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Rapid ethnography and data mining approaches have been used individually to study clinical workflows, but have seldom been used together to overcome the limitations inherent in either type of method. For rapid ethnography, how reliable are the findings drawn from small samples? For data mining, how accurate are the discoveries drawn from automatic analysis of big data, when compared with observable data? This paper explores the combined use of rapid ethnography and process mining, aka ethno-mining, to study and compare metrics of a typical clinical documentation task, vital signs charting. The task was performed with different electronic health records (EHRs) used in three different hospital sites. The individual methods revealed substantial discrepancies in task duration between sites. Specifically, means of 159.6(78.55), 38.2(34.9), and 431.3(283.04) seconds were captured with rapid ethnography. When process mining was used, means of 518.6(3,808), 345.5(660.6), and 119.74(210.3) seconds were found. When ethno-mining was applied instead, outliers could be identified, explained and removed. Without outliers, mean task duration was similar between sites (78.1(66.7), 72.5(78.5), and 71.7(75) seconds). Results from this work suggest that integrating rapid ethnography and data mining into a single process may provide more meaningful results than a siloed approach when studying of workflow.

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Clinician task performance is significantly impacted by the navigational efficiency of the system interface. Here we propose and evaluate a navigational complexity framework useful for examining differences in electronic health record (EHR) interface systems and their impact on task performance. The methodological approach includes 1) expert-based methods-specifically, representational analysis (focused on interface elements), keystroke level modeling (KLM), and cognitive walkthrough; and 2) quantitative analysis of interactive behaviors based on video-captured observations. Medication administration record (MAR) tasks completed by nurses during preoperative (PreOp) patient assessment were studied across three Mayo Clinic regional campuses and three different EHR systems. By analyzing the steps executed within the interfaces involved to complete the MAR tasks, we characterized complexities in EHR navigation. These complexities were reflected in time spent on task, click counts, and screen transitions, and were found to potentially influence nurses' performance. Two of the EHR systems, employing a single screen format, required less time to complete (mean 101.5, range 106-97 s), respectively, compared to one system employing multiple screens (176 s, 73% increase). These complexities surfaced through trade-offs in cognitive processes that could potentially influence nurses' performance. Factors such as perceptual-motor activity, visual search, and memory load impacted navigational complexity. An implication of this work is that small tractable changes in interface design can substantially improve EHR navigation, overall usability, and workflow.

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Preoperative care is a critical, yet complex, time-sensitive process. Optimization of workflow is challenging for many reasons, including a lack of standard workflow analysis methods. We sought to comprehensively characterize electronic health record-mediated preoperative nursing workflow. We employed a structured methodological framework to investigate and explain variations in the workflow. Video recording software captured 10 preoperative cases at Arizona and Florida regional referral centers. We compared the distribution of work for electronic health record tasks and off-screen tasks through quantitative analysis. Suboptimal patterns and reasons for variation were explored through qualitative analysis. Although both settings used the same electronic health record system, electronic health record tasks and off-screen tasks time distribution and patterns were notably different across two sites. Arizona nurses spent a longer time completing preoperative assessment. Electronic health record tasks occupied a higher proportion of time in Arizona, while off-screen tasks occupied a higher proportion in Florida. The contextual analysis helped to identify the variation associated with the documentation workload, preparation of the patient, and regional differences. These findings should seed hypotheses for future optimization efforts and research supporting standardization and harmonization of workflow across settings, post-electronic health record conversion.

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The impact of EHRs conversion on clinicians' daily work is crucial to evaluate the success of the intervention for Hospitals and to yield valuable insights into quality improvement. To assess the impact of different EHR systems on the preoperative nursing workflow, we used a structured framework combining quantitative time and motion study and qualitative cognitive analysis to characterize, visualize and explain the differences before and after an EHR conversion. The results showed that the EHR conversion brought a significant decrease in the patient case time and a reduced percentage of time using EHR. PreOp nurses spent a higher proportion of time caring for the patient, while the important tasks were completed in a more continuous pattern after the EHR conversion. The workflow variance was due to different nurse's cognitive process and the task time change was reduced because of some new interface features in the new EHR systems.

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We studied the medication reconciliation (MedRec) task through analysis of computer logs and ethnographic data. Time spent by healthcare providers performing MedRec was compared between two different EHR systems used at four different regional perioperative settings. Only one of the EHRs used at two settings generated computer logs that supported automatic discovery of the MedRec task. At those two settings, 53 providers generated 383 MedRec instances. Findings from the computer logs were validated with ethnographic data, leading to the identification and removal of 47 outliers. Without outliers, one of the settings had slightly smaller mean (SD) time in seconds 67.3 (40.2) compared with the other, 92.1 (25). The difference in time metrics was statistically significant (p<.001). Reusability of an existing task-based analytic method allowed for rapid study of EHR-based workflow and task.

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EHRs transform work practices in ways that enhance or impede the quality of care. There is a need for in-depth analysis of EHR workflows, particularly in complex clinical environments. We investigated EHR-basedpre-operative workflows by combining findings from 18 interviews, 7 days of observations, and process mining of EHR interactions from 31 personnel caring for 375 patients at one tertiary referral center. We provided high-definition descriptions of workflows and personnel roles. One third (32.2%) of the time with each patient was spent interacting with the EHR and 4.2% using paper-based artifacts. We also mined personnel social networks validating observed personnel's EHR-interactions. When comparing workflows between two similar pre-operative settings at different hospitals, we found significant differences in physical organization, patient workflow, roles, use of EHR, social networks and time efficiency. This study informs Mayo Clinic's enterprise-wide conversion to a single EHR and will guide before and after workflow comparisons.

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Vital sign documentation is an essential part of perioperative workflow. Health information technology can introduce complexity into all facets of documentation and burden clinicians with high cognitive load3-4. The Mayo Clinic enterprise is in the process of documenting current EHR-mediated workflow prior to a system-wide EHR conversion. We compared and evaluated three different vital sign documentation interfaces in pre-operative nursing assessments at three different Mayo Clinic sites. The interfaces differed in their modes of interaction, organization of patient information and cognitive support. Analyses revealed that accessing displays and the organization of interface elements are often unintuitive and inefficient, creating unnecessary complexities when interacting with the system. These differences surface through interface workflow models and interactive behavior measures for accessing, logging and reviewing patient information. Different designs differentially mediate task performance, which can ultimately mitigate errors for complex cognitive tasks, risking patient safety. Identifying barriers to interface usability and bottlenecks in EHR-mediated workflow can lead to system redesigns that minimize cognitive load while improving patient safety and efficiency.

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Information technologies have transformed healthcare delivery and promise to improve efficiency and quality of care. However, in-depth analysis of EHR-mediated workflows is challenging. Our goal was to apply process mining, in combination with observational techniques, to understand EHR-based workflows. We reviewed nearly 76,000 event logs from 15 providers and supporting staff, and 142 patients in a pre-operative setting and we inspected 3 weeks of interviews and video observations. We found that on average 44 minutes were spent per patient interacting with the EHR, 55% of the time of the patient visit was spent by personnel interacting with the EHR and for over 5% of the time personnel used or reviewed paper-based artifacts. We also discovered the handover-of-care network and compared frequency of interactions between personnel. This study suggests that applying process mining in combination with observational techniques has vast potential for informing Mayo Clinic in the forthcoming EHR conversion.

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There are numerous methods to study workflow. However, few produce the kinds of in-depth analyses needed to understand EHR-mediated workflow. Here we investigated variations in clinicians' EHR workflow by integrating quantitative analysis of patterns of users' EHR-interactions with in-depth qualitative analysis of user performance. We characterized 6 clinicians' patterns of information-gathering using a sequential process-mining approach. The analysis revealed 519 different screen transition patterns performed across 1569 patient cases. No one pattern was followed for more than 10% of patient cases, the 15 most frequent patterns accounted for over half ofpatient cases (53%), and 27% of cases exhibited unique patterns. By triangulating quantitative and qualitative analyses, we found that participants' EHR-interactive behavior was associated with their routine processes, patient case complexity, and EHR default settings. The proposed approach has significant potential to inform resource allocation for observation and training. In-depth observations helped us to explain variation across users.

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Failure to understand clinical workflow across electronic health record (EHR) tasks is a significant contributor to usability problems. In this paper, we employed sequential data analysis methods with the aim of characterizing patterns of 5 clinicians' information-gathering across 66 patients. Two analyses were conducted. The first one characterized the most common sequential patterns as reflected in the screen transitions. The second analysis was designed to mine and quantify the frequency of sequence occurrence. We observed 27 screen-transition patterns that were employed from 2 to 7 times. Documents/Images and Intake/Output screens were viewed for nearly all patients indicating the importance of these information sources. In some cases, they were viewed more than once which may show that users are following inefficient patterns in the information gathering process. New quantitative methods of analysis as applied to interaction data can yield critical insights in robust designs that better support clinical workflow.

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This research seeks to extend the process of novel therapeutic gene target discovery for the treatment of Alzheimer's disease (AD). Gene-gene and gene-pathway annotation tools as well as human analysis are used to explore likely connections between potential gene targets and biochemical mechanisms of AD and associated genes. Rule-based annotation systems, such as GeneRanker, can be applied to the continuously growing volume of literature to extract relevant gene lists. The subsequent challenge is to abstract biological significance from associated genes to aid in discovery of novel therapeutic gene targets. Automatic annotation of genes deemed significant by data-driven assays and knowledge-driven analysis is limited. Therefore, human analysis is still crucial to exploring novel gene targets and new disease models. This research illustrates a method of analysis of an extracted gene list which lead to the discovery of KNG1 as a possible therapeutic target, suggests a connection between inflammation and AD pathogenesis.