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Building clinical natural language processing (NLP) systems that work on widely varying data is an absolute necessity because of the expense of obtaining new training data. While domain adaptation research can have a positive impact on this problem, the most widely studied paradigms do not take into account the realities of clinical data sharing. To address this issue, we lay out a taxonomy of domain adaptation, parameterizing by what data [...]

Cross-institutional distributed healthcare/genomic predictive modeling is an emerging technology that fulfills both the need of building a more generalizable model and of protecting patient data by only exchanging the models but not the patient data. In this article, the implementation details are presented for one specific blockchain-based approach, ExplorerChain, from a software development perspective. The healthcare/genomic use cases of myocardial infarction, cancer biomarker, and length of hospitalization after surgery are [...]

Healthcare organizations need to rapidly adapt to new technology, policy changes, evolving payment strategies, and other environmental changes. We report on the development and application of a structured methodology to support technology and process improvement in healthcare organizations, Systematic Iterative Organizational Diagnostics (SIOD). SIOD was designed to evaluate clinical work practices, diagnose technology and workflow issues, and recommend potential solutions.

The objective of this study was to assess the clinical and financial impact of a quality improvement project that utilized a modified Early Warning Score (mEWS)-based clinical decision support intervention targeting early recognition of sepsis decompensation.

Delay or failure to view test results in a hospital setting can lead to delayed diagnosis, risk of patient harm, and represents inefficiency. Factors influencing this were investigated to identify how timeliness and completeness of test review could be improved through an evidence-based redesign of the use of clinical test review software.

The study sought to explore information needs arising from a gap in clinicians' knowledge that is not met by current evidence and identify possible areas of use and target groups for a future clinical decision support system (CDSS), which will guide clinicians in cases where no evidence exists.

Informatics tools that support next-generation sequencing workflows are essential to deliver timely interpretation of somatic variants in cancer. Here, we describe significant updates to our laboratory developed bioinformatics pipelines and data management application termed Houston Methodist Variant Viewer (HMVV).

Determine if deep learning detects sepsis earlier and more accurately than other models. To evaluate model performance using implementation-oriented metrics that simulate clinical practice.

One primary consideration when developing predictive models is downstream effects on future model performance. We conduct experiments to quantify the effects of experimental design choices, namely cohort selection and internal validation methods, on (estimated) real-world model performance.