Computerized clinical decision support software (CDSS) are digital health technologies that have been traditionally categorized as medical devices. However, the evaluation frameworks for traditional medical devices are not well adapted to assess the value and safety of CDSS. In this study, we identified a range of challenges associated with CDSS evaluation as a medical device and investigated whether and how CDSS are evaluated in Australia.

Using a qualitative approach, we interviewed 11 professionals involved in the implementation and evaluation of digital health technologies at national and regional levels. Data were thematically analyzed using both data-driven (inductive) and theory-based (deductive) approaches.

Our results suggest that current CDSS evaluations have an overly narrow perspective on the risks and benefits of CDSS due to an inability to capture the impact of the technology on the sociotechnical environment. By adopting a static view of the CDSS, these evaluation frameworks are unable to discern how rapidly evolving technologies and a dynamic clinical environment can impact CDSS performance. After software upgrades, CDSS can transition from providing information to specifying diagnoses and treatments. Therefore, it is not clear how CDSS can be monitored continuously when changes in the software can directly affect patient safety.

Our findings emphasize the importance of taking a living health technology assessment approach to the evaluation of digital health technologies that evolve rapidly. There is a role for observational (real-world) evidence to understand the impact of changes to the technology and the sociotechnical environment on CDSS performance.

Aim was to examine depressive symptoms in acutely ill schizophrenia patients on a single symptom basis and to evaluate their relationship with positive, negative and general psychopathological symptoms.

Two hundred and seventy-eight patients suffering from a schizophrenia spectrum disorder were analysed within a naturalistic study by the German Research Network on Schizophrenia. Using the Calgary Depression Scale for Schizophrenia (CDSS) depressive symptoms were examined and the Positive and Negative Syndrome Scale (PANSS) was applied to assess positive, negative and general symptoms. Correlation and factor analyses were calculated to detect the underlying structure and relationship of the patient’s symptoms.

The most prevalent depressive symptoms identified were depressed mood (80%), observed depression (62%) and hopelessness (54%). Thirty-nine percent of the patients suffered from depressive symptoms when applying the recommended cut-off of a CDSS total score of > 6 points at admission. Negligible correlations were found between depressive and positive symptoms as well as most PANSS negative and global symptoms despite items on depression, guilt and social withdrawal. The factor analysis revealed that the factor loading with the PANSS negative items accounted for most of the data variance followed by a factor with positive symptoms and three depression-associated factors.

The naturalistic study design does not allow a sufficient control of study results for the effect of different pharmacological treatments possibly influencing the appearance of depressive symptoms.

Results suggest that depressive symptoms measured with the CDSS are a discrete symptom domain with only partial overlap with positive or negative symptoms.