Abstract
Pressure ulcers are a critical issue not only for patients, decreasing their quality of life, but also for healthcare professionals, contributing to burnout from continuous monitoring, with a consequent increase in healthcare costs. Due to the relevance of this problem, many hardware and software approaches have been proposed to ameliorate some aspects of pressure ulcer prevention and monitoring. In this article, we focus on reviewing solutions that use sensor-based data, possibly in combination with other intrinsic or extrinsic information, processed by some form of intelligent algorithm, to provide healthcare professionals with knowledge that improves the decision-making process when dealing with a patient at risk of developing pressure ulcers. We used a systematic approach to select 21 studies that were thoroughly reviewed and summarized, considering which sensors and algorithms were used, the most relevant data features, the recommendations provided, and the results obtained after deployment. This review allowed us not only to describe the state of the art regarding the previous items, but also to identify the three main stages where intelligent algorithms can bring meaningful improvement to pressure ulcer prevention and mitigation. Finally, as a result of this review and following discussion, we drew guidelines for a general architecture of an intelligent pressure ulcer prevention system.
Highlights
A pressure ulcer (PU) is a localized injury to the skin or underlying tissue, usually over a bony prominence, as a result of unrelieved pressure [1]
These data were used to answer a predefined set of research questions: (1) which sensors are commonly used in PU prevention systems; (2) what features are acquired from the processing of raw data provided by the sensors; (3) which algorithms are used for ulcer prevention and at which stage of the process are they applied; (4) what type of recommendations these systems are capable to provide and (5) what were the practical results of their deployment
After selecting and reviewing the relevant articles, we identified three stages where intelligent algorithms are being used to help in pressure ulcer prevention and management
Summary
A pressure ulcer (PU) is a localized injury to the skin or underlying tissue, usually over a bony prominence, as a result of unrelieved pressure [1]. Risk assessment and monitoring allows healthcare professionals to implement specific prevention measures, such as following a patient repositioning schedule, minimizing possible consequences, and helping to reduce the risk of developing or worsening a pressure ulcer This is a health care intensive task and many technological approaches have been proposed to help both to improve the outcome of patients with PU (or in risk of developing it) and to alleviate the burnout risk of healthcare professionals who must monitor many patients throughout the day. These solutions typically rely on intrinsic data about the patient (e.g., limited mobility, poor nutrition, comorbidities, aging skin) [1,2,3,4] and/or extrinsic data (e.g., pressure from hard surfaces, shearing from involuntary muscle movements, excessive moisture) to provide the healthcare provider with additional information that can facilitate the definition of appropriate monitoring schedule and treatment
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