Aging in Place: Adapting the Environment



 


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Aging in Place: Adapting the Environment


Marilyn J. Rantz, Kari R. Lane, Lori L. Popejoy, Colleen Galambos, Lorraine J. Phillips, Lanis Hicks, Greg Alexander, Laurel Despins, Richelle Koopman, Marjorie Skubic, Mihail Popescu, and James Keller


It was clear to any young nurse, in the 1970s and 1980s, that the looming problem facing our society was the coming “explosion” of the aging population. Predictions in every data source about the U.S. population repeated the impending “crisis” in which increased numbers of older people would simply overload and “implode” the long-term care system. At the same time, health care advances were amazing; many people who previously would have been identified as having “terminal” diagnoses suddenly had a better prognosis both for survival and for a good quality of life for more years than had ever been experienced before.


The predictions were not lost on the federal Health Care Financing Administration (HCFA), now the Centers for Medicare and Medicaid Services (CMS). Concerned about the predictions, they launched several HCFA-sponsored large demonstration pilot projects to test potential approaches for modifying long-term care services in an effort to find potential solutions (Hughes, 1985; Kemper, 1990). These demonstrations preceded state and federal public policy decisions to encourage the development of home- and community-based services and the HCFA-sponsored demonstrations of community nursing organizations of the 1990s (Abt Associates Inc., 2000; Collins, Butler, Gueldner, & Palmer, 1997; Elkan et al., 2001).


Nurses were at the forefront, not only providing direct services to older adults in the community and long-term care settings, but also working to influence the development of new models of care, and encouraging older adults to maintain independence and function, and receive health promotion services where they lived. For instance, the Minnesota Block Nurse Program was started more than 30 years ago. The program combined public health and home care ideas and used the skills of nurses to keep people independent and manage chronic illness; that program is still active today (Living at Home Network of Block Nurse Programs, 2016; Martinson, Jamieson, O’Grady, & Sime, 1985; Metropolitan Council of the Twin Cities Area, 1990). Nurse-run clinics, which are often affiliated with schools of nursing and funded by small grants and demonstrations, have been operating in public housing, nursing homes, rehabilitation centers, and community sites since the early 1980s (Matherlee, 1999).


Local efforts in the 1980s attempted to work through county agencies in some states, not only to build affordable elder housing, but also to offer nursing services within the housing. Stated goals of these efforts were to help people stay healthier, and (it was hoped) avoid nursing home care. In the mid-1990s, the potential solutions to the projected explosion of aging people seemed to be directed at modifying traditional approaches to long-term care, with none of the real revolutionary change that the problem was demanding. Environments were clearly ripe for change—both actual living environments for people to age well and the public policies and political environment that influenced neighborhoods and communities. In this chapter, we present one such example of a program of research and university faculty effort to revolutionize traditional models of long-term care for older adults and influence public policy to disseminate a new model of care delivery promoting independence and function through the end of life.


THE AGING IN PLACE PROJECT AT THE SINCLAIR SCHOOL OF NURSING


In 1996, at the American Academy of Nursing (AAN) meeting, participants were challenged to create a new vision for care and services for the looming surge of elders in the United States. Several faculty and administrators from the school were attending and gathered informally just after the session. We began brainstorming about what we could do at our school of nursing to launch a visionary new approach for serving elders. We realized we had a group of faculty with strengths and expertise in gerontological nursing to provide the critical mass and skills that could launch such a new vision to better meet the needs of elders in the future. The question was, what would that new vision be?


We came home from the academy meeting with some preliminary ideas and convened a large group of interdisciplinary faculty from across the campus. With sage advice and creative ideas, we decided to “blow up” the traditional long-term care system, talk to elders and their families about what they wanted, and start anew (Marek & Rantz, 2000; Rantz et al., 2008). We conducted a series of focus groups, and the message from older people was consistent throughout the process. Seniors wanted to remain independent as long as possible and in the home of their choice, if at all possible, through the end of life. Families, on the other hand, wanted their mom (or dad or other relative) to be safe, above all else. Interesting verbal exchanges among participants ensued during the group discussions, and the seemingly conflicting values emerged during the sessions. Our interdisciplinary team was challenged and continued to meet to create a new vision—Aging in Place (AIP). This AIP vision continues now, 20 years later. Highlights include:


  A dramatic change to the way long-term care is provided in this country through a new approach to care and service delivery


  The foundation of care is RN care coordination/community case management supported by an interdisciplinary team of social workers, activity personnel, rehabilitation specialists, physicians, and aides managed by the RN care coordinator


  Providing seniors with the right services at the right time and early illness detection and intervention to maximize regaining or maintaining health and independence


  Provide research and education opportunities for students and faculty to support and train the next generation of health care workers


To achieve this AIP vision, two complementary parts were designed: an innovative home care agency (Sinclair Home Care) and an innovative independent living environment, TigerPlace, named after the University of Missouri (MU) mascot, the tiger. For the AIP Project to be fully realized in Missouri, legislation was required; the necessary legislation was successfully passed in 1999 and 2001 (Rantz et al., 2008). These two pieces of legislation made the AIP Project an official Missouri demonstration (without any state funds); this was our first public policy change. The next large policy change was to work with state regulators to be able to construct and operate TigerPlace under the new visionary approach, which did not match current regulatory requirements; this second change took 3 years. TigerPlace was envisioned to be an ideal AIP independent living environment where people could live through the end of life, with care and services from Sinclair Home Care brought to them in their private homes as needed. It opened and rapidly filled to capacity in 2004.


SINCLAIR HOME CARE


In 1999, prior to the development of TigerPlace and after 3 years of business planning on the AIP vision, the project team successfully applied for a CMS grant to test the concept of AIP for home- and community-based services delivered through a home health and home care agency. A new agency, Sinclair Home Care (initially named Senior Care), was started as a department within the Sinclair School of Nursing. The new agency specialized in services for older adults, was certified for both Medicare and Medicaid clients, and also served those people funded by private insurers and private pay.


With grant funding (1999–2003), we were able to develop a critical research foundation for the AIP work. Our goals were to test the effectiveness and cost-effectiveness of RN care coordination. The project to facilitate AIP in community living sites was funded by the grant, as these nursing services were not reimbursable at the time. Some decisions that were critical to success were:


  Use an electronic health record (EHR) for medical records and charting of services.


  Use a carefully selected set of standardized measures of health with known validity and reliability to use in the care of clients, and also use for assessment of outcomes and quality of care. These standardized measures enabled comparisons with other groups and state and national databases.


  Collect cost, staffing, and quality outcomes data to monitor the agency operations, as well as the AIP evaluation.


For 10 years, Sinclair Home Care provided Medicare and Medicaid home health services to the older residents in six counties in the mid-Missouri region using RN nursing care coordination, which enabled an excellent evaluation of that AIP service approach for older adults (Marek et al., 2005, 2010; Marek, Popejoy, Petroski, & Rantz, 2006; Marek, Stetzer, Adams, Popejoy, & Rantz, 2012). The team wanted to be able to potentially influence legislators and other public policy makers about the value of RN care coordination to not only older adults and their families, but also to pose possible solutions to the growing demand for traditional long-term care services and the rapid escalation of older adult health care costs. Importantly, we carefully examined outcomes of care and costs using Medicare and Medicaid files, recognizing that both were essential to influencing public policy.


For outcomes, the CMS evaluation (1999–2003) demonstrated that clients who received care from Sinclair Home Care with RN care coordination had improved clinical outcomes (cognition, depression, activities of daily living [ADLs], and incontinence) compared with individuals of similar case mix in nursing homes (Marek et al., 2005). Outcomes (pain, shortness of breath, and ADLs) were also significantly better for clients with RN care coordination than without RN care coordination in a community-based waiver program called Missouri Care Options (MCO; Marek et al., 2006). A later study comparing AIP to traditional home care found that even though AIP clients were significantly older, more likely to be on Medicaid, be cognitively impaired, and be depressed, they had significantly lower rates of decline in ADLs and instrumental activities of daily living (IADLs; Popejoy et al., 2015). Additionally, they experienced significantly fewer rehospitalizations and emergency department visits, but all-cause hospitalization rates were not different (Popejoy et al., 2015).


For costs to Medicare, monthly costs were significantly lower ($686) for MCO clients with RN care coordination compared to those without care coordination (Marek et al., 2010). Finally, total costs to Medicare and Medicaid were $1,592 lower per month in the AIP group than a nursing home comparison group over a 12-month period (Marek et al., 2012). The measurement of significant cost savings and better clinical outcomes has been essential to our efforts, the efforts of other nurses, and the efforts of organizations to change public policy for funding of RN care coordination services. Importantly, Medicare regulations were changed so that limited funding is now available for some of these services (AMA, 2013). This is evidence that yes, indeed, research and practice demonstrations with continuous evaluation and publication of measurable results can influence and change public policy.


TIGERPLACE, IDEAL HOUSING FOR AIP, STATE DEMONSTRATION


As explained previously, legislation was required and successfully passed in 1999 and 2001 with the assistance of a group of interdisciplinary stakeholders, including a state legislator, that grew from our original interdisciplinary group initiated in 1996 (Rantz et al., 2008; Rantz, Popejoy, Musterman, & Miller, 2014). The legislation enabled the construction of TigerPlace as the state’s first AIP site and officially recognized the Sinclair School of Nursing AIP Project as a Missouri demonstration. The legislation did not fund the evaluation or construction, as no funds were associated with its passage, but regulators were directed to work with the AIP site and develop an approach that would enable the construction, operation, and regulation of the demonstration.


Recognizing that additional capital would be needed to fund the project, the school, through a publicly announced Request for Proposals process, sought a partner for the initiative. Americare Systems, Inc., of Sikeston, Missouri, a well-respected long-term care owner/operator of numerous assisted living and nursing homes in several states in the Midwest, stepped forward. The plan was for the school to provide the RN care coordination, as well as health promotion and care services through Sinclair Home Care; Americare would build, own, and operate the building and services related to housing, dining, and hospitality. Key staff of Americare and faculty of the school worked on detailed plans for day-to-day operations, planned with regulators, created architectural plans, and developed detailed operational agreements between the entities. We created a private–public partnership dedicated to the success of TigerPlace as a business, incorporating the AIP research/state demonstration evaluation, and including educational experiences for students of all disciplines at the university.


The foundation for the care delivery system at TigerPlace is RN care coordination, and there is a commitment from all staff to maximize independence and function through the end of life. Just as in the community-based evaluation of AIP, funded by CMS (explained earlier), we made the same three decisions that were critical to success:


1.  The continued use of an EHR for all health records


2.  Collection of routine health assessment information on admission and every 6 months using standardized health measures (these assessments are used in everyday clinical decision making as well as for continued research about the effectiveness of AIP, and can be used in other related studies)


3.  Continuously collecting cost, quality, and staffing data for longitudinal evaluation of AIP for publication of results in order to potentially influence public policy


We also made an important fourth decision:


4.  To create a research infrastructure for ongoing development and testing of technology to help older people age well, remaining as independent as possible as long as possible.


Infrastructure for Interdisciplinary Research


All older persons who move into TigerPlace understand and agree that they are participating in a state evaluation of AIP and that their de-identified health information will be used in research. Faculty from the College of Engineering were solicited to join the planning team to design the building’s electronic networking infrastructure that would support technology research. The joint team helped plan for appropriate network cabling, a server room, and easy access to each apartment via an attic with walkway and lighting for future retrofitting as needed.


TigerPlace opened in 2004 with 31 well-designed apartments, each with screened porches and easy access via sunlit interior corridors and exterior exits via the screened porch. It was expanded in 2008 to a total of 54 apartments due to demand. Then, in 2011 as planned, Americare built an 85-bed Medicare skilled nursing facility, the Neighborhoods of TigerPlace, with five distinct “neighborhoods” in the facility, on property adjacent to TigerPlace. The rehabilitation focus of this facility has served the general community demand for these services; also, some residents of TigerPlace have used the services episodically for rehabilitation following joint replacement or other health events and then they return to their home at TigerPlace.


All residents who move into TigerPlace agree upon admission (via written informed consent) to actively participate in ongoing wellness activities and complete biannual health assessments. These assessments are used as clinical information by the health care staff and are also considered outcome measures to evaluate the effectiveness of the AIP Project. Assessment measures include: Minimum Data Set, Geriatric Depression Scale (GDS), Mini-Mental State Examination (MMSE), SF12® Health Survey, ADLs, and IADLs, and fall risk assessments. Other functional and health data collected include gait speed, Timed Up and Go (TUG), length of stay, hospitalizations, chronic illnesses, and medications. As in the earlier AIP CMS evaluation, we found that, in addition to these regular assessments, the RN care coordinators were able to make some early illness connections related to subtle changes in a resident’s condition (Marek et al., 2005, 2006; Rantz et al., 2005). It was our vision that in addition to testing a new model of care in the AIP Project, there could also be new technological discoveries for early illness detection that could enhance care coordination with ongoing assessments and observation.


Technology Research to Enhance AIP


In 2005, the Center for Eldercare and Rehabilitation Technology (CERT) in the College of Engineering at the MU installed the first version of an environmentally embedded sensor system to potentially measure functional status (Rantz et al., 2005, 2008). Early focus groups of older people revealed that they did not want to do anything with the technology, that they did not want to wear anything, and they wanted it to work seamlessly (Courtney, Demiris, Rantz, & Skubic, 2008; Demiris, Hensel, Skubic, & Rantz, 2008; Demiris, Parker-Oliver, Dickey, Skubic, & Rantz, 2008). Years of ongoing research and development have resulted in a sensor system that continuously (24×7) collects data; automated algorithms analyze the data and send care coordinators health alerts days and even weeks before health status changes are apparent to residents or care coordinators (Rantz et al., 2012, 2013; Rantz, Skubic et al., 2015; Skubic, Guevara, & Rantz, 2015).


Residents kept asking for reliable fall detection from a sensor that they did not have to wear, and the research team developed new methods for privacy-protecting depth image processing for not only fall detection (Figure 12.1), but also automated fall risk assessment (Stone & Skubic, 2013, 2014; Stone, Skubic, Rantz, Abbott, & Miller, 2015). Recent analyses revealed that using automated analysis of in-home gait speed can predict falls 3–4 weeks before they occur. In these analyses, the odds of a resident falling within 3 weeks after a cumulative in-home gait speed change of 2.54 cm/sec is 4.22 times the odds of a resident falling within 3 weeks after no change (Phillips et al., 2017). The sensor system can send fall risk alerts, just as it does health alerts, weeks before events, providing adequate time for older people, their family members, and care coordinators to pursue ways to intervene with fall prevention strategies to prevent a fall from occurring (Stone, Skubic, & Back, 2014).


Research results from the Eldertech team have added to the success of the AIP model at TigerPlace. Since 2005, sensor systems have included various sensors: passive infrared motion sensors; a bed sensor that captures pulse, respiration, and restlessness; and gait analysis/fall detection systems using vision, radar, acoustic arrays, and the Microsoft Kinect depth camera (Skubic et al., 2013; Skubic, Alexander, Popescu, Rantz, & Keller, 2009). Sensor systems are installed in the apartments of those residents who elect to participate in the technology research. The current version includes: (a) bed sensor (monitors heart rate, restlessness, and respirations; Rosales, Su, Skubic, & Ho, in press); (b) motion sensors to monitor activity in rooms (Wang, Skubic, & Zhu, 2012); and (c) Kinect depth images to monitor walking, gait parameters, report falls in real time, and automatically assess fall risk (Stone et al., 2015; Stone & Skubic, 2013). The sensors send health, increasing fall risk, and fall alerts in real time to direct care staff (Rantz, Skubic, et al., 2015). Figure 12.2 is an illustration of the health sensor system developed by the Eldertech team and used by the care coordinators at TigerPlace.



FIGURE 12.1     Images of a fall. A sequence of depth images showing a fall that was captured in a TigerPlace apartment.The depth image captures a distance measure for each pixel.The fall detection and gait analysis systems extract a 3D silhouette of a person moving about in the scene. These are colored in the depth images shown. Blue shows the detected ground plane. The grayscale readings show depth; darker gray is closer and lighter gray is farther away. The black regions are noise, often from glass or metal surfaces (see the color version of this image on the inside back cover).



FIGURE 12.2     In-home health sensor system.


Since 2010, health alerts have been automatically triggered by computer algorithms designed to detect changes in trends in each resident’s sensor data that may indicate a change in health status. The technology with health, fall risk, and fall alerts enhances care coordination effectiveness (Rantz, Skubic, et al., 2010, 2012). Recently, we compared length of stay in TigerPlace between residents (n = 52) with environmentally embedded alert-generating sensor systems and residents (n = 81) without such sensor systems, all of whom received care coordination services (Rantz, Lane, et al., 2015). Care coordinators receive health alerts for the group living with the sensor system, assess and intervene to determine if the health alert was clinically significant. Strikingly, the group living with sensors had an average length of stay of 4.3 years, whereas length of stay for the comparison group without sensors was 2.6 years. It appears that the embedded sensor technology increases length of stay even further at TigerPlace by another 1.7 years. That being said, the RN care coordination model at TigerPlace appears to have increased the average length of stay by about a year, even for those living without sensors, compared to the national median residential senior housing length of stay of 22 months (i.e., 1.8 years; Caffrey et al., 2012). Cost to residents was analyzed and compared to skilled nursing home costs, which demonstrated a projected cost savings of $29,920 per person (Rantz, Lane, et al., 2015).


Advancements in early illness recognition using environmentally embedded sensors are pushing the envelope to help not only older people living at TigerPlace, but also those individuals living in senior housing and long-term care settings, and at home. Fourteen research projects have been funded by the National Science Foundation, National Institutes of Health, the Agency for Healthcare Research and Quality, the U.S. Administration on Aging, the RAND/Hartford Foundation, and the Alzheimer’s Association, for a total over $10 million. The research infrastructure at TigerPlace, with two groups of residents, with and without the sensor system, living there through the end of life has facilitated research projects and enabled evaluation of the effectiveness of the technology in a real-world setting. Efforts are now under way to commercialize the sensor-based health alert system. This will allow deployment in other types of senior housing and target user groups for scaled-up studies, as well as making the technology available to a wide range of older adults who can benefit.


Policy makers are interested in the results of our technology research. Our research team is committed to dissemination of results and also has assistance from the MU News Bureau to prepare national press releases with new findings in major publications. Keeping the public informed about the work is important, so news articles and film coverage are actively planned. We maintain two websites about the work to share with other researchers, health care providers, students, and the public (www.eldertech.missouri.edu and www.agingmo.com).


In July 2015, Dr. Skubic, the engineering lead for the Eldertech Research team, testified to the federal Senate Committee on Aging about the advancements that could enable elders to remain at home as independent as possible and as long as possible. She was challenged to analyze the cost implications of using the technology to reduce the burden on long-term care services. This analysis led to the cost savings mentioned previously ($29,920 per person). In addition, an analysis was done using a reimbursement model, comparing the average annual cost of long-term care paid by Medicaid (in Missouri) to the cost of reimbursing expenses for the in-home sensor-based health alert system and the nursing care coordination. The cost savings was estimated at $87,000 per person. This finding was of interest to policy makers, and we are hopeful that they will seriously consider technology in the home as an alternative to facility care for elders in the future. Our team will continue working toward additional cost-effective technological advancements in the home, publicizing these results, informing policy makers, and influencing their decisions.


Results of the Missouri Demonstration of AIP at TigerPlace


The AIP results from the state demonstration have been consistent (Rantz, Phillips, et al., 2011; Rantz, Popejoy, Galambos, et al., 2014) and consistent with the Sinclair Home Care CMS evaluation (1999–2003) explained earlier (Marek et al., 2005, 2006, 2010, 2012). Using the health data routinely collected for all residents who have lived at TigerPlace, two 4-year evaluations have been conducted. The first 4 years (2005–2008) at TigerPlace (n = 66) revealed that the program was effective in restoring health and maintaining independence while being cost-effective (Rantz, Phillips, et al., 2011).


Similar results were found for the subsequent 4 years (2009–2012) of the program (n = 128) (Rantz, Popejoy, Galambos, et al., 2014). Positive health outcomes (fall risk, gait velocity, functional ambulation profile [FAP], handgrips, SF-12 PH, SF-12 MH, GDS), slightly negative ADLs, IADLs, and MMSE, and positive cost-effectiveness results were found. Combined care and housing costs for any resident who was receiving additional care services and qualified for nursing home care (n = 44) were about $20,000 less per year per person than nursing home care. As discussed previously in the section “Technology Research to Enhance AIP,” in a follow-up analysis of length of stay, residents living at TigerPlace without sensors had a length of stay of 2.6 years (Rantz, Lane, et al., 2015). This is nearly a year more than that of the national median of people living in residential senior housing (1.8 years), and may be attributable to the RN care coordination model at TigerPlace. These evaluation results are of interest to public policy makers in our state and other states, as they search for options for their state to address the growing demand for elder housing, assisted living, and other long-term care options.


Notice in the evaluations that outcome data as well as cost data are key elements. They must be planned and measured repeatedly to be able to attract and influence policy decisions. Using these data to publish findings is important; also, disseminating results so that the public knows the potential impact that could benefit them is essential. In 2008, the AAN recognized the AIP Project at the Sinclair School of Nursing as an Edge Runner. As explained on the AAN website,



Raise the Voice: Edge Runners are the practical innovators who have led the way in bringing new thinking and new methods to a wide range of health care challenges. Edge Runners have developed care models and interventions that demonstrate significant, sustained clinical and financial outcomes.

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Apr 21, 2018 | Posted by in NURSING | Comments Off on Aging in Place: Adapting the Environment
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