the dynamic evolution of health problems and monitor interventions, thus providing effective primary healthcare services for the community.

In an era where healthcare is increasingly dependent on digital infrastructure, the resilience of health information technology (IT) systems has become a cornerstone of patient safety and operational continuity. As cyber threats grow in frequency and sophistication, healthcare organizations have turned to advanced cybersecurity tools to safeguard their systems. Yet even the most robust defenses can falter. On July 19, 2024, a routine update from a widely used cybersecurity platform triggered a widespread IT disruption. A flawed sensor configuration led to 8,647 “blue screen of death” (BSOD) events, with 729 devices requiring manual remediation. What unfolded was not just a technical crisis, but a test of organizational agility, collaboration, and resilience. This viewpoint traces the response to that disruption, highlighting the pivotal role of clinical informaticists (CI) and the coordinated efforts that enabled a rapid recovery. From the formation of an incident response team to the triage and mitigation of impacted systems, the response was swift and strategic. Clinical informaticists emerged as key players, bridging the gap between technical teams and frontline care providers. They identified workflow disruptions, facilitated communication, and ensured that patient care remained as uninterrupted as possible. Despite the scale of the outage, operations continued with minimal disruption—thanks to early recognition, decisive action, and cross-disciplinary collaboration. This incident underscored the importance of a well-practiced response plan, clear communication channels, and the integration of clinical expertise in technical recovery efforts. As we reflect on this event, several lessons emerge: the need for continuous refinement of incident response strategies, the value of regular training exercises, and the critical role of clinical informatics in navigating digital crises. This paper calls for a renewed commitment to building resilient health IT ecosystems—ones that can withstand disruption and continue to support the delivery of safe, effective care.