An Interface-driven Analysis of User Interactions with an Electronic Health Records System
- aSchool of Public Health Department of Health Management and Policy, School of Information, The University of Michigan, Ann Arbor, MI
- bThe H. John Heinz III College, Carnegie Mellon University, Pittsburgh, PA
- cDepartment of Public Policy and Public Affairs, John W. McCormack Graduate School of Policy Studies, The University of Massachusetts-Boston, Boston, MA
- dDepartment of Medicine, The Western Pennsylvania Hospital, Pittsburgh, PA
- Correspondence: Kai Zheng, PhD, School of Public Health, School of Information, The University of Michigan, M3531 SPH II, 109 Observatory Street, Ann Arbor, MI 48109-2029; e-mail: <kzheng{at}umich.edu>
- Received 9 May 2008
- Accepted 5 December 2008
Abstract
Objectives This study sought to investigate user interactions with an electronic health records (EHR) system by uncovering hidden navigational patterns in the EHR usage data automatically recorded as clinicians navigated through the system's software user interface (UI) to perform different clinical tasks.
Design A homegrown EHR was adapted to allow real-time capture of comprehensive UI interaction events. These events, constituting time-stamped event sequences, were used to replay how the EHR was used in actual patient care settings. The study site is an ambulatory primary care clinic at an urban teaching hospital. Internal medicine residents were the primary EHR users.
Measurements Computer-recorded event sequences reflecting the order in which different EHR features were sequentially accessed.
Methods We apply sequential pattern analysis (SPA) and a first-order Markov chain model to uncover recurring UI navigational patterns.
Results Of 17 main EHR features provided in the system, SPA identified 3 bundled features: “Assessment and Plan” and “Diagnosis,” “Order” and “Medication,” and “Order” and “Laboratory Test.” Clinicians often accessed these paired features in a bundle together in a continuous sequence. The Markov chain analysis revealed a global navigational pathway, suggesting an overall sequential order of EHR feature accesses. “History of Present Illness” followed by “Social History” and then “Assessment and Plan” was identified as an example of such global navigational pathways commonly traversed by the EHR users.
Conclusion Users showed consistent UI navigational patterns, some of which were not anticipated by system designers or the clinic management. Awareness of such unanticipated patterns may help identify undesirable user behavior as well as reengineering opportunities for improving the system's usability.
