A time motion study of manual versus artificial intelligence methods for wound assessment
- PMID: 35901189
- PMCID: PMC9333325
- DOI: 10.1371/journal.pone.0271742
A time motion study of manual versus artificial intelligence methods for wound assessment
Abstract
Objectives: This time-motion study explored the amount of time clinicians spent on wound assessments in a real-world environment using wound assessment digital application utilizing Artificial Intelligence (AI) vs. manual methods. The study also aimed at comparing the proportion of captured quality wound images on the first attempt by the assessment method.
Methods: Clinicians practicing at Valley Wound Center who agreed to join the study were asked to record the time needed to complete wound assessment activities for patients with active wounds referred for a routine evaluation on the follow-up days at the clinic. Assessment activities included: labelling wounds, capturing images, measuring wounds, calculating surface areas, and transferring data into the patient's record.
Results: A total of 91 patients with 115 wounds were assessed. The average time to capture and access wound image with the AI digital tool was significantly faster than a standard digital camera with an average of 62 seconds (P<0.001). The digital application was significantly faster by 77% at accurately measuring and calculating the wound surface area with an average of 45.05 seconds (P<0.001). Overall, the average time to complete a wound assessment using Swift was significantly faster by 79%. Using the AI application, the staff completed all steps in about half of the time (54%) normally spent on manual wound evaluation activities. Moreover, acquiring acceptable wound image was significantly more likely to be achieved the first time using the digital tool than the manual methods (92.2% vs. 75.7%, P<0.004).
Conclusions: Using the digital assessment tool saved significant time for clinicians in assessing wounds. It also successfully captured quality wound images at the first attempt.
Conflict of interest statement
The authors have declared that no competing interests exist.
Figures
Similar articles
-
Development of a Method for Clinical Evaluation of Artificial Intelligence-Based Digital Wound Assessment Tools.JAMA Netw Open. 2021 May 3;4(5):e217234. doi: 10.1001/jamanetworkopen.2021.7234. JAMA Netw Open. 2021. PMID: 34009348 Free PMC article.
-
Image-Based Artificial Intelligence in Wound Assessment: A Systematic Review.Adv Wound Care (New Rochelle). 2022 Dec;11(12):687-709. doi: 10.1089/wound.2021.0091. Epub 2021 Dec 20. Adv Wound Care (New Rochelle). 2022. PMID: 34544270
-
Artificial intelligence in wound care: diagnosis, assessment and treatment of hard-to-heal wounds: a narrative review.J Wound Care. 2024 Apr 2;33(4):229-242. doi: 10.12968/jowc.2024.33.4.229. J Wound Care. 2024. PMID: 38573907 Review.
-
Wound Image Quality From a Mobile Health Tool for Home-Based Chronic Wound Management With Real-Time Quality Feedback: Randomized Feasibility Study.JMIR Mhealth Uhealth. 2021 Jul 30;9(7):e26149. doi: 10.2196/26149. JMIR Mhealth Uhealth. 2021. PMID: 34328440 Free PMC article. Clinical Trial.
-
Utilizing Image Processing Techniques for Wound Management and Evaluation in Clinical Practice: Establishing the Feasibility of Implementing Artificial Intelligence in Routine Wound Care.Adv Skin Wound Care. 2025 Jan-Feb 01;38(1):31-39. doi: 10.1097/ASW.0000000000000246. Adv Skin Wound Care. 2025. PMID: 39836554
Cited by
-
Empowering Patients and Caregivers to Use Artificial Intelligence and Computer Vision for Wound Monitoring: Nonrandomized, Single-Arm Feasibility Study.J Particip Med. 2025 Jun 4;17:e69470. doi: 10.2196/69470. J Particip Med. 2025. PMID: 40466054 Free PMC article.
-
Assessing Clinician Consistency in Wound Tissue Classification and the Value of AI-Assisted Quantification: A Cross-Sectional Study.Int Wound J. 2025 Jun;22(6):e70691. doi: 10.1111/iwj.70691. Int Wound J. 2025. PMID: 40421826 Free PMC article.
-
Clinical, Operational, and Economic Benefits of a Digitally Enabled Wound Care Program in Home Health: Quasi-Experimental, Pre-Post Comparative Study.JMIR Nurs. 2025 Apr 8;8:e71535. doi: 10.2196/71535. JMIR Nurs. 2025. PMID: 40198913 Free PMC article.
-
Controlled Pilot Intervention Study on the Effects of an AI-Based Application to Support Incontinence-Associated Dermatitis and Pressure Injury Assessment, Nursing Care and Documentation: Study Protocol.Res Nurs Health. 2025 Aug;48(4):419-428. doi: 10.1002/nur.22469. Epub 2025 Apr 16. Res Nurs Health. 2025. PMID: 40237306 Free PMC article.
-
Current status, challenges, and prospects of artificial intelligence applications in wound repair theranostics.Theranostics. 2025 Jan 2;15(5):1662-1688. doi: 10.7150/thno.105109. eCollection 2025. Theranostics. 2025. PMID: 39897550 Free PMC article. Review.
References
-
- Flanagan M. Improving accuracy of wound measurement in clinical practice. Ostomy/wound Management. 2003; 49(10):28–40. . - PubMed
-
- Hettiarachchi N, Mahindaratne R, Mendis G, Nanayakkara H, Nanayakkara N. Mobile based wound measurement. IEEE Point Care Healthc Technol (PHT). 2013: 298–301, doi: 10.1109/PHT.2013.6461344 - DOI
MeSH terms
LinkOut - more resources
Full Text Sources