| Literature DB >> 28347856 |
Daniel Morrow1, Mark Hasegawa-Johnson2, Thomas Huang2, William Schuh3, Renato Ferreira Leitão Azevedo4, Kuangxiao Gu2, Yang Zhang2, Bidisha Roy4, Rocio Garcia-Retamero5.
Abstract
We describe a project intended to improve the use of Electronic Medical Record (EMR) patient portal information by older adults with diverse numeracy and literacy abilities, so that portals can better support patient-centered care. Patient portals are intended to bridge patients and providers by ensuring patients have continuous access to their health information and services. However, they are underutilized, especially by older adults with low health literacy, because they often function more as information repositories than as tools to engage patients. We outline an interdisciplinary approach to designing and evaluating portal-based messages that convey clinical test results so as to support patient-centered care. We first describe a theory-based framework for designing effective messages for patients. This involves analyzing shortcomings of the standard portal message format (presenting numerical test results with little context to guide comprehension) and developing verbally, graphically, video- and computer agent-based formats that enhance context. The framework encompasses theories from cognitive and behavioral science (health literacy, fuzzy trace memory, behavior change) as well as computational/engineering approaches (e.g., image and speech processing models). We then describe an approach to evaluating whether the formats improve comprehension of and responses to the messages about test results, focusing on our methods. The approach combines quantitative (e.g., response accuracy, Likert scale responses) and qualitative (interview) measures, as well as experimental and individual difference methods in order to investigate which formats are more effective, and whether some formats benefit some types of patients more than others. We also report the results of two pilot studies conducted as part of developing the message formats.Entities:
Keywords: Aging; Cognition; Computer agent; Electronic Medical Record; Learning; Patient portal
Mesh:
Year: 2017 PMID: 28347856 PMCID: PMC5492515 DOI: 10.1016/j.jbi.2017.03.015
Source DB: PubMed Journal: J Biomed Inform ISSN: 1532-0464 Impact factor: 6.317