Causal Complexity and Causal Ontology of Health-Related Quality of Life Model

Patient-centered care (PCC) is an approach to healthcare that values patients’ preference, need, and autonomy. The estimation of healthcare partly depends on how well PCC is implemented. In addition, the result of clinical research can inform the assessment of the implementation of PCC. In clinical research, health-related quality of life (HRQL) theoretical models offer a conceptual toolbox that informs clinical research and guides the hypotheses generation. Wilson and Cleary (1995) developed the most widely used HRQL theoretical model (Bakas et al., 2012). Ontological assumptions about causation in Wilson and Cleary’s model will influence which kind of hypotheses will be generated. I will argue that Wilson and Cleary’s model instilled a kind of causal bias into hypothesis generation in clinical research on HRQL. Causation from biomedical factors to non-biomedical factors is frequently hypothesized while causation from non-biomedical factors to biomedical factors is rarely hypothesized. It leads to that the interdependence and interaction between constituent parts of patients are ignored, which is an obstacle to the implementation of PCC. In addition, I will propose a revised HRQL theoretical model which avoids the causal bias brought by Wilson and Cleary’s model. By doing so, I leave room for the improvement of the practice of healthcare by analyzing the ontological assumptions about causation.