Older adults and others with chronic health conditions benefit most from early detection of health changes and early treatment to prevent hospitalization or nursing home placement. Since 2005, our interdisciplinary research team has rigorously tested a state of the art sensor monitoring system for older adults in TigerPlace, a unique aging-in-place facility near the University of Missouri (MU) campus. We have shown that unobtrusive, continuous monitoring of individuals with in-home sensors provides useful embedded health assessment. While in-home sensors hold enormous potential for identifying early changes in health, analyzing the resulting data is challenging for clinicians due to its variety, velocity and volume. We propose to develop a new knowledge generation framework based on linguistic health summaries as a tool to integrate and summarize vast amounts of in-home sensor data. We investigate the innovative use of linguistic summaries for effective communication, and as a natural way to address the big data issues that arise from large amounts of health related evidence coming from heterogeneous sensors. We will apply our framework for improving early illness recognition in nursing homes. We will evaluate our knowledge generation framework in TigerPlace and 14 other Americare Inc., Sikeston, Missouri nursing homes in Missouri where we have deployed sensor networks in the last two years.

Public Health Relevance

Older adults and others benefit most from early detection of health changes and early treatment to prevent hospitalization or nursing home placement. In-home sensors hold enormous potential for identifying early changes in health but analyzing the resulting data is challenging for clinicians due to its variety and volume. We propose to develop a methodology for linguistic summarization of the sensor data that will facilitate clinical decision making. We will apply our summarization methodology for early illness detection and validate it in 15 different nursing homes from within the state of Missouri where we deployed remote monitoring technology.

Agency
National Institute of Health (NIH)
Institute
National Library of Medicine (NLM)
Type
Research Project (R01)
Project #
1R01LM012221-01A1
Application #
9175867
Study Section
Biomedical Library and Informatics Review Committee (BLR)
Program Officer
Vanbiervliet, Alan
Project Start
2016-08-01
Project End
2019-07-31
Budget Start
2016-08-01
Budget End
2017-07-31
Support Year
1
Fiscal Year
2016
Total Cost
Indirect Cost
Name
University of Missouri-Columbia
Department
Miscellaneous
Type
Schools of Medicine
DUNS #
153890272
City
Columbia
State
MO
Country
United States
Zip Code
65211