Information Technology and Systems in Home Health Care
Home health care faces several challenges, such as funding limitations, large geographic distances that make such resources often more costly for rural patients, and issues of clinical workforce distribution that impose access barriers to these services. It is a general premise that information technology (IT) can address these challenges and enhance home health care services. Advances in telecommunications, web solutions, and social networking tools have the potential to support health care delivery and education. The use of IT can lead to a fundamental redesign of home care processes based on the use and integration of electronic communication at all levels. Many anticipate that IT platforms may lead to patient empowerment and a transition from a passive role, in which the patient is the recipient of care services, to an active role, in which the patient is informed, has choices, and is involved in the decision-making process. Such a transition may be possible due to the active involvement of patients in the management of their disease using home monitoring devices and software, the access to information and online communities, and the use of personal health records maintained by patients themselves.
Information technology can be introduced in home care in a multitude of ways. The following taxonomy captures the multiple levels of IT function and functionality in the context of home care:
Active monitoring and management (requiring end-user involvement and participation)
Telehealth applications for home-based disease management (that link patients and their families to their health care providers)
Web-based communities for home care patients (that link patients and their families to health care providers, peers, and the community)
Personal health records (that enable patients to create and store their personal health information)
Passive monitoring and management (for which IT implementation does not require training or operation by the end-user)
Robotic applications (standalone artificial intelligence applications that support home care needs)
“Smart homes” (in which IT based on the use of sensors becomes part of the residential infrastructure)
This chapter describes these different types of IT applications and discusses technical, practical, and ethical implications.
TELEHEALTH APPLICATIONS FOR HOME-BASED DISEASE MANAGEMENT
Telehealth applications offer a platform to support disease management for home care patients diagnosed with chronic conditions and their families. This section is organized by the disease or condition addressed by the application.
For asthma management, an example of Internet utilization is the home asthma telemonitoring system (Finkelstein, O’Connor, and Friedmann, 2001), which provides patients with continuous individualized help in the daily routine of asthma self-care and coping and alerts health care providers if specific conditions or patterns emerge. The system is operated by the patient or an informal caregiver (including family members or friends) and involves web-based questionnaires and the operation of a spirometer to assess lung capacity. The data sets (including the spirometry readings) are transmitted to health care providers.
Another example involves diabetes. As diabetes has in many cases an asymptomatic nature, the time frame between sustained hyperglycemia and observable complications can be extended, thus making a long-term program of secondary prevention an essential part of appropriate diabetes care and a suitable domain for technology-based diabetes management applications. McKay, exploring the development and feasibility of a web tool for diabetes self-management that emphasized personalized goal setting, feedback, and social support (McKay et al., 1998) found that patients were satisfied with the system and appreciated the social support and the availability of information.
Similarly, the Center for Health Services Research’s Henry Ford Health System in Detroit developed the web-based Diabetes Care Management
Support System to support the provision of routine care to patients with diabetes (Baker et al., 2001). The system was evaluated in a nonrandomized, longitudinal study, and the findings indicated that web-based systems using clinical practice guidelines, patient registries, and performance feedback have the potential to improve the rate of routine testing among patients with diabetes.
The Telematic Management of Insulin-Dependent Diabetes Mellitus project, funded by the European Union, implemented and evaluated a distributed computer-based system for the management of insulin-dependent diabetes mellitus. The goal was to use Internet technology to support health care providers and patients by providing them with a set of automated services ranging from data collection and transmission to data analysis and decision support (Riva, Bellazzi, and Stefanelli, 1997). The system included a module allowing patients to automatically download their monitoring data from the blood glucose monitoring device and to send them to the hospital information system. The system provided physicians with a set of tools for data visualization, data analysis, and decision support and allowed them to send messages, including therapeutic advice, to the patients (Bellazzi et al., 2002).
Other application domains for web-based systems include congestive heart failure, chronic obstructive pulmonary disease, and wound care. The TeleHomeCare project at the University of Minnesota included a system based on the use of low-cost commercially available monitoring devices and an Internet application designed for patients diagnosed with congestive heart failure or chronic obstructive pulmonary disease or requiring wound care. The system included web pages customized to address the information needs of individual patients and included an online diary with questionnaires to be filled out daily. The daily questionnaire included questions about symptoms, vital signs (such as weight, blood pressure, temperature), overall well-being, and compliance with dietary guidelines. When one or more responses to these questions indicated a situation that required immediate clinical attention, alerts were triggered according to predefined rules and sent to the home care agency staff (Demiris, Speedie, and Finkelstein, 2001).
Oncology patients also often face the challenges of disease management and handling treatment side-effects at home. The National Cancer Institute’s Common Terminology Criteria for Adverse Events schema for seven common symptoms was adapted into a web-based patient reporting system, accessible from desktop computers in outpatient clinics and from home computers (Basch et al., 2005). In this study, 80 patients with gynecological malignancies, about to begin standard chemotherapy regimens, were enrolled and encouraged to log into the system and report symptoms at each follow-up visit or, alternatively, to access the system from home.
Numerous toxicities (grades 3 to 4) reported from home prompted clinician interventions. Patients were capable of reporting symptoms experienced during chemotherapy, and their reporting often led to clinical interventions and changes in the care plan—indicating that the use of the Internet can be beneficial for the treatment and monitoring of home patients diagnosed with cancer (Basch et al., 2005).
Finally, care following organ transplant requires an ongoing monitoring of the patient’s health status as well as the patient’s active involvement in this process. Regular spirometry monitoring of lung transplant recipients, for example, is essential to early detection of acute infection and rejection of the allograft. A prospective study investigated the impact of a web-based telemonitoring system providing direct transmission of home spirometry to the hospital. The study demonstrated that home monitoring of pulmonary function in lung transplant recipients via the Internet is feasible and provides very reproducible data, yet “it has only a mild sensitivity for the detection of acute allograft dysfunction” (Morlion et al., 2002).
As the use of telehealth technologies emerged in the area of home care, most of the earlier studies were either pilot exploratory projects or clinical trials with small sample sizes. One of the earliest clinical trials in the area of telehealth in home care (also referred to as telehomecare) with a large sample size was a study by Johnston and colleagues (2000). This was a quasi-experimental study in which newly referred patients diagnosed as having congestive heart failure, chronic obstructive pulmonary disease, cerebral vascular accident, cancer, diabetes, anxiety, or need for wound care were randomly assigned to either routine home care or a remote video system with peripheral monitoring devices that also allowed nurses and patients to interact in real time. A total of 102 subjects were enrolled in the experimental group and 110 in the control group. The study findings demonstrated no differences in the quality indicators (medication compliance, knowledge of disease, and ability for self-care) or patient satisfaction. Although the average direct cost for home health services was $1,830 in the intervention group and $1,167 in the control group, the total mean costs of care, excluding home health care costs, were $1,948 in the intervention group and $2,674 in the control group.
An extensive recently completed randomized clinical trial of telehomecare, and currently the largest telehomecare randomized study reported in scientific literature, is the Informatics for Diabetes Education and Telemedicine study (Shea et al., 2009). This project compared telehomecare case management with usual care of older, ethnically diverse, medically underserved Medicare beneficiaries with diabetes mellitus residing in medically underserved areas of New York State. The sample included 1,665 Medicare recipients with diabetes, age 55 or older. Findings demonstrate that telehomecare case management resulted in net
improvements in blood glucose, cholesterol, and blood pressure levels over 5 years.
Another large (although not randomized) study of home telehealth, carried out by the Veterans Health Administration, introduced a national home telehealth program called Care Coordination/Home Telehealth (Darkins et al., 2008). The purpose of this ongoing initiative is to coordinate the care of veteran patients with chronic conditions in order to avoid or reduce unnecessary admission to long-term institutional care. Routine analysis of data from a cohort of 17,025 patients in 2008 shows the benefits of a 25 percent reduction in number of bed days of care, a 19 percent reduction in number of hospital admissions, and overall high satisfaction rates for patients enrolled in the program (Darkins et al., 2008). The cost of the program was estimated to be $1,600 per patient per year in 2008, which the authors argue is substantially less than other noninstitutional care programs or nursing home care (Darkins et al., 2008).
Rojas and Gagnon conducted a systematic review of the key indicators for assessing telehomecare cost-effectiveness (Rojas and Gagnon, 2008). Their analysis showed that there is fair evidence of cost-effectiveness for many telehomecare applications. However, the heterogeneity among cost-effectiveness indicators in the applications reviewed and the methodological limitations of the studies impede the generalizability of the findings.
These telehealth applications require operation by the patients or their families (including use of a web interface and, in most cases, operation of a monitoring device, such as a glucose reader, a blood pressure cuff, or a spirometer). This obviously has implications for eligibility criteria, as training is often required for patients or families before they can operate the system (requiring the presence and involvement of an informal caregiver when the patient has cognitive or functional limitations). An additional implication for the health care provider at the other end, who receives the collected data sets and, in some cases, conducts videoconference-based consultations, pertains to the training of providers as well as the need for technical support when technical problems arise at either end.
WEB-BASED COMMUNITIES FOR HOME CARE PATIENTS
In addition to web-based applications that follow an institution-centric approach and link home care patients to health care providers, the Internet also supports a consumer-centric model and enables the creation of networks between home care patients diagnosed with the same condition, families or other informal caregivers, communities, and the general public. Such networks are often referred to as virtual communities. A virtual community is a social entity involving several individuals who relate to one another by the use of a specific communication technology that bridges
geographic distance (Demiris, 2005). While traditional communities are determined by such factors as geographic proximity, organizational structures, or activities shared by the members of the community, the label “virtual” declares properties that, unlike these of a traditional community, are based on the use of advanced technologies that support interactions and exchange of information between members who may never physically meet (Demiris, 2005).
Virtual communities demonstrate core attributes wherein members have a shared goal, interest, need, or activity that is the primary reason for being part of the community. They engage in repeated, active participation with access to shared resources. Defined policies determine the type and frequency of access to those resources. The sustainability of the community relies on reciprocity of information, support, and services among members (Whittaker, Isaacs, and O’Day, 1997). A virtual community with a health care purpose or focus is a group of people, as well as the social structure that they collectively create, based on the use of telecommunication with the purposes of educating, providing support, discussing issues, sharing resources, consulting with experts, and sustaining relationships beyond or without face-to-face events. Numerous such applications function as self-help groups of individuals diagnosed with the same clinical condition or undergoing similar treatment. As Finn (1999) demonstrated, virtual self-help groups can provide many of the processes used in face-to-face self-help and mutual aid groups. The emphasis in such virtual communities is on mutual problem solving, information sharing, expression of feelings, mutual support, and empathy.
Technologies for virtual communities include, among others, online message boards and automatic mailing list servers for asynchronous communication, videoconferencing, Internet relay chat, group and private chat rooms for synchronous communication, and even social networking platforms, such as Facebook or Twitter. In some cases, communication is not “moderated”; that is, there is no entity responsible for reviewing and filtering posts that are thought to be inappropriate or in violation of any of the rules and terms of the virtual community. In these cases, the community relies largely on the normative processes of its own internal social norms “to define and enforce the acceptable behavior of the community members” (Burnett, Besant, and Chatman, 2001). In other cases, a moderator or group of moderators oversees and facilitates the interaction among members.
In a systematic review of online health care communities in 2004 (Eysenbach et al., 2004), researchers compiled and evaluated the evidence on the effects on health and social outcomes of computer-based peer-to-peer communities and electronic support groups. The authors identified a lack of robust evidence of the effectiveness of consumer-led peer-to-peer communities, partly because most of these communities have been evaluated only in
conjunction with more complex interventions or involvement with health professionals (Eysenbach et al., 2004). However, given the great number of unmoderated web-based peer-to-peer groups, further research is needed to assess when and how electronic support groups can be effective (Eysenbach et al., 2004).
Virtual communities can involve patients, family members, informal caregivers, and even researchers. The Comprehensive Health Enhancement Support System (CHESS), developed by the University of Wisconsin, is a platform that provides services designed to help individuals cope with a health crisis or medical concern, but it also invites researchers to use resources and share knowledge and findings (Gustafson et al., 1992). The system provides timely access to such resources as information, social support, and decision-making and problem-solving tools when needed most. This application and its modules and consortia are good examples of a virtual community that serves individual patients’ and caregiver needs while also providing an active laboratory for researchers and organizations (Gustafson et al., 1992).
The same advanced telecommunication technologies that can facilitate virtual communities of patients and their families can also enable health care providers to form virtual teams, interacting and collaborating on cases even when separated by large geographic distances. Numerous health care settings lack the interdisciplinary resources required for efficient chronic disease management. Clinicians and researchers at Rush University Medical Center in Chicago developed the Virtual Integrated Practice, a process that creates virtual care teams (Rothschild et al., 2004) that target four strategies: (1) communications, (2) process standardization, (3) group activities, and (4) self-management. The conditions covered are diabetes, chronic obstructive pulmonary disease, and urinary incontinence. Communication among members of the virtual team is both synchronous and asynchronous. Virtual health care provider teams in general can ensure continuity of care as they use a common platform for exchange of messages, opinions, and resources. Such teams can be essential to successful disease management and to providing continuity of care for the patients.
PERSONAL HEALTH RECORDS
A concept emerging from the proliferation of web technologies in people’s homes is the personal health record (PHR). The National Alliance for Health Information Technology defines a personal health record 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” (National Alliance for Health Information Technology,
2008). Specifically, a personal health record is a tool to use in “sharing health information, increasing health understanding and helping transform patients into better-educated consumers of health care” (Kahn, Aulakh, and Bosworth, 2009).
A recent initiative to implement a PHR system was launched in the Veterans Health Administration system (U.S. Department of Veterans Affairs, 2010). Called MyHealtheVet, this system focuses at the moment primarily on appointments, medication requests, protecting the identity of the users, and helping veterans obtain a variety of services. The electronic medical record software vendor, Epic, has also introduced a PHR application currently used by Kaiser Permanente, the Cambridge Health Alliance, and others. These systems are widely used by consumers because they provide important functionality, which could lead to improved health (Mechanic, 2008).
The PHR concept is expected to enable a shift from institution-centric to patient-centric models of care as personal health records can be used for sharing such health information as health finances, diagnoses (problem lists), allergies, immunizations, insurance information, and medications in an easy way that helps people manage their own health (Hassol et al., 2004). In that context, it is the patient and not a health care facility who owns and controls his or her data. For that reason, the industry is showing a growing interest in PHR applications. Such applications introduced recently by Google (Google Health) and Microsoft (HealthVault) can potentially enable consumers to gain access to their health information via the Internet without having to use special hardware or have organizational agreements in place.
Traditional electronic medical record (EMR) systems are controlled and maintained by health care providers, whereas a PHR system is controlled and maintained by the patient. The integration of EMR and PHR systems is envisioned to enable a synergistic model in which PHR data can augment EMR data, allowing for a holistic and collaborative model of care and shared decision making; however, this is not yet a reality. This goal requires addressing several challenges, including technical issues (enabling patient control and authentication, synchronization of records, data encryption, diffusion of interoperability standards), sociotechnical issues (e.g., providers needing to develop trust in PHR data, consumers called on to assume a more active role in the health care delivery process), changes in health care providers’ workflow, and education of both consumers and providers, as well as legal and regulatory challenges.
PHR systems potentially can be used in combination with telehealth or other web-based applications, allowing patients to store and process their own data resulting from disease management efforts or communication with health care providers. PHR systems can therefore also be used for disease prevention and wellness promotion, in which consumers who are
not necessarily labeled as patients (as they may not have a clinical condition) can manage their lifestyle choices, plans, finances, encounters with the health care system, etc. With their potential to empower consumers and place the patient at the center of decision making and management of his or her own health, PHR tools may in the near future significantly affect home care.
Robotic applications using artificial intelligence principles and, in some cases, with anthropomorphic features have traditionally been used in the clinical setting, mostly in an experimental mode (e.g., robotic-assisted surgery, including robotic-assisted laparoscopic pyeloplasty, cystectomy, etc.) However, technology advances have introduced robotic applications into the home to address cognitive, functional, and psychological issues.
The Robot/CAMR suite by Johnson and colleagues (2007) includes a robotic application with a conventional force-reflecting joystick, a modified joystick therapy platform, and a steering wheel platform with embedded software to provide extrinsic motivation and outcome assessment for stroke rehabilitation home care patients. Recent reports from a number of laboratories using enhanced sensorimotor training protocols, particularly those with robotic devices, have indicated modest success in reducing impairment and increasing motor power in the exercised limb of patients with stroke when compared with control individuals (Volpe, Krebs, and Hogan, 2001).
The Nursebot project, led by the University of Pittsburgh and Carnegie Mellon University (Montemerlo et al., 2002), focuses on a robot as a platform for intelligent reminding (including reminders of medication or upcoming appointments), telepresence (connecting providers with patients via video), surveillance (to detect emergencies), mobile manipulation (which integrates robotic strength with a person’s senses and intellect), and social interaction (with the robot that can take over certain social functions) for older home care patients.
The use of robotic pets has been explored in long-term care facilities, where residents often experience social isolation and loneliness. Banks, Willoughby, and Banks (2008) explored the use of a robotic dog as part of animal-assisted therapy to treat loneliness and compared it with the use of actual living dogs that are in many cases not allowed in these facilities. Findings indicated that the two groups were comparable in terms of outcomes (both groups had statistically significant and comparable improvements in residents’ levels of loneliness).
Another robotic application that has been tested in different settings is Paro (Wada and Shibata, 2007), a therapeutic robot baby harp seal that has been designed to create a calming effect on, and elicit emotional responses
among, older adults and their caregivers. The robotic application has five kinds of sensors: (1) tactile, (2) light, (3) audition, (4) temperature, and (5) posture sensors, with which it can perceive its environment and people in it. It can recognize light and dark with the light sensor, being stroked and beaten with the tactile sensor, or being held with the posture sensor. Finally, it recognizes the direction of voices and words, such as its name, greetings, and praise with its audio sensor. The system has been tested with encouraging findings for its sociopsychological and physiological influences on older people and their caregivers in homes and assisted living facilities and for both healthy elders and elders with dementia.
A “smart home” is a residence equipped with technology installed as an integral part of the infrastructure to facilitate monitoring of residents, or promote independence, and increase residents’ quality of life (Demiris, 2008). The technology does not require training of or operation by the resident, thereby distinguishing smart home applications from standalone units that can be used in the home setting and need to be operated by the end-user (e.g., blood pressure cuffs, videophones) or software applications that require end-user initiation and training.
As technology advances, smart home applications are being developed worldwide. The Center for Future Health at the University of Rochester, for example, has developed a Smart Medical Home as a highly controlled environment that includes infrared sensors, biosensors, and video cameras (Marsh, 2002). The Aware Home at the Georgia Institute of Technology explores 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 (Finland, Ireland, Lithuania, Norway, and the United Kingdom) joined their efforts for the ENABLE project (Cash, 2003), which promotes the well-being of people with early dementia with several features, such as a locator for lost objects, a temperature monitor, and an automatic bedroom light. In Toulouse, France, the PROSAFE project is using a set of infrared motion sensors to support automatic recognition of resident activity and of possible falls (Chan et al., 1999).
Hayes evaluated the use of continuous, long-term in-home monitoring to assess neurological function in healthy and cognitively impaired elders (Hayes et al., 2008). A total of 14 older adults (ages 65 and older) living independently in the community were monitored in their homes by using an unobtrusive sensor system that enabled assessment of walking speed and level of activity. Findings demonstrate the feasibility of this approach and also suggest clear potential advantages to this methodology over conven-
tional episodic testing in a clinic environment. A sensor system was also used to address the challenges of medication adherence. In another study (Hayes et al., 2009), a context-aware reminder system, which generated reminders at an opportune time to take medication, was evaluated with 10 participants age 65 or older, living alone and managing their own medications. Adherence and activity in the home were measured using a system of sensors, including an instrumented pillbox. The study indicates that context-aware prompting may provide improved adherence over standard time-based reminders.
A systematic review of smart home projects identified 114 publications for 21 distinct ongoing smart home projects and initiatives (Demiris and Hensel, 2008). The majority of these projects address safety monitoring and assistance (e.g., use of heat sensors detect environmental hazards, such as fire or gas leaks, and safety features, such as automatically turning on bathroom lights when the resident gets out of bed), security monitoring and assistance (e.g., use of motion sensors that detect intruders), cognitive and sensory assistance (e.g., automated or self-initiated reminders, cognitive aids, such as lost key locators, and technologies that aid users with sensory deficits in vision, hearing, and touch), and overall wellness (e.g., combination of motion sensors, pressure pads, and gait monitors to assess activity levels, use of bed sensors to assess sleep quality).
In spite of the growing number of initiatives in this area, the field is in relatively early stages, focusing on feasibility testing and currently lacking an extensive body of evidence of clinical effectiveness. Most of the identified studies demonstrate the feasibility of the technological solution or describe preliminary evaluation approaches with a limited number of subjects, most commonly in a laboratory setting; only a few present results of testing in actual homes or communities (Elite Care, 2005; Demiris et al., 2006; Rialle et al., 2006).
HUMAN FACTORS CHALLENGES AND CONSIDERATIONS
The use of technology applications and tools in home health care raises a number of issues that human factors expertise is called on to address. The sections below address the issues of privacy and confidentiality; usability; data transmission and interoperability; and policy, economic, and ethical considerations.
Privacy and Confidentiality
Systems that use the Internet or other means to transmit and exchange clinical data call for an examination of how privacy and confidentiality with regard to individuals’ health information are protected. Information
privacy is the right of care recipients to control the use and dissemination of information that relates to them, and confidentiality is a tool for protecting the patients’ privacy. In the United States, the Notice of the Proposed Rule from the Department of Health and Human Services concerning Security and Electronic Signature Standards was introduced in 1998 (U.S. Department of Health and Human Services, 1999) as part of the Health Insurance Portability and Accountability Act (HIPAA) that was passed in 1996. This rule, effective in 2000, proposes standards for the security of individual health information and electronic signature use for health care providers, systems, and agencies. These standards refer to the security of all electronic health information and have a great impact on the design and operation of information technology applications in home care.
The use of the Internet in disease management calls for a clarification and definition of the issues of ownership of and access to monitoring data. In many web-based applications, patients record monitoring data and transmit them daily to a web server, owned and maintained by a private third party that allows providers to log in and access their patients’ data. In this context, it is important to determine who is authorized to access part or all of the patient record that is stored on a web server and to control such access rights. This process needs to address not only possible threats to data privacy but also to ethical debates about the restructuring of the care delivery process and introduction of new key players (such as third-party vendors who store and maintain data repositories).
When it comes to personal health records, the privacy issues can be complex. This is because new PHR tools are not necessarily covered by HIPAA regulations. Many PHR developers (e.g., Google, Microsoft) are not covered entities as defined by HIPAA. There is an urgent need to address this gap in the current HIPAA regulations and to establish “additional legal protections to reach these new PHR developers and hosting organizations” (Kahn et al., 2009).
Usability is critical to the design of information technology applications in home care, as it refers to the accessibility of the design and the specifics of an interface that lead to rapid learning, good skill retention, and low error rates. The implication for IT-based systems in home care is that a usable system is one in which end-users are able to communicate with each other, find information, and navigate the software and hardware with ease (Preece, 2000). A large segment of home care patients are elders and in some cases have functional limitations due to aging, or their diagnosis, or both. A functional limitation describes a “reduced sensory, cognitive or motor capability associated with human aging, temporary injury, or permanent
disability that prevents a person from communicating, working, playing or simply functioning in an environment where other people in the population can function” (Electronic Industries Alliance and the Electronic Industries Foundation, 1996, p. 20).
Although information technology can play a great role in disease management, the fastest growing segment of the U.S. population—people over age 50—is often at a disadvantage in spite of emerging innovative tools, because system designers often fail to consider them as a potential user group. Accessibility is a major feature of an interface, but in many cases it is ignored by system designers. Web-based applications targeting home care patients should aim to reach a high level of functional accessibility (Demiris, Finkelstein, and Speedie, 2001) and undergo rigorous usability tests. For that purpose, there are design considerations and guidelines that can inform the implementation of information technology applications in home care (Demiris, Finkelstein, and Speedie, 2001).
Although the opportunities to use human factors methods in designing health IT systems are many, these methods have too seldom been employed in such design efforts. The challenges of usability offer many examples of such opportunities.
Human factors methodologies that can be applied to ensure that end-users’ needs and expectations are reflected in the design and implementation of a system include paper prototyping and sketching, scenarios and storytelling, field studies and observations, interviews and focus groups, and simulation and modeling. These methods aim to capture the end-user’s perspective, needs, and preferences as well as their current workflow or routine. Paper prototyping, for example, allows designers to create system interfaces on paper and explore numerous options to solicit end-user feedback before developing actual prototypes that can be costly. Similarly, scenarios and storytelling allow the end-user to describe real and hypothetical situations that reveal ways in which an IT system can be used to enhance or redesign the process and information flow. Finally, simulation and modeling allow designers to assess how end-users react to conditions or situations that would be introduced with a new system, without actually developing the finished product.
Arsand and Demiris (2008) propose a framework for user-centered methods for designing patient-centric self-help tools that have implications for home care systems. Specifically, they recommend developing and testing a prototype with real patients who have a need for the tools’ functionalities, using scenarios and storytelling as effective ways of explaining how a technical solution works for the patients, as well as of assisting caregivers to gain an understanding of the patient’s experience, needs, and expectations. Such a process is dynamic and iterative and requires designers to allocate sufficient time for several meetings with end-users so they can understand
the possibilities that the technology provides and let their own creative ideas bloom. Furthermore, they recommend planning for extra iterations on the prototype design and testing with real users, selecting user-centered methods of human-computer interaction that are most relevant for a given context and user group and using the triangulation approach in the process of designing good patient-centric tools.
Ultimately, as design specifications and usability testing become widely diffused and allow for a repository of specifications and standards for commonly used IT tools, it may be possible for clinicians to systematically identify characteristics and conditions of patients, their associated environments, and available resources in order to accurately prescribe the appropriate technology tool that will support their care and disease management at home (or to determine technological approaches that will not work with a given patient’s or environment’s conditions).
Data Transmission and Interoperability
Technology-based applications in home care require in many cases the secure exchange of clinical data between different systems or data sources to group together the wide range of data required for disease management. In order to facilitate the appropriate transmission and interpretation of information, a semantically sound and technically feasible set of standards to facilitate this information exchange is required. Goossen defined a framework of relevant standards for using clinical data in information technology (Goossen, 2003). These standards include clinical, vocabulary, messages, workflow, and technical standards.
Clinical standards, such as guidelines indicating evidence-based care, must be clearly reflected in the domain knowledge included in programs for disease management and wellness.
Vocabulary standards pertain to terminologies in different formats and usually developed for specific purposes, such as clinical documentation, comparison of data, or statistical reporting.
Standards for messages address the issue of interoperability and focus on the electronic exchange of information within or between health information systems. The classic example is Health Level Seven (HL7) (Aditya et al., 2003), which provides standards for the exchange, management, and integration of data that support clinical patient care and the management, delivery, and evaluation of health care services. Such an interoperability standard is essential when it comes to exchange of data between a home-based application and the electronic medical record of a clinical facility. Current HL7 v3 message models, e.g., for patient care, do allow for the
patient to be “author of health information,” thus respecting self-care responsibilities.
Workflow standards describe the tasks and processes of the care plan, involved stakeholders and timeline, required interactions, and transactions. For example, in home care, there is a detailed care plan that dictates the number of home care visits, their goals, who conducts them (registered nurse, nursing aid, social worker, etc.), and rules for specific processes (e.g., capturing of vital signs).
Technical standards address infrastructure, networking, and security issues. Particularly relevant for disease management applications are the Internet protocol (TCP/IP) for the infrastructure and Extensible Markup Language (XML) for the technical expression of messages.
Different entities are working toward promoting interoperability among software or hardware applications in home care and disease management. Continua Health Alliance, for example, is a nonprofit, open-industry coalition that aims to establish a system of interoperable personal telehealth solutions (Continua Health Alliance, 2010). Specific objectives of this coalition include the development of design guidelines that will enable vendors to build interoperable sensors, home networks, telehealth platforms, and other services and the establishment of a product certification program pertaining to interoperability across certified products.
The public policy issues related to the use of information technology in home health care are the same as those that arise for the use of IT in health care in general and involve several levels (state, federal) as well as numerous stakeholders. Policy considerations include access to care; the quality, safety, and efficacy of the delivered services; and the issues of cost and reimbursement. The issue of access to care is actually a challenging one. At first sight, because information technology bridges geographic distance, it can be seen to increase access to care. However, the widespread use of technology in home care may have the effect of reducing access to care, when its use actually increases the cost of the care.
For example, commercially available devices that function as standalone units can often be purchased at a relatively low cost and used by a patient with a chronic condition to monitor that condition at home. A monitoring unit, however, that becomes part of an information technology application, allowing the transmission of the same monitoring data sets through a regular phone line or the Internet to a central server, can often cost 50 times that price or more. Given the limited resources of the health
care system and the challenges that home care agencies face, it may be that only a subset of home care patients will have access to such sophisticated and perhaps more beneficial IT-based systems.
In addition, access to care may be affected by the so-called digital divide. In the late 1980s, the term “digital divide” was used to describe the division between people who had a computer and those who did not. Nowadays, however, a similar divide may pertain to infrastructure requirements for the technology. Several web-based home care applications, for example, require high-speed Internet infrastructure in the home, which may not be available in all homes, especially in rural and underserved areas.
Reimbursement becomes an essential component of the planning for maintenance of existing systems and the design of new ones. The Centers for Medicare & Medicaid Services (CMS) states that videoconferencing and related technologies can be used to provide appropriate medical care over geographic distances, but that reimbursement, aside from a small fee paid to the site where the patient is located, will be equivalent to what would have been provided for a face-to-face visit. There is only a token reimbursement for the costs of the associated technologies when used in a rural setting (Center for Drugs and Devices, 1996). Specifically in the field of home care, CMS reimburses for virtual visits (videoconferencing to the home) and remote monitoring at a set amount (prospective payment system) that makes no specific provision for the costs of the technologies (Harris, Gottlieb, and Weiner, 2005). CMS is moving away from reimbursement for services to payments for outcomes (pay for performance) (Hyler and Gangure, 2004), and this will ultimately affect technology-based solutions as well.
A further public policy issue pertains to potential concerns about safety and efficacy of IT devices and systems. The Food and Drug Administration (FDA) has the responsibility for ensuring the safety and efficacy of all such devices marketed in the United States (Hallowell et al., 2003). Devices and tools used for monitoring of disease conditions, such as pulse oximeters, spirometers, and the like are subject to FDA oversight. Embedded IT (and specifically software) is reviewed as an integral device component. It is important in this context to assess FDA’s evolving position on software that is used for medical purposes but is not intrinsically bound to a particular device, such as an electronic medical record system, a decision support system, or a web-based disease management program. While the FDA currently defines such systems as tools that provide assistance to health care professionals in the treatment of their patients (thus, these tools themselves have no direct patient impact), this position may change in the near future. In this context, the proliferation of mobile phone devices and computing technologies introduces a new definition for the term “medical device” beyond the traditional standalone appliance that was a “closed” system and served only one clinical purpose. The diffusion of open-source applications
and the development of clinical applications for mobile phones and other platforms are creating multipurpose tools that can also function as medical devices. The extent to which mobile phones or other platforms can be validated or tested as medical devices because of specific functions or features they support is currently a fundamental unresolved regulatory issue.
A policy implication well documented in the telehealth literature that applies to the broader use of information technology in home care is the delivery of health care across state borders (Kluge, 2004). For most health care professions, the site of practice is considered to be where the patient (not the practitioner) is located. Health insurance is regulated by the states. Thus, this affects cases in which reimbursement for direct care is sought but technology is used to provide services across state borders.
Finally, policy barriers exist when technology developments are rapid and introduce new realities that have not been appropriately addressed by policy makers, as is the case with personal health records. For example, the financial and clinical data held by provider organizations are not well linked, even within an organization. This limits the kinds of tools that could be developed for personal health records to help consumers understand their treatment options available from their own health plans (Kahn et al., 2009). The policy changes that are likely to lead to improved consumer adoption of personal health records include establishing standards for PHR information, facilitating secure exchange of health information, and improving consumers’ access to the records and their understanding of their role and capabilities. The diffusion of PHR systems will also be facilitated by a large body of evidence demonstrating their effectiveness; thus, longitudinal studies and rigorous research initiatives can further this field and provide insight into new paradigms of home care.
As Polisena and colleagues (2009) point out, an analysis of the economic impact of home-based IT applications must focus on the incremental costs and health benefits associated with the application of the program to a population of patients. Such economic studies must specify and justify the perspective from which the home-based IT programs and health resource use are measured. Societal, health care system, third-party, and patient/family perspectives have a unique focus that informs the costs that need to be included in the analysis. In addition to direct costs (which include cost of program administration, IT delivery, training and maintenance, health care costs, and patient-borne costs pertaining to disease management), indirect costs (such as patient or caregiver’s productivity losses, providers’ traveling time to the patient’s residence) also need to be considered.
Polisena et al. (2009) developed a framework for the conduct of economic evaluation of home telehealth projects for patients with chronic conditions, calling for the assessment of incremental costs and incremental outcomes of each health care program evaluated. They argue that the majority of published studies are not economic evaluations of home telehealth and cannot assist in determining whether a treatment is justifiable based on the impact on costs and treatment outcomes; often studies interpret a reduced use of health care resources as evidence of improved outcomes (Polisena et al., 2009). Use of health care resources use may be limited, however, due to fewer contacts with home telehealth, meaning reduced frequency of access to other services but not necessarily a reduced need for these services.
This highlights the significance of inclusion of clinical outcomes (which may be surrogate outcomes, such as disease markers or patient’s quality of life) in economic evaluations. Introducing technology into the residential setting may initially increase overall costs (by adding costs of software/hardware, training, installation, and maintenance). In these cases, a cost-effectiveness, or cost-utility analysis, can highlight the potential long-term impact of the IT-based application. A cost-effectiveness analysis needs to include data on clinical outcomes associated with the particular disease or condition studied, such as event rates and deaths. Often it is the case that an economic evaluation takes place within a limited time frame that does not facilitate a demonstration of differences in long-term clinical outcomes, as would be the case with longitudinal studies. In these cases, and especially when studying populations with chronic diseases, surrogate markers (such as glycemic control for diabetes, systolic blood pressure for congestive heart failure) can be used to address clinical outcomes. An economic evaluation should include a sensitivity analysis to determine the robustness of the study findings based on the assumptions made (and by varying the underlying assumptions over a range of possible values).
A systematic review of economic evaluations for home telehealth identified a total of 22 studies on home telehealth for chronic diseases published between 1998 and 2008 (Polisena et al., 2009). Home telehealth was found to have cost savings from the health care system and insurance provider perspectives in all but two studies, but, the authors argue, the quality of the studies was generally not high. Current evidence suggests that home telehealth has the potential to reduce costs, but its impact from a societal perspective remains uncertain until higher quality studies become available.
When a system allows stakeholders of health care delivery services to interact while separated by distance, the issue of what has been called “pro-
gressive dehumanization” of interpersonal relationships is raised, namely, the conduct not only of the professional but also of the interpersonal aspect of communication online or via communication technologies with a decreasing number of face-to-face interactions. IT-based home care interventions have the potential to bridge geographic distances and in some cases allow for anonymity that might be desired for a specific medical condition; however, such applications might be lacking the sense of touch and interhuman close contact that occurs in face-to-face meetings. Virtual communities represent a physically disembodied social order, and some argue (Winner, 1990) that it will eventually compete with a structure or network of entities that occupy spatial locations. In this context, the argument is that “the fabric of human relationships and communities rests on real presences, real physical meetings and relationships” (Horner, 2001), and their elimination may affect the patient-provider relationship and perhaps even the traditional dimensions of home care.
A theoretical framework for the definition of obtrusiveness in home telehealth technologies was developed by Hensel, Demiris, and Courtney (2006). In this framework, obtrusiveness pertains to the features of information technology that may be perceived as prominently undesirable by an individual user. In all, 22 categories of what may be perceived as obtrusiveness were identified on the basis of a review of the literature and were grouped into 8 dimensions (including, among others, the physical dimension, privacy, usability, human interaction). This effort represents an initial step toward developing measures of obtrusiveness associated with information technology applications in home care and a tool to systematically address ethical considerations involved in such applications.
This review highlights the diversity of technology applications and tools in home health care and the promising role they can play for a variety of stakeholders (including patients, families, health care providers, communities, and the general public) and for a multitude of clinical areas (covering physiological, functional, cognitive, social, and psychological parameters as well as holistic aspects of wellness and quality of life). The clear advantages that IT integration in home care carries include the introduction of several stakeholders who can more easily and efficiently communicate in spite of geographic distance and the ability to generate new types of data (e.g., activity levels, sleep quality) and more frequently collect well-established parameters (such as vital signs) without requiring the actual presence of health care providers in the residential setting.
The Use of IT: Shared Decision Making and Patient Empowerment
One of the expectations resulting from the use of IT in home health care is that it will empower patients and their families by providing them with access to information, peers, and other networks and by actively engaging them in the disease management or wellness promotion initiatives. The empirical evidence that involvement in healthcare decisions makes a significant and enduring difference to health care outcomes is not unequivocal (Savage and Armstrong, 1990; Stewart, 1995; Kinmonth et al., 1998), although some studies support this hypothesis. One difficulty (among many) is that the involvement of patients in decisions has been left undefined. It is usually conceptualized as patient-centeredness (Roter, 1989; Stewart et al., 1995), which is a broad and variably interpreted concept that is difficult to assess using current tools (Mead and Bower, 2000a, 2000b). Nevertheless, the ethical need to respect autonomy and respond to home care patients’ desire for more involvement in decision making is becoming widely recognized (Richards, 1998; Coulter, Entwistle, and Gilbert, 1999).
A treatment decision-making framework based on information exchange, deliberation about treatment options, and agreement on the treatment to implement has been developed by Charles and colleagues (2003). In this framework, three approaches are presented to label the process and outcome of decision making:
The pure paternalistic approach is characterized by health care provider control, whereby the provider determines the amount and kind of information that is given to the patient. The information flow is unidirectional. The provider deliberates about the benefits and risks of available options and reaches a decision without patient input (Charles et al., 2003).
The pure informed approach is characterized by a division of labor and the preservation of patient autonomy. The provider makes available to the patient information on treatment options, challenges, and risks. The patient assesses the situation in the context of her or his own value system and preferences and makes a treatment decision.
The pure shared approach is characterized by ongoing interaction and information exchange between patient and provider in all stages of the decision-making process. The information flow is bidirectional. The provider offers information about all available options and risks, and the patient discusses personal preferences, his or her value system, lifestyle, and personal preferences. The decision-making process includes an extensive discussion and nego-
tiations in search of the best option to pursue. The decision-making process is a dynamic one in which both providers and patients may shift away from their initial position (Charles et al., 2003).
Shared decision making is increasingly advocated as an ideal model of decision making about treatment in the clinical encounter in general and in home care specifically. In the shared model, the process by which the interaction is conducted to reach an agreement may be determined at the outset of the encounter or may develop as the encounter unfolds and be shaped dynamically by the ongoing communication. Information sharing is a prerequisite to shared decision making.
It is a challenge to expect all patients to enroll in this process as equal partners, as one may argue that there may often be a power imbalance in the provider-patient relationship. Obviously health care providers have superior knowledge of the options and issues involved, as well as clinical experience, and therefore join the process as experts (Charles, Gafni, and Whelan, 1999). A patient may often participate in the encounter feeling vulnerable due to their illness or fear of the unknown. Additional issues, such as health literacy, income, gender, and cultural barriers, may impede patients and prevent them from expressing their preferences or negotiating with the physician (Charles et al., 1999). As Guadagnoli and Ward point out, it is a challenge for providers who want to practice a shared approach to provide a safe environment for patients, allowing them to be comfortable in exploring information and negotiating options (Guadagnoli and Ward, 1998). The use of information technology (and personal health records specifically) can increase access to information for patients and provide them with options as well as tools to capture their health behaviors and their needs. However, it remains to be explored whether IT use in home health care can indeed support shared decision making and ultimately lead to patient empowerment.
As technology advances, rapid developments in the areas of robotic applications and smart homes are anticipated. Currently, research is under way in Japan to explore the role of humanoids in home health care and nursing homes. The term “humanoid” describes a robotic application with artificial intelligence features that is anthropomorphic. Japan’s aging population has ignited efforts to design fully functional robots that can aid elders in their homes or long-term care facilities and address the nursing workforce shortage.
While such developments may not be fully explored in the immediate future but may become long-term trends, there are developments that are
anticipated to affect the use of IT in home health care in the very near future. These include Web 2.0 and the proliferation of wireless communications.
Web 2.0 refers to web development and web design that facilitates interactive information sharing, interoperability, and collaboration. A Web 2.0 site allows its users to interact with other users or to change website content, in contrast to noninteractive websites that limit users to the passive viewing of the information provided. Examples of Web 2.0 include web-based communities, social networking sites, and video-sharing sites. The concept of Web 2.0 enables virtual community tools and PHR applications, as well as new and innovative ways for different stakeholders to communicate and collaborate.
Wireless handheld computers and cell phones with expanded computing abilities are widely used and continue their diffusion in the U.S. population. Smart phones and other similar devices can play a role in home care, whether as tools to record daily activities (e.g., nutrition, exercise), to provide reminders, or for even more sophisticated services (e.g., use of global positioning systems to identify health care providers and facilities, built-in sensors to assess amount and type of physical activity and compare with predefined goals).
As is the case with any IT implementation, when exploring options for new and innovative technologies in home health care, one has to predict or prepare for unintended consequences. As new systems are implemented to enhance home care services, one needs to address the possibility of such technologies removing choice and control from users as they learn to rely on automation. There are fears that sophisticated applications, like robotic tools or smart homes, may result in a reduction of social interaction, or that they may provide tools that substitute for personal forms of care and communication (Tetley, Hanson, and Clarke, 2001). Since the technologies are introduced into one’s home, the warning by Wylde and Valins (1996) against creating “societies of high-tech hermits” becomes even more relevant.
In addition, the degree to which automated applications lessen the sense of personal responsibility on the part of users or their caregivers must be weighed against associated benefits. Informal caregivers may become less vigilant in monitoring health changes in their loved ones, and the patients themselves may become less vigilant in health self-monitoring or self-management. Further research and dialogue need to address eligibility criteria and user characteristics or clinical conditions that may be more suitable for IT applications in home care. Which populations may benefit the most from telehealth or smart home applications? When do the require-
ments for infrastructure and training outweigh anticipated benefits? As Stip and Rialle point out (2005), the issues of individual freedom, personal autonomy, informed consent, and confidentiality have to be examined in the context of the target population. They use an example of an IT application for patients with schizophrenia, a condition that causes distortion of reality, often in the form of delusions of persecution and psychosensory phenomena, and highlight the likelihood that surveillance technologies may exacerbate such symptoms. It becomes clear that technology toolkits should be developed and used when appropriate and should demonstrate flexibility to address the profile of every user, including not only clinical (physiological, functional, cognitive) but also psychological and social parameters.
ABOUT THE AUTHOR
George Demiris is associate professor of biobehavioral nursing and health systems in the School of Nursing and Biomedical and Health Informatics, School of Medicine, at the University of Washington. His research interests include the design and evaluation of home-based technologies for older adults and patients with chronic conditions and disabilities, smart homes and ambient assisted living applications, and the use of telehealth in home care and hospice.
Aditya, B.S., Sharma, J.C., Allen, S.C., and Vassallo, M. (2003). Predictors of a nursing home placement from a non-acute geriatric hospital. Clinical Rehabilitation, 17(1), 108-113.
Arsand, E., and Demiris, G. (2008). User centered methods for designing patient-centric self-help tools. Informatics for Health and Social Care, 33, 158-169.
Baker, A.M., Lafata, J.E., Ward, R.E., Whitehouse, F., and Divine, G. (2001). A web-based diabetes care management support system. Joint Commission Journal on Quality Improvement, 27(4), 179-190.
Banks, M.R., Willoughby, L.M., and Banks, W.A. (2008). Animal-assisted therapy and loneliness in nursing homes: Use of robotic versus living dogs. Journal of the American Medical Directors Association, 9(3), 173-177.
Basch, E., Artz, D., Dulko, D., Scher, K., Sabbatini, P., Hensley, M., et al. (2005). Patient online self-reporting of toxicity symptoms during chemotherapy. Journal of Clinical Oncology, 23(15), 3,552-3,561.
Bellazzi, R., Larizza, C., Montani, S., Riva, A., Stefanelli, M., d’Annunzio, G., et al. (2002). A telemedicine support for diabetes management: the T-IDDM project. Computer Methods and Programs in Biomedicine, 69(2), 147-161.
Burnett, G., Besant, M., and Chatman, E.A. (2001). Small worlds: Normative behavior in virtual communities and feminist bookselling. Journal of the American Society for Information Science and Technology, 52, 536-547.
Cash, M. (2003). Assistive technology and people with dementia. Reviews in Clinical Gerontology, 13, 313-319.
Center for Devices and Radiological Health, Food and Drug Administration. (1996). Telehealth related activities. Available: http://www.fda.gov/AboutFDA/CentersOffices/CDRH/DivisionofCommunicationMediaFDATVStudio/ucm125788.htm [accessed August 2010].
Chan, M., Bocquet, H., Campo, E., Val, T., and 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.
Charles, C.A., Gafni, A., and Whelan, T. (1999). Decision-making in the physician-patient encounter: Revisiting the shared treatment decision making model. Social Science and Medicine, 49, 651-661.
Charles, C.A., Whelan, T., Gafni, A., Willan, A., and Farrell, S. (2003). Shared treatment decision making: What does it mean to physicians? Journal of Clinical Ontology, 21, 932-936.
Continua Health Alliance. (2010). About the Alliance. Available: http://www.continuaalliance.org/about-the-alliance.html [accessed June 2010].
Coulter, A., Entwistle, V., and Gilbert, D. (1999). Sharing decisions with patients: Is the information good enough? British Medical Journal, 318, 318-322.
Darkins, A., Ryan, P., Kobb, R., Foster, L., Edmonson, E., Wakefield, B., and Lancaster, A.E. (2008). Care coordination/home telehealth: The systematic implementation of health informatics, home telehealth, and disease management to support the care of veteran patients with chronic conditions. Telemedicine Journal and E-Health, 14(10), 1,118-1,126.
Demiris, G. (2005). Virtual communities in health care. In B. Silverman, A. Jain, A. Ichalkaranje, and L. Jain (Eds.), Intelligent paradigms for healthcare enterprises (vol. 184, pp. 121-137). New York: Springer Verlag.
Demiris, G. (2008). Smart homes and ambient assisted living in an aging society: New opportunities and challenges for biomedical informatics. Methods of Information in Medicine, 47(1), 56-57.
Demiris, G., and Hensel, B.K. (2008). Technologies for an aging society: A systematic review of “smart home” applications. Yearbook of Medical Informatics, 33-40.
Demiris, G., Finkelstein, S.M., and Speedie, S.M. (2001). Considerations for the design of a web-based clinical monitoring and educational system for elderly patients. Journal of the American Medical Informatics Association, 8, 468-472.
Demiris, G., Skubic, M., Keller, J., Rantz, M., Parker Oliver, D., Aud, M., Lee, J., Burks, K., and Green, N. (2006). Nurse participation in the design of user interfaces for a smart home system. In Proceedings of the International Conference on Smart Homes and Health Telematics (pp. 66-73), Belfast, Northern Ireland. Amsterdam, the Netherlands: IOS Press.
Demiris, G., Speedie, S.M., and Finkelstein, S. (2001). Change of patients’ perceptions of telehomecare. Telemedicine Journal and E-Health, 7(3), 241-248.
Electronic Industries Alliance and the Electronic Industries Foundation. (1996). Resource guide for accessible design of consumer electronics: Linking product design to the needs of people with functional limitations. A joint venture of the Electronic Industries Alliance and the Electronic Industries Foundation. Arlington, VA: Telecommunications Industry Association.
Elite Care. (2005). Creating an autonomy-risk equilibrium (CARE). Available: http://legacy.lclark.edu/~moss/oatfield%20page.html [accessed May 2010].
Eysenbach, G., Powell, J., Englesakis, M., Rizo, C., and Stern, A. (2004). Health-related virtual communities and electronic support groups: Systematic review of the effects of online peer to peer interactions. British Medical Journal, 328(7449), 1,166.
Finkelstein, J., O’Connor, G., and Friedmann, R.H. (2001). Development and implementation of the home asthma telemonitoring (HAT) system to facilitate asthma self-care. Studies in Health Technology and Informatics, 84(Pt 1), 810-814.
Finn, J. (1999). An exploration of helping processes in an online self-help group focusing on issues of disability. Health and Social Work, 24(3), 220-231.
Goossen, W.T.F. (2003). Templates: An organizing framework to link evidence, terminology and information models in the nursing profession. In Proceedings of the Eighth International Congress in Nursing Informatics, Rio de Janeiro, Brazil.
Guadagnoli, E., and Ward, P. (1998). Patient satisfaction in decision making. Social Science and Medicine, 47, 329-339.
Gustafson, D.H., Bosworth, K., Hawkins, R.P., Boberg, E.W., and Bricker, E. (1992). CHESS: A computer-based system for providing information, referrals, decision support and social support to people facing medical and other health-related crises. In Proceedings of the Annual Symposium on Computer Application in Medical Care (pp. 161-165). Available: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2248029/pdf/procascamc00003-0178.pdf [accessed June 2010].
Hallowell, N., Foster, C., Eeles, R., Ardern-Jones, A., Murday, V., and Watson, M. (2003). Balancing autonomy and responsibility: The ethics of generating and disclosing genetic information. Journal of Medical Ethics, 29, 74-79.
Harris, S.B., Gottlieb, B.L., and Weiner, S. (2005). Regulating broadband. Communications Lawyer, 23, 1-10.
Hassol, A., Walker, J.M., Kidder, D., Rokita, K., Young, D., Pierdon, S., Deitz, D., Kuck, S., and Ortiz, E. (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.
Hayes, T.L., Abendroth, F., Adami, A., Pavel, M., Zitzelberger, T.A., and Kaye, J.A. (2008). Unobtrusive assessment of activity patterns associated with mild cognitive impairment. Alzheimer’s and Dementia, 4, 395-405.
Hayes, T.L., Cobbinah, K., Dishongh, T., Kaye, J.A., Kimel, J., Labhard, M., et al. (2009). A study of medication-taking and unobtrusive, intelligent reminding. Telemedicine Journal and E-Health 15, 770-776.
Hensel, B.K., Demiris, G., and Courtney, K.L. (2006). Defining obtrusiveness in home telehealth technologies: A conceptual framework. Journal of the American Medical Informatics Association, 13, 428-431.
Horner, D.S. (2001). The moral status of virtual action. In T.W. Bynum (Ed.), Proceedings of the Fifth International Conference on the Social and Ethical Impacts of Information and Communication Technologies (vol. 2, pp. 226-235). Gdansk, Poland: Technical University of Gdansk: Ethicomp.
Hyler, S.E., and Gangure, D.P. (2004). Practitioner’s corner: Legal and ethical challenges in telepsychiatry. Journal of Psychiatric Practice, 10, 272-276.
Johnson, M.J., Feng, X., Johnson, L.M., and Winters, J.M. (2007). Potential of a suite of robot/computer-assisted motivating systems for personalized, home-based, stroke rehabilitation. Journal of Neuroengineering and Rehabilitation, 4, 6.
Johnston, B., Wheeler, L., Deuser, J., and Sousa, K.H. (2000). Outcomes of the Kaiser Permanente tele-home health research project. Archives of Family Medicine, 9(1), 40-45.
Kahn, J.S., Aulakh, V., and Bosworth, A. (2009). What it takes: Characteristics of the ideal personal health record. Health Affairs, 28, 369-376.
Kidd, C.D., Orr, R., Abowd, G.D., Atkeson, C.G., Essa, I.A., MacIntyre, B., Mynatt, E., Starner, T.E., and Newstetter, W. (1999). The aware home: A living laboratory for ubiquitous computing research. Lecture Notes in Computer Science Series, vol. 1670. Available: http://www.springerlink.com/content/d598506140k50v26/fulltext.pdf [accessed June 2010].
Kinmonth, A.L., Woodcock, A., Griffin, S., Spiegal, N., and Campbell, M.J. (1998). Randomized controlled trial of patient-centered care of diabetes in general practice: Impact on current wellbeing and future disease risk. British Medical Journal, 317, 1,202-1,208.
Kluge, E.H. (2004). Informed consent to the secondary use of EHRs: Informatics rights and their limitations. Medinfo, 11, 635-638.
Marsh, J. (2002). House calls. Rochester Review, 64, 22-26.
McKay, H.G., Feil, E.G., Glasgow, R.E., and Brown, J.E. (1998). Feasibility and use of an Internet support service for diabetes self-management. Diabetes Education, 24(2), 174-179.
Mead, N., and Bower, P. (2000a). Measuring patient centeredness: A comparison of three observation based instruments. Patient Education and Counseling, 39, 71-80.
Mead, N., and Bower, P. (2000b). Patient centeredness: A conceptual framework and review of the empirical literature. Social Science and Medicine, 51, 1,087-1,110.
Mechanic, D. (2008). Rethinking medical professionalism: The role of information technology and practice innovations. Milbank Quarterly, 86(2), 327-358.
Montemerlo, M., Pineau, J., Roy, N., Thrun, S., and Verma, V. (2002). Experiences with a mobile robotic guide for the elderly. Proceedings of the Eighteenth AAAI National Conference on Artificial Intelligence. Cambridge, MA: MIT Press
Morlion, B., Knoop, C., Paiva, M., and Estenne, M. (2002). Internet-based home monitoring of pulmonary function after lung transplantation. American Journal of Respiratory and Critical Care Medicine, 165(5), 694-697.
National Alliance for Health Information Technology. (2008). Defining key health information technology terms. Available: http://healthit.hhs.gov/portal/server.pt/gateway/PTARGS_0_10741_848133_0_0_18/10_2_hit_terms.pdf [accessed June 2010].
Polisena, J., Coyle, D., Coyle, K., and McGill S. (2009). Home telehealth for chronic disease management: A systematic review and an analysis of economic evaluations. International Journal of Technology Assessment in Health Care, 25(3), 339-349.
Preece, J. (2000). Online communities: Designing usability, supporting sociability. Chichester, UK: John Wiley and Sons.
Rialle, V., Rumeau, P., Ollivet, C., and Herve, C. (2006). Smart homes. In R. Wootton, S.L. Dimmick and J.C. Kvedar (Eds.), Home telehealth: Connecting care within the community. London: RSM Press.
Richards, T. (1998). Partnership with patients. British Medical Journal, 316, 85-86.
Riva, A., Bellazzi, R., and Stefanelli, M. (1997). A web-based system for the intelligent management of diabetic patients. M.D. Computing: Computers in Medical Practice, 14(5), 360-364.
Rojas, S.V., and Gagnon, M.P. (2008). A systematic review of the key indicators for assessing telehomecare cost-effectiveness. Telemedicine Journal and E-Health 14(9), 896-904.
Roter, D. (1989). Which facets of communication have strong effects on outcome: A meta analysis. In M. Stewart (Ed.), Communicating with medical patients. Thousand Oaks, CA: Sage.
Rothschild, S.K., Lapidos, S., Minnick, A., Fogg, L., and Catrambone, C. (2004). Using virtual teams to improve the care of chronically ill patients. Journal of Clinical Outcomes Management, 11, 346-350.
Salvendy, G. (2006). Handbook of human factors and ergonomics (3rd ed.). Hoboken, NJ: Wiley.
Savage, R., and Armstrong, D. (1990). Effect of a general practitioner’s consulting style on patient satisfaction: A controlled study. British Medical Journal, 301, 968-970.
Shea, S., Weinstock, R.S., Teresi, J.A., Palmas, W., Starren, J., Cimino, J.J., Lai, A., Field, L., Morin, P.C., Goland, R., Izquierdo, R.E., Ebner, S., Silver, S., Petkova, E., Kong, J., and Eimicke, J.P. (2009). A randomized trial comparing telemedicine case management with usual care in older, ethnically diverse, medically underserved patients with diabetes mellitus: 5-year results of the IDEATel study. Journal of the American Medical Informatics Association, 16(4), 446-456.
Stewart, M. (1995). Studies of health outcomes and patient-centered communication. In M. Stewart, J.B. Brown, and W.W. Weston (Eds.), Patient-centered medicine: Transforming the clinical methods. Thousand Oaks, CA: Sage.
Stewart, M., Brown, J.B., and Weston, W.W. (Eds.). (1995). Patient-centered medicine: Transforming the clinical method. Thousand Oaks, CA: Sage.
Stip, E., and Rialle, V. (2005). Environmental cognitive remediation in schizophrenia: Ethical implications of “smart home” technology. Canadian Journal of Psychiatry, 50, 281-291.
Tetley, J., Hanson, E., and Clarke, A. (2001). Older people, telematics and care. In A.M. Warnes, L. Warren and M. Nolan (Eds.), Care services for later life: Transformations and critiques (pp. 243-258). London: Jessica Kingsley.
U.S. Department of Health and Human Services, Office of the Secretary. (1999). Standards for privacy of individually identifiable health information; proposed rule. Federal Register, 64(212), 59,917-60,016.
U.S. Department of Veterans Affairs. (2010). My HealtheVet. Available: http://www.myhealth.va.gov [accessed June 2010].
Volpe, B.T., Krebs, H.I., and Hogan, N. (2001). Is robot-aided sensorimotor training in stroke rehabilitation a realistic option? Current Opinion in Neurology, 14(6), 745-752.
Wada, K., and Shibata, T. (2007). Living with seal robots: Its sociopsychological and physiological influences on the elderly at a care house. IEEE Transactions on Robotics, 23, 972-980.
Whittaker, S., Isaacs, E., and O’Day, V. (1997). Widening the net. Workshop report on the theory and practice of physical and network communities. SIGCHI Bulletin, 29, 27-30.
Winner, L. (1990). Living in electronic space. In T. Casey and L. Embree (Eds.), Lifeworld and technology (pp. 1-14): Lanham MD: Center for Advanced Research on Phenomenology and University Press of America.
Wylde, M., and Valins, M.S. (1996). The impact of technology. In Valins M.S. and D. Salter (Eds.), Futurecare: New directions in planning health and care environments (pp. 15-24). Oxford: Blackwell Science.