Article type
Abstract
"Background
Many international health authorities have requested that economic evidence (EE) plays a more prominent role in the development of Clinical Practice Guidelines (CPG). In Spain, due to the lack of methodological guidance, there is wide variation in how EE is incorporated in the formulation of recommendations about health technologies. We addressed this gap by developing a decision-making algorithm to assist CPG developers in incorporating evidence on resource use, costs or cost-effectiveness. This tool can help to identify and prioritize areas where EE is needed, and to decide how to obtain it (systematic review of economic evaluations and/or de novo economic evaluation).
Objectives
To apply the newly-developed algorithm for incorporating EE in the development of a CPG for patients with Chronic Non-Cancer Pain (CNCP) in the Spanish National Health System.
Methods
The algorithm has three stages: 1) anticipation of the impact of EE on recommendations, 2) search and/or generation of EE, and 3) use of EE in the formulation of recommendations. As a case study, we consider the CNCP CPG, which is an adaptation of NICE’s guideline NG193. We describe how the algorithm was used to identify areas where additional EE was needed, how the evidence was obtained, and how it impacted the recommendations.
Results
It was anticipated that EE could be relevant to six of the eleven questions in the CNCP CPG. The main criteria for conducting a systematic review of EE were: i) whether evidence in NG193 was up-to-date, and ii) the generalizability of its key methodological standpoints. In Stage 2, new studies that could influence the recommendations in the CNCP CPG were identified (e.g., about the cost-effectiveness of exercise-based interventions). Where EE was found to be insufficient, a de novo economic evaluation was conducted.
Conclusions
CPG developers need to respond to the call for a more central role of EE in their recommendations. This could lead to more efficient resource-allocation decisions and, ultimately, better patient outcomes. The algorithm can help to incorporate EE when adapting existing CPGs by helping developers assess the extent to which recommendations can be generalized and the need for further EE."
Many international health authorities have requested that economic evidence (EE) plays a more prominent role in the development of Clinical Practice Guidelines (CPG). In Spain, due to the lack of methodological guidance, there is wide variation in how EE is incorporated in the formulation of recommendations about health technologies. We addressed this gap by developing a decision-making algorithm to assist CPG developers in incorporating evidence on resource use, costs or cost-effectiveness. This tool can help to identify and prioritize areas where EE is needed, and to decide how to obtain it (systematic review of economic evaluations and/or de novo economic evaluation).
Objectives
To apply the newly-developed algorithm for incorporating EE in the development of a CPG for patients with Chronic Non-Cancer Pain (CNCP) in the Spanish National Health System.
Methods
The algorithm has three stages: 1) anticipation of the impact of EE on recommendations, 2) search and/or generation of EE, and 3) use of EE in the formulation of recommendations. As a case study, we consider the CNCP CPG, which is an adaptation of NICE’s guideline NG193. We describe how the algorithm was used to identify areas where additional EE was needed, how the evidence was obtained, and how it impacted the recommendations.
Results
It was anticipated that EE could be relevant to six of the eleven questions in the CNCP CPG. The main criteria for conducting a systematic review of EE were: i) whether evidence in NG193 was up-to-date, and ii) the generalizability of its key methodological standpoints. In Stage 2, new studies that could influence the recommendations in the CNCP CPG were identified (e.g., about the cost-effectiveness of exercise-based interventions). Where EE was found to be insufficient, a de novo economic evaluation was conducted.
Conclusions
CPG developers need to respond to the call for a more central role of EE in their recommendations. This could lead to more efficient resource-allocation decisions and, ultimately, better patient outcomes. The algorithm can help to incorporate EE when adapting existing CPGs by helping developers assess the extent to which recommendations can be generalized and the need for further EE."