PhD defence of Mr. Di Lin -- Resource allocation in wireless health monitoring systems

Abstract

Overcrowding in the emergency department is a worldwide problem impairing the ability of hospitals to over emergency care within a reasonable time frame. Not merely a problem of patient satisfaction, the problem of overcrowding is leading to an increased number of waiting room death cases, which refer to the death of patients while staying in a hospital's waiting room due to a lack of sufficient medical care, and this problem underscores the significance of improving healthcare quality. As a potential way of improving healthcare quality, a wireless healthcare monitoring system (HMS) could help healthcare staff monitor the condition of patients by automatically sending alert messages to a doctor device (e.g. a smartphone, a personal digital assistant, or a laptop) once emergent conditions occur.

From a network design perspective, a wireless HMS should be capable of supporting the number of patients that will be using the system; being able to assess the network's capability to serve a given number of patients (defined as network patient capacity) is a critical factor in promoting adoption of such systems. This thesis investigates schemes for enhancing the network patient capacity within a HMS. The major objective is to explore the tradeoff between the network patient capacity and the Quality-of-Service (QoS) requirements of each patient, so that a fairly good network capacity is achieved subject to the constraints of QoS requirements within real-world transmission scenarios.

In the first part of this thesis, we develop novel methods to estimate the average waiting time of a patient to access the Emergency Department (ED) of a hospital, showing why developing a HMS and allocating its limited wireless resources are important to improve the quality of medical care.

The following part of this thesis presents various schemes for resource allocation within a HMS, in view of several factors that need to be taken into account in a real scenario, including different QoS requirements, Electromagnetic Interference (EMI) on medical equipments, as well as imperfect channel state information. We propose three novel techniques for improving the network patient capacity within a HMS, including a statistical multiplexing scheme, a channel prediction based scheme, and a medical decision support based scheme.

The last part of this thesis focuses on the performance evaluation of a decision support system, a result that is important to assess the validity and acceptability of the decision support based resource allocation scheme proposed above.