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The Role of Human Factors in Home Health Care: Workshop Summary 6 The Health Care Challenge: Matching Care to People in Their Home Environments Neil Charness My tasks in this chapter are to (1) review the demographics concerning current and future home health care users; (2) examine data on their capabilities; (3) assess their attitudes and how these might be expected to impact successful interaction with current and future home health care technology; (4) provide some examples of how user characteristics may influence the ability to perform home health care tasks, particularly involving technology; and (5) identify important gaps in the understanding of these relationships and make some recommendations. I stress a human factors perspective in attempting to address these issues. THE SHIFT TO HOME HEALTH CARE Justification for concern with these issues lies in the remarkable shift in the way in which health care has been delivered to individuals in the past century in the United States. The major changes include the diversity in the population being treated and in their attitudes about health care, who pays for treatment, what type of health conditions are treated, where people are treated, and the demands made on those who are treated by current technology products. As one example of change in treatment locale, today about 99 percent of children in the United States are born in hospitals or clinics (DeClercq, Paine, and Winter, 1995), whereas home births probably predominated before 1900. In contrast, place of death has begun to shift away from hospital settings (dropping from 78 to 61 percent from 1994 to 2004 in a Canadian study; Wilson et al., 2009) to homes and hospice facilities. Finally, except for occasional programs that cater to housebound
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The Role of Human Factors in Home Health Care: Workshop Summary older adults (e.g., Beck et al., 2009), one travels to an office or clinic to see a health care professional today or is taken by ambulance to a hospital in an emergency. (The author can remember a childhood visit by a physician to diagnose and schedule an emergency appendectomy.) Perhaps the greatest change is the treatment of patients with serious health conditions at home instead of in hospitals, a trend being driven in part by treatment cost considerations. A motivator for such treatment locale changes is the rapidly rising cost of health care coupled with a shift in the burden of payment. Individuals now pay directly for less than half their medical care expenses, with public and private insurance entities picking up the bulk of the payment, whereas as few as 50 years ago these relationships were reversed. Finally, people a century ago came in contact (sparingly) with medical care providers to address acute health concerns, such as communicable illnesses and injuries. In contrast, it was estimated that about 78 percent of health care expenditures in the United States in 1996 were made to treat people with chronic conditions (Anderson and Horvath, 2002). By 2005 that figure had risen to 90 percent (Machlin, Cohen, and Beauregard, 2008), in part because of the high prevalence of these conditions in the population. About 60 percent of adult civilian noninstitutionalized people have at least one chronic condition, although only about half of total medical care expenditures were for treatment of them. (Those with chronic conditions also experience disproportionate treatment for acute conditions.) The definition of chronic diseases by the Centers for Disease Control and Prevention is that they are “noncommunicable illnesses that are prolonged in duration, do not resolve spontaneously, and are rarely cured completely.” The five most costly ones in 2006 were (1) heart conditions, (2) cancer, (3) trauma-related disorders, (4) mental disorders, and (5) asthma that includes chronic obstructive pulmonary disease (Soni, 2009). Many of these disorders are experienced throughout the life course (trauma-related, such as auto accidents), although some are more strongly associated with childhood (asthma), some are associated more with young adulthood (mental disorders, such as schizophrenia), and some are most associated with old age (heart disease, cancer, Alzheimer’s disease). I focus primarily on older adult health care examples because that part of the population bears the greatest burden from chronic diseases. Given the aging of the population, the percentage of health care cost expended to treat chronic diseases will undoubtedly rise because of the strong relation between age and chronic disease prevalence (see Figure 6-1). The Government Accountability Office projected a quadrupling of spending on older adult long-term care alone between 2000 and 2050 (Allen, 2005). There is also concern that other trends, such as increased prevalence of diabetes, which is variously projected to increase from 11 million in 2000
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The Role of Human Factors in Home Health Care: Workshop Summary FIGURE 6-1 Relation between age and prevalence of a chronic condition. SOURCE: Based on data from Machlin, Cohen, and Beauregard (2008). to 29 million in 2050 (Boyle et al., 2001) or to 38 million in 2031 (Mainous et al., 2007), coupled with improved survival from traumas that used to lead to death (e.g., traffic crashes and battlefield injuries), will also increase demands on the health care system. The future population of home health care users is already in place: it is the current U.S. population ranging from young to old and living with a variety of ailments whose treatment regimen makes a variety of demands on themselves and their caregivers. Examples are prematurely born infants on ventilators, children with diabetes requiring insulin injections, young adults with AIDS who must adhere to complicated medication regimens, middle-aged adults with “silent” hypertension that requires medication with unpleasant side effects, older adults with sleep apnea who must use uncomfortable equipment to maintain continuous positive airway pressure, and people with renal failure who use home dialysis to avoid costly kidney transplants (with comparable outcomes; Pauly et al., 2009). As well, children and adults also experience acute conditions, such as infections (influenza) and injuries (broken bones), that make demands of shorter duration
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The Role of Human Factors in Home Health Care: Workshop Summary on their capabilities and those of their caregivers but are typically treated mostly at home. What will change is that today’s relatively healthy children and young adults will, as they age, develop chronic conditions in addition to the acute conditions that affect health for shorter periods of time. In addition, many of those who have chronic conditions now (e.g., spinal cord injuries, diabetes) will continue to consume home health care services as their general health deteriorates and as comorbidities develop. The changing ethnic composition of the population, coupled with differential susceptibility to some diseases, means that one can also anticipate a change in the mix of morbidities, such as diabetes, whose incidence tends to be greater in minority groups (Mainous et al., 2007). Given the enormous expenditures made on health care in the United States (discussed below), the model of providing continuing care through the traditional hospital and physician system is being questioned. One can expect to see more and more health care migrating into the home, with increased monitoring of health status being accomplished through technological systems, such as remote vital sign monitoring equipment. The goal is that such systems will provide more efficient health care delivery. However, designers of technology systems need to consider human factors in their design and deployment, because a badly designed system may fail to accomplish the goal of efficient delivery of health care and can even lead to fatal errors (e.g., Leveson and Turner, 1993; Institute of Medicine, 2000). Another example is assistive devices such as hearing aids, which have a high rate of abandonment (e.g., 30-50 percent) despite their potential benefit to users (Fuhrer, 2001). Some of the problems that arise may be the result of a poor fit between a device and the abilities and expectations of a specific user. Human factors and ergonomics specialists can offer insights into how best to design better health self-care systems. THE HEALTH CARE ENVIRONMENT People in the United States consumed $2.1 trillion of health care in 2006, that is, $7,026 per capita, representing 16 percent of gross domestic product (National Center for Health Statistics, 2008). About 84 percent of the expenditures were on personal health care and about 16 percent were on administrative costs, government public health activities, research, structures, and equipment. The highest percentage of those personal expenditures was for in-hospital care, followed by physician services (see Figure 6-2). As many have noted (e.g., Schoen et al., 2006), Americans pay more than citizens of most other developed countries for their health care, yet by most health outcome measures, they fail to obtain benefits commensurate with these expenditures. Thus, using the figure above as a guide, in order
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The Role of Human Factors in Home Health Care: Workshop Summary FIGURE 6-2 U.S. health care expenditures, 2006. SOURCE: National Center for Health Statistics (2008). to cut health care costs, the most likely candidates would be to diminish unnecessary contacts with hospitals and physicians and to consume fewer unnecessary over-the-counter and prescription drugs. Deployment of home health care technologies may reduce some of these expenditures by enabling people to be monitored from home by less skilled health care workers (in some cases, the user and family members) and by identifying health care problems before they require expensive treatments. THE PERSON-ENVIRONMENT FIT FRAMEWORK I consider health care somewhat broadly to include not only treatment of acute or chronic disorders but also such processes as information seeking and advice provision for health-related activities (e.g., exercise and diet) that might occur through a search of the Internet.1 A useful framework for envisioning how best to match care to people in their home environments is that of capability-demand fit. A sample framework, based on Czaja et al. (2001), is presented in Figure 6-3, one that exemplifies demands for a telehealth tool, such as a videoconferencing system used in telemedicine interventions. The person would be asked to use this device at home (e.g., to receive therapy for a mental health disorder). The device presents challenges 1 A 2008 Pew study indicated that 61 percent of adults in the United States had sought health information from the Internet (Fox and Jones, 2009).
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The Role of Human Factors in Home Health Care: Workshop Summary FIGURE 6-3 Capability-demand fit framework. SOURCE: Adapted from Czaja et al. (2001). in the form of its hardware interface, software interface, and instructional support. The user brings a range of abilities to bear, including perceptual, cognitive, and psychomotor capabilities. In short, systems make demands on users and the capabilities of the users will determine whether there is an adequate fit, which can affect acceptance and use of the system. Use of the (health care) system may in turn determine whether someone has a positive or negative health outcome. As another example, think of some of the demands made by newer mobile vital sign monitoring devices now entering the home health care market. Consider a wristwatch-like device that, in the presence of a wireless network in the home, streams information, such as the user’s temperature, location, and potential falls (via an embedded accelerometer), to a remote server. The information is aggregated, filtered through an intelligent program that tests for out-of-bounds values for vital sign parameters, and is presented to a health care provider via a password-protected website. However, the watch is battery-operated and needs to be recharged once a week on a charging station. If the user is somewhat cognitively impaired or simply forgetful, it is possible for the watch to fail based on a too-low battery state. Worse yet, the charging station may require precise placement of the watch within the station for effective charging to take place. If the
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The Role of Human Factors in Home Health Care: Workshop Summary older adult has a tremor, he or she may fail to align the watch with the charging contacts. Although the watch can signal its low-battery state to the server and present an alarm (via the web-based interface, or with an alert sent through the cellular phone system), unless someone is monitoring for the low-battery alarm, a fall could be missed before the user is alerted and reminded to recharge the watch (or the fall could occur while the watch is being charged). Even if the watch puts out a low-battery warning on the watch face, unless the user is carefully monitoring the watch and remembers what the low-battery icon means, that signal could be missed. Thus, user perceptual, cognitive, and psychomotor capabilities set a limit on how effectively the tool can function, despite the presence of a sophisticated hardware and software interface and instructional support. As an example of how user characteristics, such as attitudes, can affect degree of fit for technology, consider a newly diagnosed older adult diabetic who is told to monitor blood sugar levels and inject insulin accordingly,2 as well as to change diet and exercise levels. Blood glucose meters are relatively easy (although somewhat painful) to use with appropriate instruction (e.g., Mykityshyn, Fisk, and Rogers, 2002), but they may come with inadequate instructions, hobbling both cognitively fit young adults and less fit older adults (Rogers et al., 2001). Similarly, advice to change diet and to increase exercise levels may not yield adherence if the senior sees little linkage between glucose meter readings and short-term diet or exercise changes. The user may have unrealistic expectations and attitudes at the outset, which are reinforced by difficulties and unpleasantness associated with glucose meter use and the injection of insulin. All these difficulties may lead to poor adherence to diet, exercise, and monitoring/injection schedules. Better instructional materials, the training of expectations, quick access to skilled health care professionals for troubleshooting with equipment (e.g., through videoconferencing), and better designed equipment (e.g., noninvasive glucose measurement, automated insulin pumps) could lead to superior outcomes by making the treatment demands better match user capabilities and attitudes. One could also argue that having better health instruction earlier in the life span might have led to a lifestyle that would have avoided adult-onset diabetes. For most health self-care, the proximal environment includes the home, its residents, and health care devices. In the United States, some relevant characteristics of households for the noninstitutionalized civilian population are shown in Table 6-1 and Figure 6-4. Of the roughly 117 million households in 2008, about two-thirds are family households, although composition varies with age of householder. For those ages 20-24, about half dwell 2 This is admittedly an extreme case, as most forms of Type 2 diabetes are managed through diet and exercise changes and typically don’t require insulin injections.
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The Role of Human Factors in Home Health Care: Workshop Summary TABLE 6-1 U.S. Households by Type and Age, 2008 (numbers in thousands) U.S. Households Total Age of Householder Under 20 Years 20-24 Years 25-29 Years 30-34 Years Total all households 116,783 862 5,691 9,400 9,825 Family households Total 77,873 535 2,824 5,869 7,384 Married couple 58,370 58 1,166 3,753 5,240 Male householder 22,972 361 2,124 2,724 2,140 Female householder 35,442 443 2,401 2,923 2,444 Proportion family households 0.67 0.62 0.50 0.62 0.75 Nonfamily households Total 38,910 327 2,867 3,531 2,440 Male householder 17,872 147 1,521 2,074 1,595 Female householder 21,038 180 1,346 1,457 845 Size of household One member 32,167 143 1,507 2,167 1,764 Two members 38,737 269 1,992 2,966 2,340 Three members 18,522 215 1,230 1,934 1,988 Four members 15,865 121 611 1,394 2,133 Five members 7,332 63 222 597 1,062 Six members 2,694 29 80 222 383 Seven+ members 1,467 22 50 120 155 Proportion one-member households 0.28 0.17 0.26 0.23 0.18 SOURCE: Based on data from U.S. Census Bureau (2009). with family members, and that percentage increases to a high of 78 percent by ages 35-39 and then declines to a low of 42 percent by age 75+. Household composition is likely to affect the willingness (and ability) of another household member to provide help with activities of daily living (ADLs) and instrumental activities of daily living (IADLs) and, more specifically, with health technology products. Research on problem solving suggests that two heads are sometimes better than one (Hinsz, Tindale, and Volrath, 1997), although 28 percent of all households have single members, and the proportion by age rises from 17 percent for those under age 20 to 56 percent for those over age 75. Also, as seen in Figure 6-4, women are
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The Role of Human Factors in Home Health Care: Workshop Summary 35-39 Years 40-44 Years 45-49 Years 50-54 Years 55-64 Years 65-74 Years 75+ Years 10,900 11,548 12,685 11,851 19,909 12,284 11,829 8,605 8,996 9,438 8,511 13,218 7,503 4,990 6,406 6,583 7,105 6,737 11,144 6,365 3,813 1,956 2,113 2,363 2,163 3,457 1,765 1,804 2,537 2,852 3,217 2,951 5,308 4,153 6,213 0.79 0.78 0.74 0.72 0.66 0.61 0.42 2,295 2,552 3,247 3,340 6,690 4,781 6,839 1,442 1,567 1,766 1,696 2,944 1,568 1,552 853 985 1,481 1,644 3,746 3,213 5,287 1,760 2,040 2,702 2,877 5,995 4,542 6,671 1,974 2,152 3,183 3,954 9,307 6,243 4,358 2,060 2,280 2,494 2,350 2,535 915 521 2,803 2,790 2,593 1,707 1,248 310 156 1,465 1,503 1,102 645 473 129 70 557 522 406 187 210 68 29 281 262 205 131 140 76 24 0.16 0.18 0.21 0.24 0.30 0.37 0.56 much less likely to be living in family households than are men at advanced ages. By age 85+, of those not institutionalized, about 64 percent of men compared with 39 percent of women live in family households. Particularly when it comes to managing and maintaining complex technology, having help accessible may be the difference between succeeding or failing with a task. Technology can provide access to such support when it does not reside in the household. A more inclusive definition of the health care environment would consider other providers outside the home (physicians, nurses, formal and informal caregivers), including health care staff or advisors associated with schools and workplaces as well as other service providers who enter the home (e.g., to prepare meals, help with bathing). Thus, it is also likely that home health
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The Role of Human Factors in Home Health Care: Workshop Summary FIGURE 6-4 Male and female percentages for living with a family member, by age and sex. SOURCE: U.S. Census Bureau (2009). care will resemble a team environment, one in which team members will vary widely in their skills. Communication skills are central to expert team performance. Team environments also place a high premium on training members for their roles (Salas and Cannon-Bowers, 2001), a usually neglected aspect of home health care settings. I restrict consideration mainly to noninstitutionalized dwellings (apartments, detached and semidetached houses) rather than congregate housing (e.g., assisted living and chronic care institutions), primarily because the vast majority of Americans live in such dwellings for most of their lives, spending only a few years in assisted living or other chronic care residences. For example, for people ages 65+, only about 5 percent live in congregate housing settings, although percentages rise steeply with age. DEMOGRAPHICS OF HEALTH CARE USERS Every member of the population is a potential home health care user. So, for example, knowing the palm-down press and twist strength of children, young adults, and older adults is helpful for designing the cap mechanisms on prescription drug containers. One needs to ensure that opening requirements are too great for toddlers but not for older adults with arthritic hands. There are good sources of ergonomic information for different
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The Role of Human Factors in Home Health Care: Workshop Summary subsets of the population (e.g., Kroemer, 2005), although chronicling user capabilities for the entire population (e.g., some disabled veterans have lost limbs and use prostheses) is beyond the scope of this chapter. However, given that those with chronic conditions incur about 90 percent of the health care expenditures, they are the primary focus of this review. Although it is difficult to predict trends for home health care utilization (or cost; Manton, Lamb, and Gu, 2007), given current promising trends of increasing disability-free longevity (e.g., Manton, Gu, and Lowrimore, 2008), one relatively safe prediction is that those already suffering from impairments are candidates to reach old age with fewer financial resources and with disabilities that will complicate treatment of other chronic conditions likely to arise. Those with disabilities are much less likely to be employed full time. For example, the unemployment rate in May 2009 for those ages 16 and over who were not institutionalized in the United States was 8.9 percent for persons without a disability and 13.7 percent for those with one (see http://www.bls.gov/cps/cpsdisability.htm [accessed June 2010]). Thus, at least for expected income levels, which strongly influence health care consumption, the life chances for those with a disability are likely to be poorer. There are two obvious subgroups for disability: civilians and wounded veterans who are classified as disabled. Disability According to the American Community Survey (ACS), in 2006 there were approximately 41.3 million people in the United States who reported some form of disability. Disability rises with age, particularly after age 65. Figure 6-5 shows percentage data for men and women derived from the 2006 ACS. (Data were downloaded as an Excel spreadsheet for a Factfinder query based on the U.S. population.) Disability is defined in that survey as “a long-lasting sensory, physical, mental, or emotional condition or conditions that make it difficult for a person to do functional or participatory activities such as seeing, hearing, walking, climbing stairs, learning, remembering, concentrating, dressing, bathing, going outside the home, or working at a job.” Percentages can be misleading, so Figure 6-6 shows the numbers in millions from the ACS. Although percentage of disability rises with age past 65, the majority of disabled individuals are in their working years. Assuming that a moderate percentage of them reach old age (mortality can be expected to be higher than in the general population, e.g., three times higher for those with an intellectual disability; Tyrer, Smith, and McGorther, 2007), they will constitute a very large cohort that will need significant assistance with self-care activities, and many others in the nondisabled segments of the population can be expected to transition into their ranks as they age.
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The Role of Human Factors in Home Health Care: Workshop Summary video recordings, ask the person to think aloud and record their voice while performing the task to reveal the problem-solving processes that they engage in, make eye movement recordings, and perhaps monitor the user with EEG or neuroimaging equipment if records of brain activity are needed. The device may be a working prototype, or a “Wizard of Oz” technique might be used, in which a human substitutes for some function in a device under development. An example of the latter might be having a human listener substitute for a speech comprehension module in a computer coaching system for a smart home that is not yet fully capable of speech comprehension. The human would type the words, which appear on the user’s screen. Modeling and Simulation Either as an adjunct to usability testing or as an independent technique, models (e.g., mathematical) and simulations (e.g., computer models) can be used to predict typical human performance without the expense of bringing a user into a laboratory or following their activities in a home environment. Often this involves making use of preexisting simulation environments (e.g., for handheld medical devices, there is Bonnie John’s CogTool; http://cogtool.hcii.cs.cmu.edu/ [accessed May 2010]) or using task analysis in combination with model human processor parameters (Card, Moran, and Newell, 1983) to predict how long tasks would take for different user groups or different devices. Such simulation techniques can uncover design flaws in devices (e.g., inadequate time-out intervals for input on some mobile phones; Jastrzembski and Charness, 2007) without the need for expensive usability testing and can do so for different populations (e.g., younger and older adults) when parameter estimates are available. Typically, a task, such as accessing a health message on a mobile phone, is decomposed into unit tasks (e.g., basic cognitive, perceptual, and motor operations) for which there are estimates of the unit task time or probability of error. The times (or errors) are then summed to estimate total task completion time (or error). Such analysis takes into account technology demands and user capabilities (see Figure 6-3) with degree of fit being determined by the time to complete the task or the probability of making an error. Questionnaires and Focus Groups Questionnaires can be an efficient way to assess some of the dimensions of person-environment fit, for either a high-tech or a low-tech device (e.g., an illuminated magnifying lens to help those with low vision to read health care instructions). Either standard instruments (e.g., that assess ease of use and perceived usefulness) or tailored ones can quickly probe user attitudes,
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The Role of Human Factors in Home Health Care: Workshop Summary satisfaction, and degree of workload experienced for a device. Similarly, focus group studies with transcription and coding of user discussion can uncover concerns and preferences in a reasonably cost-effective manner. For tutorials on these techniques, see Fisk et al. (2009). GAPS IN KNOWLEDGE Research is needed to fill in gaps about user attitudes, knowledge about the home environment, and knowledge about what home health care interventions are cost-effective. Knowledge of User Attitudes There is a lack of representative data on attitudes toward health care technologies (e.g., privacy concerns and trust), health care technology adoption, and, more importantly, technology abandonment. Surveys could be commissioned to address these issues as part of the U.S. Census Bureau’s Current Population Survey. In general, there are few population-representative studies about health care technology attitudes and health care technology adoption. Few studies investigate the influence of potentially important mediators or moderators, such as ethnicity, gender, education/income, and age. There are relatively well-developed models about factors that influence technology adoption that might be adapted to studying home health care technology adoption (e.g., the technology acceptance model). Technologies (and attitudes toward technologies) change rapidly, which makes knowledge acquisition a moving target. A related gap is knowledge about factors influencing abandonment of health care technology. It is evident that maintenance of technology is not simple or easy, so maintenance and repair are important issues to address, particularly for users with low income and education. Mass adoption of home telehealth technology is not likely until there is widespread, competent, and relatively inexpensive technical support available to users. Knowledge of the Home Recall that person-environment fit depends on characteristics of both the person and the environment. One reason for the rise of specialized environments for health care delivery, such as hospitals and clinics, is that, in theory, they provide standardized environments for tending to those in ill health. They can provide well-lit, quiet, clean, well-equipped rooms for treatment of patients with fast access to highly trained health care specialists. How does the typical home or apartment environment compare?
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The Role of Human Factors in Home Health Care: Workshop Summary There are no systematic surveys of the home health care environment. How many households have access to modern telecommunications links (wireless, broadband)? How many homes have adequate wiring to support modern equipment?9 Something as simple as lighting (which influences legibility of written instructions) varies enormously in homes, partly as a function of the age of the homeowner (Charness and Dijkstra, 1999). I would recommend incorporation of such questions into health care surveys (see as well Chapter 10 on the physical environment). Knowledge of Home Health Care Efficacy and Cost-Effectiveness What are the risks and benefits of treating chronic (and acute) conditions in the home? Cochrane reports indicate that there are too few randomized trials to assess efficacy (or cost-effectiveness) of telemedicine with much confidence. Undoubtedly, clinical trials are under way and further meta-analyses are being prepared, but given the unique features of each study from the perspective of population sampled and intervention chosen and also what constitutes usual care for the control group, it will be some time before there are definitive answers to the question of what form of home health care works best. One promising way to proceed is to abandon usual treatment as a comparison point and replace it with currently recognized best treatment. RECOMMENDATIONS In order to have successful deployment of home health care, there must be access, sound design, and appropriate training to ensure good person-environment fit. I make three recommendations to promote successful deployment. Promotion of Secure High-Speed Internet Access to Households. Given the importance of access to health care information, coupled with the rapid movement of such information to the Internet, having high-speed Internet access in households is becoming more of a necessity than a luxury. Telehealth applications to homes, including diagnosis, treatment, and rehabilitation, would be facilitated by such access. However, such televisits will depend on having secure communication channels to comply with regulations (e.g., the Health Insurance Portability and Accountability Act, HIPAA, in the United States), as well as on having standardized protocols 9 The author recalls having to buy an adapter for a three-pronged plug to use computer equipment at his mother-in-law’s home, which had wiring to support only two-pronged outlets.
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The Role of Human Factors in Home Health Care: Workshop Summary and interfaces for home health care equipment (e.g., stethoscopes, scales, vital sign monitoring equipment) to minimize cost and training difficulty. Whether such access should be mandated in the same way as basic wired phone service or electricity and provided (with government subsidies if necessary) by current wired cable and phone connections (often unavailable in rural communities) or by soon-to-be-deployed wireless networks is open to debate. However, I recommend that the Federal Communications Commission carry out studies to advise Congress about the best way to bring secure, high-speed access to U.S. households capable of hosting telehealth services, such as videoconferencing. Promotion of Usability Testing for Home Health Care Devices. It is not wise to design and then deploy a health care device or system in a home and expect it to work well for an increasingly diverse population of users. Usability testing should be encouraged with relevant user populations. Universal design (see Chapter 9) is a potential solution, by designing so that anyone, from a child to an impaired adult, could use a device, but it is unrealistic given the range of abilities/disabilities in the population. So inclusive design is the more sensible goal, making it ever easier to use devices, based on a cycle of design, testing, and redesign. Ideally, users of devices would fall into a few well-defined categories so that manufacturers could target them efficiently for testing. Simulation and modeling may prove to be a viable option to potentially expensive usability testing, as models are extended to cover people with more diverse abilities. The Food and Drug Administration currently requires manufacturers of medical devices to attend to human factors concerns. However, many devices not specifically classified as medical devices either could be or are now used to provide information about health care or delivery of home health care services (e.g., mobile phones, computers equipped with webcams for videoconferencing, videogame systems intended to promote physical and mental exercise). I recommend that manufacturers of such products and system integrators be strongly encouraged to provide evidence of efficacy through usability testing (or modeling) of the device with likely user populations. Researching and Promoting Sound Instructional and Training Principles. Too little is known about the most effective techniques to instruct and train the use of home health care devices and how to search for and evaluate health care information (e.g., from the web) in the increasingly diverse population of home health care users. A good example is the recent Guidelines for Pediatric Home Health Care (American Academy of Pediatrics, 2009), which highlights, in chapter after chapter, the need to train caregivers but offers few if any suggestions for how to do this training or how to assess its efficacy. What are the optimal training principles
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The Role of Human Factors in Home Health Care: Workshop Summary and techniques for those with low health literacy, those with poor English comprehension skills, those from minority ethnic communities? Although guidelines have been proposed for training older adults (e.g., Fisk et al., 2009), the empirical support behind such recommendations is relatively weak (e.g., being based on those who volunteer for lab-based experiments rather than representative samples). Thus, I recommend that further research be conducted into potential ability-by-treatment interactions for training diverse populations of health care users, emphasizing the use of representative sampling. That is, studies need to assess whether and how ability levels for such variables as literacy, ethnicity, education, and age moderate the effectiveness of different training techniques. ABOUT THE AUTHOR Neil Charness is William G. Chase professor of psychology and an associate in the Pepper Institute on Aging and Public Policy at Florida State University. His research interests include understanding relations between age and technology use, expert performance, and work performance. REFERENCES Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50, 179-211. Ajzen, I., and Fishbein, M. (1977). Attitude-behavior relations: A theoretical analysis and review of empirical research. Psychological Bulletin, 84, 888-918. Alkema, G.E., Wilber, K.H., Shannon, G.R., and Allen, D. (2007). Reduced mortality: The unexpected impact of a telephone-based care management intervention for older adults in managed care. Health Services Research, 42, 1,632-1,650. Allen, K.G. (2005). Long-term care financing: Growing demand and cost of services are straining federal and state budgets. GAO Testimony Before the Subcommittee on Health, Committee on Energy and Commerce, House of Representatives. GAO-05-564T. Available: http://www.gao.gov/new.items/d05564t.pdf [accessed July 2009]. American Academy of Pediatrics. (2009). Guidelines for pediatric home health care (2nd ed.) Elk Grove Village, IL: Author. Anderson, G., and Horvath, J. (2002). Chronic conditions: Making the case for ongoing care. Baltimore, MD: Johns Hopkins University Press. Baer, J., Kutner, M., and Sabatini, J. (2009). Basic reading skills and the literacy of America’s least literate adults: Results from the 2003 National Assessment of Adult Literacy (NAAL) supplemental studies (NCES 2009-481). Washington, DC: National Center for Education Statistics, U.S. Department of Education. Available: http://nces.ed.gov/pubs2009/2009481.pdf [accessed August 2009]. Beach, S.R., Schulz, R., Downs, J., Matthews, J., Barron, B., and Seelman, K. (2009). Disability, age, and informational privacy attitudes in quality of life technology applications: Results from a national web survey. ACM Transactions on Accessible Computing (TACCESS), 2(1), 1-21.
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The Role of Human Factors in Home Health Care: Workshop Summary Beck, R.A., Arizmendi, A., Purnell, C., Fultz, B.A., and Callahan, C.M. (2009). House calls for seniors: Building and sustaining a model of care for homebound seniors. Journal of the American Geriatric Society, 57(6), 1,103-1,109. Bendixen, R.M., Levy, C.E., Olive, E.S., Kobb, R.F., and Mann, W.C. (2009). Cost effectiveness of a telerehabilitation program to support chronically ill and disabled elders in their homes. Telemedicine and e-Health, 15, 31-38. Benson, J.G., and Forman, W.B. (2002). Comprehension of written health care information in an affluent geriatric retirement community: Use of the Test of Functional Health Literacy. Gerontology, 48, 93-97. Blackhall, L.J., Frank, G., Murphy, S.T., Michel, V., Palmer, J.M., and Azen, S.P. (1999). Ethnicity and attitudes towards life sustaining technology. Social Science and Medicine, 48, 1,779-1,789. Bowles, K.H., and Baugh, A.C. (2007). Applying research evidence to optimize telehomecare. Journal of Cardiovascular Nursing, 22, 5-15. Boyle, J.P., Honeycutt, A.A., Venkat Narayan, V.M., Hoerger, T.J., Giess, L.S., Chen, H., and Thompson, T.J. (2001). Projection of diabetes burden through 2050: Impact of changing demography and disease prevalence in the United States. Diabetes Care, 24, 1,936-1,940. Brickfield, C.F. (1984). Attitudes and perceptions of older people toward technology. In P.K. Robinson and J.E. Birren (Eds.), Aging and technological advances (pp. 31-38). New York: Plenum. Caine, K.E., Fisk, A.D., and Rogers, W.A. (2006). Benefits and privacy concerns of a home equipped with a visual sensing system: A perspective from older adults. Proceedings of the Human Factors and Ergonomics Society, 50th Annual Meeting, pp. 180-184. Available: http://awarehome.imtc.gatech.edu/publications/Caine-Fisk-Rogers-2006P.pdf [accessed May 2010]. Cameron, K.A. (2009). A practitioner’s guide to persuasion: An overview of 15 selected persuasion theories, models and frameworks. Patient Education and Counseling, 74, 309-317. Card, S.K., Moran, T.P., and Newell, A. (1983). The psychology of human-computer interaction. Hillsdale, NJ: Lawrence Erlbaum Associates. Centers for Disease Control and Prevention. (2008). Self-reported falls and fall-related injuries among persons aged > 65 years—United States, 2006. Morbidity and Mortality Weekly Report, 57, 225-229. Available: http://www.cdc.gov/mmwr/preview/mmwrhtml/mm5709a1.htm [accessed July 2009]. Charness, N. (2008a). Technology as multiplier effect for an aging work force. In K.W. Schaie and R. Abeles (Eds.), Social structures and aging individuals: Continuing challenges (pp. 167-192). New York: Springer. Charness, N. (2008b). Computer and Internet use in the United States of America: Influence of age, attitude, employment status, and education. International Society for Gerontechnology Proceedings, June 4, Pisa, Italy. Charness, N., and Boot, W.R. (2009). Aging and information technology use: Potential and barriers. Current Directions in Psychological Science, 18, 253-258. Charness, N., and Czaja, S.J. (2006). Older worker training: What we know and don’t know. AARP Public Policy Institute #2006-22. Washington, DC: AARP. Available: http://assets.aarp.org/rgcenter/econ/2006_22_worker.pdf [accessed June 2010]. Charness, N., and Dijkstra, K. (1999). Age, luminance, and print legibility in homes, offices, and public places. Human Factors, 41, 173-193. Charness, N., Kelley, C.L., Bosman, E.A., and Mottram, M. (2001). Word processing training and retraining: Effects of adult age, experience, and interface. Psychology and Aging, 16, 110-127.
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The Role of Human Factors in Home Health Care: Workshop Summary Melenhorst, A., Rogers, W.A., and Bouwhuis, D.G. (2006). Older adults’ motivated choice for technological innovation: Evidence for benefit-driven selectivity. Psychology and Aging, 21, 190-195. Meyer, B.J.F., Talbot, A.P., and Ranalli, C. (2007). Why older adults make more immediate treatment decisions about cancer than younger adults. Psychology and Aging, 22, 505-524. Morrell, R.W., and Park, D.C. (1993). The effects of age, illustrations, and task variables on the performance of procedural assembly tasks. Psychology and Aging, 8, 389-399. Morrow, D.G., and Rogers, W.A. (2008). Environmental support: An integrative framework. Human Factors, 50, 589-613. Morrow, D., Clark, D., Tu, W., Wu, J., Weiner, M., and Steinley, D. (2006). Correlates of health literacy in patients with chronic heart failure. The Gerontologist, 46, 669-676. Mustard, C.A., Kaufert, P., Kozyrskyj, A., and Mayer, T. (1998). Sex differences in use of health care services. New England Journal of Medicine, 338, 1,678-1,683. Mykityshyn, A.L., Fisk, A.D., and Rogers, W.A. (2002). Learning to use a home medical device: Mediating age-related differences with training. Human Factors, 44, 354-364. Mynatt, E.D., Melenhorst, A.S., Fisk, A.D., and Rogers, W.A. (2004). Aware technologies for aging in place: Understanding user needs and attitudes. IEEE Pervasive Computing, 3, 36-41. National Center for Education Statistics. (2006). The health literacy of America’s adults: Results from the 2003 National Assessment of Adult Literacy (NCES 2006-483). M. Kutner, E., Greenberg, Y. Jin, and C. Paulsen, Eds. Washington, DC: U.S. Department of Education. National Center for Health Statistics. (2008). Health, United States, 2008 with chartbook. Available: http://www.cdc.gov/nchs/data/hus/hus08.pdf [accessed April 2009]. National Institute on Aging, National Institutes of Health, and U.S. Department of Health and Human Services. (2007). Growing older in America: Health and retirement study. Institute for Social Research, University of Michigan. Available: http://hrsonline.isr.umich.edu/index.php?p=dbook [accessed August 2010]. Parati, G., Omboni, S., Abini, F., Piantoni, L., Giuliano, A, Revera, M., Illyes, M., and Mancia, G. (2009). Home blood pressure telemonitoring improves hypertension control in general practice. The TeleBPCare study. Journal of Hypertension, 27, 198-203. Pauly, R.P., Gill, J.S., Rose, C.L., Asad, R.A., Chery, A., Pierratos, A., and Chan, C.T. (2009). Survival among nocturnal home haemodialysis patients compared to kidney transplant recipients. Nephrology Dialysis Transplantation, 24, 2,915-2,919. Quill, T.E. (2005). Terri Schiavo—A tragedy compounded. New England Journal of Medicine, 352, 1,630-1,633. Quinn, C.C., Gruber-Baldini, A.L., Shardell, M., Weed, K., Clough, S.S., Peeples, M., Terrin, M., Bronich-Hall, L., Barr, E., and Lender, D. (2009). Mobile diabetes intervention study: Testing a personalized treatment/behavioral communication intervention for blood glucose control. Contemporary Clinical Trials, 30, 334-346. Rahimpour, M., Lovell, N.H., Celler, B.G., and McCormick, J. (2008). Patient’s perceptions of a home telecare system. International Journal of Medical Informatics, 77, 486-498. Rogers, E.M. (2003). Diffusion of innovations (5th ed.). New York: Free Press. Rogers, W.A., Mykitshyn, A.L., Campbell, R.H., and Fisk, A.D. (2001). Analysis of a “simple” medical device. Ergonomics in Design, 9, 6-14. Salas, E., and Cannon-Bowers, J.A. (2001). The science of training: A decade of progress. Annual Review of Psychology, 52, 471-499. Salthouse, T.A. (2010). Major issues in cognitive aging. Oxford: Oxford University Press. Schoen, C., Davis, K., How, S.K., and Schoenbaum, S.C. (2006). U.S. health system performance: A national scorecard. Health Affairs, 25, W457-W475.
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The Role of Human Factors in Home Health Care: Workshop Summary Soni, A. (2009). The five most costly conditions, 1996 and 2006: Estimates for the U.S. civilian noninstitutionalized population. Statistical brief #248, July. Rockville, MD: Agency for Healthcare Research and Quality. Available: http://www.meps.ahrq.gov/mepsweb/data_files/publications/st248/stat248.pdf [accessed September 2009]. Steenbekkers, L.P.A., and van Beijsterveldt, C.E.M. (Eds.). (1998). Design-relevant characteristics of ageing users. Delft, The Netherlands: Delft University Press. Story, M.T., Neumark-Stzainer, D.R., Sherwood, N.E., Holt, K., Sofak, D., Trowbridge, F.L., and Barlow, S.E. (2002). Management of child and adolescent obesity: Attitudes, barriers, skills, and training needs among health care professionals. Pediatrics, 110, 210-214. Tacken, M., Marcellini, F., Mollenkopf, H., Ruoppila, I., and Széman, Z. (2005). Use and acceptance of new technology by older people. Findings of the international MOBILATE survey: “Enhancing mobility in later life.” Gerontechnology, 3, 126-137. Thang, L., Patrick, L., and Nash, S.G. (2009). Help-seeking attitudes among Vietnamese Americans: The impact of acculturation, cultural barriers, and spiritual beliefs. Social Work in Mental Health, 7, 476-493. Tyrer, F., Smith, L.K., and McGrother, C.W. (2007). Mortality in adults with moderate to profound intellectual disability: A population-based study. Journal of Intellectual Disability Research, 51, 520-527. U.S. Census Bureau. (2009). Current population survey, 2008 annual social and economic supplement. Available: http://www.census.gov/population/socdemo/hh-fam/cps2008/tabH2-all.xls [accessed June 2009]. U.S. Census Bureau. (2010). American community survey, 2006. Available: http://www.census.gov/acs/www/ [accessed July 2010]. Varshney, U. (2007). Pervasive healthcare and wireless health monitoring. Mobile Network Applications, 12, 113-127. DOI 10.1007/s11036-007-0017-1. Venkatesh, V., and Davis, F.D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal case studies. Management Science, 46, 186-204. Wilson, D.M., Truman, C.D., Thomas, R., Fainsinger, R., Kovacs-Burns, K., Forggatt, K., and Justice, C. (2009). The rapidly changing location of death in Canada, 1994-2004. Social Science and Medicine, 68, 1,752-1,758. Zhu, C.W. (2004). Effects of the balanced budget act on Medicare home health utilization. Journal of the American Geriatric Society, 52, 989-994. Suggested General Readings Carayon, P., and Friesdorf, W. (2006). Human factors and ergonomics in medicine. In G. Salvendy (Ed.), Handbook of human factors and ergonomics (3rd ed., pp. 1,517-1,537). Hoboken, NJ: Wiley. Fisk, A.D., Rogers, W.A., Charness, N., Czaja, S.J., and Sharit, J. (2009). Designing for older adults: Principles and creative human factors approaches (2nd ed.). Boca Raton, FL: CRC Press. Jacko, J.A., and Sears, A. (Eds.). (2008). The human-computer interaction handbook Part V: Designing for diversity (2nd ed.). New York: Lawrence Erlbaum Associates. Krupinski, E., Dimmick, S., Grigsby, J., Mogel, G., Puskin, D., Speedie, S., Stamm, B., Wakefield, B., Whited, J., Whitten, P., and Yellowlees, P. (2006). Research recommendations for the American Telemedicine Association. Telemedicine and e-Health, 12, 579-589.