OBJECTIVE: This report provides updated estimates on use of electronic medical records (EMRs) in US home health and hospice (HHH) agencies, describes utilization of EMR functionalities, and presents novel data on telemedicine and point of care documentation (PoCD) in this setting. DESIGN: Nationally representative, cross-sectional survey of US HHH agencies conducted in 2007. MEASUREMENTS: Data on agency characteristics, current use of EMR systems as well as use of telemedicine and PoCD were collected. RESULTS: In 2007, 43% of US HHH agencies reported use of an EMR system. Patient demographics (40%) and clinical notes (34%) were the most commonly used EMR functions among US HHH agencies. Only 20% of agencies with EMR systems had health information sharing functionality and about half of them used it. Telemedicine was used by 21% of all HHH agencies, with most (87%) of these offering home health services. Among home health agencies using telemedicine, greater than 90% used telephone monitoring and about two-thirds used non-video monitoring. Nearly 29% of HHH agencies reported using electronic PoCD systems, most often for Outcome and Assessment Information Set (OASIS) data capture (79%). Relative to for-profit HHH agencies, non-profit agencies used considerably more EMR (70% vs 28%, p<0.001) and PoCD (63% vs 9%, p<0.001). CONCLUSIONS: Between 2000 and 2007, there was a 33% increase in use of EMR among HHH agencies in the US. In 2007, use of EMR and PoCD technologies in non-profit agencies was significantly higher than for-profit ones. Finally, HHH agencies generally tended to use available EMR functionalities, including health information sharing.

OBJECTIVES: This study sought to define the extent of utilization of 12 types of electronic information system (EIS) function in U.S. nursing homes (NH), to relate EIS utilization to selected facility characteristics and to contrast these findings to previous estimates of EIS use in NH. DESIGN: This study used data from the National Nursing Home Survey (NNHS), a nationally representative, cross-sectional sample of U.S. NH. MEASUREMENTS: Data on current use of EIS in 12 functional areas, including administrative and resident care activities, were collected. Information was also collected on facility characteristics including ownership, bed size, and whether the facility was a member of a chain. RESULTS: Essentially all (99.6%) U.S. NH had >or=1 EIS, a figure that was driven by the nearly universal use of EIS for Minimum Data Set (MDS) reporting (96.4%) and for billing (95.4%). Nearly 43% of U.S. NH had EIS for medical records, including nurse's notes, physician notes, and MDS forms. EIS use ranged from a high of 79.6% for admission, transfer, and discharge to a low of 17.6% for daily care by certified nursing assistants (CNAs). Ownership, membership in a chain, and bed size were associated with use of selected EIS. Larger facilities and those that were part of a chain used more EIS than smaller standalone facilities. CONCLUSION: In 2004, NH use of EIS for functions other than MDS and billing was highly variable, but considerably higher than previous estimates.

Techniques such as ballistocardiography (BCG) that can provide noninvasive long-term physiological monitoring have gained interest due to a growing recognition of adverse effects from poor sleep and sleep disorders. The noninvasive analysis of physiological signals (NAPS) system is a BCG-based monitoring system developed to measure heart rate, breathing rate, and musculoskeletal movement that shows promise as a general sleep analysis tool. Overnight sleep studies were conducted on 40 healthy subjects during a clinical trial at the University of Virginia. The NAPS system's measures of heart rate and breathing rate were compared to ECG, pulse oximetry, and respiratory inductance plethysmography (RIP). The subjects were split into a training dataset and a validation dataset, maintaining similar demographics in each set. The NAPS system accurately detected heart rate, averaged over the prescribed 30-s epochs, to within less than 2.72 beats per minute of ECG, and accurately detected breathing rate, averaged over the same epochs, to within 2.10 breaths per minute of RIP bands used in polysomnography.

In this paper, we examine at-home activity rhythms and present a dozen of behavioral patterns obtained from an activity monitoring pilot study of 22 residents in an assisted living setting with four case studies. Established behavioral patterns have been captured using custom software based on a statistical predictive algorithm that models circadian activity rhythms (CARs) and their deviations. The CAR was statistically estimated based on the average amount of time a resident spent in each room within their assisted living apartment, and also on the activity level given by the average number of motion events per room. A validated in-home monitoring system (IMS) recorded the monitored resident's movement data and established the occupancy period and activity level for each room. Using these data, residents' circadian behaviors were extracted, deviations indicating anomalies were detected, and the latter were correlated to activity reports generated by the IMS as well as notes of the facility's professional caregivers on the monitored residents. The system could be used to detect deviations in activity patterns and to warn caregivers of such deviations, which could reflect changes in health status, thus providing caregivers with the opportunity to apply standard of care diagnostics and to intervene in a timely manner.

This paper describes a study designed to assess the acceptance and some psychosocial impacts of monitoring technology in assisted living. Monitoring systems were installed in 22 assisted living units to track the activities of daily living (ADLs) and key alert conditions of residents (15 of whom were nonmemory care residents). Activity reports and alert notifications were sent to professional caregivers who provided care to residents participating in the study. Diagnostic use of the monitoring data was assessed. Nonmemory care residents were surveyed and assessed using the Satisfaction With Life Scale (SWLS) instrument. Pre- and post-installation SWLS scores were compared. Older adult participants accepted monitoring. The results suggest that monitoring technologies could provide care coordination tools that are accepted by residents and may have a positive impact on their quality of life.

This paper presents a characterization study of the HOKUYO PBS-03JN Infrared range-finder and compares it to the characterization of the SICK LMS-200 laser range-finder for use in indoor 2-D mapping. Many parameters that could affect the performance of the sensor including warm-up time, divergence of the detection beam, usable detection range in the azimuth, target surface, color, and size properties, incidence angle at the target, and the mixed pixels problem have been studied. This characterization, quantification of errors, and 3-D confidence in the distance readings of the sensor is vital for practical applications. These characteristics are compared to the counterpart characteristics of the laser range-finder. The PBS-03JN is a cost effective alternative to laser range-finders in indoor environments. The sensor is attractive due to lower power consumption, and its lightweight.

With the rapid growth of the elderly population, there is a need to support the ability of elders to maintain an independent and healthy lifestyle in their homes rather than through more expensive and isolated care facilities. One approach to accomplish these objectives employs the concepts of ambient intelligence to remotely monitor an elder's activities and condition. The SmartHouse project uses a system of basic sensors to monitor a person's in-home activity; a prototype of the system is being tested within a subject's home. We examined whether the system could be used to detect behavioral patterns and report the results in this paper. Mixture models were used to develop a probabilistic model of behavioral patterns. The results of the mixture-model analysis were then evaluated by using a log of events kept by the occupant.

This paper presents a physics-based model approach to infer navigational intent of the user of a walker, based on measuring forces and moments applied to the walker's handles. Our experiments use two 6-DOF force/moment sensors on the walker's handles, a 2-D kinematic-dynamic model of the walker and a digital motion capture system to trace the path of the walker. The motion capture system records the path the walker follows while the 6 DOF sensors record the handle forces used to guide the walker along that path. A dynamic model of the walker that determines user navigational intent from force/moment data was developed and validated against the motion capture data streams. This paper describes the development and validation of the model as well as plans for using the model as a path predictor. The inferred user intent is incorporated into a passive shared steering control system for the walker.

This paper presents a novel approach to infer navigational intent of the user of a walker, based on measuring forces and moments applied to the walker's handles. While there are many types of "intent" that could be inferred for a given user action, the experiments conducted here focused on the determining user's navigational intent, i.e. their desired heading. Our experiments used two 6-DOF force/moment sensors on the walker's handles and a digital motion capture system to correlate applied force with actual motion. Preliminary results revealed that the intent to turn, represented by changes in the heading angle, highly correlates with the overall turning moment around the vertical axis as well as the side forces applied by the user. Other force/moment components reveal additional information, such as support needs. The inferred user intent will be incorporated into a passive shared steering control system for the walker.