- Email: [email protected]
Information Technology to Support Aging
Chapter 12
Information Technology to Support Aging: Implications for Caregiving George Demiris School of Medicine & School of Nursing, University of Washington, Seattle, WA, USA
INTRODUCTION The rapidly growing segment of the population 65 years of age or older, along with the limited human and other resources available to provide high-quality healthcare services, have led to the exploration of innovative tools and services with the potential to support new models of aging (see Chapter 6). With the creation of mobile tools, pervasive and ubiquitous monitoring devices, and the proliferation of the Internet and information systems, new applications are being assessed to determine their empowering role in allowing older adults to lead meaningful lives in the community while preserving quality of life and independence. In addition to active monitoring (where an older adult, a caregiver, or both need to operate a device or application to facilitate monitoring), technology can support continuous passive monitoring of people (without requiring them to operate any software or hardware) in their homes to detect but also prevent emergencies and maximize their well-being, increase access to health information, facilitate archiving of health-related transactions and preferences, and improve communication between individuals and their clinicians and family members. Technology applications to support aging directly or indirectly affect not only older adults, but also their formal (i.e., paid) and informal caregivers. Informal caregivers (namely, spouses, family members, friends, or others who assume the unpaid caregiving role) are essential to the delivery of healthcare and support services to older adults. Caregiving activities include a broad range of tasks and responsibilities, such as providing assistance with personal care (such as dressing, bathing, feeding, and toileting), helping with household chores (such as cooking, shopping, laundry, and cleaning), providing transportation, assisting with finances, administering medications, providing companionship, and arranging for and coordinating help and paid services. Technology solutions can facilitate these tasks or provide more data to inform effective care Family Caregiving in the New Normal. DOI: http://dx.doi.org/10.1016/B978-0-12-417046-9.00012-X © 2015 2013 Elsevier Inc. All rights reserved.
211
212 SECTION | IV A New Vision for Supporting Caregivers in the Future
coordination or support the delivery of coping services to caregivers so they can more effectively engage in these activities. This chapter describes and discusses the role of a wide range of technologies in supporting aging and caregiving in the home, including telehomecare and smart-home systems, personal health records (PHRs), and robotic applications that are designed to monitor and support older adults at home and to facilitate the delivery of supportive services to their caregivers.
TELEHEALTH IN THE HOME With aging, people often try to cope with health-related issues such as falls, sensory impairment, diminished mobility, isolation, and in some cases the challenge of complex medication management and cognitive decline. Telehealth, defined as the use of video, text, and biometric devices to monitor and provide care at a distance, has been used since the early 1990s. Telehomecare (telehealth that takes place in the home) enables people to communicate with remote clinicians and transmit information about their well-being and health at home. Steventon et al. (2012) conducted a pragmatic, multisite cluster randomized trial comparing home telehealth with the usual care for patients with chronic conditions (diabetes, chronic obstructive pulmonary disease, or heart failure). The study involved 179 general practices in the United Kingdom, and 3230 patients were recruited. Telehomecare was associated with lower mortality and emergency admission rates. Takahashi et al. (2012) conducted a randomized controlled trial (RCT) among adults 60 years or older at high risk for rehospitalization in the United States. While older adults in the study embraced the concept of home telemonitoring and found it provided a sense of safety in their home, telemonitoring in this study did not result in fewer hospitalizations or emergency department visits, and surprisingly, mortality was higher in the telemonitoring group. Another RCT addressed whether coronary artery bypass graft surgery patients and their caregivers who received telehealth follow-up had greater improvements in anxiety levels, from presurgery to 3 weeks after discharge, than those who received standard care (Keeping-Burke et al., 2013). No group differences were noted in changes in patients’ anxiety and depressive symptoms, but caregivers in the telehealth group experienced a greater decrease in depressive symptoms than those in the standard care group. Finally, an RCT compared two remote telehealth monitoring intensity levels (low and high) and the usual care in patients with type 2 diabetes and hypertension (Wakefield et al., 2012). No significant differences were found across the groups in self-efficacy, adherence, or patient perceptions of the intervention mode. The study indicated that telehomecare can enhance the detection of key clinical symptoms that occur between regular clinical visits, but it called for further investigation of the mechanism of the effect of the telehomecare intervention.
Information Technology to Support Aging Chapter | 12 213
The studies described here targeted patients in the home. In a recent systematic review by Chi and Demiris (in press), examining the effect of telehealth interventions on family caregivers or the use of telehealth specifically to support family caregivers and address their own needs, 95% of the 65 studies identified reported significant improvements in caregiver outcomes, including psychological health (anxiety, depression stress, burden, and isolation), improved caregiving knowledge and skills, higher quality of life, increased social support, improved coping and problem-solving skills, communication with providers, and enhanced physical health and productivity. The articles included 52 experimental studies, 11 evaluation studies, 1 case study, and 1 secondary analysis. A total of 33 articles focused on family caregivers of adult and older patients, while 32 articles focused on parental caregivers of pediatric patients. The technologies included video, web-based, and telemetry/ remote-monitoring technologies. There were six main categories of intervention delivered via technology: education, consultation (including decision support aid), psychosocial/cognitive behavioral therapy (including problemsolving training), social support, data collection and monitoring, and clinical care delivery. In a study by Eisdorfer et al. (2003), caregivers who received family therapy and a technology-based intervention that facilitated communication between a therapist and family members who were separated by geographic distance experienced a significant reduction in depressive symptoms at 6 months compared to caregivers in the family therapy group (without the technology component) and the minimal support control group. Similarly, in a study by Czaja et al. (2013), a technology-based multicomponent psychosocial intervention was delivered in home and via videophone technology to Hispanic and African-American caregivers of patients with dementia. Caregivers who received the intervention reported a decrease in burden, an increase in perceived social support, and positive perceptions of the caregiving experience. In the telehomecare examples described here, older adults and their family members are asked to operate hardware, software, or both in order to facilitate remote monitoring and communication. This may be challenging for people who live alone and experience functional limitations, as well as those who may have limited or no previous experience with computers. Issues of training and device maintenance in this context may generate additional tasks for caregivers who are called to assist or operate the technology.
SMART HOMES A smart home often refers to a digitally augmented residence, or a residential setting with embedded technological features that enable passive monitoring of the daily activities of their residents, with the aim of detecting or even preventing emergencies and ultimately increasing the independence of the involved residents. The number of research projects and commercial initiatives exploring
214 SECTION | IV A New Vision for Supporting Caregivers in the Future
the concept of smart homes has been growing worldwide. The Center for Future Health at the University of Rochester in the United States has developed a Smart Medical Home as a highly controlled environment, including infrared sensors, biosensors, and video cameras (Marsh, 2002). The Aware Home at the Georgia Institute of Technology explored ubiquitous computing technologies that sense and identify potential crises, assist a senior adult’s memory, and track behavioral trends (Kidd et al., 1999). Researchers from five countries (the United Kingdom, Ireland, Finland, Lithuania, and Norway) joined their efforts for the ENABLE project (Cash, 2003), which promoted the well-being of people with early dementia with features such as a locator for lost objects, a temperature monitor, and an automatic bedroom light. In Toulouse, France, the PROSAFE project is utilizing a set of infrared motion sensors to support automatic recognition of resident activity and possible falls (Chan, Bocquet, Campo, Val, & Pous, 1999). As part of the Home-based Environmental and Assisted Living Technologies for Healthy Elders (HEALTH-E) initiative at the University of Washington, researchers have installed various sensor technologies in apartments of older adults who live in retirement communities in the Pacific Northwest (Reeder, Chung, Le, Thompson, & Demiris, 2014). The sensor technologies include regular motion sensors to detect how one moves from one room to another in the home or within a room, as well as infrastructure-mediated sensing (namely, a sensor that can detect electricity consumption by electricity source or water consumption by water source). These features allow the detection of activities such as meal preparation or bathroom visits with a level of detail (e.g., which faucet or electric device was used when and for how long) that simple motion sensors cannot provide. Additionally, the integration of door sensors can provide information on visitors and time spent outside the home. Complex data analysis techniques allow both the detection of activities and potential changes over time, calling for a timely intervention to prevent an adverse event (e.g., if data indicate a more sedentary behavior over time, an increase in bathroom visits at night or other sleep interruptions, less number of meals prepared, etc.) Most projects demonstrate the potential of smart homes with small feasibility studies or exploratory case studies. A 2007 Cochrane review (Martin, Kelly, Kernohan, McCreight, & Nugent, 2008) found no randomized clinical trial in this domain and an overall lack of empirical evidence to support or refute the use of smart-home technologies within health and social care. In more recent years, the number of smart-home studies continued to increase significantly. A 2013 study by Reeder et al. (2013) identified 34 studies, with 21 classified as emerging, 10 as promising, and 3 as effective using an evidence-based public health typology. Smart homes can address various healthcare needs and areas of monitoring. Research and commercially available smart-home solutions address a variety of areas. Table 12.1 summarizes various functionalities that smart-home applications can support and their implications for caregiving. The diffusion of smart homes and their adoption by the population ultimately depend on the extent to which older adults and their families accept and embrace this concept. In this context, it is important to address obtrusiveness, defined as
TABLE 12.1 Smart-Home Functionalities and Implications for Caregiving Functionality
Definition
Implications for Caregiving
Physiological monitoring
Collecting and processing physiological measurements such as vital signs of pulse, respiration, temperature, bladder and bowel output, etc.
Providing summary data sets describing physiological status to inform care coordination, preparation of meals, and symptom management
Functional monitoring
Collecting and processing functional measurements such as general activity level, motion, gait, meal intake, and other activities-of-daily-living
Informing potential interventions to reduce environmental elements that pose a fall risk, determining need for assistance with personal care and daily activities
Safety monitoring
Collecting and processing measurements that detect environmental hazards such as fire or gas leak. Safety assistance includes functions such as automatic turning on/off bathroom lights when getting out of bed, facilitating safety by reducing trips and falls. Location technologies including global-positioning system (GPS)–based tracking aimed at safety also fit into this type
Providing peace of mind for caregivers
Security monitoring and assistance
Enables measurements that detect human threats such as intruders. Assistance includes responses to identified threats
Provides peace of mind for caregivers and enables them to adjust their schedule if loved one needs to be briefly unattended
Social interaction monitoring and assistance
Collecting and processing of data pertaining to frequency of social interactions such as phone calls, visitors, and participation in activities. Social interaction assistance includes technologies that facilitate social interaction, such as videobased components that support video-mediated communication with friends and loved ones, virtual participation in group activities, etc.
Providing virtual presence for remote/distant caregivers, informing interventions to increase social interactions and facilitating the engagement of other family members and friends who can participate in group discussions and activities
Cognitive and sensory assistance
This feature supports automated or self-initiated reminders and other cognitive aids such as medication reminder and management tools, lost key locators, etc., for users with identified memory deficits. Cognitive assistance applications also include task instruction technologies, such as verbal instructions in using an appliance. Sensory assistance includes technologies that aid users with sensory deficits such as for sight, hearing, and touch
Assist caregiver with medication administration, coordination of tasks, and assistance with instrumental activities of daily living
216 SECTION | IV A New Vision for Supporting Caregivers in the Future
“a summary evaluation by the user based on characteristics or effects associated with the technology that are perceived as undesirable and physically and/or psychologically prominent” (Hensel, Demiris, & Courtney, 2006). Obtrusiveness assessments address the cumulative effect of a number of characteristics or attributes that may be important or prominent to a user. Obtrusiveness is an individualized subjective assessment; what one person perceives as obtrusive may not be perceived the same way by another. The user in this context is not only the older adult, but also all other residents in the home in general, and the family caregiver more specifically. People weigh their perceived need for such healthcare technology against potential privacy considerations (Courtney, Demiris, Rantz, & Skubic, 2008). As is the case with all emerging technologies that cause a paradigm shift in models of care, it is important to fully address ethical considerations associated with the concept of smart homes, and these extend beyond the concept of obtrusiveness. Moran (1993) noted the potential of advanced technology to change relationships between household members, as well as the role and function of the home and its relationship with the wider environment. Such passive monitoring technologies can remove choice and control from older adults and their caregivers as they learn to rely on automation. Smart homes could reduce social interaction or may provide tools that substitute for personal forms of care and communication (Tetley, Hanson, & Clarke, 2001). As we consider ways to implement smart-home systems, we need to address the warning by Wylde and Valins (1996) that we may be indeed creating “societies of high-tech hermits.” Smart homes may also lessen residents’ or their caregivers’ sense of personal responsibility. Family caregivers, for example, may become less vigilant in monitoring health changes in their loved one as they rely on an automated process. Stip and Rialle (2005) emphasize that the issues of individual freedom, personal autonomy, informed consent, and confidentiality have to be examined in the context of the target population. They note that surveillance technologies may exacerbate delusions of persecution and psychosensorial phenomena among persons with schizophrenia. Similarly, while smart-home systems may facilitate the monitoring and detection of emergencies among people with dementia, it may be difficult to assess residents’ true wishes in terms of wanting to be monitored if they cannot provide consent for such an intervention and their participation is determined by a loved one who acts on their behalf. Such challenges raise the question how smart homes may affect or alter the relationship between a patient/resident and the family caregiver or other members of their social network. Smart-home systems affect caregivers both in cases where they reside with an older adult and thus are also monitored in that setting, and in cases where they receive information from the smart-home system and are called to act upon it. Rialle, Ollivet, Guigui, and Hervé (2008) conducted a survey of 270 families of patients with Alzheimer’s disease or related disorders, located in the greater
Information Technology to Support Aging Chapter | 12 217
Paris area in France, to assess their perceptions and attitudes toward smarthome technologies. Interestingly, the authors identified two opposite clusters in the responses: family caregivers in favor of substantial use of technology and those “rather or totally hostile.” The two technologies that were most appreciated by caregivers were tracking devices that allow location of a wandering patient, and videoconferencing devices or tools that promote social connectedness and also allow family caregivers to interact with or supervise the patient when at a distance. As the authors of this study point out, their findings indicate that caregivers appreciate most smart-home technologies that increase patient safety and those that increase the caregiver’s social connectedness and ability to leave the home and still communicate with the patient. The study findings also serve as a reminder that it is important to assess caregiver needs and preferences and ensure their buy-in prior to implementing a smart-home system that would require their involvement and affect their daily lives.
PERSONAL HEALTH RECORDS Technology allows patients to store and manage their own health information as a PHR. The National Alliance for Health Information Technology (National Alliance for Health Information Technology, 2008) defines a PHR as “an individual’s electronic record of health-related information that conforms to nationally recognized interoperability standards and that can be drawn from multiple sources while being managed, shared, and controlled by the individual.” A PHR is basically a tool to use in “sharing health information, increasing health understanding, and helping transform patients into better-educated consumers of health care,” (Kahn, Aulakh, & Bosworth, 2009, p. 369). There have been several efforts from industry, government, and not-for-profit entities to explore the design and implementation of such PHR tools. The Veterans Health Administration introduced a PHR system called MyHealtheVet (Department of Veterans Affairs, 2012), which documents and manages appointments and medication requests. It also assists veterans with selecting and obtaining a variety of healthcare services. Epic, the electronic medical record (EMR) software vendor, has also introduced a PHR application that is currently used by Kaiser Permanente, the Cambridge Health Alliance, and other healthcare organizations. These systems are widely used by consumers because they offer important functionality that could lead to improved health (Mechanic, 2008). PHRs enable the sharing of information, such as health finances (e.g., billing and insurance paperwork), diagnoses or conditions, allergies, immunizations, and medications to assist patients with managing their own health (Hassol et al., 2004). In such systems, the patient (not any healthcare facility or provider) owns and controls/manages her data. PHR systems can be powerful tools for both patients and their caregivers, as the tools can be jointly managed and maintained by the patient-caregiver dyad, allowing caregivers to have a detailed record of transactions and preferences when they have to act as proxy decision
218 SECTION | IV A New Vision for Supporting Caregivers in the Future
makers for the patient. Furthermore, a PHR could support the documentation of the caregiver’s own concerns and fears that relate to patient care (such as concerns around pain medication administration or barriers to effective caregiving). There is a lack of documented efforts to utilize PHR systems specifically for older adults and their families, but web-based mobile tools can play a great role for older adults as they transition through multiple settings of care with many challenges and redundancies in the information that needs to be collected at every site and a lack of tools that solicit older adults’ own specific preferences and needs. Pillemer et al. (2012) conducted a study measuring the effects of electronic health information technology (IT) implementation on nursing home resident outcomes. While their study did not focus on a PHR system, the introduction of an EMR system in the nursing home led to overall residents’ positive reception of the health IT. This indicates that potentially residents themselves and caregivers could in the future engage in data entry and oversight of health information leading to a joint EMR-PHR system that could assist with care coordination.
BIG DATA New technologies such as wearable devices or sensors that facilitate ongoing monitoring of physiological, behavioral, or other information generate vast amounts of data over time. These large amounts of data, similar in size to ones found in computational biology, are often referred to as Big Data. The size and complexity of such data sets, often found in biomedicine, call for new tools to clean and process data as traditional data processing approaches are no longer sufficient. Big Data carry great potential to support significant discoveries in biomedicine and lead to better care, assuming that appropriate forecasting and data mining tools are applied. Participatory medicine, defined as “an approach of cooperative health care that actively and continuously involves patients and other stakeholders (i.e., healthcare providers and caregivers), across the continuum of care” (Hood & Auffray, 2013), aided by multisource data integration to support health management and clinical decision making, is anticipated to help save our healthcare system from unsustainability by transforming the way that caregivers, biological researchers, patients, and the population at large interact. Fueled by greater ubiquity of Internet access, the proliferation of social media, and increased government support for health IT, the amount of information related to health and health care has seen remarkable growth. Many online resources that foster social participation in health and health care have emerged. The next big challenges to enabling participatory medicine are the creation of PHRs that integrate open-source and personal health and healthcare information, as well as the use of integrated PHRs to develop models and analytics in support of health management and clinical decision making. Specifically in support of aging, a participatory approach to health care can be achieved through the collection, integration, analysis, and modeling of massive, heterogeneous health
Information Technology to Support Aging Chapter | 12 219
and healthcare data, including personal and open-source information, from multiple sources. The use of ensuing knowledge to monitor and forecast patients’ health and wellness can then support shared decision making for treatment. To achieve this goal, Big Data computing challenges need to be addressed, including gaining access to data; organizing, managing, and processing biomedical Big Data; and developing new methods and tools for analyzing and using biomedical Big Data. This calls for developing innovative methods, processes, and techniques in four areas: Harvesting health and healthcare data from heterogeneous sources (including health information systems in clinical sites, smart-home data, behavioral data from homes and communities, self-reporting, EMRs, and PHRs); ● Structuring and integrating heterogeneous health and healthcare data; ● Developing diagnostic and forecasting health and wellness models; and ● Developing a user application for shared clinical decision making that facilitates direct control and access to older adults, their family members or other stakeholders who they choose to have actively involved in the process. ●
Let us consider an example in the context of smart homes as described earlier. Such a system may perform continuous assessment of residents’ activities and mobility and extract models and metrics for providing feedback. Applying these scientific advances of processing Big Data to the challenge of mobility for older adults, such a system can synthesize and visualize information generated from the sensing system to support decision making for older adults and healthcare providers using context-driven messaging that includes cues, prompts, and alerts. Implementation of such a system can be scalable, ranging from personal tools for individual users to community-based applications that could assess mobility and well-being at a population level to inform the design of communitywide health promotion services and interventions. Such an approach would allow customized community-based interventions with clear societal implications based on trend detection. The information provided from smart-home sensors could allow facility administrators and public health providers to detect trends in overall community well-being and identify and implement systematic quality and safety improvement interventions. On the community level, aggregate data can also be used by public health or facility administrators to benchmark mobility between various institutions or communities and has the potential to be used as a population-level metric of health.
ROBOTIC APPLICATIONS Robots are programmable artificial intelligence machines that carry out complex tasks automatically and, in doing so, resemble human movements and functions. As technology advances, robotic applications have entered many areas of manufacturing, and more recently, health care and other aspects of daily life. Service robots include commercialized domestic machines, such as
220 SECTION | IV A New Vision for Supporting Caregivers in the Future
self-navigating vacuum cleaners and mops (Kuzma, 2006), but also robotic pets that apply pet therapy principles to assist older people to maintain mobility and stay active when having real pets is not an option (e.g., due to a residential policy or the inability of a resident to continuously care for a pet) (Carelli, Gaggioli, Pioggia, de Rossi, & Riva, 2009). Pearce, Adair, and Miller (2012) conducted a systematic review to identify the breadth of robotic applications to support older adults living at home and the evidence of their effectiveness. The majority of robotic studies in the home focus on lower-limb “exoskeleton” technologies. Robotic exoskeletons are fitted to the outside of the limbs. One of the most widely tested robotic exoskeletons for the lower limbs is the Lokomat, designed as a supportive structure for walking (Jezernik, Schärer, Colombo, & Morari, 2003). Other applications to assist with walking and mobility included robotic walkers and robotic guidance systems (Mehrholz, Werner, Kugler, & Pohl, 2007; Rentschler, Simpson, Cooper, & Boninger, 2008). In the same review, upper-limb technologies included both upper-limb exoskeleton systems to guide arm movements and haptic visuomotor feedback systems to assist in compensating for disorders of sensation and visual impairment. Such a visuomotor guidance system, the MIT-MANUS, was the most widely utilized and examined upper-limb robotic system, especially for patients recovering from stroke (Krebs et al., 2008). The review by Pearce et al. (2012) highlights that only four investigations tested robots within a home, residential-care setting, or simulated home environment. These studies demonstrated that robots have the potential to assist older people with mobility issues around the home environment. As the authors point out, however, the findings presented in those studies presented only low to moderate levels of evidence. While robotic applications carry promise for the future, research in this area is still in the very early stages. In the future, care givers may have the option to rely on service robotics, social robotics, or rehabilitation and physical performance support robotics to delegate some of their caregiving tasks (such as providing social support and assisting with personal care and daily activities). To date, research does not include any investigations with robotic systems in the healthcare context that specifically target caregivers or measure how such systems affect caregiving outcomes.
CONCLUSION Technological advances have the potential to support new models of empowering older adults and their caregivers. Computerized PHRs can facilitate the engagement of older adults in their own care and shared decision making. Big Data generated by continuous active and passive monitoring can be integrated into PHRs, allowing a more comprehensive assessment and documentation of one’s health and informing potential lifestyle changes that can prevent disease and improve management. The behavioral-sensing component that is enabled by smart-home technology can be integrated into personal records to provide
Information Technology to Support Aging Chapter | 12 221
a comprehensive view of one’s status; such aggregate information can then be shared with healthcare providers and informal caregivers to facilitate shared decision making. One such example would be for decisions about transitions of care; older adults and their families often have to make decisions about moving to a different setting with a higher level of institutionalized care based on subjective or incomplete information. Healthcare providers in this case may have only episodic, fragmented snapshots of overall wellness without information on the actual health-related trajectory. A PHR that integrates telehomecare or smarthome data could provide performance information that could facilitate decision making and improve communication between all the involved stakeholders. As technology advances, smart homes and other home-based solutions for older adults will strengthen invisibility, ubiquity, and adaptivity. As technologies integrate into architecture, furnishings, appliances, and clothing, they become effectively invisible to residents and visitors. Additionally, they are located in multiple rooms, making them ubiquitous in the home, and some support monitoring and data collection outside the home as well. Smart-home systems often include artificial intelligence features, allowing them to learn and adapt to the particular patterns of residents. At face value, such technological developments hold promise for caregivers who can use sophisticated tools to better monitor their loved ones and coordinate their caregiving tasks and responsibilities. However, relying on caregivers to operate software and other devices, or processing and maintaining health information data and making decisions pertaining to complex technologyrelated privacy and confidentiality implications, may be an added burden rather than an asset. Thus, it is important to expand our evidence base documenting the effectiveness of these new tools and systems beyond small feasibility and pilot studies. As technological innovations enable more sophisticated and tailored, home-based solutions, we need to ensure that the design and implementation of IT solutions are not determined simply by the latest technological trends, but by actual needs of older adults and their caregivers. We must address the technical, practical, ethical, and legal aspects of technology implementation if we are to maximize the potential of technology to support caregivers.
REFERENCES Carelli, L., Gaggioli, A., Pioggia, G., de Rossi, F., & Riva, G. (2009). Affective robot for elderly assistance. Studies in Health Technology and Informatics, 144, 44–49. Cash, M. (2003). Assistive technology and people with dementia. Reviews in Clinical Gerontology, 13(4), 313–319. Chan, M., Bocquet, H., Campo, E., Val, T., & Pous, J. (1999). Alarm communication network to help carers of the elderly for safety purposes: A survey of a project. International Journal of Rehabilitation Research, 22, 131–136. Chi, N., & Demiris, G. (2015). A systematic review of telehealth tools and interventions to support family caregivers. Journal of Telemedicine and Telecare, 21(1),37–44.
222 SECTION | IV A New Vision for Supporting Caregivers in the Future Courtney, K. L., Demiris, G., Rantz, M., & Skubic, M. (2008). Needing smart home technologies: The perspectives of older adults in continuing care retirement communities. Informatics in Primary Care, 16(3), 195–201. Czaja, S. J., Loewenstein, D., Schulz, R., Nair, S. N., & Perdomo, D. (2013). A videophone psychosocial intervention for dementia caregivers. American Journal of Geriatric Psychiatry, 21(11), 1071–1081. Department of Veterans Affairs. (2012). My healthevet.. Retrieved 11.06.14. Eisdorfer, C., Czaja, S. J., Loewenstein, D. A., Rubert, M. P., Arguelles, S., Mitrani, V. B., et al. (2003). The effect of a family therapy and technology-based intervention on caregiver depression. Gerontologist, 43(4), 521–531. Hassol, A., Walker, J. M., Kidder, D., Rokita, K., Young, D., Pierdon, S., et al. (2004). Patient experiences and attitudes about access to a patient electronic health care record and linked web messaging. Journal of the American Medical Informatics Association, 11(6), 505–513. Hensel, B. K., Demiris, G., & Courtney, K. L. (2006). Defining obtrusiveness in home telehealth technologies: A conceptual framework. Journal of the American Medical Informatics Association, 13(4), 428–431. Hood, L., & Auffray, C. (2013). Participatory medicine: A driving force for revolutionizing healthcare. Genome Medicine, 5(12), 110. Jezernik, S., Schärer, R., Colombo, G., & Morari, M. (2003). Adaptive robotic rehabilitation of locomotion: A clinical study in spinally injured individuals. Spinal Cord, 41(12), 657–666. Kahn, J. S., Aulakh, V., & Bosworth, A. (2009). What it takes: Characteristics of the ideal personal health record. Health Affairs (Millwood), 28(2), 369–376. Keeping-Burke, L., Purden, M., Frasure-Smith, N., Cossette, S., McCarthy, F., & Amsel, R. (2013). Bridging the transition from hospital to home: Effects of the VITAL telehealth program on recovery for CABG surgery patients and their caregivers. Research in Nursing & Health, 36(6), 540–553. Kidd, C.D., Orr, R., Abowd, G.D., Atkeson, C.G., Essa, I.A., MacIntyre, B., et al. (1999). The aware home: A living laboratory for ubiquitous computing research. Proceedings of CoBuild’99. Position paper. Krebs, H. I., Mernoff, S., Fasoli, S. E., Hughes, R., Stein, J., & Hogan, N. (2008). A comparison of functional and impairment-based robotic training in severe to moderate chronic stroke: A pilot study. NeuroRehabilitation, 23(1), 81–87. Kuzma, C. (2006). The human touch. Science and Spirit, 4, 15–17. Marsh, J. (2002). House calls. Rochester Review, 64(3), 22–26. Martin, S., Kelly, G., Kernohan, W. G., McCreight, B., & Nugent, C. (2008). Smart home technologies for health and social care support. Cochrane Database Systematic Review (4), CD006412. Mechanic, D. (2008). Rethinking medical professionalism: The role of information technology and practice innovations. Milbank Quarterly, 86(2), 327–358. Mehrholz, C., Werner, C., Kugler, J., & Pohl, M. (2007). Electromechanical-assisted training for walking after stroke. Cochrane Database of Systematic Reviews (4). Article ID CD006185. Moran, R. (1993). The electronic home: Social and spatial aspects. Dublin: European Foundation for the Improvement of Living and Working Conditions. National Alliance for Health Information Technology. (2008). Defining key health information technology terms. 28 April 2008. . Retrieved 03.06.14. Pearce, A. J., Adair, B., Miller, K., et al. (2012). Robotics to enable older adults to remain living at home. Journal of Aging Research, 2012, 10. Article ID 538169.
Information Technology to Support Aging Chapter | 12 223 Pillemer, K., Meador, R. H., Teresi, J. A., Chen, E. K., Henderson, C. R., Jr, et al., Lachs, M. S., et al. (2012). Effects of electronic health information technology implementation on nursing home resident outcomes. Journal of Aging Health, 24(1), 92–112. Reeder, B., Chung, J., Le, T., Thompson, H., & Demiris, G. (2014). Assessing older adults’ perceptions of sensor data and designing visual displays for ambient environments. An exploratory study. Methods of Information in Medicine, 53(3), 152–159. Reeder, B., Meyer, E., Lazar, A., Chaudhuri, S., Thompson, H. J., & Demiris, G. (2013). Framing the evidence for health smart homes and home-based consumer health technologies as a public health intervention for independent aging: A systematic review. International Journal of Medical Informatics, 82(7), 565–579. Rentschler, J., Simpson, R., Cooper, R. A., & Boninger, M. L. (2008). Clinical evaluation of Guido robotic walker. Journal of Rehabilitation Research and Development, 45(9), 1281–1294. Rialle, V., Ollivet, C., Guigui, C., & Hervé, C. (2008). What do family caregivers of Alzheimer’s disease patients desire in smart home technologies? Contrasted results of a wide survey. Methods of Information in Medicine, 47(1), 63–69. Steventon, A., Bardsley, M., Billings, J., Dixon, J., Doll, H., Hirani, S., et al. (2012). Whole System Demonstrator Evaluation Team. Effect of telehealth on use of secondarycare and mortality: Findings from the Whole System Demonstrator cluster randomised trial. British Medical Journal, 344, e3874. Stip, E., & Rialle, V. (2005). Environmental cognitive remediation in schizophrenia: Ethical implications of “Smart Home” technology. Canadian Journal of Psychiatry, 50(5), 281–291. Takahashi, P. Y., Pecina, J. L., Upatising, B., Chaudhry, R., Shah, N. D., Van Houten, H., et al. (2012). A randomized controlled trial of telemonitoring in older adults with multiple health issues to prevent hospitalizations and emergency department visits. Archives of Internal Medicine, 172(10), 773–779. Tetley, J., Hanson, E., & Clarke, A. (2001). Older people, telematics and care. In A. M. Warnes, L. Warren, & M. Nolan (Eds.), Care services for later life: Transformations and critiques (pp. 243–258). London: Jessica Kingsley Publications. Wakefield, B. J., Holman, J. E., Ray, A., Scherubel, M., Adams, M. R., Hills, S. L., et al. (2012). Outcomes of a home telehealth intervention for patients with diabetes and hypertension. Telemedicine Journal and E-Health, 18(8), 575–579. Wylde, M., & Valins, M. S. (1996). The impact of technology. In M. S. Valins & D. Salter (Eds.), Futurecare: New directions in planning health and care environments (pp. 15–24). Oxford: Blackwell Science.
Information Technology to Support Aging: Implications for Caregiving George Demiris School of Medicine & School of Nursing, University of Washington, Seattle, WA, USA
INTRODUCTION The rapidly growing segment of the population 65 years of age or older, along with the limited human and other resources available to provide high-quality healthcare services, have led to the exploration of innovative tools and services with the potential to support new models of aging (see Chapter 6). With the creation of mobile tools, pervasive and ubiquitous monitoring devices, and the proliferation of the Internet and information systems, new applications are being assessed to determine their empowering role in allowing older adults to lead meaningful lives in the community while preserving quality of life and independence. In addition to active monitoring (where an older adult, a caregiver, or both need to operate a device or application to facilitate monitoring), technology can support continuous passive monitoring of people (without requiring them to operate any software or hardware) in their homes to detect but also prevent emergencies and maximize their well-being, increase access to health information, facilitate archiving of health-related transactions and preferences, and improve communication between individuals and their clinicians and family members. Technology applications to support aging directly or indirectly affect not only older adults, but also their formal (i.e., paid) and informal caregivers. Informal caregivers (namely, spouses, family members, friends, or others who assume the unpaid caregiving role) are essential to the delivery of healthcare and support services to older adults. Caregiving activities include a broad range of tasks and responsibilities, such as providing assistance with personal care (such as dressing, bathing, feeding, and toileting), helping with household chores (such as cooking, shopping, laundry, and cleaning), providing transportation, assisting with finances, administering medications, providing companionship, and arranging for and coordinating help and paid services. Technology solutions can facilitate these tasks or provide more data to inform effective care Family Caregiving in the New Normal. DOI: http://dx.doi.org/10.1016/B978-0-12-417046-9.00012-X © 2015 2013 Elsevier Inc. All rights reserved.
211
212 SECTION | IV A New Vision for Supporting Caregivers in the Future
coordination or support the delivery of coping services to caregivers so they can more effectively engage in these activities. This chapter describes and discusses the role of a wide range of technologies in supporting aging and caregiving in the home, including telehomecare and smart-home systems, personal health records (PHRs), and robotic applications that are designed to monitor and support older adults at home and to facilitate the delivery of supportive services to their caregivers.
TELEHEALTH IN THE HOME With aging, people often try to cope with health-related issues such as falls, sensory impairment, diminished mobility, isolation, and in some cases the challenge of complex medication management and cognitive decline. Telehealth, defined as the use of video, text, and biometric devices to monitor and provide care at a distance, has been used since the early 1990s. Telehomecare (telehealth that takes place in the home) enables people to communicate with remote clinicians and transmit information about their well-being and health at home. Steventon et al. (2012) conducted a pragmatic, multisite cluster randomized trial comparing home telehealth with the usual care for patients with chronic conditions (diabetes, chronic obstructive pulmonary disease, or heart failure). The study involved 179 general practices in the United Kingdom, and 3230 patients were recruited. Telehomecare was associated with lower mortality and emergency admission rates. Takahashi et al. (2012) conducted a randomized controlled trial (RCT) among adults 60 years or older at high risk for rehospitalization in the United States. While older adults in the study embraced the concept of home telemonitoring and found it provided a sense of safety in their home, telemonitoring in this study did not result in fewer hospitalizations or emergency department visits, and surprisingly, mortality was higher in the telemonitoring group. Another RCT addressed whether coronary artery bypass graft surgery patients and their caregivers who received telehealth follow-up had greater improvements in anxiety levels, from presurgery to 3 weeks after discharge, than those who received standard care (Keeping-Burke et al., 2013). No group differences were noted in changes in patients’ anxiety and depressive symptoms, but caregivers in the telehealth group experienced a greater decrease in depressive symptoms than those in the standard care group. Finally, an RCT compared two remote telehealth monitoring intensity levels (low and high) and the usual care in patients with type 2 diabetes and hypertension (Wakefield et al., 2012). No significant differences were found across the groups in self-efficacy, adherence, or patient perceptions of the intervention mode. The study indicated that telehomecare can enhance the detection of key clinical symptoms that occur between regular clinical visits, but it called for further investigation of the mechanism of the effect of the telehomecare intervention.
Information Technology to Support Aging Chapter | 12 213
The studies described here targeted patients in the home. In a recent systematic review by Chi and Demiris (in press), examining the effect of telehealth interventions on family caregivers or the use of telehealth specifically to support family caregivers and address their own needs, 95% of the 65 studies identified reported significant improvements in caregiver outcomes, including psychological health (anxiety, depression stress, burden, and isolation), improved caregiving knowledge and skills, higher quality of life, increased social support, improved coping and problem-solving skills, communication with providers, and enhanced physical health and productivity. The articles included 52 experimental studies, 11 evaluation studies, 1 case study, and 1 secondary analysis. A total of 33 articles focused on family caregivers of adult and older patients, while 32 articles focused on parental caregivers of pediatric patients. The technologies included video, web-based, and telemetry/ remote-monitoring technologies. There were six main categories of intervention delivered via technology: education, consultation (including decision support aid), psychosocial/cognitive behavioral therapy (including problemsolving training), social support, data collection and monitoring, and clinical care delivery. In a study by Eisdorfer et al. (2003), caregivers who received family therapy and a technology-based intervention that facilitated communication between a therapist and family members who were separated by geographic distance experienced a significant reduction in depressive symptoms at 6 months compared to caregivers in the family therapy group (without the technology component) and the minimal support control group. Similarly, in a study by Czaja et al. (2013), a technology-based multicomponent psychosocial intervention was delivered in home and via videophone technology to Hispanic and African-American caregivers of patients with dementia. Caregivers who received the intervention reported a decrease in burden, an increase in perceived social support, and positive perceptions of the caregiving experience. In the telehomecare examples described here, older adults and their family members are asked to operate hardware, software, or both in order to facilitate remote monitoring and communication. This may be challenging for people who live alone and experience functional limitations, as well as those who may have limited or no previous experience with computers. Issues of training and device maintenance in this context may generate additional tasks for caregivers who are called to assist or operate the technology.
SMART HOMES A smart home often refers to a digitally augmented residence, or a residential setting with embedded technological features that enable passive monitoring of the daily activities of their residents, with the aim of detecting or even preventing emergencies and ultimately increasing the independence of the involved residents. The number of research projects and commercial initiatives exploring
214 SECTION | IV A New Vision for Supporting Caregivers in the Future
the concept of smart homes has been growing worldwide. The Center for Future Health at the University of Rochester in the United States has developed a Smart Medical Home as a highly controlled environment, including infrared sensors, biosensors, and video cameras (Marsh, 2002). The Aware Home at the Georgia Institute of Technology explored ubiquitous computing technologies that sense and identify potential crises, assist a senior adult’s memory, and track behavioral trends (Kidd et al., 1999). Researchers from five countries (the United Kingdom, Ireland, Finland, Lithuania, and Norway) joined their efforts for the ENABLE project (Cash, 2003), which promoted the well-being of people with early dementia with features such as a locator for lost objects, a temperature monitor, and an automatic bedroom light. In Toulouse, France, the PROSAFE project is utilizing a set of infrared motion sensors to support automatic recognition of resident activity and possible falls (Chan, Bocquet, Campo, Val, & Pous, 1999). As part of the Home-based Environmental and Assisted Living Technologies for Healthy Elders (HEALTH-E) initiative at the University of Washington, researchers have installed various sensor technologies in apartments of older adults who live in retirement communities in the Pacific Northwest (Reeder, Chung, Le, Thompson, & Demiris, 2014). The sensor technologies include regular motion sensors to detect how one moves from one room to another in the home or within a room, as well as infrastructure-mediated sensing (namely, a sensor that can detect electricity consumption by electricity source or water consumption by water source). These features allow the detection of activities such as meal preparation or bathroom visits with a level of detail (e.g., which faucet or electric device was used when and for how long) that simple motion sensors cannot provide. Additionally, the integration of door sensors can provide information on visitors and time spent outside the home. Complex data analysis techniques allow both the detection of activities and potential changes over time, calling for a timely intervention to prevent an adverse event (e.g., if data indicate a more sedentary behavior over time, an increase in bathroom visits at night or other sleep interruptions, less number of meals prepared, etc.) Most projects demonstrate the potential of smart homes with small feasibility studies or exploratory case studies. A 2007 Cochrane review (Martin, Kelly, Kernohan, McCreight, & Nugent, 2008) found no randomized clinical trial in this domain and an overall lack of empirical evidence to support or refute the use of smart-home technologies within health and social care. In more recent years, the number of smart-home studies continued to increase significantly. A 2013 study by Reeder et al. (2013) identified 34 studies, with 21 classified as emerging, 10 as promising, and 3 as effective using an evidence-based public health typology. Smart homes can address various healthcare needs and areas of monitoring. Research and commercially available smart-home solutions address a variety of areas. Table 12.1 summarizes various functionalities that smart-home applications can support and their implications for caregiving. The diffusion of smart homes and their adoption by the population ultimately depend on the extent to which older adults and their families accept and embrace this concept. In this context, it is important to address obtrusiveness, defined as
TABLE 12.1 Smart-Home Functionalities and Implications for Caregiving Functionality
Definition
Implications for Caregiving
Physiological monitoring
Collecting and processing physiological measurements such as vital signs of pulse, respiration, temperature, bladder and bowel output, etc.
Providing summary data sets describing physiological status to inform care coordination, preparation of meals, and symptom management
Functional monitoring
Collecting and processing functional measurements such as general activity level, motion, gait, meal intake, and other activities-of-daily-living
Informing potential interventions to reduce environmental elements that pose a fall risk, determining need for assistance with personal care and daily activities
Safety monitoring
Collecting and processing measurements that detect environmental hazards such as fire or gas leak. Safety assistance includes functions such as automatic turning on/off bathroom lights when getting out of bed, facilitating safety by reducing trips and falls. Location technologies including global-positioning system (GPS)–based tracking aimed at safety also fit into this type
Providing peace of mind for caregivers
Security monitoring and assistance
Enables measurements that detect human threats such as intruders. Assistance includes responses to identified threats
Provides peace of mind for caregivers and enables them to adjust their schedule if loved one needs to be briefly unattended
Social interaction monitoring and assistance
Collecting and processing of data pertaining to frequency of social interactions such as phone calls, visitors, and participation in activities. Social interaction assistance includes technologies that facilitate social interaction, such as videobased components that support video-mediated communication with friends and loved ones, virtual participation in group activities, etc.
Providing virtual presence for remote/distant caregivers, informing interventions to increase social interactions and facilitating the engagement of other family members and friends who can participate in group discussions and activities
Cognitive and sensory assistance
This feature supports automated or self-initiated reminders and other cognitive aids such as medication reminder and management tools, lost key locators, etc., for users with identified memory deficits. Cognitive assistance applications also include task instruction technologies, such as verbal instructions in using an appliance. Sensory assistance includes technologies that aid users with sensory deficits such as for sight, hearing, and touch
Assist caregiver with medication administration, coordination of tasks, and assistance with instrumental activities of daily living
216 SECTION | IV A New Vision for Supporting Caregivers in the Future
“a summary evaluation by the user based on characteristics or effects associated with the technology that are perceived as undesirable and physically and/or psychologically prominent” (Hensel, Demiris, & Courtney, 2006). Obtrusiveness assessments address the cumulative effect of a number of characteristics or attributes that may be important or prominent to a user. Obtrusiveness is an individualized subjective assessment; what one person perceives as obtrusive may not be perceived the same way by another. The user in this context is not only the older adult, but also all other residents in the home in general, and the family caregiver more specifically. People weigh their perceived need for such healthcare technology against potential privacy considerations (Courtney, Demiris, Rantz, & Skubic, 2008). As is the case with all emerging technologies that cause a paradigm shift in models of care, it is important to fully address ethical considerations associated with the concept of smart homes, and these extend beyond the concept of obtrusiveness. Moran (1993) noted the potential of advanced technology to change relationships between household members, as well as the role and function of the home and its relationship with the wider environment. Such passive monitoring technologies can remove choice and control from older adults and their caregivers as they learn to rely on automation. Smart homes could reduce social interaction or may provide tools that substitute for personal forms of care and communication (Tetley, Hanson, & Clarke, 2001). As we consider ways to implement smart-home systems, we need to address the warning by Wylde and Valins (1996) that we may be indeed creating “societies of high-tech hermits.” Smart homes may also lessen residents’ or their caregivers’ sense of personal responsibility. Family caregivers, for example, may become less vigilant in monitoring health changes in their loved one as they rely on an automated process. Stip and Rialle (2005) emphasize that the issues of individual freedom, personal autonomy, informed consent, and confidentiality have to be examined in the context of the target population. They note that surveillance technologies may exacerbate delusions of persecution and psychosensorial phenomena among persons with schizophrenia. Similarly, while smart-home systems may facilitate the monitoring and detection of emergencies among people with dementia, it may be difficult to assess residents’ true wishes in terms of wanting to be monitored if they cannot provide consent for such an intervention and their participation is determined by a loved one who acts on their behalf. Such challenges raise the question how smart homes may affect or alter the relationship between a patient/resident and the family caregiver or other members of their social network. Smart-home systems affect caregivers both in cases where they reside with an older adult and thus are also monitored in that setting, and in cases where they receive information from the smart-home system and are called to act upon it. Rialle, Ollivet, Guigui, and Hervé (2008) conducted a survey of 270 families of patients with Alzheimer’s disease or related disorders, located in the greater
Information Technology to Support Aging Chapter | 12 217
Paris area in France, to assess their perceptions and attitudes toward smarthome technologies. Interestingly, the authors identified two opposite clusters in the responses: family caregivers in favor of substantial use of technology and those “rather or totally hostile.” The two technologies that were most appreciated by caregivers were tracking devices that allow location of a wandering patient, and videoconferencing devices or tools that promote social connectedness and also allow family caregivers to interact with or supervise the patient when at a distance. As the authors of this study point out, their findings indicate that caregivers appreciate most smart-home technologies that increase patient safety and those that increase the caregiver’s social connectedness and ability to leave the home and still communicate with the patient. The study findings also serve as a reminder that it is important to assess caregiver needs and preferences and ensure their buy-in prior to implementing a smart-home system that would require their involvement and affect their daily lives.
PERSONAL HEALTH RECORDS Technology allows patients to store and manage their own health information as a PHR. The National Alliance for Health Information Technology (National Alliance for Health Information Technology, 2008) defines a PHR as “an individual’s electronic record of health-related information that conforms to nationally recognized interoperability standards and that can be drawn from multiple sources while being managed, shared, and controlled by the individual.” A PHR is basically a tool to use in “sharing health information, increasing health understanding, and helping transform patients into better-educated consumers of health care,” (Kahn, Aulakh, & Bosworth, 2009, p. 369). There have been several efforts from industry, government, and not-for-profit entities to explore the design and implementation of such PHR tools. The Veterans Health Administration introduced a PHR system called MyHealtheVet (Department of Veterans Affairs, 2012), which documents and manages appointments and medication requests. It also assists veterans with selecting and obtaining a variety of healthcare services. Epic, the electronic medical record (EMR) software vendor, has also introduced a PHR application that is currently used by Kaiser Permanente, the Cambridge Health Alliance, and other healthcare organizations. These systems are widely used by consumers because they offer important functionality that could lead to improved health (Mechanic, 2008). PHRs enable the sharing of information, such as health finances (e.g., billing and insurance paperwork), diagnoses or conditions, allergies, immunizations, and medications to assist patients with managing their own health (Hassol et al., 2004). In such systems, the patient (not any healthcare facility or provider) owns and controls/manages her data. PHR systems can be powerful tools for both patients and their caregivers, as the tools can be jointly managed and maintained by the patient-caregiver dyad, allowing caregivers to have a detailed record of transactions and preferences when they have to act as proxy decision
218 SECTION | IV A New Vision for Supporting Caregivers in the Future
makers for the patient. Furthermore, a PHR could support the documentation of the caregiver’s own concerns and fears that relate to patient care (such as concerns around pain medication administration or barriers to effective caregiving). There is a lack of documented efforts to utilize PHR systems specifically for older adults and their families, but web-based mobile tools can play a great role for older adults as they transition through multiple settings of care with many challenges and redundancies in the information that needs to be collected at every site and a lack of tools that solicit older adults’ own specific preferences and needs. Pillemer et al. (2012) conducted a study measuring the effects of electronic health information technology (IT) implementation on nursing home resident outcomes. While their study did not focus on a PHR system, the introduction of an EMR system in the nursing home led to overall residents’ positive reception of the health IT. This indicates that potentially residents themselves and caregivers could in the future engage in data entry and oversight of health information leading to a joint EMR-PHR system that could assist with care coordination.
BIG DATA New technologies such as wearable devices or sensors that facilitate ongoing monitoring of physiological, behavioral, or other information generate vast amounts of data over time. These large amounts of data, similar in size to ones found in computational biology, are often referred to as Big Data. The size and complexity of such data sets, often found in biomedicine, call for new tools to clean and process data as traditional data processing approaches are no longer sufficient. Big Data carry great potential to support significant discoveries in biomedicine and lead to better care, assuming that appropriate forecasting and data mining tools are applied. Participatory medicine, defined as “an approach of cooperative health care that actively and continuously involves patients and other stakeholders (i.e., healthcare providers and caregivers), across the continuum of care” (Hood & Auffray, 2013), aided by multisource data integration to support health management and clinical decision making, is anticipated to help save our healthcare system from unsustainability by transforming the way that caregivers, biological researchers, patients, and the population at large interact. Fueled by greater ubiquity of Internet access, the proliferation of social media, and increased government support for health IT, the amount of information related to health and health care has seen remarkable growth. Many online resources that foster social participation in health and health care have emerged. The next big challenges to enabling participatory medicine are the creation of PHRs that integrate open-source and personal health and healthcare information, as well as the use of integrated PHRs to develop models and analytics in support of health management and clinical decision making. Specifically in support of aging, a participatory approach to health care can be achieved through the collection, integration, analysis, and modeling of massive, heterogeneous health
Information Technology to Support Aging Chapter | 12 219
and healthcare data, including personal and open-source information, from multiple sources. The use of ensuing knowledge to monitor and forecast patients’ health and wellness can then support shared decision making for treatment. To achieve this goal, Big Data computing challenges need to be addressed, including gaining access to data; organizing, managing, and processing biomedical Big Data; and developing new methods and tools for analyzing and using biomedical Big Data. This calls for developing innovative methods, processes, and techniques in four areas: Harvesting health and healthcare data from heterogeneous sources (including health information systems in clinical sites, smart-home data, behavioral data from homes and communities, self-reporting, EMRs, and PHRs); ● Structuring and integrating heterogeneous health and healthcare data; ● Developing diagnostic and forecasting health and wellness models; and ● Developing a user application for shared clinical decision making that facilitates direct control and access to older adults, their family members or other stakeholders who they choose to have actively involved in the process. ●
Let us consider an example in the context of smart homes as described earlier. Such a system may perform continuous assessment of residents’ activities and mobility and extract models and metrics for providing feedback. Applying these scientific advances of processing Big Data to the challenge of mobility for older adults, such a system can synthesize and visualize information generated from the sensing system to support decision making for older adults and healthcare providers using context-driven messaging that includes cues, prompts, and alerts. Implementation of such a system can be scalable, ranging from personal tools for individual users to community-based applications that could assess mobility and well-being at a population level to inform the design of communitywide health promotion services and interventions. Such an approach would allow customized community-based interventions with clear societal implications based on trend detection. The information provided from smart-home sensors could allow facility administrators and public health providers to detect trends in overall community well-being and identify and implement systematic quality and safety improvement interventions. On the community level, aggregate data can also be used by public health or facility administrators to benchmark mobility between various institutions or communities and has the potential to be used as a population-level metric of health.
ROBOTIC APPLICATIONS Robots are programmable artificial intelligence machines that carry out complex tasks automatically and, in doing so, resemble human movements and functions. As technology advances, robotic applications have entered many areas of manufacturing, and more recently, health care and other aspects of daily life. Service robots include commercialized domestic machines, such as
220 SECTION | IV A New Vision for Supporting Caregivers in the Future
self-navigating vacuum cleaners and mops (Kuzma, 2006), but also robotic pets that apply pet therapy principles to assist older people to maintain mobility and stay active when having real pets is not an option (e.g., due to a residential policy or the inability of a resident to continuously care for a pet) (Carelli, Gaggioli, Pioggia, de Rossi, & Riva, 2009). Pearce, Adair, and Miller (2012) conducted a systematic review to identify the breadth of robotic applications to support older adults living at home and the evidence of their effectiveness. The majority of robotic studies in the home focus on lower-limb “exoskeleton” technologies. Robotic exoskeletons are fitted to the outside of the limbs. One of the most widely tested robotic exoskeletons for the lower limbs is the Lokomat, designed as a supportive structure for walking (Jezernik, Schärer, Colombo, & Morari, 2003). Other applications to assist with walking and mobility included robotic walkers and robotic guidance systems (Mehrholz, Werner, Kugler, & Pohl, 2007; Rentschler, Simpson, Cooper, & Boninger, 2008). In the same review, upper-limb technologies included both upper-limb exoskeleton systems to guide arm movements and haptic visuomotor feedback systems to assist in compensating for disorders of sensation and visual impairment. Such a visuomotor guidance system, the MIT-MANUS, was the most widely utilized and examined upper-limb robotic system, especially for patients recovering from stroke (Krebs et al., 2008). The review by Pearce et al. (2012) highlights that only four investigations tested robots within a home, residential-care setting, or simulated home environment. These studies demonstrated that robots have the potential to assist older people with mobility issues around the home environment. As the authors point out, however, the findings presented in those studies presented only low to moderate levels of evidence. While robotic applications carry promise for the future, research in this area is still in the very early stages. In the future, care givers may have the option to rely on service robotics, social robotics, or rehabilitation and physical performance support robotics to delegate some of their caregiving tasks (such as providing social support and assisting with personal care and daily activities). To date, research does not include any investigations with robotic systems in the healthcare context that specifically target caregivers or measure how such systems affect caregiving outcomes.
CONCLUSION Technological advances have the potential to support new models of empowering older adults and their caregivers. Computerized PHRs can facilitate the engagement of older adults in their own care and shared decision making. Big Data generated by continuous active and passive monitoring can be integrated into PHRs, allowing a more comprehensive assessment and documentation of one’s health and informing potential lifestyle changes that can prevent disease and improve management. The behavioral-sensing component that is enabled by smart-home technology can be integrated into personal records to provide
Information Technology to Support Aging Chapter | 12 221
a comprehensive view of one’s status; such aggregate information can then be shared with healthcare providers and informal caregivers to facilitate shared decision making. One such example would be for decisions about transitions of care; older adults and their families often have to make decisions about moving to a different setting with a higher level of institutionalized care based on subjective or incomplete information. Healthcare providers in this case may have only episodic, fragmented snapshots of overall wellness without information on the actual health-related trajectory. A PHR that integrates telehomecare or smarthome data could provide performance information that could facilitate decision making and improve communication between all the involved stakeholders. As technology advances, smart homes and other home-based solutions for older adults will strengthen invisibility, ubiquity, and adaptivity. As technologies integrate into architecture, furnishings, appliances, and clothing, they become effectively invisible to residents and visitors. Additionally, they are located in multiple rooms, making them ubiquitous in the home, and some support monitoring and data collection outside the home as well. Smart-home systems often include artificial intelligence features, allowing them to learn and adapt to the particular patterns of residents. At face value, such technological developments hold promise for caregivers who can use sophisticated tools to better monitor their loved ones and coordinate their caregiving tasks and responsibilities. However, relying on caregivers to operate software and other devices, or processing and maintaining health information data and making decisions pertaining to complex technologyrelated privacy and confidentiality implications, may be an added burden rather than an asset. Thus, it is important to expand our evidence base documenting the effectiveness of these new tools and systems beyond small feasibility and pilot studies. As technological innovations enable more sophisticated and tailored, home-based solutions, we need to ensure that the design and implementation of IT solutions are not determined simply by the latest technological trends, but by actual needs of older adults and their caregivers. We must address the technical, practical, ethical, and legal aspects of technology implementation if we are to maximize the potential of technology to support caregivers.
REFERENCES Carelli, L., Gaggioli, A., Pioggia, G., de Rossi, F., & Riva, G. (2009). Affective robot for elderly assistance. Studies in Health Technology and Informatics, 144, 44–49. Cash, M. (2003). Assistive technology and people with dementia. Reviews in Clinical Gerontology, 13(4), 313–319. Chan, M., Bocquet, H., Campo, E., Val, T., & Pous, J. (1999). Alarm communication network to help carers of the elderly for safety purposes: A survey of a project. International Journal of Rehabilitation Research, 22, 131–136. Chi, N., & Demiris, G. (2015). A systematic review of telehealth tools and interventions to support family caregivers. Journal of Telemedicine and Telecare, 21(1),37–44.
222 SECTION | IV A New Vision for Supporting Caregivers in the Future Courtney, K. L., Demiris, G., Rantz, M., & Skubic, M. (2008). Needing smart home technologies: The perspectives of older adults in continuing care retirement communities. Informatics in Primary Care, 16(3), 195–201. Czaja, S. J., Loewenstein, D., Schulz, R., Nair, S. N., & Perdomo, D. (2013). A videophone psychosocial intervention for dementia caregivers. American Journal of Geriatric Psychiatry, 21(11), 1071–1081. Department of Veterans Affairs. (2012). My healthevet.
Information Technology to Support Aging Chapter | 12 223 Pillemer, K., Meador, R. H., Teresi, J. A., Chen, E. K., Henderson, C. R., Jr, et al., Lachs, M. S., et al. (2012). Effects of electronic health information technology implementation on nursing home resident outcomes. Journal of Aging Health, 24(1), 92–112. Reeder, B., Chung, J., Le, T., Thompson, H., & Demiris, G. (2014). Assessing older adults’ perceptions of sensor data and designing visual displays for ambient environments. An exploratory study. Methods of Information in Medicine, 53(3), 152–159. Reeder, B., Meyer, E., Lazar, A., Chaudhuri, S., Thompson, H. J., & Demiris, G. (2013). Framing the evidence for health smart homes and home-based consumer health technologies as a public health intervention for independent aging: A systematic review. International Journal of Medical Informatics, 82(7), 565–579. Rentschler, J., Simpson, R., Cooper, R. A., & Boninger, M. L. (2008). Clinical evaluation of Guido robotic walker. Journal of Rehabilitation Research and Development, 45(9), 1281–1294. Rialle, V., Ollivet, C., Guigui, C., & Hervé, C. (2008). What do family caregivers of Alzheimer’s disease patients desire in smart home technologies? Contrasted results of a wide survey. Methods of Information in Medicine, 47(1), 63–69. Steventon, A., Bardsley, M., Billings, J., Dixon, J., Doll, H., Hirani, S., et al. (2012). Whole System Demonstrator Evaluation Team. Effect of telehealth on use of secondarycare and mortality: Findings from the Whole System Demonstrator cluster randomised trial. British Medical Journal, 344, e3874. Stip, E., & Rialle, V. (2005). Environmental cognitive remediation in schizophrenia: Ethical implications of “Smart Home” technology. Canadian Journal of Psychiatry, 50(5), 281–291. Takahashi, P. Y., Pecina, J. L., Upatising, B., Chaudhry, R., Shah, N. D., Van Houten, H., et al. (2012). A randomized controlled trial of telemonitoring in older adults with multiple health issues to prevent hospitalizations and emergency department visits. Archives of Internal Medicine, 172(10), 773–779. Tetley, J., Hanson, E., & Clarke, A. (2001). Older people, telematics and care. In A. M. Warnes, L. Warren, & M. Nolan (Eds.), Care services for later life: Transformations and critiques (pp. 243–258). London: Jessica Kingsley Publications. Wakefield, B. J., Holman, J. E., Ray, A., Scherubel, M., Adams, M. R., Hills, S. L., et al. (2012). Outcomes of a home telehealth intervention for patients with diabetes and hypertension. Telemedicine Journal and E-Health, 18(8), 575–579. Wylde, M., & Valins, M. S. (1996). The impact of technology. In M. S. Valins & D. Salter (Eds.), Futurecare: New directions in planning health and care environments (pp. 15–24). Oxford: Blackwell Science.