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Process Re-Engineering and Data Integration Using Fast Healthcare Interoperability Resources for the Multidisciplinary Treatment of Lung Cancer

Process Re-Engineering and Data Integration Using Fast Healthcare Interoperability Resources for the Multidisciplinary Treatment of Lung Cancer

Our primary objective was to conduct process re-engineering in a multidisciplinary lung cancer team to address the difficulties and challenges faced by the teams in their workflow. To achieve this goal, we applied the user experience design approach to gain in-depth insights into the processes and requirements of lung cancer MDT. This project uses “step reduction” as an indicator to assess workflow rather than “time reduction.”

Ching-Hsiung Lin, Bing-Yen Wang, Sheng-Hao Lin, Pei Hsuan Shih, Chin-Jing Lee, Yung Ting Huang, Shih Chieh Chen, Mei-Lien Pan

JMIR Cancer 2025;11:e53887

Oxidative Stress Markers and Prediction of Severity With a Machine Learning Approach in Hospitalized Patients With COVID-19 and Severe Lung Disease: Observational, Retrospective, Single-Center Feasibility Study

Oxidative Stress Markers and Prediction of Severity With a Machine Learning Approach in Hospitalized Patients With COVID-19 and Severe Lung Disease: Observational, Retrospective, Single-Center Feasibility Study

Recent observational studies have reported that increasing COVID-19 severity may be responsible for a worse prognosis [17] among diabetes patients [18] and increased severity of lung disease [19]. Two meta-analyses showed that supplementation with antioxidants (in the form of vitamins and trace elements) in critically ill patients was associated with decreased mortality and counteracted OS damage [20,21].

Olivier Raspado, Michel Brack, Olivier Brack, Mélanie Vivancos, Aurélie Esparcieux, Emmanuelle Cart-Tanneur, Abdellah Aouifi

JMIR Form Res 2025;9:e66509

Estimation of Static Lung Volumes and Capacities From Spirometry Using Machine Learning: Algorithm Development and Validation

Estimation of Static Lung Volumes and Capacities From Spirometry Using Machine Learning: Algorithm Development and Validation

Given this context, we hypothesized that ML models could predict static lung volumes using spirometry alone across a diverse cohort of lung conditions. Such an approach could reduce the need for identifying those who would benefit most from formal lung volume assessments. In this study, we applied ML approaches to develop and validate an algorithm for estimating lung volumes and capacities from standard spirometry.

Scott A Helgeson, Zachary S Quicksall, Patrick W Johnson, Kaiser G Lim, Rickey E Carter, Augustine S Lee

JMIR AI 2025;4:e65456

Prehabilitation Program for Lung and Esophageal Cancers (Boosting Recovery and Activity Through Early Wellness): Protocol for a Nonrandomized Trial

Prehabilitation Program for Lung and Esophageal Cancers (Boosting Recovery and Activity Through Early Wellness): Protocol for a Nonrandomized Trial

Among these cancers, lung cancer is the most frequently diagnosed in the country, resulting in 25% of cancer-related deaths [2]. Esophageal cancer, while less common, presents unique challenges due to its effect on patients’ nutritional status, its tendency to metastasize rapidly, and, consequently, its poorer prognosis [3]. Based on surgical candidacy and disease stage, surgical resection by a thoracic surgeon is a mainstay of treatment for lung and esophageal cancers.

Jodi E Langley, Daniel Sibley, Joy Chiekwe, Melanie R Keats, Stephanie Snow, Judith Purcell, Stephen Sollows, Leslie Hill, David Watton, Abbigael E Gaudry, Ibrahim Hashish, Alison Wallace

JMIR Res Protoc 2025;14:e60791

Performance of an Electronic Health Record–Based Automated Pulmonary Embolism Severity Index Score Calculator: Cohort Study in the Emergency Department

Performance of an Electronic Health Record–Based Automated Pulmonary Embolism Severity Index Score Calculator: Cohort Study in the Emergency Department

The PESI score combines demographics (age and sex), comorbidities (history of cancer, heart failure, and chronic lung disease), vital signs (temperature, heart rate, respiratory rate, blood pressure, oxygen saturation), and mental status to stratify acute PE patients according to 30 days all-cause mortality, with classes I (PESI score One potential barrier to increasing outpatient management of low-risk PE is identifying appropriate patients [5,14,15].

Elizabeth Joyce, James McMullen, Xiaowen Kong, Connor O'Hare, Valerie Gavrila, Anthony Cuttitta, Geoffrey D Barnes, Colin F Greineder

JMIR Med Inform 2025;13:e58800

User Perceptions of Wearability of Knitted Sensor Garments for Long-Term Monitoring of Breathing Health: Thematic Analysis of Focus Groups and a Questionnaire Survey

User Perceptions of Wearability of Knitted Sensor Garments for Long-Term Monitoring of Breathing Health: Thematic Analysis of Focus Groups and a Questionnaire Survey

This might lead to the perception that smart garments are mainly for fitness rather than for the people who need wearable health devices for continuous health monitoring, such as those with cardiovascular or lung disease [19]. Thus, the style of currently available smart garments can influence consumer acceptance, especially for the older generation or those less fitness inclined.

Kristel Fobelets, Nikita Mohanty, Mara Thielemans, Lieze Thielemans, Gillian Lake-Thompson, Meijing Liu, Kate Jopling, Kai Yang

JMIR Biomed Eng 2024;9:e58166

Remote Patient Monitoring and Machine Learning in Acute Exacerbations of Chronic Obstructive Pulmonary Disease: Dual Systematic Literature Review and Narrative Synthesis

Remote Patient Monitoring and Machine Learning in Acute Exacerbations of Chronic Obstructive Pulmonary Disease: Dual Systematic Literature Review and Narrative Synthesis

The main types of data collected are physiological measures (blood pressure, heart rate [HR] or pulse rate [PR], respiratory rate [RR], weight, oxygen saturation [Sp O2], and temperature), functional measures (lung function and physical activity [PA]), PRO (dyspnea, sputum, sleep quality, depression, anxiety, and HRQo L), self-report (physiological measures, medication-usage, exacerbation history, demographics, and medical history), and meteorological data.

Henry Mark Granger Glyde, Caitlin Morgan, Tom M A Wilkinson, Ian T Nabney, James W Dodd

J Med Internet Res 2024;26:e52143

Gamified Crowdsourcing as a Novel Approach to Lung Ultrasound Data Set Labeling: Prospective Analysis

Gamified Crowdsourcing as a Novel Approach to Lung Ultrasound Data Set Labeling: Prospective Analysis

In lung POCUS, B-lines are hyperechoic linear artifacts that extend from the pleural line and appear dynamically with the respiratory cycle [23]. B-lines are known markers of pulmonary congestion and their presence, quantity, and thickness (discrete vs confluent B-lines) correlate with pathological severity of conditions such as congestive heart failure exacerbations, pneumonia, or inflammatory lung disease [23,26,27].

Nicole M Duggan, Mike Jin, Maria Alejandra Duran Mendicuti, Stephen Hallisey, Denie Bernier, Lauren A Selame, Ameneh Asgari-Targhi, Chanel E Fischetti, Ruben Lucassen, Anthony E Samir, Erik Duhaime, Tina Kapur, Andrew J Goldsmith

J Med Internet Res 2024;26:e51397

Preferences, Needs, and Values of Patients With Chronic Obstructive Pulmonary Disease Attending a Telehealth Service: Qualitative Interview Study

Preferences, Needs, and Values of Patients With Chronic Obstructive Pulmonary Disease Attending a Telehealth Service: Qualitative Interview Study

The Global Initiative for Chronic Obstructive Lung Disease (GOLD), established in 1998, has developed a set of recommendations for managing COPD. Evidence shows that self-management improves outcomes for patients with COPD and reduces the likelihood of hospitalization [8,9]. The recommendations by GOLD also address how increased self-management can help motivate and engage people, leading to positive adaptations in their health behaviors.

Camilla Wong Schmidt, Karen Borgnakke, Anne Frølich, Lars Kayser

JMIR Hum Factors 2024;11:e53131