Impact of passive health status monitoring to care providers and payers in assisted living.

The objective of this study was to assess the impact of passive health status monitoring on the cost of care, as well as the efficiencies of professional caregivers in assisted living. We performed a case-controlled study to assess economic impact of passive health status monitoring technology in an assisted-living facility. Passive monitoring systems were installed in the assisted-living units of 21 residents to track physiological parameters (heart rate and breathing rate), the activities of daily living (ADLs), and key alert conditions. Professional caregivers were provided with access to the wellness status of the monitored residents they serve. The monitored individuals' cost of medical care was compared to that of an age, gender, and health status matched cohort. Similarly, efficiency and workloads of professional caregivers providing care to the monitored individuals were compared to those of caregivers providing care to the control cohort in the control site. Over the 3-month period of the study, a comparison between the monitored and control cohorts showed reductions in billable interventions (47 vs. 73, p = 0.040), hospital days (7 vs. 33, p = 0.004), and estimated cost of care (21,187.02 dollars vs. 67,753.88 dollars with monitoring cost included, p = 0.034). A comparison between efficiency normalized workloads of monitoring and control sites' caregivers revealed significant differences both at the beginning (0.6 vs. 1.38, p = 0.041) and the end (0.84 vs. 1.94, p = 0.002) of the study. The results demonstrate that monitoring technologies have significantly reduced billable interventions, hospital days, and cost of care to payers, and had a positive impact on professional caregivers' efficiency.

Improving the detection of stage I pressure ulcers by enhancing digital color images.

It has been observed in previous studies that the detection of stage I pressure ulcers becomes more difficult by unaided visual inspection and/or by using currently available techniques with darker skin subjects, due to increased melanin content. This difficulty is indicated by the elevated proportion of black and hispanic patients developing more serious stage III and IV pressure ulcers compared to white patients. The ultimate goal of this project, undertaken by MARC at the University of Virginia, is to develop a low-cost, non-contact imaging-based stage I pressure ulcer detection system for use by support staff in assisted living and skilled nursing facilities to increase the ulcer detection rate over a wide range of skin colors. This paper describes an image enhancement procedure that improves the detection of pressure ulcers when applied to the color images of ulcer sites. Preliminary results clearly indicate that the enhanced images exhibit higher contrast and make the pressure ulcer site more conspicuous to the examiner. The experiments show promising results even for subjects with black and dark brown skin colors.

A non-contact imaging-based approach to detecting stage I pressure ulcers.

This paper describes a non-contact imaging-based method to detect stage I pressure ulcers over a wide range of melanin levels. Two approaches were explored: the first used broad and narrow band visible spectrum imaging, and the second used near infrared (NIR) imaging. Preliminary results are presented together with results of numerical analysis of different erythema indices derived from the visible spectrum images. The results have shown that a low-cost imaging-based approach to detecting pressure ulcers is feasible and can yield promising results when applied to subjects with darker skin pigmentation.

Validation of rule-based inference of selected independent activities of daily living.

This paper explores the validity of a rule-based inference method of selected independent activities of daily living (ADLs). An inexpensive ADL monitoring system was installed in the community for 37 days to monitor a middle-aged, healthy individual living alone. The subject was given a personal digital assistant (PDA), running custom activity diary software, and asked to record activities in real-time. Rule-based activity inference algorithms were refined on data from 17 days, and data from the remaining 20 days were used for validation. The chisquare statistic was computed for 2 x 2 contingency tables comparing activities detected by the algorithms to user-logged activities. The phi (r()) and Cohen's kappa (kappa) coefficients were computed as measures of correlation. After correcting for subject noncompliance in logging activities, the kappa correlation between the meal detection algorithm and the PDA record was 0.84, with 91% sensitivity, and 100% specificity. Similarly, the kappa correlation between the shower detection algorithm and the PDA record is 0.69, with 67% sensitivity and 100% specificity. The detection algorithms and the sensory data did not miss any main meals or showering activities recorded on the PDA. The results suggest that rule-based algorithms can successfully detect meal preparation and showering activities using simple low-cost detectors. The sensors and detection algorithms reported events not recorded by the occupant on the PDA attributed to reporting noncompliance. Overall, the PDA activity journal was a compromise between paper diaries, which are more time consuming to keep, and may result in higher noncompliance errors, and video recording, which is considered intrusive.