to better approximate the probability of an event and use this as a basis for learning processes. EHR data can be useful for learning systems, but it must be of high quality and mitigated through triangulation of multiple resources.


In the context of public health surveillance, data quality has varying definitions. As James Buehler of the CDC explained in his comments, quality requirements depend on the public health purpose the data are serving. For those working to prevent and contain specific diseases or adverse health events, the required data includes information about disease characteristics and severity, where and when it is occurring, its antecedents, its evolution over time, and its consequences. Moreover, public health professionals need data on those who are affected, individuals’ risk factors and whether certain groups of people are affected more than others, outcomes, and disease susceptibility to treatment. All of this information, often generated by individuals’ utilization of health care services, provides insight into what can be done to craft, target, and direct and redirect public health interventions. As such, public health surveillance is not simply about collecting information; it is about analyzing and using that data for a purpose, and that purpose can vary from disease surveillance, to situational awareness of a community’s status, to local, sometimes individual, interventions. While the data-quality requirements vary for each of these different purposes, Buehler continued, some apply to the broad range of public health surveillance uses. The data should be complete, reliable, timely, and inexpensive, and they should provide accurate insights into the local surveillance context. In practice, it is often not possible for a surveillance system to achieve all of these desirable attributes, requiring balance of desirable and sometimes competing attributes to maximize utility and value.

In order to meet these requirements, current public health surveillance data sources and systems are becoming progressively more automated. Attention is increasingly directed toward integrating EHRs into both the reporting and feedback arms of surveillance, so that individuals’ direct interactions with the health care system can serve as an additional source of electronic public health data. However, the process of moving this automation and integration forward faces a number of challenges Buehler noted, outlining several priorities for addressing those challenges. It is critical that public health surveillance systems are prepared to take full advantage of the data influx resulting from implementation of meaningful use, he said. The public health workforce likewise must be equipped to make the best use of this information, as it presents a great opportunity for more effective and

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