Quantifying the impact of health IT implementations on clinical workflow: A new methodological perspective

School of Public Health, Department of Health Management and Policy, The University of Michigan, Ann Arbor, Michigan 48109-2029, USA.
Journal of the American Medical Informatics Association (Impact Factor: 3.93). 07/2010; 17(4):454-61. DOI: 10.1136/jamia.2010.004440
Source: PubMed

ABSTRACT Health IT implementations often introduce radical changes to clinical work processes and workflow. Prior research investigating this effect has shown conflicting results. Recent time and motion studies have consistently found that this impact is negligible; whereas qualitative studies have repeatedly revealed negative end-user perceptions suggesting decreased efficiency and disrupted workflow.
We speculate that this discrepancy may be due in part to the design of the time and motion studies, which is focused on measuring clinicians' ‘time expenditures' among different clinical activities rather than inspecting clinical ‘workflow’ from the true ‘flow of the work’ perspective. In this paper, we present a set of new analytical methods consisting of workflow fragmentation assessments, pattern recognition, and data visualization, which are accordingly designed to uncover hidden regularities embedded in the flow of the work. Through an empirical study, we demonstrate the potential value of these new methods in enriching workflow analysis in clinical settings.

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    • "In most cases, the primary task was one that was initiated first. The transition probability from one task (predecessor) to another task (successor) was defined as the frequency of this transition divided by the total number of transitions originating from the predecessor [17]. Data were then analyzed by physician groups (resident and attending physicians) and data collection periods (pre-and post-EHR implementation). "
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    ABSTRACT: To assess the impact of EHR technology on the work and workflow of ICU physicians and compare time spent by ICU resident and attending physicians on various tasks before and after EHR implementation. EHR technology with electronic order management (CPOE, medication administration and pharmacy system) and physician documentation was implemented in October 2007. We collected a total of 289h of observation pre- and post-EHR implementation. We directly observed the work of residents in three ICUs (adult medical/surgical ICU, pediatric ICU and neonatal ICU) and attending physicians in one ICU (adult medical/surgical ICU). EHR implementation had an impact on the time distribution of tasks as well as the temporal patterns of tasks. After EHR implementation, both residents and attending physicians spent more of their time on clinical review and documentation (40% and 55% increases, respectively). EHR implementation also affected the frequency of switching between tasks, which increased for residents (from 117 to 154tasks per hour) but decreased for attendings (from 138 to 106tasks per hour), and the temporal flow of tasks, in particular around what tasks occurred before and after clinical review and documentation. No changes in the time spent in conversational tasks or the physical care of the patient were observed. The use of EHR technology has a major impact on ICU physician work (e.g., increased time spent on clinical review and documentation) and workflow (e.g., clinical review and documentation becoming the focal point of many other tasks). Further studies should evaluate the impact of changes in physician work on the quality of care provided. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.
    International Journal of Medical Informatics 04/2015; 84(8). DOI:10.1016/j.ijmedinf.2015.04.002 · 2.72 Impact Factor
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    • "Given the limited understanding regarding patterns of handoff communication, we then used sequential analysis as an exploratory data analysis approach [46] to characterize the nature of temporal patterns of communicative interactions by computing the probability of transitions between the CEs. Researchers have used similar sequential analysis approaches to examine temporal co-occurring patterns of human interaction with tools and artifacts [47] [48] [49] [50] [51] [52] [53] (additional details can be found in Section 6 of Appendix A). "
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    ABSTRACT: Handoffs vary in their structure and content, raising concerns regarding standardization. We conducted a comparative evaluation of the nature and patterns of communication on 2 functionally similar but conceptually different handoff tools: Subjective, Objective, Assessment and Plan, based on a patient problem-based format, and Handoff Intervention Tool (HAND-IT), based on a body system-based format. A nonrandomized pre-post prospective intervention study supported by audio recordings and observations of 82 resident handoffs was conducted in a medical intensive care unit. Qualitative analysis was complemented with exploratory sequential pattern analysis techniques to capture the characteristics and types of communication events (CEs) and breakdowns. Use of HAND-IT led to fewer communication breakdowns (F1,80 = 45.66: P < .0001), greater number of CEs (t40 = 4.56; P < .001), with more ideal CEs than Subjective, Objective, Assessment and Plan (t40 = 9.27; P < .001). In addition, the use of HAND-IT was characterized by more request-response CE transitions. The HAND-IT's body system-based structure afforded physicians the ability to better organize and comprehend patient information and led to an interactive and streamlined communication, with limited external input. Our results also emphasize the importance of information organization using a medical knowledge hierarchical format for fostering effective communication.
    Journal of critical care 11/2013; 29(2). DOI:10.1016/j.jcrc.2013.11.014 · 2.19 Impact Factor
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    • "Timeline belt visualizations also display variations in data over time (Tufte, 1990). Others have used timeline belt visualizations to present clinicians' macrolevel work process steps before and after the implementation of health information technology (Zheng et al., 2010). We use timeline belt visualizations to show the order of behaviors and eye fixations nurses complete as they administer medications to patients. "
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    ABSTRACT: 10.1177/1555343412457683
    Journal of Cognitive Engineering and Decision Making 06/2013; 7(2):198-210. DOI:10.1177/1555343412457683
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