Medical information technology is often thought of in the modern context of computers, but the careful collection and analysis of information related to observation of patient condition, effectiveness of different treatments, and design of new treatments dates back to the time of Hippocrates (ca. 460 BC – ca. 370 Be) (Washburn & Hornberger, 2008). Hippocrates took meticulous notes that enabled him to make numerous breakthroughs both in the understanding of the workings of the human body and in the ethics and approach to thinking that are essential to modern medical practice and investigation (Olguin, Gloor & Pentland, 2009). Comparatively little innovation took place in furthering, the practice of medicine from the time of Hippocrates until the early 20th century, with developments such as the smallpox vaccine in 1901.
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During the 20th century, the growth of medical technology has increased continuously, with innovations such as penicillin, X-ray, PET/MRI scanning, computers, robotic surgery, radiation therapy, chemo-therapy, and many other forms of technology and treatments (Garson, 2008; Munnelly & Clarke, 2007). While the use of medical hardware and information technology has been essential to healthcare for thousands of years, these same tools can create difficult problems (Appari & Johnson, 2010; Ziefle & Rocker, 2010). For example, the over-use of antibiotics has caused a new form of pathogen commonly called super-bugs, such as methicillin-resistant staphylococcus aureus (MRSA) and other antibiotic resistance strains that are extremely difficult and expensive to treat.
Healthcare facilities (buildings) can also be considered to be a form of technology. As with other forms of technology, physical facilities involve a mutual interaction between users of the technology and the technology Anderson & Wittwer, 2011). In healthcare contexts, the physical facilities are often intimately interrelated with the staff and other technology that the building contains (Munnelly & Clarke, 2007). Often, technology is integrated into the building itself. As with other forms of technology in healthcare, organizations spend significant sums of money on their facilities. If these funds are not spent wisely, they contribute to the rising cost of healthcare and can affect the financial or operational viability of the organization (Aziz et al. 2006; Washburn & Hornberger, 2008).
Among the pioneers of Healthcare Technology, the National Aeronautics and Space Administration (NASA) has been one of the most supportive. Concerned with the wellness of the astronauts during space missions, NASA scientists developed technological devices for the measurement and transmission of physiological and medical data between space and earth stations in the 1960s (Lankton & Wilson, 2007). This effort was later applied in the 1970s to support medical services to the rural Papago Native American Reservation in Arizona using a manned mobile medical unit linked to local hospitals. The first full service Healthcare Technology operation appeared in 1968 between Logan Airport Health station and the Massachusetts General Hospital (MGH) of Harvard Medical School (Garson, 2008; Munnelly & Clarke, 2007). The service included 10 remote sites linked through the New Hampshire-Vermont Medical Interactive Television Network with a central hub stationed at Dartmouth. The service supported medical education and specialty medical services including psychiatry, cancer, and dermatology (Pai & Huang, 2011).
Another significant Healthcare Technology event occurred in the 1990s when NASA launched the first large scale international Healthcare Technology project, Spacebridge. Spacebridge currently supplies a variety of medical specialist consultations and medical educational opportunities to the Eastern European region (Sneha & Varshney, 2007; Varshney, 2009). Modern Healthcare Technology in the last century evolved from basic telephone consultations as experimental projects. Propelled by emerging technologies and the information superhighway, Healthcare Technology has resurfaced with new content and meaning. Healthcare Technology experiments that are currently used in pilot form will prove to be routine in the future.
Impact of Technology on Healthcare
The purpose of this section is to review the literature on the impacts of technology in healthcare. Evidence on the impact of technology in healthcare is mixed. Literature on technology impacts in healthcare have looked at both final outcome measures, such as productivity or output or mortality , as well as intermediate performance measures such as error rates, cycle times, utilization, and complications (Pai & Huang, 2011). A recurring theme among studies on technology and healthcare is the role of time lags; the empirical evidence generally supports the notion that technology investments require a substantial time period for users to learn how to use the technology (Ziefle & Rocker, 2010).
Studies drawing from technology literature base, consistent with the literature on technology investment, appeared more likely to include complementary investment factors such as business process reengineering (BPR) and training (Varshney, 2009). These studies find positive impacts to technology and often included (Varshney, 2009). Studies based in the medical literature painted a more mixed view of outcomes technology investment (Bardram, 2008; Coronato & Pietro, 2010). These studies generally did not include complementary investments and generally took a “tool view” of technology investments. The studies based in the medical literature used a more nuanced choice of outcomes; consistent with the idea that healthcare is a unique context, including outcome measures such as error rate, differential mortality, utilization rates, and complication rates (Sneha & Varshney, 2007; Varshney, 2009).
What is missing from this literature is a study that takes into account the unique nature of technology investment, as well as the unique context of healthcare. Theory and evidence about the impacts of technology investment suggest that technology: a) is a general-purpose technology which often requires complementary investments to yield positive returns, b) lowers search costs, which lower the variance of outcomes, c) facilities the accumulation of “memory capital” over time, d) lowers monitoring costs, e) speeds information diffusion, and f) exhibits network effects (Ziefle & Rocker, 2010). While many of the potential impact of technology would seem to result in positive returns in healthcare, findings on the impact of technology in healthcare to date are mixed. Most studies on the impacts of technology in healthcare have either: a) used a rich understanding of technology investments focused upon the impact of technology on traditional outcome measures such as profitability or response time, or b) used a simplified view of technology investment with a rich understand of the particular phenomena which arise out of the unique context of healthcare (Coronato & Pietro, 2010). What is needed in this literature is a study which takes into account the particular impacts of technology investments on phenomena which are unique to healthcare, such as treatment inconsistency.
Research Conceptual Framework and Theoretical Background
Present research examines the factors that influence patient Healthcare Technology adoption drawing support from the following theory.
Theory of Reasoned Action
The Theory of Reasoned Action asserts that beliefs influence attitudes. Attitudes, in turn, influence the intentions that guide behaviour, and acceptance of technology is then demonstrated through behaviour. TRA is well-tested and has been proven valid in predicting and explaining behaviours in general human behaviours. The concept of Theory of Reasoned Action was founded on Fishbein and Ajzen’s social psychology research. TRA suggested that significant relations exist between beliefs, attitudes, intentions, and behaviours (Aziz et al. 2006; Washburn & Hornberger, 2008). According to TRA, most social behaviours are not automatic actions; instead, they are under volitional controls. TRA asserts that people consider the implications of their action based on the information available to them before they decide to perform behaviour (Aziz et al. 2006; Washburn & Hornberger, 2008).
Since behaviour is a result of cognitive reasoning, behaviour is predictable. Theory of Reasoned Action is built on three constructs: attitude (AT), subjective norm (SN), and behavioural intention (BI). TRA has been examined and tested through numerous research studies. In TRA, attitude reflects personal behavioural beliefs and subjective norm refers to social influences. TRA suggests that behaviour intention is a function of two determinants, a person’s attitude and the subjective norm. A person’s behavioural intention, in turn, is the immediate determinant of the actual action (Aziz et al. 2006; Washburn & Hornberger, 2008). Based on the pictorial presentation of TRA by Ajzen and Fishbein, TRA may be expressed as:
BI = AT + SN and actual behaviour = BI.
A person holds different beliefs from past experience about objects, actions, and events. Beliefs serve as the immediate determining factors of a person’s attitude (Aziz et al. 2006; Washburn & Hornberger, 2008). Positive belief means stronger conviction and acceptance toward the behaviour in question. With positive beliefs, a person tends to gather positive attitudinal intention to behaviour, which in turn leads to more potential realization of the behaviour. Attitude is a person’s evaluation of the entity in question (Lankton & Wilson, 2007). Attitude arises as a function of beliefs. Beliefs may change due to time and circumstances or be replaced by new beliefs; these changes in turn affect a person’s attitude. Social scientists have long established that attitude is a critical behavioural disposition (Lankton & Wilson, 2007).
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However, a person’s favourable or unfavourable perception to behaviour in consideration alone does not always produce the behavioural outcome. To accurately predict attitude, an additional variable must be taken into account of the attitude-behaviour relationship. This additional variable in TRA is the subjective norm (Aziz et al. 2006; Washburn & Hornberger, 2008). Subjective norm refers to a person’s perceived expectations from relevant individuals or groups on whether or not to perform the behaviour in question (Varshney, 2009). Subjective norm is a function of normative beliefs, the resulting influence of the social environment. Social pressure can force an individual to perform or avoid behaviour in consideration regardless of the person’s existing intention. Since it has the potential of overriding a person’s own intention, subjective norm is an independent construct to attitude in the TRA model.
Concept of Pervasive Healthcare Technology
Many Pervasive Healthcare Technology devices have undergone experimental trials in hospitals as well as in patients’ homes. Infrared technology, motion sensors (infra-red detection or acoustical detection), video cameras, and so on, that use wireless, Internet, ISDN, and telephone lines have been installed in healthcare facilities (Snyder, 2007). Traditional non-invasive Pervasive Healthcare Technology often requires patient engagement with devices at a set time and location. For at risk cases, such as post-stroke and postoperative wound-related complications where a close un-obstructive monitor is crucial in the recovery process, periodic monitoring may not catch episodic signs at the critical time (Washburn & Hornberger, 2008). Recent development of pervasive monitoring systems focuses on automated and un-obstructive Pervasive Healthcare Technology without the restrictions of time and place.
Pervasive healthcare requires wireless technologies and the matching infrastructure capabilities. Pervasive services are supported through wireless LANs, cellular GSM/3G networks, satellite-based systems, and so forth (Varshney, 2007). Pervasive healthcare applications include “pervasive health monitoring, intelligent emergency management system, pervasive healthcare data access, and ubiquitous mobile Healthcare Technology” (Varshney, 2007). Research on pervasive Healthcare Technology started in the early 2000s using the then budding pervasive computing technologies. The goal was to utilize ubiquitous communication technologies to improve patient autonomy and healthcare mobility through continuous monitoring. In cases such as myocardial ischemia and post abdominal operations, continuous physiological data for timely detection of deterioration can change the entire care outcome.
Extended from Varshney’s definition for pervasive healthcare (2007), present research defines pervasive Healthcare Technology as a Pervasive Healthcare Technology for anyone, anytime, and anywhere without location, time, and other restraints. Earlier pervasive Healthcare Technology experimented with video-telephony installations (Thuemmler et al. 2009). These devices provide live video interactive communication through plain old POTS for its wide availability and relatively low costs (Lankton & Wilson, 2007). Using video-telephony, the healthcare professional can review the therapies and provide support in real-time. More importantly, these devices alleviate the gap of distance, allowing care providers to monitor the patient’s emotional and mental states and not simply physiological information (Olguin, Gloor & Pentland, 2009).
Other types of pervasive Healthcare Technology are enabled by portable topical sensors that integrate wireless technology with clinical devices. Tele-devices such as tele-ECG and ring-sensors are worn by the patients for Pervasive Healthcare Technology. Data, such as ECG, pulse rate, respiration rate, and oxygen saturation levels, is collected and forwarded to the healthcare providers automatically (Tu, Zhou, & Piramuthu, 2009; Varshney, 2007). This continuously monitored data can provide important clinical insight for timely and accurate diagnosis. Advanced pervasive devices for automatically collecting multiple clinical parameters have shown success in a body sensor network system (Nachman et al. 2010).
This Pervasive Healthcare Technology system equipped with multiple sensors is able to collect, process, and wirelessly transmit the received data via a secured link to a laptop for further diagnosis. Pervasive Healthcare Technology devices that do not require patients to wear the tele-devices also have been developed in the past years. For example, mattresses, toilets, kitchen appliances, and clothing embedded with monitors can sense sleep pattern, body weight, body temperature, pulse rate, and so forth (Bardram, 2008; Coronato & Pietro, 2010).
Further experiments on advanced tele-sensing systems utilize the Doppler radar technique to gather scattered vital signs from throughout the body (Ziefle & Rocker, 2010).
These systems can gather multiple clinical parameters and are able to operate autonomously without disturbing the lives of the patients. Pervasive Healthcare Technology is built on widely deployed wireless networks and advanced computing technologies. Pervasive Healthcare Technology solutions have focused mainly on at risk disease management Anderson & Wittwer, 2011). However, a growing market in a wide range of the healthcare field is ready to propel the development and consumption of pervasive Healthcare Technology. This practice has had
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