An interface-driven analysis of user interactions with an electronic health records system.

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.

Understanding technology adoption in clinical care: clinician adoption behavior of a point-of-care reminder system.

BACKGROUND: Evaluation studies of clinical decision support systems (CDSS) have tended to focus on assessments of system quality and clinical performance in a laboratory setting. Relatively few studies have used field trials to determine if CDSS are likely to be used in routine clinical settings and whether reminders generated are likely to be acted upon by end-users. Moreover, such studies when performed tend not to identify distinct user groups, nor to classify user feedback. AIM: To assess medical residents' acceptance and adoption of a clinical reminder system for chronic disease and preventive care management and to use expressed preferences for system attributes and functionality as a basis for system re-engineering. DESIGN OF STUDY: Longitudinal, correlational study using a novel developmental trajectory analysis (DTA) statistical method, followed by a qualitative analysis based on user satisfaction surveys and field interviews. SETTING: An ambulatory primary care clinic of an urban teaching hospital offering comprehensive healthcare services. 41 medical residents used a CDSS over 10 months in their daily practice. Use of this system was strongly recommended but not mandatory. METHODS: A group-based, semi-parametric statistical modeling method to identify distinct groups, with distinct usage trajectories, followed by qualitative instruments of usability and satisfaction surveys and structured interviews to validate insights derived from usage trajectories. RESULTS: Quantitative analysis delineates three types of user adoption behavior: "light", "moderate" and "heavy" usage. Qualitative analysis reveals that clinicians of distinct types tend to exhibit views of the system consistent with their demonstrated adoption behavior. Drawbacks in the design of the CDSS identified by users of all types (in different ways) motivate a redesign based on current physician workflows. CONCLUSION: We conclude that this mixed methodology has considerable promise to provide new insights into system usability and adoption issues that may benefit clinical decision support systems as well as information systems more generally.

Handling anticipated exceptions in clinical care: investigating clinician use of 'exit strategies' in an electronic health records system.

Unpredictable yet frequently occurring exception situations pervade clinical care. Handling them properly often requires aberrant actions temporarily departing from normal practice. In this study, the authors investigated several exception-handling procedures provided in an electronic health records system for facilitating clinical documentation, which the authors refer to as 'data entry exit strategies.' Through a longitudinal analysis of computer-recorded usage data, the authors found that (1) utilization of the exit strategies was not affected by postimplementation system maturity or patient visit volume, suggesting clinicians' needs to 'exit' unwanted situations are persistent; and (2) clinician type and gender are strong predictors of exit-strategy usage. Drilldown analyses further revealed that the exit strategies were judiciously used and enabled actions that would be otherwise difficult or impossible. However, many data entries recorded via them could have been 'properly' documented, yet were not, and a considerable proportion containing temporary or incomplete information was never subsequently amended. These findings may have significant implications for the design of safer and more user-friendly point-of-care information systems for healthcare.

Social networks and physician adoption of electronic health records: insights from an empirical study.

OBJECTIVE: To study how social interactions influence physician adoption of an electronic health records (EHR) system. DESIGN: A social network survey was used to delineate the structure of social interactions among 40 residents and 15 attending physicians in an ambulatory primary care practice. Social network analysis was then applied to relate the interaction structures to individual physicians' utilization rates of an EHR system. MEASUREMENTS: The social network survey assessed three distinct types of interaction structures: professional network based on consultation on patient care-related matters; friendship network based on personal intimacy; and perceived influence network based on a person's perception of how other people have affected her intention to adopt the EHR system. EHR utilization rates were measured as the proportion of patient visits in which sentinel use events consisting of patient data documentation or retrieval activities were recorded. The usage data were collected over a time period of 14 months from computer-recorded audit trail logs. RESULTS: Neither the professional nor the perceived influence network is correlated with EHR usage. The structure of the friendship network significantly influenced individual physicians' adoption of the EHR system. Residents who occupied similar social positions in the friendship network shared similar EHR utilization rates (p<0.05). In other words, residents who had personal friends in common tended to develop comparable levels of EHR adoption. This effect is particularly prominent when the mutual personal friends of these 'socially similar' residents were attending physicians (p<0.001). CONCLUSIONS: Social influence affecting physician adoption of EHR seems to be predominantly conveyed through interactions with personal friends rather than interactions in professional settings.