Human Factors Lab

Electronic Health Record (EHR) systems are revolutionizing health care, with potential to improve patient/provider collaboration and patient self-care. A barrier to realizing this potential is that information in EHRs can be technical and not patient-specific. Patients may misunderstand this information, especially numeric information presented without context. Tools are needed to translate EMR information into language that supports collaboration and informs and empowers patients, and to present this information in ways that engage patients for self-care. We are developing a Natural Language Processing (NLP)-based tool that takes as input technical medication information from EHRs and generates language that is easy for patients to understand. This patient-centered language is integrated into a Computer Agent (CA)-based ‘adviser’ system that supports collaboration and patient self-care. The CA emulates best practices for face-to-face communication in distributed contexts, such as patient portals. Eventually, the CA will be interactive, querying patients to ensure they understand presented information.

Grant: Jump Applied Research for Community Health through Engineering and Simulation (ARCHES) program

Publications

In this project we focus on improving understanding and use of information provided through patient portals to Electronic Health Record (EHR) systems by older adults with diverse numeracy and literacy abilities. Portals are intended to support patient-centered care but are underutilized by older adults because they serve more as information repositories than as collaborative tools for patient education. Self-managing health often hinges on comprehension of numeric information, which challenges lower numeracy patients. Older adults, the most frequent consumers of health information, also have lower numeracy. Patients often interpret numeric information in terms of their goals and knowledge in order to create gist representations organized in terms of evaluative/affective categories that capture the ‘bottom-line’ for their health. Clinicians traditionally help patients create gist by using verbal and nonverbal strategies that contextualize the information. However, clinicians have less and less time to provide this support. While patient portals have the potential to support patient comprehension by providing access to information when needed, portals may not be effective because they eliminate context supporting gist comprehension. We will help older adults use numeric information by emulating in portal environments best practices from face-to-face communication. Leveraging progress in developing computer-based agents (CA) for human-computer systems, we are developing a CA to serve as an expert clinical intermediary that provides succinct commentary about portal-based test results reinforced by nonverbal cues (facial expressions, tone of voice) to help patients create gist representations that support understanding, engender trust, and encourage them to accomplish self-care goals.