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: 3.5). 07/2010; 17(4):454-61. DOI: 10.1136/jamia.2010.004440
Source: PubMed


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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Available from: Kai Zheng, Sep 06, 2015
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    • "In order to make informed changes to maximize resources and improve care, efficient data collection and analysis methods are required. Methodological challenges are the most significant barrier to delivering the promised benefits from workflow studies [2] [9]. Clinical workflow directly impacts patient safety and the quality of clinical care, yet existing methods to describe clinical workflow that examine the linkages between clinical workflow and patient outcomes are inefficient and limited in response to rapid clinical changes [2] [10]. "
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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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    • "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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