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The diabetes healthcare provider plays a key role in interpreting blood glucose trends, but few institutions have successfully integrated patient home glucose data in the electronic health record (EHR). Published implementations to date have required custom interfaces, which limit wide-scale replication. We piloted automated integration of continuous glucose monitor data in the EHR using widely available consumer technology for 10 pediatric patients with insulin-dependent diabetes. Establishment of a passive data [...]

To investigate how individuals with diabetes and diabetes educators reason about data collected through self-monitoring and to draw implications for the design of data-driven self-management technologies.

The proposed Meaningful Use Stage 3 recommendations require healthcare providers to accept patient-generated health data (PGHD) by 2017. Yet, we know little about the tensions that arise in supporting the needs of both patients and providers in this context. We sought to examine these tensions when designing a novel, patient-centered technology - mobile Post-Operative Wound Evaluator (mPOWEr) - that uses PGHD for post-discharge surgical wound monitoring.

To evaluate the feasibility of automatically assessing the Social Rhythm Metric (SRM), a clinically-validated marker of stability and rhythmicity for individuals with bipolar disorder (BD), using passively-sensed data from smartphones.

Given the increasing emphasis on delivering high-quality, cost-efficient healthcare, improved methodologies are needed to measure the accuracy and utility of ordered diagnostic examinations in achieving the appropriate diagnosis. Here, we present a data-driven approach for performing automated quality assessment of radiologic interpretations using other clinical information (e.g., pathology) as a reference standard for individual radiologists, subspecialty sections, imaging modalities, and entire departments. Downstream diagnostic conclusions from the electronic medical record [...]

To review literature assessing the impact of speech recognition (SR) on clinical documentation.

To identify patients in a human immunodeficiency virus (HIV) study cohort who have fallen by applying supervised machine learning methods to radiology reports of the cohort.

Stage 2 and proposed Stage 3 meaningful use criteria ask providers to support patient care coordination by electronically generating, exchanging, and reconciling key information during patient care transitions.