No abstract

The aim of this investigation was to assess helpful and challenging aspects of electronic health information with respect to clinical workflow and identify a set of characteristics that support patient care processes. We conducted 20 semi-structured interviews at a Veterans Affairs Medical Center, with a fully implemented electronic health record (EHR), and elicited positive and negative examples of how information technology (IT) affects the work of healthcare employees. Responses naturally shed light on information characteristics that aid work processes. We performed a secondary analysis on interview data and inductively identified characteristics of electronic information that support healthcare workflow. Participants provided 199 examples of how electronic information affects workflow. Seventeen characteristics emerged along with four primary domains: trustworthy and reliable; ubiquitous; effectively displayed; and adaptable to work demands. Each characteristic may be used to help evaluate health information technology pre- and post-implementation. Results provide several strategies to improve EHR design and implementation to better support healthcare workflow.

Healthcare organizations are increasingly implementing electronic medical records (EMRs) and other related health information technology (IT). Even in institutions which have long adopted these computerized systems, there are still instances where employees rely on paper to complete their work. The use of paper suggests that parts of the EMR may not be sufficiently designed to support clinicians and their work processes. To understand the use of paper-based alternatives, we conducted 14 key-informant interviews in a large Veterans Affairs Medical Center (VAMC), with a fully implemented EMR. We found nine distinct categories of paper-based workarounds to the use of the EMR. In several cases, paper served as an important tool and assisted healthcare employees in their work. In other cases, paper use circumvented the intended EMR design, introduced potential gaps in documentation, and generated possible paths to medical error. We discuss implications of these findings for EMR design and implementation.