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To identify and describe clinical drug data sources that have the potential to serve as a repository of information for developing drug knowledge translation products.

Despite increased risk for negative outcomes, cognitive impairment (CI) is greatly under-detected during hospitalization. While automated EHR-based phenotypes have potential to improve recognition of CI, they are hindered by widespread under-diagnosis of underlying etiologies such as dementia-limiting the utility of more precise structured data elements. This study examined unstructured data on symptoms of CI in the acute-care EHRs of hip and stroke fracture patients with dementia from two hospitals. Clinician [...]

To estimate the national cost of ADEs resulting from inappropriate medication-related alert overrides in the U.S. inpatient setting.

U.S. policy on interoperable HIT has focused on increasing inter-system (ie, between different organizations) health information exchange. However, interoperable HIT also supports the movement of information within the same organization (ie, intra-system exchange).

The installation of EHR systems can disrupt operations at clinical practice sites, but also lead to improvements in information availability. We examined how the installation of an ambulatory EHR at OB/GYN practices and its subsequent interface with an inpatient perinatal EHR affected providers' satisfaction with the transmission of clinical information and patients' ratings of their care experience.

Although family and friends (FF) often play a significant support role in the health of older adults (OA), we know little about their role in personal health information management (PHIM). To address this gap and inform the design of PHIM tools, we describe the work, needs, and barriers of FF in the context of PHIM for OAs.

To develop a conceptual prediction model framework containing standardized steps and describe the corresponding open-source software developed to consistently implement the framework across computational environments and observational healthcare databases to enable model sharing and reproducibility.

Deep learning has become a promising approach for automated support for clinical diagnosis. When medical data samples are limited, collaboration among multiple institutions is necessary to achieve high algorithm performance. However, sharing patient data often has limitations due to technical, legal, or ethical concerns. In this study, we propose methods of distributing deep learning models as an attractive alternative to sharing patient data.

As available data increases, so does the opportunity to develop risk scores on more refined patient populations. In this paper we assessed the ability to derive a risk score for a patient no-showing to a clinic visit.