A Real-Time, Knowledge-Based Decision Support System for Healthcare - The case A & E department - Funded by The North West London Hospitals NHS Trust
This research investigates and develops a new approach to the management of service quality with the emphasis on patient and staff satisfaction in the healthcare sector. The challenge of measuring the quality of service in healthcare requires us to view the problem from multiple perspectives. At the philosophical level, the true nature of quality is still debated; at the psychological level, an accurate conceptual representation is problematic; whilst at the physical level, an accurate measurement of the concept still remains elusive to practitioners and academics. This research focuses on the problem of quality measurement in the healthcare sector. The contributions of this research are fourfold:
Firstly, it argues that from the technological point of view the research to date into quality of service in healthcare has not considered methods of real-time measurement and monitoring. This research identifies the key elements that are necessary for developing a real-time quality monitoring system for the healthcare environment.
Secondly, a unique index is proposed for the monitoring and improvement of healthcare performance using information-theoretic entropy formalism. The index is formulated based on five key performance indicators and was tested as a Healthcare Quality Index (HQI) based on three key quality indicators of dignity, confidence and communication in an Accident and Emergency department.
Thirdly, using an M/G/1 queuing model and its underlying Little’s Law, the concept of Effective Satisfaction in healthcare has been proposed. The concept is based on a Staff-Patient Satisfaction Relation Model (S-PSRM) developed using a patient satisfaction model and an empirically tested model developed for measuring staff satisfaction with workload (service time). The argument is presented that a synergy between patient satisfaction and staff satisfaction is the key to sustainable improvement in healthcare quality.
The final contribution is the proposal of a Discrete Event Simulation ( DES ) modelling platform as a descriptive model that captures the random and stochastic nature of healthcare service provision process to prove the applicability of the proposed quality measurement models.
Resource Management and Product Traceability Decision Tool( e-Track) - BridgeLink Feasibility Study Funded by Department of Environment, Food and Rural Affairs (AFM218br)
This PROJECT investigates the feasibility of a quick-response decision modeller to help managers deal with the demands of volatile food production environment. It argues the need for a focused, real-time, high quality information apparatus to support production managers on the shopfloor. This is achieved by providing specified definitions for the key performance factors (KPF) within the industry. A cost/benefit analysis tool will be presented that reports on system performance.
The aim of the proposed system is to improve the quality of decisions made during food production process with the aim to realise the real operational costs thus improve profit margins. By creating the proper information environment and equipping the system with suitable prediction platform stressed managers can be advised on various action plans and the consequences for each decision. This is to be achieved by firstly, creating a comprehensive information environment for precise measurement of the state of the system in real-time. Secondly, provide an accurate prediction platform for improved decision-making.
Our research to date reveals that there is no quick response decision- making tool in the industry that helps production managers to evaluate the gains and losses behind each option available to them.
