The Impact Of Electronic Health Records And Machine Learning On Antibiotic Prescribing Practices In Haematology Patients With Bacteraemia.

Abstract

Background: Bloodstream infections (BSI) are a leading cause of morbidity and mortality among haematology patients, particularly those immunocompromised due to chemotherapy-induced neutropenia. Delays in diagnosis and reliance on broad-spectrum antibiotics contribute to antimicrobial resistance (AMR), now recognised as a critical global health threat. Electronic Health Records (EHR) combined with Machine Learning (ML) offer potential to support early infection recognition and optimise antibiotic prescribing, yet their adoption in high-risk haematology populations remains underexplored. Aim: This dissertation explores how HER and ML-driven tools influence antibiotic prescribing practices for bacteraemia in haematology patients, with specific attention to their potential roles in antimicrobial stewardship, early detection, and safe clinical decision-making. Methods: A case study approach was adopted, drawing on Yin’s structured framework to examine evidence at the intersection of digital health and haematology care. A systematic search of PubMed, MEDLINE, and CINAHL identified 143 records, of which 11 primary studies met inclusion criteria. These studies were critically appraised using Joanna Briggs Institute (JBI) checklists. Thematic synthesis was employed to integrate findings across quantitative and qualitative evidence, with particular attention to methodological quality, relevance to practice, and stakeholder perspectives. Findings: Four themes were identified. First, early infection prediction: ML models consistently outperformed conventional scores (e.g., qSOFA, NEWS2), offering potential for earlier alerts. Second, outcome prognostication: algorithms improved risk stratification for mortality and antimicrobial resistance. Third, support for antibiotic selection and de-escalation: personalised antibiograms and interpretable resistance forecasts showed promise for AMS. Fourth, model and data design: most studies lacked haematology-specific variables such as duration of neutropenia, central venous access, or MDR colonisation, limiting generalisability. Across themes, facilitators and barriers shaped implementation: clinician trust, usability, workflow alignment, governance, and the need for continuous recalibration. Evidence quality overall was moderate, constrained by retrospective single-centre designs and scarce prospective validation. Conclusion: ML–EHR systems hold promise for enhancing infection recognition and optimising antibiotic use in haematology, but their translation into practice is hindered by methodological weaknesses and implementation barriers. Future progress requires prospective, haematology-specific trials, incorporation of relevant risk factors, and development of governance frameworks. Embedding nurses, pharmacists, and wider stakeholders within stewardship structures will be essential to ensure safe, accountable, and effective deployment.