A philosophical perspective on the development and application of patient-reported outcomes measures (PROMs).
Keith Ashton MeadowsPublished in: Quality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation (2021)
Questionnaires are a common method in healthcare and clinical research to collect self-reported data on patients' behaviour and outcomes rather than the clinician's perspective. As a consequence there is a plethora of questionnaires and rating forms developed to measure a range of concepts such as health-related quality of life and health status. Given that these measures have been developed within a nomothetic paradigm to enhance our understanding of peoples self-perceived health status by translating complex personal feelings and experiences into a simple numeric score, the patient's illness narrative is lost along the way. This commentary discusses the limitations of the nomothetic approach as completion of a questionnaire is a social and contextually orientated activity and that their development is best viewed within the philosophical tradition of pragmatism, based on sound qualitative methods and rigorous psychometric testing. The commentary discusses the philosophical orientation underpinning PROM development and argues the case for a pragmatic epistemology based on a mixed methods research paradigm which goes beyond the current practice of informing the content validity of a PROM in the early phase of its development but to work towards developing a more composite and holistic picture through mixed methods in the interpretation of a patient's PROM score. Therefore, it is argued that the quality of data obtained will be enhanced but, also importantly and rightly places the participant at the centre of the research.
Keyphrases
- patient reported outcomes
- healthcare
- mental health
- end stage renal disease
- case report
- chronic kidney disease
- psychometric properties
- primary care
- systematic review
- ejection fraction
- newly diagnosed
- electronic health record
- physical activity
- machine learning
- social support
- metabolic syndrome
- patient reported
- weight loss
- health information
- glycemic control
- double blind