ABSTRACT
BACKGROUND: The effectiveness of mental health care is currently monitored through routine quantitative symptom-driven measurements in most clinical settings. These measurements seem inadequate, especially for target groups with complex, multi-faceted problems. There is as yet no alternative method. AIM: 1. To describe why quantitative symptom-driven measurements are inadequate for measuring healthcare effectiveness; and 2. to introduce a new data platform that adjusts for socioeconomic and environmental factors to monitor the effectiveness of healthcare. METHOD: Overview of developments based on literature and introduction of a unique data platform. RESULTS: In the case of complex, multi-faced problems, such as in children with mild intellectual disability and comorbid psychopathology, mental health problems cannot be quantified, isolated, and individualized, i.e., decontextualized. To evaluate care for external benchmarking and scientific research, a shift is advised from measuring clinical symptoms within the treatment period to measuring longer-term group-level social functioning across multiple life domains, with a focus on socio-demographic differences. The Extramuraal LUMC Academisch Netwerk Gezond & Gelukkig Den Haag (ELAN-GGDH ; in English: Extramural LUMC Academic Network Healthy & Happy The Hague) data platform accomplishes this by combining mental health data with Statistics Netherlands microdata. CONCLUSION: The data platform could add value to external benchmarking and scientific research at group level.
