Objective: Mobile health (mHealth) apps may prove to be useful tools for supporting chronic disease management. We assessed the feasibility of implementing a clinically integrated mHealth app and practice model to facilitate between-visit asthma symptom monitoring as per guidelines and with the help of patient-reported outcomes (PRO).
Methods: We implemented the intervention at two pulmonary clinics and conducted a mixed-methods analysis of app usage data and semi-structured interview of patients and clinician participants over a 25-week study period.
Results: Five physicians, 1 physician's assistant, 1 nurse, and 26 patients participated. Twenty-four patients (92%) were still participating in the intervention at the end of the 25-week study period. On average, each patient participant completed 21 of 25 questionnaires (84% completion rate). Weekly completion rates were higher for participants who were female (88 vs. 73%, = 0.02) and obtained a bachelor's degree level or higher (94 vs. 74%, = 0.04). On average, of all questionnaires, including both completed and not completed (25 weekly questionnaires times 26 patient participants), 25% had results severe enough to qualify for a callback from a nurse; however, patients declined this option in roughly half of the cases in which they were offered the option. We identified 6 key themes from an analysis of 21 patients and 5 clinician interviews. From the patient's perspective, these include more awareness of asthma, more connected with provider, and app simplicity. From the clinician's perspective, these include minimal additional work required, facilitating triage, and informing conversations during visits.
Conclusion: Implementation of a clinically integrated mHealth app and practice model can achieve high patient retention and adherence to guideline-recommended asthma symptom monitoring, while minimally burdening clinicians. The intervention has the potential for scaling to primary care and reducing utilization of urgent and emergency care.
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http://dx.doi.org/10.1055/s-0039-1697597 | DOI Listing |
Int J Med Inform
December 2024
Department of Hepatobiliary Surgery, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China. Electronic address:
Background: Solid organ transplantation (SOT) is vital for end-stage organ failure but faces challenges like organ shortage and rejection. Artificial intelligence (AI) offers potential to improve outcomes through better matching, success prediction, and automation. However, the evolution of AI in SOT research remains underexplored.
View Article and Find Full Text PDFJMIR Med Inform
January 2025
Medical Big Data Research Center, Chinese PLA General Hospital, Beijing, China.
Background: Machine learning models can reduce the burden on doctors by converting medical records into International Classification of Diseases (ICD) codes in real time, thereby enhancing the efficiency of diagnosis and treatment. However, it faces challenges such as small datasets, diverse writing styles, unstructured records, and the need for semimanual preprocessing. Existing approaches, such as naive Bayes, Word2Vec, and convolutional neural networks, have limitations in handling missing values and understanding the context of medical texts, leading to a high error rate.
View Article and Find Full Text PDFJ Med Internet Res
January 2025
Nursing Pharmacology and Physiotherapy Department University of Córdoba, Lifestyles Innovation and Health (GA-16) Maimonides Biomedical Research Institute of Córdoba (IMIBIC) Spain, University of Córdoba, Córdoba, Spain.
Background: Chronic obstructive pulmonary disease (COPD) primarily originates from exposure to tobacco smoke, although factors, such as air pollution and exposure to chemicals, also play a role. One of the primary treatments for COPD is oxygen therapy, which helps manage dyspnea and improve survival rates. Mobile health (mHealth) technologies have demonstrated significant potential in monitoring patients with chronic diseases, offering new avenues for enhancing patient care and disease management.
View Article and Find Full Text PDFJ Clin Oncol
January 2025
Liangyu Mi, MD, Third Hospital of Shanxi Medical University, Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Tongji Shanxi Hospital, Taiyuan, China, Shanxi Province Clinical Research Center for Dermatologic and Immunologic Diseases (Rheumatic Diseases), Taiyuan, China; James Cheng-Chung Wei, MD, Department of Allergy, Immunology & Rheumatology, Chung Shan Medical University Hospital, Taichung, Taiwan, Institute of Medicine, Chung Shan Medical University, Taichung, Taiwan, Department of Nursing, Chung Shan Medical University, Taichung, Taiwan, Graduate Institute of Integrated Medicine, China Medical University, Taichung, Taiwan, Office of Research and Development, Asia University, Taichung, Taiwan; and Ke Xu, MD, Jinfang Gao, MD, Yalin Zhao, MD, and Liyun Zhang, MD, Third Hospital of Shanxi Medical University, Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Tongji Shanxi Hospital, Taiyuan, China, Shanxi Province Clinical Research Center for Dermatologic and Immunologic Diseases (Rheumatic Diseases), Taiyuan, China.
J Comput Assist Tomogr
November 2024
From the Department of Nutritional Sciences, University of Wisconsin-Madison, Madison, WI.
Objective: Computed tomography (CT) measured muscle density is prognostic of health outcomes. However, the use of intravenous contrast obscures prognoses by artificially increasing CT muscle density. We previously established a correction to equalize contrast and noncontrast muscle density measurements.
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