Defining and Measuring Quality in Primary Dental Care

Abstract

The concept of quality within primary dental care is poorly understood and reported. There is no consensus on a definition of quality within primary dental care nor how quality should be measured. The research in this thesis sets out to define an approach to quality assessment in dentistry and develop ways of measuring quality. The literature review in chapter 1 describes the current understanding of quality in healthcare and identifies reasons for an approach for quality assessment that is specific to dentistry. Chapter 2 proposes an approach to quality assessment built around a framework that intersects previously reported dimensions of quality against Donabedian’s domains of structure, process and outcome and suggests that the use of a common approach to quality improvement is more important than a single definition for quality in dentistry. A systematic review in chapter 3 describes the current state of quality measures available within the peer review and grey literature. 11 validated measure sets from the peer reviewed literature and 24 measure sets from the grey literature were identified. Many measure concepts were repeated between measure sets. Few measures were developed from an a priori conceptualisation of quality. Chapter 4 describes A RAND/UCLA appropriateness method study to gain consensus on the dimensions of quality that are important in defining quality in dentistry and appropriate quality measures for use in primary care in the UK. This study constructs 45 indicators that may be used to assess quality in primary dental care and calls for similar studies in different contexts to build an international consensus of quality. Chapter 5 describes a qualitative interview study performed with members of the public (n=10) and primary care dentists (n=10) in the UK. It assesses the barriers to quality measurement use in primary dental care, with focus on novel concepts of data collection and reporting. This showed a desire from patients to be able to access objective information regarding the quality of dentists and acknowledgment of the benefits of automation of quality measurement from dentists. However, some dentists believed that quality measurement would be used as a mechanism to increase surveillance and reduce clinical autonomy. Chapters 6 and 7 describe a novel method of quality assessment using cloud-based sentiment analysis tools to assess online reviews of dental services. Chapter 6 describes the technical process of generating sentiment analysis scores using different cloud-based platforms. The sentiments described by these systems has good agreement with humans reviewing the same data; Amazon Web Services (k=0.660) Google (k=0.706) and Monkeylearn (k=0.728). Chapter 7 demonstrates a big data approach to the use of sentiment analysis from reviews, showing the sentiment of patients across England. It compares the results of sentiment analysis to satisfaction displayed by the Friends and family test. There was a statistically significant difference between the sentiment displayed in the FFT and in online reviews (Sentiment analysis 82% Positive, Friends and Family Test 96% positive, �2=5025.375 df =1 p =

Date of Award	1 Aug 2021
Original language	English
Awarding Institution	The University of Manchester
Supervisor	Anne-Marie Glenny (Supervisor), Martin Tickle (Supervisor) & Lucy O'Malley (Supervisor)