Knowledge optimization theory and application to point-of-care testing.
Point-of-care testing is defined as testing at or near the site of patient care wherever that medical care is needed. The number and types of tests available at the point of care are increasing dramatically. Point-of-care testing provides test results to the clinical team immediately, usually within a therapeutic turnaround time of five minutes. Immediacy of data is a primary benefit, but we must evaluate when, why, and where point-of-care testing should be used because in most cases this new modality is more expensive than conventional testing in clinical laboratories. Knowledge optimization integrates key components of testing, patient focusing, performance, and test clusters, in concert with synthesis of temporal and diagnostic-therapeutic process information that the physician can assimilate quickly and act on to improve patient outcomes. We identify through literature searches, meta-analysis, and documented outcomes results, the optimal applications of point-of-care testing in critical care and other medical settings. The basis for optimality is improved medical and/or economic outcomes. Results show that point-of-care testing is strongly justified for emergency resuscitations under any circumstances, in critical care, such as the operating room and intensive care unit, and in disease management. Long-range implications are: (1) the point-of-care testing trend will accelerate; (2) viable optimization tools include integrative strategies, clinical algorithms, care paths, and performance maps; (3) attention should be focused on rapid communication and understanding of critical results; and (4) enhanced human interfaces are crucial for efficient medical decision making and efficacious patient therapy.
Kost
G J
GJ
School of Medicine, University of California, Davis 95616, USA.
D001185:Artificial Intelligence; D003362:Cost-Benefit Analysis; D006801:Humans; D017063:Outcome Assessment, Health Care; D019095:Point-of-Care Systems