Psychosis, characterised by chronic symptoms often emerging in youth, imposes a substantial burden on individuals and healthcare systems. While early detection and intervention can mitigate this burden, there is limited evidence on the cost-effectiveness of such approaches. To address this lack of evidence, this study protocol outlines the health economic implications of an artificial intelligence (AI)-based intervention, the Computer-Assisted Risk-Evaluation (CARE), designed to prevent psychosis. The intervention uses AI technologies to enhance the diagnosis and treatment quality for individuals at high risk of psychosis.

The health economic evaluation has been designed alongside a 12-month multicentre randomised controlled trial comparing CARE with treatment as usual from both payer and societal perspectives. An implementation cost analysis will complement the evaluation, and long-term consequences beyond the trial will be explored descriptively. Based on a literature review, an initial economic logic model will guide subsequent analyses by depicting CARE’s programme theory.

The cost-effectiveness assessment will include averted cases of manifest psychosis and quality-adjusted life-years using the EuroQol 5-Dimensions 3-Level instrument. Other effectiveness outcomes will also be incorporated into a cost–consequence analysis. Cost-effectiveness acceptability curves reflecting statistical uncertainty will be constructed, incorporating various payer and societal willingness-to-pay values. The implementation cost analysis will follow a mixed-methods approach to capture facility-specific costs.

A dark logic model, emphasising negative outcomes, will be developed to investigate long-term consequences. Further, the initial economic logic model will be refined using trial data and expert interviews. This comprehensive approach aims to provide decision-makers not only with evidence on the cost-effectiveness of CARE, but also with a broader understanding of the implications of the intervention.

The study has received ethical approval and plans to disseminate its findings through publication in a peer-reviewed journal and conference presentations.

NCT05813080.

Polypharmacy is associated with an increased risk of adverse patient outcomes across various settings, including inpatient care. To enhance the appropriateness of medication therapy management for patients during hospital stays, computerised interventions have shown promise with regard to patient safety. This study assesses whether the implementation of a clinical decision support system will optimise the process of inpatient medication therapy to prevent inappropriate medication use and thus promote patient safety.

The intervention will be evaluated in a prospective, cluster-randomised controlled trial using a stepped-wedge design. The study will be conducted in 12 hospitals across Germany over a total period of 33 months. Patients will be treated according to the group status of the hospital and receive either standard care or the Transsektorale Optimierung der Patientensicherheit or trans-sectoral optimisation of patient safety intervention. The primary outcome is the combined endpoint of all-cause mortality and all-cause hospitalisation. Secondary endpoints are, for example, inappropriate prescriptions, utilisation of different health services, cost-effectiveness, as well as patient-reported outcome measures. Parameters describing the attitudes of patients and healthcare professionals towards the intervention and organisational change processes will be collected as part of the process evaluation. The primary endpoint will be evaluated using hospital and outpatient claims data from participating statutory health insurances at the population level. There are multiple secondary endpoints with data linkage of primary and secondary data at study participant level. Statistical analysis will make use of (generalised) linear mixed models or generalised estimating equations, taking account of independent covariables. All data analyses of the process evaluation will be descriptive and explorative.

Data collection, storage and evaluation meet all applicable data protection regulations. The trial has been approved by the Ethics Committees of the University of Wuppertal and the Medical Association of Saarland, Germany. Results will be disseminated through workshops, peer-reviewed publications and local and international conferences.

DRKS00025485.