The Transparent, Concurrent, and Collaborative Health Record
Subtitle: Methods to Improve Patients' Comprehension of Health Information
By: Lisa Grossman Liu
Publisher: [publisher not identified]
Published: 2021
Place of publication: [New York, N.Y.?]
Language: eng
Description: A decade ago, only 10% of U.S. healthcare organizations used an electronic health record (EHR), whereas more than 99% do today. The rapid adoption of EHRs has radically transformed communication of health information. Previously, health records consisted of providers handwriting notes in paper charts, rarely seen by outsiders. Today, EHRs integrate information from dozens of sources, to be used by providers, administrators, researchers, and increasingly, patients. Last year alone, an estimated 100 million Americans interacted with their own health records through patient-facing systems. This information has been used to prevent medical errors, reduce nonadherence to treatment, increase shared decision-making, and improve health outcomes. However, failure to comprehend this information can negate any potential benefits and even cause medically-harmful miscommunication. Therefore, it is critical to represent health information using methods that promote patients' comprehension. Despite the need for better representation, today's patient-facing systems do little more than present unexplained data, and limited guidance has been given by research or policy.

In this thesis, we present new evidence about representation of health information in patient-facing systems, and we use this evidence to develop informatics methods that promote comprehension. Two aims center on (1) medical abbreviations and acronyms, one of the biggest barriers to patients' comprehension of their health records, and (2) changes in patient-reported outcomes, one of the most important informants of chronic disease management. We assess challenges with representing this information to patients, using randomized trials and qualitative studies. Then, we develop and evaluate an array of informatics methods for overcoming challenges, specifically: (1) machine learning methods for automated expansion of medical abbreviations and acronyms, and (2) information visualization methods for representing changes in patient-reported outcomes. In the future, these interventions can be implemented in patient-facing systems to optimize comprehension. Our evidence will guide strategies for meaningful communication that, ultimately, will build trust between patients and the healthcare system that serves them.
Notes: Department: Biomedical Informatics.

Thesis advisor: David K. Vawdrey.

Thesis (Ph.D.)--Columbia University, 2021.