Comparison of Anticipatory Glancing and Risk Mitigation of Novice Drivers and Exemplary Drivers when Approaching Curves

Novice drivers are overrepresented in run-off-the-road crashes. Indeed, the previous literature demonstrates that novice drivers are less likely to anticipate hazards or maintain attention to the forward roadway and as a result fail to mitigate hazards by slowing. This research was an effort to compare the linked hazard anticipation and hazard mitigation behaviors of novice drivers with exemplary experienced drivers at curves, locations that are known to have a greater crash risk. Each driver navigated three drives in a driving simulator, one of which included a moderate curve left and one of which included a tightening curve right. Experienced drivers made more anticipatory glances and began slowing significantly earlier in the curves than did novice drivers. However, novice drivers who anticipated hazards were much more likely to also mitigate the hazard. The use of these results in a PC-based driver hazard mitigation training program will be discussed

The Use of Technology to Support Precision Health in Nursing Science

PurposeThis article outlines how current nursing research can utilize technology to advance symptom and self‐management science for precision health and provides a roadmap for the development and use of technologies designed for this purpose.ApproachAt the 2018 annual conference of the National Institute of Nursing Research (NINR) Research Centers, nursing and interdisciplinary scientists discussed the use of technology to support precision health in nursing research projects and programs of study. Key themes derived from the presentations and discussion were summarized to create a proposed roadmap for advancement of technologies to support health and well‐being.ConclusionsTechnology to support precision health must be centered on the user and designed to be desirable, feasible, and viable. The proposed roadmap is composed of five iterative steps for the development, testing, and implementation of technology‐based/enhanced self‐management interventions. These steps are (a) contextual inquiry, focused on the relationships among humans, and the tools and equipment used in day‐to‐day life; (b) value specification, translating end‐user values into end‐user requirements; (c) design, verifying that the technology/device can be created and developing the prototype(s); (d) operationalization, testing the intervention in a real‐world setting; and (e) summative evaluation, collecting and analyzing viability metrics, including process data, to evaluate whether the technology and the intervention have the desired effect.Clinical RelevanceInterventions using technology are increasingly popular in precision health. Use of a standard multistep process for the development and testing of technology is essential.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/151985/1/jnu12518.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/151985/2/jnu12518_am.pd

Overcoming challenges integrating patient-generated data into the clinical EHR: lessons from the CONtrolling Disease Using Inexpensive IT--Hypertension in Diabetes (CONDUIT-HID) Project

INTRODUCTION: The CONDUIT-HID intervention integrates patients\u27 electronic blood pressure measurements directly into the clinical EHR using Microsoft HealthVault as an intermediary data store. The goal of this paper is to describe generalizable categories of patient and technical challenges encountered in the development and implementation of this inexpensive, commercial off-the-shelf consumer health informatics intervention, examples of challenges within each category, and how the example challenges were resolved prior to conducting an RCT of the intervention. METHODS: The research team logged all challenges and mediation strategies during the technical development of the intervention, conducted home visits to observe patients using the intervention, and conducted telephone calls with patients to understand challenges they encountered. We then used these data to iteratively refine the intervention. RESULTS: The research team identified a variety of generalizable categories of challenges associated with patients uploading data from their homes, patients uploading data from clinics because they did not have or were not comfortable using home computers, and patients establishing the connection between HealthVault and the clinical EHR. Specific challenges within these categories arose because: (1) the research team had little control over the device and application design, (2) multiple vendors needed to coordinate their actions and design changes, (3) the intervention use cases were not anticipated by the device and application designers, (4) PHI accessed on clinic computers needed to be kept secure, (5) the research team wanted the data in the clinical EHR to be valid and reliable, (6) patients needed the ability to share only the data they wanted, and (7) the development of some EHR functionalities were new to the organization. While these challenges were varied and complex, the research team was able to successfully resolve each one prior to the start of the RCT. CONCLUSIONS: By identifying these generalizable categories of challenges, we aim to help others proactively search for and remedy potential challenges associated with their interventions, rather than reactively responding to problems as they arise. We posit that this approach will significantly increase the likelihood that these types of interventions will be successful

Just What the Doctor Ordered?: The Role of Cognitive Decision Support Systems in Clinical Decision-Making & Patient Safety

Human expertise is limited by both cognitive workload and the boundaries of attention. With the spread and integration of healthcare informatics, cognitive decision support (CDS) technologies have been suggested as a means for improving the effectiveness and efficiency of healthcare. The current panel brings together leading human factors and medical experts in the fields of decision-making, design, and human-system interaction to provide their insight and perspective on the following question: What contributions can human factors science bring to bear on (1) the design, (2) integration, and (3) training necessary for effective CDS implementation