Search AMIA.org

We developed the Medication Extraction and Normalization (MedXN) system to extract comprehensive medication information and normalize it to the most appropriate RxNorm concept unique identifier (RxCUI) as specifically as possible.

This study describes the implementation and impact of an electronic test result acknowledgement (RA) system in the Mater Mothers' Hospital in Brisbane, Australia. The Verdi application electronically records clinicians' acknowledgement of the review of results. Hospital data (August 2011-August 2012) were extracted to measure clinicians' acknowledgement practices. There were 27,354 inpatient test results for 6855 patients. All test results were acknowledged. 60% (95% CI 59% to 61%) of laboratory and [...]

To compare the agreement of electronic health record (EHR) data versus Medicaid claims data in documenting adult preventive care. Insurance claims are commonly used to measure care quality. EHR data could serve this purpose, but little information exists about how this source compares in service documentation. For 13 101 Medicaid-insured adult patients attending 43 Oregon community health centers, we compared documentation of 11 preventive services, based on EHR versus Medicaid claims [...]

This paper presents an automated system for classifying the results of imaging examinations (CT, MRI, positron emission tomography) into reportable and non-reportable cancer cases. This system is part of an industrial-strength processing pipeline built to extract content from radiology reports for use in the Victorian Cancer Registry.

To examine whether there is a common sequence of adoption of electronic health record (EHR) functions among US hospitals, identify differences by hospital type, and assess the impact of meaningful use.

mHealth interventions have shown promise for helping people sustain healthy behaviors such as weight loss. However, few have assessed treatment fidelity, that is, the accurate delivery, receipt, and enactment of the intervention. Treatment fidelity is critical because the valid interpretation and translation of intervention studies depend on treatment fidelity assessments. We describe strategies used to assess treatment fidelity in mobile health (mHealth) interventions aimed at sustaining healthy behaviors in weight [...]

To specify the problem of patient-level temporal aggregation from clinical text and introduce several probabilistic methods for addressing that problem. The patient-level perspective differs from the prevailing natural language processing (NLP) practice of evaluating at the term, event, sentence, document, or visit level.

The PaTH (University of Pittsburgh/UPMC, Penn State College of Medicine, Temple University Hospital, and Johns Hopkins University) clinical data research network initiative is a collaborative effort among four academic health centers in the Mid-Atlantic region. PaTH will provide robust infrastructure to conduct research, explore clinical outcomes, link with biospecimens, and improve methods for sharing and analyzing data across our diverse populations. Our disease foci are idiopathic pulmonary fibrosis, atrial fibrillation [...]

The Mid-South Clinical Data Research Network (CDRN) encompasses three large health systems: (1) Vanderbilt Health System (VU) with electronic medical records for over 2 million patients, (2) the Vanderbilt Healthcare Affiliated Network (VHAN) which currently includes over 40 hospitals, hundreds of ambulatory practices, and over 3 million patients in the Mid-South, and (3) Greenway Medical Technologies, with access to 24 million patients nationally. Initial goals of the Mid-South CDRN include [...]