ML-CDSS Implementation

Risks & contraindications

Evidence quality

Highlighted claims

• A systematic review found that only 34.2% of CDSS tools have been used in clinical practice, reflecting a longstanding implementation gap. — 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
• Implementing the ML-CDSS would require changes to established processes anchored in national guidelines and evidence-based best practice. — 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
• Clinicians noted that staff are deeply accustomed to existing protocols aligned with NICE guidelines, with nurses described as strictly adhering to written protocol and avoiding deviations. — 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
• Whole-unit buy-in and formalised processes for recording ML-CDSS recommendations were identified as prerequisites for implementation, alongside extensive staff training. — 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
• Because the ML-CDSS relies solely on blood chemistry data, it cannot account for behavioural factors or comorbidities, which could produce recommendations that do not fully reflect a patient's true clinical state. — 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
• Known barriers to CDSS adoption in healthcare include new administrative burdens, usability issues, and friction with existing clinical workflows. — 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
• Reduced frequency of visits or blood tests enabled by the ML-CDSS could be perceived negatively by patients who value face-to-face interaction during treatment. — 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