Efficient allocation of public resources requires identification, measurement and quantification of costs and benefits of alternative programs. Patient reported outcomes (PROs) are routinely incorporated into economic evaluations of health technologies, but patient experience is often overlooked. This thesis aims to develop a descriptive system for patient experience that can be valued and used to inform economic evaluation. The generation and selection of items is key in the development of any PRO measure. The thesis provides a contemporary overview of recommended methods and those actually used by instrument developers. Frequently a staged approach is used to establish dimensions first, using exploratory factor analysis, followed by item selection using item response theory (IRT), Rasch or structural equation modelling (SEM). I demonstrate the use of different methods for item selection and its underlying mechanics, followed by comparison of the methods. An existing patient dataset, the Inpatient survey (2014) that collected information on nearly 70 aspects of healthcare delivery from NHS users was used. Logistic regression analyses were applied with respondents' rating of overall patient experience specified as dependent variable. Advanced statistical analyses focussed mostly on patients who had an operation or procedure. Latent construct or dimensions were derived and measurement model was confirmed using confirmatory factor analysis. IRT and factor analysis were employed in each one-factor model for item selection. Regression analyses identified many significant variables but most overlapped conceptually. An 11 and 8 factor model for patients with A&E and planned admissions respectively was determined. A generalised partial credit model and a factor analysis model identified different items to include in each dimension. Broadly the items identified by different methods related to respect, comfort and clear communication to patients. This thesis presents descriptive systems for patient experience that is amenable to valuation. It also demonstrates that different patient experience instruments are generated based on patient population used and item selection technique adopted.

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