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Investigate sociodemographic differences in the use of a patient-facing family health history (FHH)-based risk assessment platform.

Alcohol misuse is present in over a quarter of trauma patients. Information in the clinical notes of the electronic health record of trauma patients may be used for phenotyping tasks with natural language processing (NLP) and supervised machine learning. The objective of this study is to train and validate an NLP classifier for identifying patients with alcohol misuse.

We describe a stratified sampling design that combines electronic health records (EHRs) and United States Census (USC) data to construct the sampling frame and an algorithm to enrich the sample with individuals belonging to rarer strata.

The aim of this study was to generate synthetic electronic health records (EHRs). The generated EHR data will be more realistic than those generated using the existing medical Generative Adversarial Network (medGAN) method.

Thirty-day hospital readmissions are a quality metric for health care systems. Predictive models aim to identify patients likely to readmit to more effectively target preventive strategies. Many risk of readmission models have been developed on retrospective data, but prospective validation of readmission models is rare. To the best of our knowledge, none of these developed models have been evaluated or prospectively validated in a military hospital.

Patient-generated health data (PGHD) may help providers monitor patient status between clinical visits. Our objective was to describe our medical center's early experience with an electronic flowsheet allowing patients to upload self-monitored blood glucose to their provider's electronic health record (EHR).

Data modeling for electronic health records (EHRs) is complex, requiring technological and cognitive sophistication. The openEHR approach leverages the tacit knowledge of domain experts made explicit in a model development process aiming at interoperability and data reuse.

When a paucity of clinical information is communicated from ordering physicians to radiologists at the time of radiology order entry, suboptimal imaging interpretations and patient care may result.

More patients are receiving their test results via patient portals. Given test results are written using medical jargon, there has been concern that patients may misinterpret these results. Using sample colonoscopy and Pap smear results, our objective was to assess how frequently people can identify the correct diagnosis and when a patient should follow up with a provider.