Does an Eye Tracker Tell the Truth About Visualizations?: Findings While Investigating Visualizations for Decision Making

For information visualization researchers, eye tracking has been a useful tool to investigate research participants’ underlying cognitive processes by tracking their eye movements while they interact with visual techniques. We used an eye tracker to better understand why participants with a variant of a tabular visualization called ‘SimulSort’ outperformed ones with a conventional table and typical one-column sorting feature (i.e., Typical Sorting). The collected eye-tracking data certainly shed light on the detailed cognitive processes of the participants; SimulSort helped with decision-making tasks by promoting efficient browsing behavior and compensatory decision-making strategies. However, more interestingly, we also found unexpected eye-tracking patterns with Simul- Sort. We investigated the cause of the unexpected patterns through a crowdsourcing-based study (i.e., Experiment 2), which elicited an important limitation of the eye tracking method: incapability of capturing peripheral vision. This particular result would be a caveat for other visualization researchers who plan to use an eye tracker in their studies. In addition, the method to use a testing stimulus (i.e., influential column) in Experiment 2 to verify the existence of such limitations would be useful for researchers who would like to verify their eye tracking results

Home telemonitoring effects on frailty transitions, hospitalizations and emergency department visits, and cost among older adults: Evaluation of a clinical trial

The U.S. Census Bureau predicted that 1 in 5 people in the U.S. would be over 65 by 2030, making older adults the fastest growing segment of the population by number ( U.S. Census Bureau National Population Projections, 2012). Aging comes with increased incidence of chronic diseases, such as cardiovascular disease, chronic obstructive pulmonary disease, and diabetes. This is a major reason healthcare expenditures are distorted. In 2002, the top 5% of patients were responsible for 49% of the healthcare expenses in U.S. population and 43% of the top 5% spenders were from people 65 and over. Additionally, the average healthcare expense for elderly people in the U.S. was $11,089 per year, while for younger adults (ages 19-64), the annual average was$3,352 (Stanton, 2006). Therefore, it is crucial that we find a suitable care model for this population and determine who benefits most from this intervention to ensure quality care is provided at a good value. Telemonitoring has emerged as a potential solution to efficiently and effectively manage care of older adults through the use of audio, video, and other telecommunication technology to monitor patient status at a distance. There have been numerous publications on telemonitoring that focused on individual benefits as well as system-wide benefits, such as effects on costs and use of health services. However, the evaluations of older adults with multiple chronic diseases are understudied. This research is based on data from a randomized controlled trial conducted at Mayo Clinic called the Tele-ERA trial. The primary aim of the clinical trial was to determine the effectiveness of home telemonitoring compared with usual care in reducing the combined outcomes of hospitalization and emergency department (ED) visits in an at-risk population 60 years of age or older with multiple medical conditions. First, we evaluated the effect of home telemonitoring in reducing the decline to a worsened frailty state and death since frailty is highly prevalent in older adults and confers a high risk for falls, disability, hospitalization, and mortality. The evidence did not indicate a difference between telemonitoring and usual care group. Second, we investigated how elderly participants who were telemonitored compared with those receiving usual care in the rate at which inpatient hospital and emergency department visit incidence changed over time. We also estimated how other personal characteristics impacted the rate of change. The evidence showed that an average telemonitoring participant did not significantly differ from usual care participant on the combined hospital and ED visit rate, but the intervention reduced the incident rate for ED visits and increased the incident rate for inpatient hospital visits. Key personal characteristics that lowered the rate of combined hospital and ED visits were being male, married, frail at baseline, living alone, and/or a having higher than Elder Risk Assessment (ERA) index of 15. Among those with a higher than average ERA Index score, telemonitoring is associated with a higher rate of combined visits. Third, we analyzed the cost consequence for participants in telemonitoring and usual care groups by examining the total cost of care as well as inpatient, outpatient, and ED costs. The result indicated that the estimated mean total cost difference between the two groups did not differ even though the mean estimated inpatient and outpatient costs were lower and ED costs were higher for telemonitoring group compared to usual care

Mortality Associated With Emergency Department Boarding Exposure: Are There Differences Between Patients Admitted to ICU and Non-ICU Settings

BACKGROUND: Emergency Department (ED) boarding threatens patient safety. It is unclear whether boarding differentially affects patients admitted to intensive care units (ICUs) versus non-ICU settings. RESEARCH DESIGN AND SUBJECTS: We performed a 2-hospital, 18-month, cross-sectional, observational, descriptive study of adult patients admitted from the ED. We used Kaplan-Meier estimation and Cox Proportional Hazards regression to describe differences in boarding time among patients who died during hospitalization versus those who survived, controlling for covariates that could affect mortality risk or boarding exposure, and separately evaluating patients admitted to ICUs versus non-ICU settings. MEASURES: We extracted age, race, sex, time variables, admission unit, hospital disposition, and Elixhauser comorbidity measures and calculated boarding time for each admitted patient. RESULTS: Among 39,781 admissions from the EDs (21.3% to ICUs), non-ICU patients who died in-hospital had a 1.2-fold risk (95% confidence interval, 1.03-1.36; P=0.016) of having experienced longer boarding times than survivors, accounting for covariates. We did not observe a difference among patients admitted to ICUs. CONCLUSIONS: Among non-ICU patients, those who died during hospitalization were more likely to have had incrementally longer boarding exposure than those who survived. This difference was not observed for ICU patients. Boarding risk mitigation strategies focused on ICU patients may have accounted for this difference, but we caution against interpreting that boarding can be safe. Segmentation by patients admitted to ICU versus non-ICU settings in boarding research may be valuable in ensuring that the safety of both groups is considered in hospital flow and boarding care improvements