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encapsulated in incompatible systems with uncoordinated de nitions of formats and terms It is essential that the different parts of this organization, which have different data systems, nd ways to work together in order to improve quality performance The VAMTA has been developed to contribute to that effort by enhancing the electronic management of patient data Much has been written about end-user perceptions of IT, but few studies address user evaluations of IT as it applies to voice activated healthcare data collection This case focuses on the development and testing of an instrument to measure the performance and end-user evaluation of the VAMTA in preventive healthcare delivery It is not inconceivable that, in the future, a signi cant amount of healthcare data will be collected via applications such as VAMTA The TTF model (Figure 42) is a popular model for assessing user evaluations of information systems The central premise for the TTF model is that users will give evaluations based on the extent to which systems meet their needs and abilities For the purpose of our study, we de ne user evaluations as the user perceptions of the t of systems and services they use, based on their personal task needs The TTF model represented in Figure 42 is very general, and using it for a particular setting requires special consideration Among the three factors that determine user evaluations of information systems, technology is the most complex factor to measure in healthcare Technology in healthcare is used primarily for reporting, electronic information sharing and connectivity, and staff and equipment scheduling Reporting is important in a healthcare setting because patient lives depend on accurate and timely information Functional departments within the healthcare facility must be able to access and report new information in order to respond properly to changes in the healthcare environment Our ideas about smart data include the dimension of the interoperability of the technology Our case shows that there are four types of smart data that are reported in a healthcare facility These include scienti c and technical smart data, patient-care smart data, customer satisfaction smart data, and administrative smart data These four types of smart data embrace data engineering, model-driven data exchange, semantic mediation, metadata management, automated mapping tools, and the service-oriented enterprise paradigm that includes smart data, smart grid, smart services, and credentialing and privileging Scienti c and technical smart data requires metadata management in order to provide the knowledge base for identifying, organizing, retrieving, analyzing, delivering, and reporting clinical and managerial journal literature, reference smart
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Figure 42 Task-technology t (TFF) model
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data, and research data for use in designing, managing, and improving patient-speci c and departmental processes Semantic mediation is required to manage the volume of content that confronts healthcare personnel Patient-care information is speci c data and information on patients that is essential for maintaining accurate medical records of the patients medical histories and physical examinations This process requires automated mapping tools because patient-speci c smart data are critical to tracking all diagnostic and therapeutic procedures and tests Maintaining model-driven data exchange of accurate smart data about patient-care results and discharges is imperative to delivering quality healthcare Customer satisfaction smart data is gathered from external customers, such as a patient and his or her family and friends Customer satisfaction smart data is gathered from surveys and takes into account sociodemographic characteristics, physical and psychological status, attitudes, and expectations concerning medical care, the outcome of treatment, and the healthcare setting The adoption of a service-oriented enterprise paradigm that includes smart data, smart grid, and smart services are essential to maintaining customer satisfaction Credentialing and privileging are necessary for managing the administrative smart data that is reported in a healthcare facility It is essential for formulating and implementing effective policies at both the organizational and departmental levels Administrative information is necessary to determine the degree of risk involved in nancing expansion of services As the technological infrastructure of organizations becomes increasingly complex, IT and the adoption of the smart data paradigm is increasingly being used to improve coordination of activities within and across organizations Computers and video networks provide long-distance healthcare through medical connectivity Doctors interact with each other and ancillary medical personnel through e-mail, video, and audio means A dif cult patient case in a rural area or on board a ship can be given expert specialist attention simply by using distance medicine Not only can patient records, text, and documents be transmitted instantaneously via electronic means, but live video, X-rays, and other diagnostic parameters can be discussed in an interactive manner with live discussions As the availability of external consultative services increases, sharing of smart data through connectivity has become increasingly important Connectivity allows diagnoses to be made in remote locations using electronic means, and information sharing decreases the chances that mistakes will be made in a healthcare setting Connectivity leads to shared smart data and care that comprises the continued, coordinated, and integrated activities of different people from different institutions applying different methods in different time frames, all in a combined effort to aid patients medically, psychologically, and socially in the most bene cial ways In addition to reporting and electronic information sharing and connectivity, smart data is used hand in hand with IT to extensively improve staff and equipment scheduling in healthcare settings IT-based scheduling, utilizing smart data, can lower healthcare costs and improve the utilization of physical and human resources Scheduling using statistical, time series, and regression analysis is conducted to
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