23 A real time data dashboard for smart infusion pump patient data

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

Advances in Medical Education, Research, and Ethics - Handbook of Research on Advancing Health Education through Technology ◽

10.4018/978-1-4666-9494-1.ch019 ◽

2016 ◽

pp. 452-479

Author(s):

Masoud Mohammadian ◽

Dimitrios Hatzinakos ◽

Petros Spachos ◽

Ric Jentzsh

Keyword(s):

Data Acquisition ◽

Real Time ◽

Radio Frequency Identification ◽

Data Classification ◽

Patient Data ◽

Hospital Environment ◽

Time Data ◽

Privacy And Security ◽

Real Time Data ◽

Frequency Identification

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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User and Data Classification for a Secure and Practical Approach for Patient-Doctor Profiling Using an RFID Framework in Hospital

Healthcare Administration ◽

10.4018/978-1-4666-6339-8.ch059 ◽

2015 ◽

pp. 1101-1122

Author(s):

Masoud Mohammadian ◽

Ric Jentzsch

Keyword(s):

Data Acquisition ◽

Real Time ◽

Radio Frequency Identification ◽

Data Classification ◽

Well Being ◽

Patient Data ◽

Time Data ◽

Privacy And Security ◽

Real Time Data ◽

Improve Patient

Utilization and application of the latest technologies can save lives and improve patient treatments and well-being. For this it is important to have accurate, near real-time data acquisition and evaluation. The delivery of patient's medical data needs to be as fast and as secure as possible. Accurate almost real-time data acquisition and analysis of patient data and the ability to update such a data is a way to reduce cost and improve patient care. One possible solution to achieve this task is to use a wireless framework based on Radio Frequency Identification (RFID). This framework can integrate wireless networks for fast data acquisition and transmission, while maintaining the privacy issue. This chapter discusses the development of an intelligent multi-agent system in a framework in which RFID can be used for patient data collection. This chapter presents a framework for the knowledge acquisition of patient and doctor profiling in a hospital. The acquisition of profile data is assisted by a profiling agent that is responsible for processing the raw data obtained through RFID and database of doctors and patients. A new method for data classification and access authorization is developed, which will assist in preserving privacy and security of data.

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User and Data Classification for a Secure and Practical Approach for Patient-Doctor Profiling Using an RFID Framework in Hospital

Cases on Healthcare Information Technology for Patient Care Management ◽

10.4018/978-1-4666-2671-3.ch014 ◽

2013 ◽

pp. 224-253

Author(s):

Masoud Mohammadian ◽

Ric Jentzsch

Keyword(s):

Data Acquisition ◽

Real Time ◽

Radio Frequency Identification ◽

Data Classification ◽

Well Being ◽

Patient Data ◽

Time Data ◽

Privacy And Security ◽

Real Time Data ◽

Improve Patient

Utilization and application of the latest technologies can save lives and improve patient treatments and well-being. For this it is important to have accurate, near real-time data acquisition and evaluation. The delivery of patient’s medical data needs to be as fast and as secure as possible. Accurate almost real-time data acquisition and analysis of patient data and the ability to update such a data is a way to reduce cost and improve patient care. One possible solution to achieve this task is to use a wireless framework based on Radio Frequency Identification (RFID). This framework can integrate wireless networks for fast data acquisition and transmission, while maintaining the privacy issue. This chapter discusses the development of an intelligent multi-agent system in a framework in which RFID can be used for patient data collection. This chapter presents a framework for the knowledge acquisition of patient and doctor profiling in a hospital. The acquisition of profile data is assisted by a profiling agent that is responsible for processing the raw data obtained through RFID and database of doctors and patients. A new method for data classification and access authorization is developed, which will assist in preserving privacy and security of data.

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Data Challenges With Real-Time Safety Event Detection And Clinical Decision Support (Preprint)

10.2196/preprints.13047 ◽

2018 ◽

Author(s):

Eric Steven Kirkendall ◽

Yizhao Ni ◽

Todd Lingren ◽

Matthew Leonard ◽

Eric S Hall ◽

...

Keyword(s):

Health Care ◽

Decision Support ◽

Real Time ◽

Clinical Decision Support ◽

Event Detection ◽

Infusion Pump ◽

Clinical Decision ◽

Time Data ◽

Real Time Data ◽

Safety Event

BACKGROUND The continued digitization and maturation of health care information technology has made access to real-time data easier and feasible for more health care organizations. With this increased availability, the promise of using data to algorithmically detect health care–related events in real-time has become more of a reality. However, as more researchers and clinicians utilize real-time data delivery capabilities, it has become apparent that simply gaining access to the data is not a panacea, and some unique data challenges have emerged to the forefront in the process. OBJECTIVE The aim of this viewpoint was to highlight some of the challenges that are germane to real-time processing of health care system–generated data and the accurate interpretation of the results. METHODS Distinct challenges related to the use and processing of real-time data for safety event detection were compiled and reported by several informatics and clinical experts at a quaternary pediatric academic institution. The challenges were collated from the experiences of the researchers implementing real-time event detection on more than half a dozen distinct projects. The challenges have been presented in a challenge category-specific challenge-example format. RESULTS In total, 8 major types of challenge categories were reported, with 13 specific challenges and 9 specific examples detailed to provide a context for the challenges. The examples reported are anchored to a specific project using medication order, medication administration record, and smart infusion pump data to detect discrepancies and errors between the 3 datasets. CONCLUSIONS The use of real-time data to drive safety event detection and clinical decision support is extremely powerful, but it presents its own set of challenges that include data quality and technical complexity. These challenges must be recognized and accommodated for if the full promise of accurate, real-time safety event clinical decision support is to be realized.

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