Feedback GAP: pragmatic,cluster-randomized trial of goal setting and action plans to increase the effectiveness of audit and feedback interventions in primary care

Background

Audit and feedback to physicians is a commonly used quality improvement strategy, but its optimal design is unknown. This trial tested the effects of a theory-informed worksheet to facilitate goal setting and action planning, appended to feedback reports on chronic disease management, compared to feedback reports provided without these worksheets.

Methods

A two-arm pragmatic cluster randomized trial was conducted, with allocation at the level of primary care clinics. Participants were family physicians who contributed data from their electronic medical records. The ‘usual feedback’ arm received feedback every six months for two years regarding the proportion of their patients meeting quality targets for diabetes and/or ischemic heart disease. The intervention arm received these same reports plus a worksheet designed to facilitate goal setting and action plan development in response to the feedback reports. Blood pressure (BP) and low-density lipoprotein cholesterol (LDL) values were compared after two years as the primary outcomes. Process outcomes measured the proportion of guideline-recommended actions (e.g., testing and prescribing) conducted within the appropriate timeframe. Intention-to-treat analysis was performed.

Results

Outcomes were similar across groups at baseline. Final analysis included 20 physicians from seven clinics and 1,832 patients in the intervention arm (15% loss to follow up) and 29 physicians from seven clinics and 2,223 patients in the usual feedback arm (10% loss to follow up). Ten of 20 physicians completed the worksheet at least once during the study. Mean BP was 128/72 in the feedback plus worksheet arm and 128/73 in the feedback alone arm, while LDL was 2.1 and 2.0, respectively. Thus, no significant differences were observed across groups in the primary outcomes, but mean haemoglobin A1c was lower in the feedback plus worksheet arm (7.2% versus 7.4%, p<0.001). Improvements in both arms were noted over time for one-half of the process outcomes.

Discussion

Appending a theory-informed goal setting and action planning worksheet to an externally produced audit and feedback intervention did not lead to improvements in patient outcomes. The results may be explained in part by passive dissemination of the worksheet leading to inadequate engagement with the intervention.

Trial registration

ClinicalTrials.gov NCT00996645

     

A multi-step directional generalized gradient vector flow snake for target tumor segmentation in US-guided high-intensity focused ultrasound ablation

Getting precise locations of target tumors can help to ensure ablation of cancerous tissues and avoid unwanted destruction of healthy tissues in high-intensity focused ultrasound (HIFU) treatment system. Because of speckle noise and spurious boundaries in ultrasound images, traditional image segmentation methods are not suitable for achieving the precise locations of target tumors in HIFU ablation. In this paper, a multi-step directional generalized gradient vector flow snake model is introduced for target tumor segmentation. In the first step, the traditional generalized gradient vector flow (GGVF) snake is used to obtain an approximate contour of the tumor. According to the approximate contour, a new distance map is generated. Subsequently, a new directional edge map is created by calculating a scalar product of the gradients of the distance map and the initial image. In this process, the gradient directional information and the magnitude information of the distance map are used to attenuate unwanted edges and highlight the real edges in the new directional edge map. Finally, a refined GGVF field is derived from a diffusion operation of the gradient vectors of the directional edge map. The GGVF field is used to refine the tumor's contour, by directing the approximate contour to edges with the desired gradient directionality. Based on the newly developed snake model, the influences of the spurious boundaries and the speckle noise are significantly reduced in the ultrasound image segmentation. Experimental results indicate that this technique is greatly useful for target tumor segmentation in HIFU treatment system