TD
T. Demaret
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Closing the gap between what we know and what we do
Designing an implementation tool for infection prevention
Healthcare-associated infections and antimicrobial resistance threaten patient safety, yet the everyday measures that prevent them, such as hand hygiene and the correct use of personal protective equipment, are applied inconsistently in hospitals. Physicians are often poorer compliers, and the cause is rarely a lack of knowledge. Compliance is shaped by time pressure, habit, hierarchy, and local ward culture, and it varies widely between departments. Hospital-wide campaigns therefore rarely produce lasting change. What works instead is tailoring: matching interventions to the behavioural determinants at play in a given context.
Within the STIP programme at Amsterdam UMC, evidence-based strategies to improve physician compliance have already been developed, but infection prevention professionals lack a structured way to bring them into practice. This project addresses that gap. Following an iterative, human-centred design approach grounded in context analysis, interviews, and the COM-B model, it delivers STIP-assist: a digital, AI-supported decision aid. The tool guides infection prevention professionals from a contextual analysis of barriers, to a set of matched and explained strategies, to a concrete action plan, without depending on external experts. This report describes the problem framing, the design insights, the current prototype, and the steps planned towards user testing and delivery. ...
Within the STIP programme at Amsterdam UMC, evidence-based strategies to improve physician compliance have already been developed, but infection prevention professionals lack a structured way to bring them into practice. This project addresses that gap. Following an iterative, human-centred design approach grounded in context analysis, interviews, and the COM-B model, it delivers STIP-assist: a digital, AI-supported decision aid. The tool guides infection prevention professionals from a contextual analysis of barriers, to a set of matched and explained strategies, to a concrete action plan, without depending on external experts. This report describes the problem framing, the design insights, the current prototype, and the steps planned towards user testing and delivery. ...
Healthcare-associated infections and antimicrobial resistance threaten patient safety, yet the everyday measures that prevent them, such as hand hygiene and the correct use of personal protective equipment, are applied inconsistently in hospitals. Physicians are often poorer compliers, and the cause is rarely a lack of knowledge. Compliance is shaped by time pressure, habit, hierarchy, and local ward culture, and it varies widely between departments. Hospital-wide campaigns therefore rarely produce lasting change. What works instead is tailoring: matching interventions to the behavioural determinants at play in a given context.
Within the STIP programme at Amsterdam UMC, evidence-based strategies to improve physician compliance have already been developed, but infection prevention professionals lack a structured way to bring them into practice. This project addresses that gap. Following an iterative, human-centred design approach grounded in context analysis, interviews, and the COM-B model, it delivers STIP-assist: a digital, AI-supported decision aid. The tool guides infection prevention professionals from a contextual analysis of barriers, to a set of matched and explained strategies, to a concrete action plan, without depending on external experts. This report describes the problem framing, the design insights, the current prototype, and the steps planned towards user testing and delivery.
Within the STIP programme at Amsterdam UMC, evidence-based strategies to improve physician compliance have already been developed, but infection prevention professionals lack a structured way to bring them into practice. This project addresses that gap. Following an iterative, human-centred design approach grounded in context analysis, interviews, and the COM-B model, it delivers STIP-assist: a digital, AI-supported decision aid. The tool guides infection prevention professionals from a contextual analysis of barriers, to a set of matched and explained strategies, to a concrete action plan, without depending on external experts. This report describes the problem framing, the design insights, the current prototype, and the steps planned towards user testing and delivery.