How Will the Information Be Captured and Used?

New sources of health care data, combined with existing resources, offer unprecedented opportunities to learn from health care delivery and patient care. These sources include, for example, EHR systems; registries on diseases, treatments, or specific populations; claims databases from insurers and payers; and mobile devices and sensors that capture local data. In addition to the capacity these sources bring to the collection of clinical data, they also support clinical effectiveness research; surveillance for safety, public health, and other purposes; quality improvement initiatives; population health management; cost and quality reporting; and tools for patient education.

As noted above, EHR systems provide a substantial opportunity for learning by unlocking information currently stored in paper medical records. For example, one study found that real-time analysis of clinical data from EHRs could have identified the increased risk of heart attack associated with rosiglitazone, a diabetes drug, within 18 months of its introduction, as opposed to the 7-8 years between the medication’s introduction and when concerns were raised publicly (Brownstein et al., 2010). In considering how to maximize the clinical knowledge gained from EHR systems, a tension exists between the data needs of research studies and the resources required to collect and store clinical data on care processes and patient outcomes. Given the range of health care research studies, it is likely to be infeasible for every system to capture the full amount of data needed to fulfill all potential research needs. A compromise solution to this tension is to identify those core pieces of information that are needed for many research questions and ensure that this limited set of information is captured faithfully by most digital health record systems. This method of identifying a core dataset that satisfies both research and clinical care needs has been used by several organizations. For example, the National Quality Forum’s (NQF’s) Quality Data Model defines a set of standardized clinical and administrative data that are needed to calculate quality measures using information from EHRs (National Quality Forum, 2010), while the HMO Research Network’s Virtual Data Warehouse (discussed further on page 165) maps data from the EHRs and medical claims of multiple health maintenance organization (HMO) plans into a standardized dataset. Other efforts focus on population health; for example, popHealth software integrates with providers’ EHRs to automate and simplify the reporting and exchange of quality data on the providers’ patient populations, and the Query Health project is setting data standards to enable research on population health (Fridsma, 2011; popHealth, 2012). In addition to the research benefits, routine adoption of core datasets in EHRs can enhance the

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