Using Technology to Develop Effective Continuous Quality Assurance Mechanisms

A major challenge in implementation science and program evaluation is providing rapid, useful feedback. Traditional feedback methods are too slow to use effectively with implementation science and continuous quality... [ view full abstract ]

A major challenge in implementation science and program evaluation is providing rapid, useful feedback. Traditional feedback methods are too slow to use effectively with implementation science and continuous quality improvement evaluations. Often, by the time the data is analyzed and conclusions written, the feedback is too late for it to have a meaningful impact on the implementation of a training program. Furthermore, traditional approaches are labor intensive, thereby limiting the scale at which evaluation can take place because only a small selection of programs can be evaluated without expending a large amount of resources.

During this presentation, methods for using technology to improve the evaluation process will be presented. We have developed approaches for leveraging technology to regularly process large databases and automatically analyze outcomes. Rapid feedback is made available to agency directors, program directors, and case managers using interactive web based dashboards that include graphs and interactive data visualizations. Administrators and staff can explore important process indicators such as risk assessments for all children at program entry and outcome indicators including the average length of stay, custody closure reasons, and permanency rates. As this data is processed automatically, there is essentially no extra effort in analyzing and providing feedback to a large number of programs. Agency directors can then examine program functioning at the level of an individual program or compare programs system wide. The end goal in using technology to facilitate implementation and evaluation feedback is to enable social service providers to have easily accessible and actionable data when making decisions designed to improve outcomes for children and families.