Publishing web-based guidelines using interactive decision models

Commonly used methods for guideline development and dissemination do not en able developers to tailor guidelines systematically to specific patient pop ulations and update guidelines easily. We developed a web-based system, ALC HEMIST, that uses decision models and automatically creates evidence-based guidelines that can be disseminated, tailored and updated over the web. Our objective was to demonstrate the use of this system with clinical scenario s that provide challenges for guideline development. We used the ALCHEMIST system to develop guidelines for three clinical scenarios: (1) Chlamydia sc reening for adolescent women, (2) antiarrhythmic therapy for the prevention of sudden cardiac death; and (3) genetic testing for the BRCA breast-cance r mutation. ALCHEMIST uses information extracted directly from the decision model, combined with the additional information from the author of the dec ision model, to generate global guidelines. ALCHEMIST generated electronic web-based guidelines for each of the three scenarios, Using ALCHEMIST, We d emonstrate that tailoring a guideline for a population at high-risk for Chl amydia changes the recommended policy for control of Chlamydia from contact tracing of reported cases to a population-based screening programme. We us ed ALCHEMIST to incorporate new evidence about the effectiveness of implant able cardioverter defibrillators (ICD) and demonstrate that the cost-effect iveness of use of ICDs improves from $74 400 per quality-adjusted life year (QALY) gained to $34 500 per QALY gained. Finally, we demonstrate how a cl inician could use ALCHEMIST to incorporate a woman's utilities for relevant health states and thereby develop patient-specific recommendations for BRC A testing; the patient-specific recommendation improved quality-adjusted li fe expectancy by 37 days. The ALCHEMIST system enables guideline developers to publish both a guideline and an interactive decision model on the web. This web-based tool enables guideline developers to tailor guidelines syste matically to update guidelines easily, and to make the underlying evidence and analysis transparent for users.