Search AMIA.org

To evaluate the impact of a referral manager tool on primary care practices.

This integrative review identifies convergent and divergent areas of need for collecting and using patient-generated health data (PGHD) identified by patients and providers (i.e., physicians, nurses, advanced practice nurses, physician assistants, and dietitians).

Organizational strategies for implementing eHealth tools influence patient and provider use of portal technology. This study examines whether the intensity of bidirectional secure portal messaging is associated with improved clinical outcomes.

Ensuring the ability to exchange patient information among disparate electronic health records systems is a top priority and a domain of substantial public investment across countries. However, we know little about the extent to which current capabilities meet the needs of frontline clinicians.

Distributional semantics algorithms, which learn vector space representations of words and phrases from large corpora, identify related terms based on contextual usage patterns. We hypothesize that distributional semantics can speed up lexicon expansion in a clinical domain, radiology, by unearthing synonyms from the corpus.

Representing nursing data sets in a standard way will help to facilitate sharing relevant information across settings. We aimed to populate nursing process and outcome metrics with electronic health record (EHR) data and then compare the results with event reporting systems.

The federal meaningful use (MU) program was aimed at improving adoption and use of electronic health records, but practicing physicians have criticized it. This study was aimed at quantifying the benefits (ie, usefulness) and burdens (ie, workload) of the MU program for practicing family physicians.

To develop a comprehensive value set for documenting and encoding adverse reactions in the allergy module of an electronic health record.

A key challenge in clinical data mining is that most clinical datasets contain missing data. Since many commonly used machine learning algorithms require complete datasets (no missing data), clinical analytic approaches often entail an imputation procedure to "fill in" missing data. However, although most clinical datasets contain a temporal component, most commonly used imputation methods do not adequately accommodate longitudinal time-based data. We sought to develop a new imputation algorithm [...]