Problem-centric Organization and Visualization of Patient Imaging and Clinical Data

Abstract

A patient's electronic medical record contains a large amount of unstructured textual information. As patient records become increasingly dense owing to an aging population and increased occurrence of chronic diseases, a tool is needed to help organize and navigate patient data in a way that facilitates a clinician's ability to understand this information and that improves efficiency. A system has been developed for physicians that summarizes clinical information from a patient record. This system provides a gestalt view of the patient's record by organizing information about each disease along four dimensions (axes): time (eg, disease progression over time), space (eg, tumor in left frontal lobe), existence (eg, certainty of existence of a finding), and causality (eg, response to treatment). A display is generated from information provided by radiology reports and discharge summaries. Natural language processing is used to identify clinical abnormalities (problems, symptoms, findings) from these reports as well as associated properties and relationships. This information is presented in an integrated format that organizes extracted findings into a problem list, depicts the information on a timeline grid, and provides direct access to relevant reports and images. The goal of this system is to improve the structure of clinical information and its presentation to the physician, thereby simplifying the information retrieval and knowledge discovery necessary to bridge the gap between acquiring raw data and making an informed diagnosis.

Salience of Medical Concepts of Inside Clinical Texts and Outside Medical Records for Referred Cardiovascular Patients

“…In clinical practice, an aggregation of such relevant events and concepts can be used to deliver valuable information to clinicians as a summarization of EHRs. Diverse approaches [19][20][21][22] to extract clinical concepts and variables from clinical notes have been explored. The majority of these studies, however, did not evaluate extraction in a clinical practice setting, or their systems were not deployed [23] and relied on structured data sections of the EHR to discover major concepts.…”

V-Model: a new perspective for EHR-based phenotyping

“…However, little work has been done that targets narrative clinical documents and events that have been visualized selectively. Bashyam et al [ 26 ] developed a problem-oriented medical record (POMR) system for clinical documents. The existence of problems or findings was visualized on a timeline grid which is a collection of explicit date cells.…”

V-model

“…Little work has been done on displaying narrative text in a timeline. Bashyam et al [11] designed and implemented a problem-oriented medical record (POMR) view of information extracted from clinical documents. However, problem centric visualization may exclude some useful information contained in clinical documents (e.g., narrative descriptions about situations that caused problematic symptoms).…”