The Quality Improvement Program for Missouri's Long - Term Care Facilities (QIPMO) is committed to Missouri's Elderly.
The "Aging-in-place" model allows older adults to receive health care in their preferred place of living, eliminating the need for a more restricted living space, such as a nursing home.
TigerPlace is a specially designed elder housing project initiated by the MU Sinclair School of Nursing, working to provide elders a better quality of life.
AgingMO is a centralized online home for the University of Missouri’s Aging in Place (AIP) program and its related projects. Our unique AIP model allows older adults to receive health care in their preferred place of living. As their care needs increase, residents contract for more care in the same setting, eliminating the need for a move to a more restrictive living environment such as a nursing home. This project, which began in 1996, is a multidisciplinary project including MU’s School of Nursing, College of Electrical and Computer Engineering, School of Social Work, Department of Physical Therapy, Department of Management and Informatics, Biostatistics Group, and Department of Family and Community Medicine, along with outside consultants. We have developed this website to assist you by allowing complete and easy access to the many distinctive aspects of our groundbreaking research.
America’s 75 million aging adults soon will face decisions about where and how to live as they age. Current options for long-term care, including nursing homes and assisted-living facilities, are costly and require seniors to move from place to place. University of Missouri researchers have found that a new strategy for long-term care called Aging in Place (AIP) is less expensive and provides better health outcomes. The AIP model provides services and care to meet residents’ increasing needs to avoid relocation to higher levels of care. AIP includes continuous care management, a combination of personalized health services with nursing care coordination. Click here for an AIP overview.
THERE are nearly 1.5 million older adults residing in nursing homes (NH) across the United States. Reducing avoidable hospitalizations among vulnerable NH residents has become a national priority. Estimates suggest more than $14 billion of Medicare funding is spent annually on hospitalizations for this vulnerable population. The most common causes of avoidable hospitalizations are conditions of septicemia, pneumonia, congestive heart failure, and urinary tract infections. Experts identify that NHs have limited capacity for early illness detection and/or prevention to avoid the need for hospitalization. As such,
it is vital that NHs improve their capacity to identify and treat acute illness in their residents to avoid hospitalization. Evidence suggests that advanced practice registered nurses (APRNs) improve NH outcomes including reducing avoidable hospitalizations. Moreover, major health care cost savings have been measured when APRNs work in NHs. This improvement appears to be related to the APRNs expertise in clinical management of health conditions, early detection of illness, and problem solving with NH staff to provide the needed care to manage clinical conditions within the NH. Despite evidence that APRNs improve resident outcomes, little evidence exists to describe how APRNs actually integrate their advanced practice role into NHs to influence care delivery. This article reports on a series of 3 focus groups held with APRNs who were embedded full-time in 16 Missouri NHs. The purpose of the focus groups was to describe the integration of APRNs into the NH setting including their their perceived challenges and successes.
Vogelsmeier, A., Popejoy, L., Rantz, M., Flesner, M., Lueckenotte, A., & Alexander, G. (2015). Integrating advanced practice registered nurses into nursing homes: The Missouri Quality Initiative experience. Journal of Nursing Care Quality, 30(2), 93-98.
Objective This research identifies specific care coordination activities used by Aging In Place (AIP) nurse care coordinators and home healthcare nurses when coordinating care for older community dwelling adults and suggests a method to quantify care coordination.
Methods A care coordination ontology was built based on activities extracted from 11,038 notes labeled with the Omaha Case Management category. From the parsed narrative notes of every patient, we mapped the extracted activities to the ontology, from which we computed problem profiles and quantified care coordination for all patients.
Results We compared two groups of patients: Aging in Place who received enhanced care coordination (n=217) and Home Healthcare (HHC) who received traditional care (n=691) using 128,135 narratives notes. Patients were tracked from the time they were admitted to AIP or HHC until they were discharged. We found that patients in AIP received a higher dose of care coordination than HHC in most Omaha problems, with larger doses being given in AIP than in HHC in all four Omaha categories.
Conclusion "Communicate" and "manage" activities are widely used in care coordination. That confirmed the expert hypothesis that nurse care coordinators spent most of their time communicating about their patients and managing problems. Overall, nurses performed care coordination in both AIP and HHC, but the aggregated dose across Omaha problems and categories is larger in AIP.
Popejoy, L., Khalilia, M., Popescu, P., Galambos, C., Lyons, V., Rantz, M., Hicks, L., & Stetzer, F. (2015). Quantifying care coordination using natural language processing and domain-specific ontology. Journal of the American Medical Informatics Association, currently published online.*
We propose in this paper the use of Wavelet transform (WT) to detect human falls using a ceiling mounted Doppler range control radar. The radar senses any motions from falls as well as nonfalls due to the Doppler effect. The wavelet transform is very effective in distinguishing the falls from other activities, making it a promising technique for radar fall detection in non-obtrusive in-home elder care applications. The proposed radar fall detector consists of two stages. The prescreen stage uses the coefficients of wavelet decomposition at a given scale to identify the time locations in which fall activities may have occurred. The classification stage extracts the time-frequency content from the wavelet coefficients at many scales to form a feature vector for fall vs nonfall classification. The selection of different wavelet functions is examined to achieve better performance. Experimental results using the data from the laboratory and real in-home environments validate the promising and robust performance of the proposed detector.
Su, B.Y., Ho, K.C., Rantz, M., & Skubic, M. (2015). Doppler radar fall activity detection using the wavelet transform. IEEE Transactions on Biomedical Engineering, 62(3), 865-875.