What are the views of cancer care administrators and clinicians in England on the use of a machine learning clinical decision support system (ML-CDSS) to predict patients' risk of hepatic and renal deterioration during chemotherapy? A qualitative study.

OBJECTIVE

To explore administrators' and clinicians' views on the factors that influence their use and adoption of a machine learning clinical decision support system (ML-CDSS) to predict patients' risk of hepatic and renal deterioration during chemotherapy.

METHODS AND ANALYSIS: This was a qualitative study that used purposive sampling. 18 participants with administration and clinical backgrounds working in cancer care in England were recruited. Qualitative data were collected by conducting semi-structured interviews and a focus group. Data were analysed thematically using the framework method to identify key themes.

RESULTS

Participants acknowledged that monitoring blood chemistry is a core component of chemotherapy as it helps clinicians assess patient fitness and treatment response. The ML-CDSS was perceived as a potentially valuable tool for identifying patients at increased risk of hepatic and renal deterioration, supporting clinical decision-making and enhancing care efficiency. However, several concerns were raised regarding its potential implementation in practice. Participants questioned clinicians' willingness and capacity to integrate the tool into their existing workflows. Participants also believed it was important to demonstrate the ML-CDSS's sensitivity, specificity and validity in accurately predicting patients' risk to build clinicians' trust in the tool, demonstrating evidence of its efficacy and effectiveness in practice.

CONCLUSION

Administrators and clinicians recognised the potential benefits of the ML-CDSS to enhance the delivery of chemotherapy by identifying patients at risk for hepatic and renal deterioration. Successful adoption in practice depends on building trust with the tool by being transparent in its development, its effectiveness and impact. Future work should demonstrate the ML-CDSS being used in practice to generate real-world evidence.