An Intelligent and Secure Framework for Wireless Information Technology in Healthcare for User and Data Classification in Hospitals

An Intelligent and Secure Framework for Wireless Information Technology in Healthcare for User and Data Classification in Hospitals

Masoud Mohammadian (University of Canberra, Australia), Dimitrios Hatzinakos (University of Toronto, Canada), Petros Spachos (University of Toronto, Canada) and Ric Jentzsh (University of Canberra, Australia)
DOI: 10.4018/978-1-4666-9494-1.ch019
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Real time data acquisition and evaluation are required to save lives. Such data with utilization and application of the latest technologies in hospitals around the world can improve patient treatments and well beings. The delivery of patient's medical data needs to be secure. Secure and accurate real time data acquisition and analysis of patient data and the ability to update such data will assist in reducing cost while improving patient's care. A wireless framework based on radio frequency identification (RFID) can integrate wireless networks for fast data acquisition and transmission, while maintaining the privacy issue. This chapter discusses the development of a framework that can be considered for secure patient data collection and communications in a hospital environment. A new method for data classification and access authorization has also been developed, which will assist in preserving privacy and security of data. Several Case studies are offered to show the effectiveness of this framework.
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1. Introduction

Radio Frequency Identification (RFID) has been shown to be a viable and promising technology in the health care industry (Finkenzeller, 1999); Glover, & Bhatt, 2006; Mohammadian & Jentzsch, 2008; Schuster, Allen, & Brock, 2007; Shepard, 2005; Angeles, 2007; Pramatari, Doukidis, & Kourouthanassis, 2005; Mickey, 2004) Whiting, 2004). RFIDs have the capability to penetrate and add value to many areas of health care. RFIDs can lower the cost of some services as well as improve services to individuals and health care providers. The real value of RFID is achieved in conjunction with the use of intelligent software systems such as intelligent multi-agent systems. The integration of these two technologies can benefit and assist health care services.

Radio Frequency Identifiers (RFID) have been around for many years. Their current and projected use has only begun to be researched in hospitals (Fuhrer, & Guinard, 2007). This research study considers the use of RFIDs and their potential in hospitals and similar environments. RFIDs can effectively be used to collect data at the source thereby providing the data for monitoring patient’s well-being in order to provide a higher level of patient health care. There are four areas where using RFIDs in their data collection role can have significant positive benefits in hospitals. These four areas are:

  • Care Tracking: This is getting the right care to the right patient at the right time;

  • Quality of Care: Improving the services given to the right patient at the right time in a timely manner;

  • Cost of Care: Finding ways to be effective in the use of available resources such that the cost per patient per incident does not adversely increase to the cost of the resources; and

  • Service of Care: More timely information to enable a more informed decision by providing more knowledge about an individual’s need for care. (Mohammadian & Jentzsch, 2007,2008)

RFID tags and readers are commonly associated with inventory and tracking goods in such places as manufacturing and warehousing, but hospitals are starting to apply the technology to new purposes. Unlike bar codes, RFID technology does not require contact or line of sight for communication. RFID data can be obtained wirelessly through clothing and other non-metallic materials. This capability makes RFIDs an appropriate technology to fit into the health care environment.

Both research and practical application of RFIDs in hospitals continues to be of increasing importance. For hospitals this has meant managing inventories in a more efficient manner. Such inventories take on a variety of different roles than those found in manufacturing. The nature of the inventory and assets can include a plethora of expensive and varied equipment, drugs, beds, chairs, as well as patients themselves and staff.

People tracking can be looked at from three perspectives:

  • 1.

    Continuous and Full Time: Often referred to as human chipping. People are tagged as an integral part of the person process 24/7.

  • 2.

    Part Time: In which people acquire some type of tag as part of their work and / or task environment. RFIDs are well suited for this type of tagging by staff in the health care industry.

  • 3.

    Casual: With tags used on and as needed basis.

The percentage of worldwide RFID projects related to people tagging has increased from eight percent to 11 percent since 2005 (Tindal, 2008). But the healthcare industry has yet to quantify or provide evidence of the benefit to people-tagging. Human chipping continuous full time tagging) is not new but does raise many ethical questions, subject for further study and beyond the scope of this paper (Angeles, 2007).

Key Terms in this Chapter

RFID: RFID or Radio Frequency Identification is a progressive technology that is easy to use and well suited for collaboration with intelligent software agents. Basically an RFID can be read-only, volatile read/write; or write once / read many times. RFID are non-contact; and non-line-of-sight operations. Being non-contact and non-line-of-sight will make RFIDs able to function under a variety of environmental conditions and while still providing a high level of data integrity ( Finkenzeller, 1999 ; Glover & Bhatt, 2006 ).

Patient or Doctor Profiling: A patient or doctor profile is a collection of information that can be used in a decision analysis situation between the doctor, domain environment, and patient. A static profile is kept in pre-fixed data fields where the period between data field updates is long such as months or years. A dynamic profile is constantly updated as per evaluation of the situation in which the situation occurs. The updates may be automated or performed manually. The automated user profile building is especially important in real time decision-making systems. The profiling of patient doctor model is based on the patient/doctor information.

Panda-Hunter Model: Routing based protocols can provide source-location privacy. The Panda-Hunter model formalizes the problem in sensor networks and it proposes a phantom routing technique based on both flooding and single path routing. Phantom routing involves two phases: a random walk phase, and a subsequent flooding/single path routing.

Location Privacy: Location privacy is an important security issue. Lack of location privacy can lead to subsequent exposure of significant traffic information on the network and the physical world entities. For instance, cardiologic data packet coming out of a hospital in a mesh network enable an eavesdropper to analyze and find out at-risk heart patients, if the source location of those packets can be determined. Toward that goal, a number of source-location communication protocols have been proposed (Deng & Mishra, 2004 AU65: The in-text citation "Deng & Mishra, 2004" is not in the reference list. Please correct the citation, add the reference to the list, or delete the citation. ; Shao, Yang, Zhu, & Cao, 2008 ), where the main idea is a mixture of valid and fake messages. Each node transmits either a valid or a fake message, consistently. The main disadvantage of this approach is that the broadcasting of fake messages consumes significant amount of the limited energy in each sensor node. Moreover, because each node has to transmit a packet in every time slot, the effect is increase in number of collisions, and decrease in the packet delivery ratio. Therefore, these approaches are not suitable especially for large scale wireless sensor networks.

Multi-Agent System (MAS): A Multi-agent System (MAS) consists of a set of intelligent agents software that are connected together to perform tasks in an environment to achieve a common goal. To achieve a common goal successfully the agents cooperate and collaborate with each other.

Discretionary Access Controls: Discretionary Access Controls provide users with permits to access allow or disallow other users access to data items stored in a hospital. Such user or user groups with certain access permissions is allowed to access data items. It is possible that authorized users can then pass such data items to other users that may not have permission access to such a data. One way to restrict access to data items can be based an intelligent agent being capable to identity user/s data access level to which data can be provided. In this case each data item contains metadata information about its security and privacy. An intelligent agent can then control data access of users by checking metadata information attached to data.

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