Empowering Patients and Caregivers to Use Artificial Intelligence and Computer Vision for Wound Monitoring: Nonrandomized, Single-Arm Feasibility Study
Abstract BackgroundChronic wounds affect 1%-2% of the global population, and pose significant health and quality-of-life challenges for patients and caregivers. Advances in artificial intelligence (AI) and computer vision (CV) technologies present new opportunities for enhanci...
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| Format: | Article |
| Language: | English |
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JMIR Publications
2025-06-01
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| Series: | Journal of Participatory Medicine |
| Online Access: | https://jopm.jmir.org/2025/1/e69470 |
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| author | Rose Raizman José Luis Ramírez-GarciaLuna Tanmoy Newaz Sheila C Wang Gregory K Berry Ling Yuan Kong Heba Tallah Mohammed Robert D J Fraser |
| author_facet | Rose Raizman José Luis Ramírez-GarciaLuna Tanmoy Newaz Sheila C Wang Gregory K Berry Ling Yuan Kong Heba Tallah Mohammed Robert D J Fraser |
| author_sort | Rose Raizman |
| collection | DOAJ |
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Abstract
BackgroundChronic wounds affect 1%-2% of the global population, and pose significant health and quality-of-life challenges for patients and caregivers. Advances in artificial intelligence (AI) and computer vision (CV) technologies present new opportunities for enhancing wound care, particularly through remote monitoring and patient engagement. A digital wound care solution (DWCS) that facilitates wound tracking using AI was redesigned as a patient-facing mobile app to empower patients and caregivers to actively participate in wound monitoring and management.
ObjectiveThis study aims to evaluate the feasibility, usability, and preliminary clinical outcomes of the Patient Connect app (Swift Medical Inc) in enabling patients and caregivers to remotely capture and share wound data with health care providers.
MethodsA feasibility study was conducted at 2 outpatient clinics in Canada between May 2020 and February 2021. A total of 28 patients with chronic wounds were recruited and trained to use the Patient Connect app for wound imaging and secure data sharing with their care teams. Wound images and data were analyzed using AI models integrated into the app. Clinicians reviewed the data to inform treatment decisions during follow-up visits or remotely. Key metrics included app usage frequency, patient engagement, and wound closure rates.
ResultsParticipants captured a median of 13 wound images per wound, with images submitted every 8 days on average. The study cohort included patients with diabetic ulcers, venous ulcers, pressure injuries, and postsurgical wounds. A median wound closure surface area closure of 80% (range 15-100) was achieved across all patients, demonstrating the app’s clinical potential. Feedback from patients and clinicians highlighted during the feasibility testing support insight into the app’s usability, data security features, and ability to enhance remote monitoring that need to be explored in further qualitative research.
ConclusionsThe Patient Connect app effectively engaged patients and caregivers in chronic wound care, demonstrating feasibility and promising clinical outcomes. By enabling secure, remote wound monitoring through AI technology, the app has the potential to improve patient adherence, enhance care accessibility, and optimize clinical workflows. Future studies should focus on evaluating its scalability, cost-effectiveness, and broader applicability in diverse health care settings. |
| format | Article |
| id | doaj-art-e697a19c33cd42faba19cb52002782a8 |
| institution | OA Journals |
| issn | 2152-7202 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | JMIR Publications |
| record_format | Article |
| series | Journal of Participatory Medicine |
| spelling | doaj-art-e697a19c33cd42faba19cb52002782a82025-08-20T02:23:57ZengJMIR PublicationsJournal of Participatory Medicine2152-72022025-06-0117e69470e6947010.2196/69470Empowering Patients and Caregivers to Use Artificial Intelligence and Computer Vision for Wound Monitoring: Nonrandomized, Single-Arm Feasibility StudyRose Raizmanhttp://orcid.org/0000-0001-6854-9532José Luis Ramírez-GarciaLunahttp://orcid.org/0000-0002-3953-9762Tanmoy Newazhttp://orcid.org/0000-0002-6655-3121Sheila C Wanghttp://orcid.org/0000-0001-8123-1713Gregory K Berryhttp://orcid.org/0000-0002-1745-7301Ling Yuan Konghttp://orcid.org/0000-0003-1329-2164Heba Tallah Mohammedhttp://orcid.org/0000-0002-0848-8384Robert D J Fraserhttp://orcid.org/0000-0003-2279-4203 Abstract BackgroundChronic wounds affect 1%-2% of the global population, and pose significant health and quality-of-life challenges for patients and caregivers. Advances in artificial intelligence (AI) and computer vision (CV) technologies present new opportunities for enhancing wound care, particularly through remote monitoring and patient engagement. A digital wound care solution (DWCS) that facilitates wound tracking using AI was redesigned as a patient-facing mobile app to empower patients and caregivers to actively participate in wound monitoring and management. ObjectiveThis study aims to evaluate the feasibility, usability, and preliminary clinical outcomes of the Patient Connect app (Swift Medical Inc) in enabling patients and caregivers to remotely capture and share wound data with health care providers. MethodsA feasibility study was conducted at 2 outpatient clinics in Canada between May 2020 and February 2021. A total of 28 patients with chronic wounds were recruited and trained to use the Patient Connect app for wound imaging and secure data sharing with their care teams. Wound images and data were analyzed using AI models integrated into the app. Clinicians reviewed the data to inform treatment decisions during follow-up visits or remotely. Key metrics included app usage frequency, patient engagement, and wound closure rates. ResultsParticipants captured a median of 13 wound images per wound, with images submitted every 8 days on average. The study cohort included patients with diabetic ulcers, venous ulcers, pressure injuries, and postsurgical wounds. A median wound closure surface area closure of 80% (range 15-100) was achieved across all patients, demonstrating the app’s clinical potential. Feedback from patients and clinicians highlighted during the feasibility testing support insight into the app’s usability, data security features, and ability to enhance remote monitoring that need to be explored in further qualitative research. ConclusionsThe Patient Connect app effectively engaged patients and caregivers in chronic wound care, demonstrating feasibility and promising clinical outcomes. By enabling secure, remote wound monitoring through AI technology, the app has the potential to improve patient adherence, enhance care accessibility, and optimize clinical workflows. Future studies should focus on evaluating its scalability, cost-effectiveness, and broader applicability in diverse health care settings.https://jopm.jmir.org/2025/1/e69470 |
| spellingShingle | Rose Raizman José Luis Ramírez-GarciaLuna Tanmoy Newaz Sheila C Wang Gregory K Berry Ling Yuan Kong Heba Tallah Mohammed Robert D J Fraser Empowering Patients and Caregivers to Use Artificial Intelligence and Computer Vision for Wound Monitoring: Nonrandomized, Single-Arm Feasibility Study Journal of Participatory Medicine |
| title | Empowering Patients and Caregivers to Use Artificial Intelligence and Computer Vision for Wound Monitoring: Nonrandomized, Single-Arm Feasibility Study |
| title_full | Empowering Patients and Caregivers to Use Artificial Intelligence and Computer Vision for Wound Monitoring: Nonrandomized, Single-Arm Feasibility Study |
| title_fullStr | Empowering Patients and Caregivers to Use Artificial Intelligence and Computer Vision for Wound Monitoring: Nonrandomized, Single-Arm Feasibility Study |
| title_full_unstemmed | Empowering Patients and Caregivers to Use Artificial Intelligence and Computer Vision for Wound Monitoring: Nonrandomized, Single-Arm Feasibility Study |
| title_short | Empowering Patients and Caregivers to Use Artificial Intelligence and Computer Vision for Wound Monitoring: Nonrandomized, Single-Arm Feasibility Study |
| title_sort | empowering patients and caregivers to use artificial intelligence and computer vision for wound monitoring nonrandomized single arm feasibility study |
| url | https://jopm.jmir.org/2025/1/e69470 |
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