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Introduction: Point-of-care decision support is best presented in the form of checklists or algorithms containing brief prioritized information. While this approach has been widespread in operating room settings, it is lacking in other acute care environments. Assessing information needs at the point-of-care is a key prerequisite for designing meaningful decision support that can fit clinician workflow. We tested the feasibility of a plan-do-study-act (PDSA) framework for designing point-of-care decision support to facilitate global adoption of systematic and disciplined evaluation and treatment of acutely ill patients. Methods: Following the comprehensive review of practice guidelines, a multidisciplinary expert group (anesthesiology, emergency and internal medicine; from Mayo Clinic Rochester MN, USA) first designed checklist drafts for acute syndromes, emergent medications and interventions. An international sample (Asia, Africa, Eastern Europe, and Latin America) of acute care providers completed a self-administered internet based survey to rank the itemized information according to point-of-care needs (from high to low priority). Experts from various international settings and backgrounds iteratively reviewed prioritized decision support content and validated it through a structured feedback. Results: Forty acute syndromes, 86 medications and 23 procedural checklist-cards were developed. Preliminary survey results were gathered from 56 practitioners (17.2 %); 34 of the overall 59 items to rate (57.6%) were assessed as very important or essential during the urgent evaluation and support of an acutely ill patient. Respondents highly rated 57.9% of the syndrome-related items, 72.7% of the medication-related items and 60% of the procedure-related items. Users reported 65 comments the first month, of which 60% related to content, 41.5% to technical issues, and 10.7% to typographic mistakes. Conclusions: A rapid 6-month cycle assessment of information needs for point-of-care decision support is feasible and allows for efficient refinement and prioritization of applied critical care knowledge.
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Bonneton et al. (2013) studied this question.
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