How to measure outcome? A perspective from the dynamic complex systems approach

Abstract

The evaluation and assessment of outcome is an important issue in psychotherapy research and practice. Since the beginning of empirical research, the effectiveness of treatments has been in the focus of interest to optimise mental health care. Despite this importance, the assessment of outcome by pre-to-post comparisons of point measures is hampered by some limitations. These include, amongst others, the predominant use of standard questionnaires neglecting personalised outcome criteria, the focus on point measures that ignore dynamic patterns representing the volatility of mental functioning, memory biases that become important if a recall of longer time periods is urged, and the non-ergodicity of trajectories of change. Based on new methods of digitalised data collection in the real-life setting of patients, some conclusions for process and outcome monitoring can be drawn: first, most mental diseases are characterised by specific dynamic patterns (dynamic diseases) whose changes can be assessed by high-frequency time sampling, for example daily assessments of patients. Second, personal criteria for self-assessments can be identified by multiperspective case formulations. Third, electronic devices such as smartphones allow for data collection in the real-world settings of patients, which gives access to experiences in their ecosystems.