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Objective This paper investigates driver engagement with vehicle automation and the transition to manual control in the context of a phenomenon that we have termed vicarious steering—drivers steering when the vehicle is under automated control. Background Automated vehicles introduce many challenges, including disengagement from the driving task and out-of-the-loop performance decrement. We examine drivers' steering behavior when the automation is engaged, and steering input has no effect on the vehicle state. Such vicarious steering is a potential indicator of engagement for evaluating automated vehicles. Method A total of 32 female and 32 male drivers between 25 and 55 years of age participated in this experiment. A 2 × 2 between-subject design combined control algorithms and instructed responsibility. The control algorithms (lane centering and adaptive) were intended to convey the capability of the automation. The adaptive algorithm drifted across the lane center when latent hazards were present. The instructed levels of responsibility (driver primarily responsible and automation primarily responsible) were intended to replicate the admonitions of owners' manuals. Results The adaptive algorithm increased vicarious steering ( p < .001), but instructed responsibility did not ( p = .67), and there was no interaction between the algorithm and the responsibility ( p = .75). Vicarious steering was associated with an increase in transitions to manual control and glances to the road but was negatively associated with driving performance immediately after the transition to manual control. Conclusion Vicarious steering is a promising indicator of driver engagement when the vehicle is under automated control and automation algorithms can promote engagement.

The purpose of this review is to consolidate existing evidence from published systematic reviews on health information system (HIS) evaluation studies to inform HIS practice and research. Fifty reviews published during 1994–2008 were selected for meta-level synthesis. These reviews covered five areas: medication management, preventive care, health conditions, data quality, and care process/outcome. After reconciliation for duplicates, 1276 HIS studies were arrived at as the non-overlapping corpus. On the basis of a subset of 287 controlled HIS studies, there is some evidence for improved quality of care, but in varying degrees across topic areas. For instance, 31/43 (72%) controlled HIS studies had positive results using preventive care reminders, mostly through guideline adherence such as immunization and health screening. Key factors that influence HIS success included having in-house systems, developers as users, integrated decision support and benchmark practices, and addressing such contextual issues as provider knowledge and perception, incentives, and legislation/policy.

IntroductionFrailty is a complex condition that affects many aspects of a patients' wellbeing and health outcomes.
ObjectivesWe used available Electronic Medical Record (EMR) and administrative data to determine definitionsof frailty. We also examined whether there were differences in demographics or health conditionsamong those identified as frail in either the EMR or administrative data.
MethodsEMR and administrative data were linked in British Columbia (BC) and Manitoba (MB) to identifythose aged 65 years and older who were frail. The EMR data were obtained from the CanadianPrimary Care Sentinel Surveillance Network (CPCSSN) and the administrative data (e.g. billing,hospitalizations) was obtained from Population Data BC and the Manitoba Population ResearchData Repository. Sociodemographic characteristics, risk factors, prescribed medications, use andcosts of healthcare are described for those identified as frail.
ResultsSociodemographic and utilization differences were found among those identified as frail from theEMR compared to those in the administrative data. Among those who were >65 years, who hada record in both EMR and administrative data, 5%-8% (n=191 of 3,553, BC; n=2,396 of 29,382,MB) were identified as frail. There was a higher likelihood of being frail with increasing age andbeing a woman. In BC and MB, those identified as frail in both data sources have approximatelytwice the number of contacts with primary care (n=20 vs. n=10) and more days in hospital (n=7.2vs. n=1.9 in BC; n=9.8 vs. n=2.8 in MB) compared to those who are not frail; 27% (BC) and 14%(MB) of those identified as frail in 2014 died in 2015.
ConclusionsIdentifying frailty using EMR data is particularly challenging because many functional deficits arenot routinely recorded in structured data fields. Our results suggest frailty can be captured along acontinuum using both EMR and administrative data.