Identification, Extraction and Display of Clinical Data Patterns with Application to Anesthesia Workflows

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 DESCRIPTION (provided by applicant): The goal of this project is to use cutting-edge methods from the data science/Big Data community to provide rapidly interpretable visualizations of complex clinical data patterns that allow clinicians to quickly answer selected clinical questions that they face many times a day. The traditional Electronic Health Record display formats of tabular numeric data and narrative clinical text make it difficult to identify complex relationships and trends among several variables at once, particularly if those relationships change over time. We have previously developed computational methods to identify clinically important, time-changing relationships and patterns among medical data, and in this project we seek to extend those methods and produce tailored visualizations of the discovered patterns to support specific clinical tasks that cover the spectrum of understanding patients, procedures, and populations. Specifically, we seek to support the cognitive tasks involved in answering the following broad clinical questions: 1) What is the preoperative clinical status of this patient? 2) What are the common anesthetic approaches for this surgical procedure? And 3) What is the acuity level and complexity of each patient in the population of those who will be operated on tomorrow? We selected these specific questions from the clinical domain of anesthesia because that domain has fairly consistent practices between institutions, but we intend for our solutions to be easily extendable to analogous questions across clinical specialties. This project includes developing web-based tools that clinicians can use to answer these questions during their daily clinical practice. We plan an iterative development approach, starting with qualitative user studies of workflows and information needs relevant to the three questions, and followed by design iterations that include end-user clinician feedback at each iteration. Additionally, we will consider at design time possible barriers to adoption, rather than leaving this until deployment time, and we expect to be able to lower those barriers with appropriate design decisions. If successful, this project will facilitate the daily practice of clnical care, increasing its efficiency, effectiveness, and quality.