Clinical audit of invasive mold disease (IMD) in hematology patients is inefficient due to the difficulties of case finding. This results in antifungal stewardship (AFS) programs preferentially reporting drug cost and consumption rather than measures that actually reflect quality of care. We used machine learning-based natural language processing (NLP) to non-selectively screen chest tomography (CT) reports for pulmonary IMD, verified by clinical review against international definitions and benchmarked against key AFS measures. NLP screened 3014 reports from 1 September 2008 to 31 December 2017, generating 784 positives that after review, identified 205 IMD episodes (44% probable-proven) in 185 patients from 50,303 admissions. Breakthrough-probable/proven-IMD on antifungal prophylaxis accounted for 60% of episodes with serum monitoring of voriconazole or posaconazole in the 2 weeks prior performed in only 53% and 69% of episodes, respectively. Fiberoptic bronchoscopy within 2 days of CT scan occurred in only 54% of episodes. The average turnaround of send-away bronchoalveolar galactomannan of 12 days (range 7–22) was associated with high empiric liposomal amphotericin consumption. A random audit of 10% negative reports revealed two clinically significant misses (0.9%, 2/223). This is the first successful use of applied machine learning for institutional IMD surveillance across an entire hematology population describing process and outcome measures relevant to AFS. Compared to current methods of clinical audit, semi-automated surveillance using NLP is more efficient and inclusive by avoiding restrictions based on any underlying hematologic condition, and has the added advantage of being potentially scalable.
Australian medical students have a strong appreciation for IR despite having suboptimal teaching, exposure and knowledge in IR. In order to complement and sustain the rapid uptake of IR techniques in modern medicine, university curricula require a greater focus on IR.
Research has been slow to leverage digitalised medical records as a data resource. Our study assessed patient acceptability of opt-out consent for secondary use of digital patient data. A questionnaire was distributed to patients in multiple languages and with an interpreter. Of 919 completed surveys, 33% were of non-English speaking background, 15% self-reported cognitive impairment and 3% were refugees. Opt-out consent was accepted in this diverse population; 87% of participants approved, or were indifferent to opt-out consent. Gender, employment and cognition status were not significant determinants of acceptability.
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