Data Driven Approach to Enhancing Efficiency and Value in Healthcare

Abstract

Healthcare is changing, and the era of data-driven healthcare organisations is increasingly popular. Data-Driven approaches (e.g., Machine Learning, Metaheuristics, Modelling and Simulation, Data Analytics, and Data Visualisation) can be used to increase efficiency and value in health services.

Despite extensive research and technological development, the evidence impact of those methodologies in the healthcare sector is limited. In this Thesis we argue that an approach without borders in terms of academic societies and field of study could help to tackle this lack of impact to enhance efficiency and value in healthcare. This Thesis is based on solving practical problems in healthcare, with the research drawing upon both theoretical and empirical analysis.

The research is organised in four stages. In the first, a variety of techniques from Modelling and Simulation were studied and used to analyse current performance and to model improved and more efficient future states of healthcare systems. The focus was the analysis of capacity, demand, activity, and queues both at hospital and population levels. In the second part, a Genetic Algorithm was used to solve a Routing Home Healthcare problem. In the third part, Social Network Analysis was used to visualise and analyse email networks. In the final part, a new healthcare system performance metric is proposed and implemented using a case study. New frameworks to implement these methodologies in the context of real-world problems are presented throughout the Thesis.

In collaboration with University of Southampton, Wessex Academic Health Science Network (AHSN), and NHS England, several projects were developed and implemented for healthcare improvement in the UK. The work aims to increase early detection of cancer and thereby reduce premature mortality. The research was conducted working closely with NHS organisations across the Wessex region in England to produce bespoke endoscopy service modelling, as well as population level models. At a regional level, a Colorectal Cancer Screening Programme model was developed. An analysis of endoscopy activity, capacity and demand across the region was conducted. Future demand for endoscopy services in five years' time was estimated, and we found that the system has enough capacity to attend the expected future activity. A new healthcare system performance metric is presented as a tool to improve healthcare services.

Genetic Algorithm metaheuristic was applied in a variant of the Home Health Care Problem (HHCP), focusing on the route planning of clinical homecare.

Working with the Hospital del Mar Medical Research Institute of Barcelona and the Agency of Health Quality and Assessment of Catalonia a study was developed to estimate future utilisation scenarios of knee arthroplasty (KA) revision in the Spanish National Health System in the short-term (2015) and long-term (2030) and their impact on primary KA utilisation. One of the findings was that the variation in the number of revisions depended on both the primary utilisation rate and the survival function applied. Future activity and resources needed was estimated.

A Social Network Analysis (SNA) project was developed in collaboration with the Wessex AHSN to analyse and extract insight from an organisational email, using SNA and Data Mining.

A new healthcare system performance metric - based on the Overall Equipment Effectiveness (OEE) measure - is proposed and evaluated using real data from and Endoscopy Unit from a UK based hospital. To summarise, this work identifies four key techniques to use in the investigation of health data - Machine Learning Algorithms, Metaheuristic, Discrete Event Simulation and Data Analytics & Visualisations. Following a review of the different subjects and associated issues, those four techniques were evaluated and used with an applied-focus to solve healthcare problems. Key learning points from all different studies, as well as challenges and opportunities for the application of data-driven methodologies are discussed in the final chapter of the Thesis.

La asistencia sanitaria está cambiando y la era de las organizaciones sanitarias basadas en datos es cada vez más popular. Los enfoques basados en datos (por ejemplo, Aprendizaje Automático; Meta-heurísticas; Modelamiento y Simulación; y Análisis y Visualización de datos) pueden utilizarse para aumentar la eficiencia y el valor en los servicios sanitarios.

A pesar de la amplia investigación y el desarrollo tecnológico, la evidencia sobre el impacto de estas metodologías en el sector sanitario es limitada.

En esta tesis argumentamos que un enfoque sin fronteras en términos de sociedades académicas y campo de estudio podría ayudar a abordar esta falta de impacto para aumentar la eficiencia y el valor en la asistencia sanitaria. Esta tesis se basa en la resolución de problemas prácticos en el sector sanitario, con un enfoque tanto teórico como práctico.

La investigación se organizó en cuatro etapas. En la primera, una variedad de técnicas de modelamiento y simulación fueron estudiadas y aplicadas en el análisis y simulación de mejores y más eficientes configuraciones de sistemas sanitarios. El objetivo fue un análisis de capacidad, demanda, actividad y listas de esperas a nivel hospitalario y poblacional. En la segunda parte, un Algoritmo Genético fue implementado para resolver un problema de ruteo de personal sanitario encargado de atención de salud en el hogar. En la tercera parte, Análisis de Redes Sociales fue utilizado para visualizar y analizar una red de usuarios de correos electrónicos. En la etapa final, se propone una nueva métrica para evaluar el rendimiento de sistemas sanitarios, la cual fue implementada a través de un caso de estudio. Diferentes marcos de referencia para la implementación de estas metodologías en problemas reales se presentan a lo largo de la tesis.