Data Warehousing and Decision Support in Mobile Wireless Patient Monitoring

Data Warehousing and Decision Support in Mobile Wireless Patient Monitoring

Barin N. Nag (Towson University, USA) and Mark Siegal (National Library of Medicine, USA)
DOI: 10.4018/978-1-60566-156-8.ch051
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Abstract

The recent advances in wireless communication technologies have made possible the development of wireless systems for monitoring the health and disease status of patients, in both in-patient hospital settings and outside. The volume of patient monitoring data requires Data Warehousing technologies for storage intended for analysis. The analysis is performed by Decision Support Systems (DSS) that provide clinical diagnoses and treatment methodology consistent with the urgency. The clinical DSS is critical in the analysis of a volume of data beyond the capabilities of a healthcare professional, and is effective in reducing workload, saving money, and providing better care for patients. This chapter also analyzes the technical aspects of the process.
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Medical Rationale

The rationale for DSS in wireless patient monitoring includes saving lives by faster diagnoses and treatments, faster turnaround and reduced workload for hospital staff saving money for hospitals, health insurance companies, and the patients [Eisenstein 2006]. The necessity of DSS arises because patient monitoring systems generate large volumes of data that is too much for the doctors and nurses to analyze [Abidi 2001].

DSSs are useful for managing, analyzing, and making decisions on large volumes of clinical data obtained from patient monitoring systems. For example, when patient monitoring is continuous, there is more data than can be analyzed by a health care professional [Falas et al. 2003]. The targeted users for these DSS consist of doctors and nurses, to aid in diagnoses and direct patient care, patients to facilitate self-maintenance of their conditions, hospital administrators in improving the ability to allocate hospital resources, and public health officials to use the abstracted data from these monitoring systems to provide high-level insights into public health matters [Nanningo & Abu-Hanna 2006].

Any disease or condition can use DSS for wireless patient monitoring, and a wide variety are seen in the literature and in practice. These systems are most useful for chronic diseases or acute attacks [Farmer et al. 2005]. These are the scenarios where the patient is at a recurring risk for health difficulties, and thus continual monitoring is preferable. These DSS are applicable to many medical situations, in the hospital, in transport, or elsewhere [Gouaux et al. 2003].

There are several important diseases and conditions that are prime targets for patient monitoring. One of the most common diseases in these systems is heart disease [Conforti et al. 2006]. Diabetes is one such disease that is common for patient monitoring using cellular networks [Farmer et al. 2005], in particular for regular glucose monitoring [Jun et al. 2006]. Another condition is sleep apnea, which is when a patient briefly stops breathing repeatedly in the night [Ishida et al. 2005]. Asthma is another example, because this common breathing illness leads to attacks that can be deadly if not treated [Ryan et al. 2005]. Other examples include wound maintenance care [Braun et al. 2005], dialysis [Nakamoto et al. 2003], urology [Liatsikos et al. 2004], and monitoring the health and safety status of the elderly [Lin et al. 2006].

Key Terms in this Chapter

Clinical DSS: Decision Support Systems for clinical use.

Wireless Data: Data generated by mobile wireless devices (same as Mobile Data).

Patient Monitoring: Collecting real time patient clinical data.

Mobile Data: Data generated by mobile wireless devices.

Decision Support Systems (DSS): Systems to evaluate critical factors in support of decision making.

Artificial Intelligence (AI): Methods of simulating human intelligence in computing.

Data Mining (DM): Searching (mining) for nuggets of information, usually using methods of AI.

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