G D Sanders1, R F Nease, D K Owens. 1. Center for Primary Care and Outcomes Research, Department of Medicine, Stanford University, California 94305-6019, USA. sanders@stanford.edu
Abstract
BACKGROUND: Local tailoring of clinical practice guidelines (CPGs) requires experts in medicine and evidence synthesis unavailable in many practice settings. The authors' computer-based system enables developers and users to create, disseminate, and tailor CPGs, using normative decision models (DMs). METHODS: ALCHEMIST, a web-based system, analyzes a DM, creates a CPG in the form of an annotated algorithm, and displays for the guideline user the optimal strategy. ALCHEMIST'S interface enables remote users to tailor the guideline by changing underlying input variables and observing the new annotated algorithm that is developed automatically. In a pilot evaluation of the system, a DM was used to evaluate strategies for staging non-small-cell lung cancer. Subjects (n = 15) compared the automatically created CPG with published guidelines for this staging and critiqued both using a previously developed instrument to rate the CPGs' usability, accountability, and accuracy on a scale of 0 (worst) to 2 (best), with higher scores reflecting higher quality. RESULTS: The mean overall score for the ALCHEMIST CPG was 1.502, compared with the published-CPG score of 0.987 (p = 0.002). The ALCHEMIST CPG scores for usability, accountability, and accuracy were 1.683, 1.393, and 1.430, respectively; the published CPG scores were 1.192, 0.941, and 0.830 (each comparison p < 0.05). On a scale of 1 (worst) to 5 (best), users' mean ratings of ALCHEMIST'S ease of use, usefulness of content, and presentation format were 4.76, 3.98, and 4.64, respectively. CONCLUSIONS: The results demonstrate the feasibility of a web-based system that automatically analyzes a DM and creates a CPG as an annotated algorithm, enabling remote users to develop site-specific CPGs. In the pilot evaluation, the ALCHEMIST guidelines met established criteria for quality and compared favorably with national CPGs. The high usability and usefulness ratings suggest that such systems can be a good tool for guideline development.
BACKGROUND: Local tailoring of clinical practice guidelines (CPGs) requires experts in medicine and evidence synthesis unavailable in many practice settings. The authors' computer-based system enables developers and users to create, disseminate, and tailor CPGs, using normative decision models (DMs). METHODS: ALCHEMIST, a web-based system, analyzes a DM, creates a CPG in the form of an annotated algorithm, and displays for the guideline user the optimal strategy. ALCHEMIST'S interface enables remote users to tailor the guideline by changing underlying input variables and observing the new annotated algorithm that is developed automatically. In a pilot evaluation of the system, a DM was used to evaluate strategies for staging non-small-cell lung cancer. Subjects (n = 15) compared the automatically created CPG with published guidelines for this staging and critiqued both using a previously developed instrument to rate the CPGs' usability, accountability, and accuracy on a scale of 0 (worst) to 2 (best), with higher scores reflecting higher quality. RESULTS: The mean overall score for the ALCHEMIST CPG was 1.502, compared with the published-CPG score of 0.987 (p = 0.002). The ALCHEMIST CPG scores for usability, accountability, and accuracy were 1.683, 1.393, and 1.430, respectively; the published CPG scores were 1.192, 0.941, and 0.830 (each comparison p < 0.05). On a scale of 1 (worst) to 5 (best), users' mean ratings of ALCHEMIST'S ease of use, usefulness of content, and presentation format were 4.76, 3.98, and 4.64, respectively. CONCLUSIONS: The results demonstrate the feasibility of a web-based system that automatically analyzes a DM and creates a CPG as an annotated algorithm, enabling remote users to develop site-specific CPGs. In the pilot evaluation, the ALCHEMIST guidelines met established criteria for quality and compared favorably with national CPGs. The high usability and usefulness ratings suggest that such systems can be a good tool for guideline development.
Authors: Ulrich Siering; Michaela Eikermann; Elke Hausner; Wiebke Hoffmann-Eßer; Edmund A Neugebauer Journal: PLoS One Date: 2013-12-09 Impact factor: 3.240