We hypothesize that by using published data that quan- tify the value of prevention practices, an empirical method can be developed to determine the efficacy of mobilehealthcare programs. Specifically, mobile health- care programs annually provide a broad array of preven- tion services to as many as 4 million otherwise un-served individuals. Recent, ground-breaking research from the National Commission on Prevention Priorities (NCPP) assigned a relative value to various prevention practices in terms of quality adjusted life years saved (QALYS). When combined with research that estimates the value of a sta- tistical life year saved, and applied to mobilehealthcare data, the result projects a return on investment (ROI) ratio of at least 30:1, a value both significant and compelling. We believe that this methodology can be enhanced, and applied to mobilehealthcare programs across the USA to quantify the value of their current services as well as sup- port the process of prioritization of target populations and interventions that promise the greatest return for healthcare dollars invested. We believe that such a ROI calculator will be an innovative and effective method of quantifying the value of mobilehealthcare programs within the US healthcare system.
The concept of a common information space (CIS) (Bannon and Bødker, 1997) is used by (Reddy et al., 2001) to analyse the cooperative work of a heterogeneous group of workers on an intensive care ward, which is based on the use of a common information repository, HealthStat. The diﬀerent clinicians in the study each have a diﬀerent representation of the underlying in- formation stored in HealthStat, so coordinating activities relies on each representation reflecting accurate shared data, with any change propagated to each representation. A richer elaboration of the CIS concept is presented in (Bossen, 2002), which examines the operation of a hospital ward, including a consideration of articulation work (a central component of the CIS frame- work). While articulation work is undoubtedly a feature of mobilehealthcare work, the analysis is not taken to the point of suggesting and exploring concrete requirements, and so we omit these aspects, while noting that articulation is central to the mobility work concept presented above. Information and Representations. Personal electronic mobile devices can support the require- ments of having di ﬀ erent representations of the same underlying information and can be tailored to support specific work practices and work groups. However this must be tempered by the need for stable communication between groups which maintain a CIS though discussion and compar- ison of representations. This requires a balancing of competing constraints, and becomes even more important if mobile technology uptake in healthcare continues to increase, as the use of personal and individually tailored representations, rather than shared representations could lead to communication problems and diﬃculties in building shared understanding. The sucessful el- ements that allow cooperation currently should be uncovered and factored into requirements for any new system.
The aging society will become a serious problem for most countries in the world. Under the con- straint of limited medical resource, the self-health management becomes important. In this paper, a mobilehealthcare system is implemented. One can easily monitor his/her physiological data through the using of a smartphone that is wirelessly connected to different medical detection de- vices. A cloud database is established for storing and analysing these physiological data. The guidance of suitable physical exercises to individuals is then given in the system. This paper shows the details of the system implementation.
Mobilehealthcare based on medical data communication technology and intelligent ter- minal has become a new telemedicine mode, and it has moved from a concept to a real- ity which its application extends to every field of medical treatment . Doukas et al.  present a mHealth system by means of Cloud Computing. In , a mHealth service system is introduced by means of RFID technology and mobile devices. David et al.  present mHealth applications and discuss possible challenges facing the development of mobile applications. Baig et al.  analyze the critical issues and challenges related to security and privacy of data in mobile phone-based sensor applications of mHealth. Rongxing et al.  introduce a secure and privacy-preserving framework based on a new access control and privacy-preserving technique. Kumar et al.  propose a novel solution of security of private data transmission. Rahman et al.  discuss the security scheme to prevent the attack of wireless communications in mHealth systems. Azzedine et al.  propose a secure multicast strategy to only permit trustworthy nodes to take part in communications. AlMuhtadi et al.  propose an emergency call mechanism with a view to preserving personal privacy. Kuan et al.  present many secure and privacy-preserving strategies in mHealth.
To gain a patient’s cardiac surveillance outside of healthcare providers’ facilities, a smart mobile phone is recognized as the method to acquire personal electrocardiogram from the low-cost hardware utilizing Bluetooth and multimedia messaging service. With the opening of the SMS text message that contains ECG samples, the vital signs about a heart condition are delivered to a PDA or a smartphone to plot the ECG on
smart card to carry its own record management applet while being hosted by a commonly available web browser, presents a powerful paradigm to support a truly mobile and open environment. Importantly, it enables medical personnel to quickly gain access of vital patient's medical record without the need of a hospital or clinic to be equipped with so-called compatible information system. Moreover, UDMHC can easily be updated as new services are performed and new medications are prescribed so your card will always
Information and communication technologies are well developed in Taiwan , and the development of EMR in hospitals is also comparatively mature . Consequently, it is proper for this study to choose Taiwanese physicians as the research subjects to validate the research model proposed in Walter and Lopez’ s study . Since Taiwan started to promote its national health insurance system in 1995, traditional (desktop and wired) EMR has been commonly used in hospitals . In addition, mobilehealthcare (also known as m-Health and m-Healthcare) is considered to have significant benefits and is in the stage of initial development . Nevertheless, presenting ubiquitous services to healthcare professionals is not easy. A key challenge is progressing m-Health approaches from pilot projects to wider implementions whilst properly engaging healthcare professionals in the process . Developers of these projects need to expend substantial effort and resources to ensure mobile service support. Thus, understanding the factors that influence healthcare professionals’ usage of mobile services is important to the development of mobile electronic medical records (MEMR). Therefore, two research questions are presented in this study: 1) Is the dual-factor model proposed by Walter and Lopez  applicable for evaluating physi- cians’ acceptance of MEMR in Eastern countries? 2) Could the feature of “perceived mobility” become a valuable antecedent variable for each of the inhibitors and enablers in Walter and Lopez’s model ?
In a relatively short period of time, mobile application has penetrated significantly into worldwide society, capturing an entire range of users with different ages, experiences, jobs or studies. Such progress has build upon a long history of use of communication devices and a rapid adoption of them. Today’s main mobilehealthcare application are grouped in some main categories as shown in figure 2 .
calls from accidents or incidents. Health-aware mobile devices detect pulse-rate, blood pressure, and level of alcohol to alert healthcare emergency system. Pervasive access to healthcare information allows patients or healthcare providers to access the current and past medical information.Pervasive lifestyle incentive management can be used to pay healthcare expenses and manage otherrelated charges automatically.With the advance of IT technology, modern healthcare has driven significant development of distributed systems that can deal with smarter environments, adapt to specific context, and respond to critical events. This section describes recent researches in emerging healthcare systems.Mobile Healthcare Service (MHS): MobileHealthcare Service (MHS) System is an infection control application that uses mobile device together with RFID (Radio Frequency System) technology to position and identify both persons and object that are inside and outside the hospital. Healthcare Alerts Management System (HAMS) Healthcare Alert Management System is a healthcare service application that runs on mobile devices to manage alerts in hospital . All the urgent requests in a hospital are referred to as alert. Example of hospital alerts are operations rescheduling, laboratory result and adverse drug events. HAMS receive alerts signal and deliver them to the right person at the right time. These alerts are delivered via short message service (SMS) to the mobile device, e-mail notification to the involved users such as doctors and nurses. By having a HAMS which could effectively route and monitor alerts, the hospital is able to provide quality and cost-effective healthcare services to its customers. 
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ireless Sensors are one of the main component of mobilehealthcare system. Sensors used in m-Healthcare are known as the wearable body sensors. These wearable body sensors are implanted in the patient’s body. Body sensor senses the various condition of patient’s body like blood pressure, heart beat, blood sugar, body temperature and others. These data are known as the physical health information. The highly sensitive physical health information is then transmitted to authorised medical healthcare centre. According to the received PHI medical experts present in the medical centre will provide a remote healthcare solution.
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Pregnancy period is a special moment of women’s life and maternity healthcare is considered as an important part of society healthcare. There are some problems and limitations with the existing services to support gravid women. The first problem is that there is no electronic system to share maternity data between hospitals and clinics. The existing systems do not exploit web and mobile technology, and there is no pervasive and ubiquities system. Most of health clinics’ activities are done with traditional approaches. The second problem is that 20% of pregnant women have to rest at hospital for some days, weeks, or months because of some pregnancy complication such as bleeding, low placenta, and so forth. There is no monitoring service at home to reduce the number of hospitalized pregnant women. The next problem is with rural enceinte women who have higher poverty rates and tend to be in poorer health. Fewer doctors and hospitals, and other health resources will cause more difficulties for them getting to health services. So far, there is no monitoring system for rural enceinte women. Using mobile devices for monitoring pregnant women is a way to overcome those problems. Maternity monitoring by mobile makes an opportunity which by using it we can share maternity data and monitor enceinte women at home instead of being hospitalized. But maternity monitoring via mobile devices can raise other technical problems. The first problem is the quality, availability, accessibility, security and privacy of patients’ data. The second problem is mobile device limitation that includes the limitation of memory, battery life span, and processor speed. In this study to solve these problems the literature review has been conducted on maternity data management, pervasive mobilehealthcare system, cloud computing, and mobilehealthcare system on cloud computing. Then a new architecture is proposed to solve those problems.
Kiholee  presented U-healthcare system in the internet of things environment(IoT) with the support of mobile gateway.Mobile healthcare applications include applications related to health/medicine, social network and human- to-human services. These mobile applications may be applied to all aspects of our lives. It provides the sensed information to a home medical station or doctor.
Information and communication technologies are transforming our social interactions, lifestyles, and workplaces. One of the most promising applications of information technology is healthcare and wellness management. Healthcare is moving from an approach based on the reactive responses to acute conditions to a proactive approach characterized by early detection, prevention, and long-term management of health conditions. The current trend places an emphasis on the watching of health conditions and the management of wellness as significant contributors to individual healthcare and well being. This is particularly important in developed countries with a significant aging population, where information technology can significantly most improve the management of chronic conditions and thereby improve quality of real life. In particular, the continuous or even occasional recording of biomedical signals is critical for the advancement of diagnosis as well as treatment of cardiovascular diseases by using wireless wearable sensors. For example, continuous recording of an electrocardiogram (ECG) or photoplethysmogram (PPG) by a wearable sensor can provide a realistic view of the heart condition of a patient during normal routines daily, and can help determine such conditions as high blood pressure, stress , anxiety, diabetes, and depression , . In addition, it is conceivable that further automated analysis of recorded biomedical signals could support doctors in their daily practices and allow the development of warning systems. This would bring several advantages: it would increase the health observability, collaboration among doctors, and doctor-to-patient efficiency , and thereby decrease healthcare costs. Moreover, such continuous monitoring would increasing early detection of abnormal health conditions and diseases
As highly skilled clinicians, nurses are constantly ana- lysing and altering their planned schedule of care as new information or events require . Constant inter- ruptions to established workflows require critical think- ing and an ability to be flexible. As interruptions to workflow increase, the fragmentation of nursing care creates the need for workarounds. Nurses modify the way they think and behave when practices no longer work as intended, become redundant or opportunities occur to incorporate new work practices that benefit workflow. This adaptation process includes recognising the new intervention’s benefits and investing in learning about the new process to enable integration into rou- tine work patterns. Sustaining change occurs when the benefits outweigh non-use . This process is being attenuated with regard to mobile technology and mo- bile learning, however. From the interviews, nurse leaders appear to absolve themselves of responsibility for advocating within the profession to advance nursing practice. Nurses continue to support a historically hier- archical system that justifies their lack of inclusion in decision-making and are consequently unable to articu- late the importance of mobile learning for enabling in- formal learning and CPD . This apparent inability to communicate the value of access to mobile technol- ogy is hindering nurses ’ capacity to demonstrate how mobile learning improves workflow, promotes continu- ity of care and potentially improves patient outcomes. It also prevents the modelling of digital professionalism to undergraduate nurses perpetuating the status quo. The current deficiency in the capacity of nurses to in- fluence the direction of mobile learning policy at sys- tem and organisation levels further marginalises them within the registered health professions [16, 60].
According to  mobile medical apps are more popular among women as compared to men. This popularity range from elaborating simple and common health patterns to experimenting personalized tests. In addition, as per the results of  on how women are using technology today which studied completive number of women using technology showed that women are always in a hunt of technology which help them to follow up with their active lifestyles. This makes women the fastest growing and one of most valuable consumer of internet and ecommerce companies.
We anticipate that there will be limited scope for meta- analysis because of the range of different outcomes mea- sured across the reasonably small number of existing mobile technology intervention trials. However, where studies have used the same type of intervention and MED, with the same outcome measure, we will use Stata v11.0  to pool the results of randomised con- trolled trials using a random-effects meta-analysis, with standardised mean differences for continuous outcomes and risk ratios for binary outcomes, and calculate 95% confidence intervals and two sided P values for each outcome. In studies where the effects of clustering have not been taken into account, we will adjust the standard deviations for the design effect, using intra-class coeffi- cients, if they are provided in the study reports, or alter- natively using external estimates obtained from similar studies . Heterogeneity between the studies in effect measures will be assessed using both the c 2 test and the I 2
3.6 Methods for the Measurement of Healthcare Preferences STI testing services are not subject to gatekeeping by referral from a clinician, they are therefore directly dependent on individuals’ preferences. Probabilities of uptake are key parameters within the economic model and early insight into the attributes which are most influential in determining whether individuals would use a new STI self-test and treatment pathway will therefore be helpful in informing product development and the assumptions used in any later economic modelling. It is also recognised that “aligning health care policy with patient preferences could improve the effectiveness of health care interventions by improving adoption of, satisfaction with, and adherence to clinical treatments or public health programmes” (Bridges et al., 2011:404).