Time motion studies were first described in the early 20th century in industrial engineering, referring to a quantitative data collection method where an external observer captured detailed data on the duration and movements required to accomplish a specific task, coupled with an analysis focused on improving efficiency. Since then, they have been broadly adopted by biomedical researchers and have become a focus of attention due to the current interest in clinical workflow related factors. However, attempts to aggregate results from these studies have been difficult, resulting from a significant variability in the implementation and reporting of methods. While efforts have been made to standardize the reporting of such data and findings, a lack of common understanding on what “time motion studies” are remains, which not only hinders reviews, but could also partially explain the methodological variability in the domain literature (duration of the observations, number of tasks, multitasking, training rigor and reliability assessments) caused by an attempt to cluster dissimilar sub-techniques. A crucial milestone towards the standardization and validation of time motion studies corresponds to a common understanding, accompanied by a proper recognition of the distinct techniques it encompasses. Towards this goal, we conducted a review of the literature aiming at identifying what is being referred to as “time motion studies”. We provide a detailed description of the distinct methods used in articles referenced or classified as “time motion studies”, and conclude that currently it is used not only to define the original technique, but also to describe a broad spectrum of studies whose only common factor is the capture and/or analysis of the duration of one or more events. To maintain alignment with the existing broad scope of the term, we propose a disambiguation approach by preserving the expanded conception, while recommending the use of a specific qualifier “continuous observation time motion studies” to refer to variations of the original method (the use of an external observer recording data continuously). In addition, we present a more granular naming for sub-techniques within continuous observation time motion studies, expecting to reduce the methodological variability within each sub-technique and facilitate future results aggregation.

No abstract

Background:Electronic health records (EHRs) have the potential to enhance patient-provider communication and improve patient outcomes. However, in order to impact patient care, clinical decision support (CDS) and communication tools targeting such needs must be integrated into clinical workflow and be flexible with regard to the changing health care landscape.Design:The Stroke Prevention in Healthcare Delivery Environments (SPHERE) team developed and implemented the SPHERE tool, an EHR-based CDS visualization, to enhance patient-provider communication around cardiovascular health (CVH) within an outpatient primary care setting of a large academic medical center.Implementation:We describe our successful CDS alert implementation strategy and report adoption rates. We also present results of a provider satisfaction survey showing that the SPHERE tool delivers appropriate content in a timely manner. Patient outcomes following implementation of the tool indicate one-year improvements in some CVH metrics, such as body mass index and diabetes.Discussion:Clinical decision-making and practices change rapidly and in parallel to simultaneous changes in the health care landscape and EHR usage. Based on these observations and our preliminary results, we have found that an integrated, extensible, and workflow-aware CDS tool is critical to enhancing patient-provider communications and influencing patient outcomes.

Clinical trials are essential for determining whether new interventions are effective. In order to determine the eligibility of patients to enroll into these trials, clinical trial coordinators often perform a manual review of clinical notes in the electronic health record of patients. This is a very time-consuming and exhausting task. Efforts in this process can be expedited if these coordinators are directed towards specific parts of the text that are relevant for eligibility determination. In this study, we describe the creation of a dataset that can be used to evaluate automated methods capable of identifying sentences in a note that are relevant for screening a patient's eligibility in clinical trials. Using this dataset, we also present results for four simple methods in natural language processing that can be used to automate this task. We found that this is a challenging task (maximum F-Score=26.25), but it is a promising direction for further research.