Assessing Wound Healing Velocity? There’s an App for That!Tobechukwu Madu; Kevin P. Keenahan; and Joshua Budman
BACKGROUND: Electronic health records (EHRs) can capture and store a growing set of high-quality wound care data using artificial intelligence present in smart applications. However, data on wound healing is highly subjective and conflicting reports pervade the literature.
OBJECTIVE: To address this issue, the authors utilized an EHR-integrated mobile application to enable the objective capture and analysis of both demographic and clinical data.
METHODS: For this study, they evaluated the medical records of 3588 wound patients at 11 facilities. Wound healing likelihood and healing rate based on visit frequency, care setting, etiology, and age were investigated.
RESULTS: The application’s machine-learning algorithm provided objective wound measurement and data on tissue composition. This preliminary analysis reveals unbiased new insights on the proportion of wounds that heal and the best predictors of wound healing velocity.
CONCLUSIONS: This methodology will inform the design of a model that equips patients, providers, and payers with clinically relevant tools to make optimal treatment decisions.
Madu T, Keenahan KP, Budman J. Assessing wound healing velocity? There’s an app for that!. Poster presented at: Symposium on Advanced Wound Care Fall; November 2-4, 2018; Las Vegas, NV.
Product: Advanced Wound Care Management (Tissue Analytics; Baltimore, MD)
This abstract was not subject to the WOUNDS peer-review process.