Modeling the Construct of an Expert Evidence-Adaptive Knowledge Base for a Pressure Injury Clinical Decision Support System

Abstract

Abstract:The selection of appropriate wound products for the treatment of pressure injuries is paramount in promoting wound healing. However, nurses find it difficult to decide on the most optimal wound product(s) due to limited live experiences in managing pressure injuries resulting from successfully implemented pressure injury prevention programs. The challenges of effective decision-making in wound treatments by nurses at the point of care are compounded by the yearly release of wide arrays of newly researched wound products into the consumer market. A clinical decision support system for pressure injury (PI-CDSS) was built to facilitate effective decision-making and selection of optimal wound treatments. This paper describes the development of PI-CDSS with an expert knowledge base using an interactive development environment, Blaze Advisor. A conceptual framework using decision-making and decision theory, knowledge representation, and process modelling guided the construct of the PI-CDSS. This expert system has incorporated the practical and relevant decision knowledge of wound experts in assessment and wound treatments in its algorithm. The construct of the PI-CDSS is adaptive, with scalable capabilities for expansion to include other CDSSs and interoperability to interface with other existing clinical and administrative systems. The algorithm was formatively evaluated and tested for usability. The treatment modalities generated after using patient-specific assessment data were found to be consistent with the treatment plan(s) proposed by the wound experts. The overall agreement exceeded 90% between the wound experts and the generated treatment modalities for the choice of wound products, instructions, and alerts. The PI-CDSS serves as a just-in-time wound treatment protocol with suggested clinical actions for nurses, based on the best evidence available.

Clinical Decision Support Systems for Pressure Ulcer Management: Systematic Review

“…Access to 6 articles was not possible, and 11 were excluded for different reasons (see Multimedia Appendix 2 ). Hence, 16 studies [ 16 - 31 ] met all the eligibility criteria and were included in this review. A flow diagram of the selection process is presented in Figure 1 .…”

“…General characteristics of included studies are shown in Multimedia Appendix 3 . Included studies were conducted between 1995 to 2017 in the following countries: United States of America [ 16 , 17 , 20 , 21 , 25 - 27 ], Italy [ 18 ], Canada [ 19 ], Norway [ 22 - 24 ], South Korea [ 28 ], Belgium [ 29 ], and Singapore [ 30 , 31 ]. The studies were published in 9 different journals and in symposium proceedings, most of which related to health informatics (9/16, 60%), followed by nursing sciences (3/16, 20%).…”

“…The clinical decision support systems were implemented to support nurses' clinical decisions in multiple clinical and health care settings such as nursing homes [ 22 - 24 , 26 , 27 , 29 ]; hospital inpatient units (medical-surgical) [ 16 - 18 , 20 , 21 , 30 , 31 ]; acute, home, and extended care [ 19 ]; intensive care [ 28 ]; and long-term care facilities [ 25 ]. The clinical decision support systems were used in pressure ulcer prevention [ 18 , 20 , 21 , 25 - 29 ], prevention and treatment [ 16 , 17 , 19 ], pressure ulcer prevention and evaluation of nutritional status [ 22 - 24 ], and treatment [ 30 , 31 ]. Interventions in the studies were based on the implementation of clinical decision support systems in clinical practice with follow-up periods ranging from 1 month [ 18 ] to 12 months [ 19 , 27 ] or more [ 31 ].…”

“…The clinical decision support systems were used in pressure ulcer prevention [ 18 , 20 , 21 , 25 - 29 ], prevention and treatment [ 16 , 17 , 19 ], pressure ulcer prevention and evaluation of nutritional status [ 22 - 24 ], and treatment [ 30 , 31 ]. Interventions in the studies were based on the implementation of clinical decision support systems in clinical practice with follow-up periods ranging from 1 month [ 18 ] to 12 months [ 19 , 27 ] or more [ 31 ].…”

“…All included studies describe knowledge-based systems—13 out of 16 systems were classified as knowledge representation, with methodologies such as decision rules ( if-then model) [ 20 , 30 , 31 ], guideline modeling language (GLIF, Guideline Interchange Format) to validate the logic of enhanced decision rules [ 21 ], or clinical practice guidelines represented through the graphic editor GUIDE, written in Java [ 18 ]; and 3 out of 16 systems were classified as multipurpose, with 2 using decision trees [ 30 , 31 ] and 1 using a Bayesian network model [ 28 ].…”