摘要：Background To improve the quality, quantity, and speed of implementation, careful monitoring of the implementation process is required. However, some health organizations have such limited capacity to collect, organize, and synthesize information relevant to its decision to implement an evidence-based program, the preparation steps necessary for successful program adoption, the fidelity of program delivery, and the sustainment of this program over time. When a large health system implements an evidence-based program across multiple sites, a trained intermediary or broker may provide such monitoring and feedback, but this task is labor intensive and not easily scaled up for large numbers of sites. We present a novel approach to producing an automated system of monitoring implementation stage entrances and exits based on a computational analysis of communication log notes generated by implementation brokers. Potentially discriminating
关键词：Machine learning ; Unobtrusive measures ; Text mining ; Social systems informatics
