CBL on FHIR: A FHIR-Based Platform for Health Professional Education.

A FHIR based platform for case-based instruction of health professions students has been developed and field tested. The system provides a non-technical case authoring tool; supports individual and team learning using digital virtual patients; and allows integration of SMART Apps into cases via its simulated EMR. Successful trials at the University of Queensland have led to adoption at the University of Melbourne.

Leveraging Unlabeled Clinical Data to Boost Performance of Risk Stratification Models for Suspected Acute Coronary Syndrome.

The performance of deep learning models in the health domain is desperately limited by the scarcity of labeled data, especially for specific clinical-domain tasks. Conversely, there are vastly available clinical unlabeled data waiting to be exploited to improve deep learning models where their training labeled data are limited. This paper investigates the use of task-specific unlabeled data to boost the performance of classification models for the risk stratification of suspected acute coronary syndrome. By leveraging large numbers of unlabeled clinical notes in task-adaptive language model pretraining, valuable prior task-specific knowledge can be attained. Based on such pretrained models, task-specific fine-tuning with limited labeled data produces better performances. Extensive experiments demonstrate that the pretrained task-specific language models using task-specific unlabeled data can significantly improve the performance of the downstream models for specific classification tasks.

A SMART on FHIR Prototype for Genomic Test Ordering.

Queensland Genomics recently undertook a number of Clinical Demonstration Projects (CDPs) to demonstrate the benefits of genomics in clinical practice. Integration of this testing requires the health system to provide the infrastructure for the appropriate ordering of these tests. Ordering of genomics tests will likely require greater exchange of information between the ordering clinician and the lab that is producing a clinical test report. The clinical demonstration projects were used to understand the information flow and the use of genomic, phenotypic and other information through the test ordering, analysis and reporting stages. This information was used to inform a set of requirements for a genomics test ordering and reporting system. A prototype of this system was developed as a SMART on FHIR application. This prototype will inform a future production system with FHIR Resources, software interfaces and interoperability requirements.

A Decision Support System for Pathology Test Result Reviews in an Emergency Department to Support Patient Safety and Increase Efficiency.

The review of pathology test results for missed diagnoses in Emergency Departments is time-consuming, laborious, and can be inaccurate. An automated solution, with text mining and clinical terminology semantic capabilities, was developed to provide clinical decision support. The system focused on the review of microbiology test results that contained information on culture strains and their antibiotic sensitivities, both of which can have a significant impact on ongoing patient safety and clinical care. The system was highly effective at identifying abnormal test results, reducing the number of test results for review by 92%. Furthermore, the system reconciled antibiotic sensitivities with documented antibiotic prescriptions in discharge summaries to identify patient follow-ups with a 91% F-measure - allowing for the accurate prioritization of cases for review. The system dramatically increases accuracy, efficiency, and supports patient safety by ensuring important diagnoses are recognized and correct antibiotics are prescribed.

Computer-Assisted Diagnostic Coding: Effectiveness of an NLP-based approach using SNOMED CT to ICD-10 mappings.

Computer-assisted (diagnostic) coding (CAC) aims to improve the operational productivity and accuracy of clinical coders. The level of accuracy, especially for a wide range of complex and less prevalent clinical cases, remains an open research problem. This study investigates this problem on a broad spectrum of diagnostic codes and, in particular, investigates the effectiveness of utilising SNOMED CT for ICD-10 diagnosis coding. Hospital progress notes were used to provide the narrative rich electronic patient records for the investigation. A natural language processing (NLP) approach using mappings between SNOMED CT and ICD-10-AM (Australian Modification) was used to guide the coding. The proposed approach achieved 54.1% sensitivity and 70.2% positive predictive value. Given the complexity of the task, this was encouraging given the simplicity of the approach and what was projected as possible from a manual diagnosis code validation study (76.3% sensitivity). The results show the potential for advanced NLP-based approaches that leverage SNOMED CT to ICD-10 mapping for hospital in-patient coding.

Assessing colonoscopic inspection skill using a virtual withdrawal simulation: a preliminary validation of performance metrics.

BACKGROUND: The effectiveness of colonoscopy for diagnosing and preventing colon cancer is largely dependent on the ability of endoscopists to fully inspect the colonic mucosa, which they achieve primarily through skilled manipulation of the colonoscope during withdrawal. Performance assessment during live procedures is problematic. However, a virtual withdrawal simulation can help identify and parameterise actions linked to successful inspection, and offer standardised assessments for trainees. METHODS: Eleven experienced endoscopists and 18 endoscopy novices (medical students) completed a mucosal inspection task during three simulated colonoscopic withdrawals. The two groups were compared on 10 performance metrics to preliminarily assess the validity of these measures to describe inspection quality. Four metrics were related to aspects of polyp detection: percentage of polyp markers found; number of polyp markers found per minute; percentage of the mucosal surface illuminated by the colonoscope (≥0.5 s); and percentage of polyp markers illuminated (≥2.5 s) but not identified. A further six metrics described the movement of the colonoscope: withdrawal time; linear distance travelled by the colonoscope tip; total distance travelled by the colonoscope tip; and distance travelled by the colonoscope tip due to movement of the up/down angulation control, movement of the left/right angulation control, and axial shaft rotation. RESULTS: Statistically significant experienced-novice differences were found for 8 of the 10 performance metrics (p's < .005). Compared with novices, experienced endoscopists inspected more of the mucosa and detected more polyp markers, at a faster rate. Despite completing the withdrawals more quickly than the novices, the experienced endoscopists also moved the colonoscope more in terms of linear distance travelled and overall tip movement, with greater use of both the up/down angulation control and axial shaft rotation. However, the groups did not differ in the number of polyp markers visible on the monitor but not identified, or movement of the left/right angulation control. All metrics that yielded significant group differences had adequate to excellent internal consistency reliability (α = .79 to .90). CONCLUSIONS: These systematic differences confirm the potential of the simulated withdrawal task for evaluating inspection skills and strategies. It may be useful for training, and assessment of trainee competence.

Radiation exposure during scoliosis surgery: a prospective study.

BACKGROUND CONTEXT: The present literature on the cancer risks related to radiation exposure in patients and surgeons during scoliosis surgery is sparse. PURPOSE: To assess the radiation exposure in patients and surgeons during scoliosis surgery and estimate the increased cancer risk of both groups. STUDY DESIGN: Over a 6-month period, we conducted a prospective study to monitor the intraoperative radiation dose received by both patients and surgeons during scoliosis cases. PATIENT SAMPLE: It included 30 consecutive patients undergoing scoliosis surgery by a team of two surgeons (S1 and S2). OUTCOME MEASURES: We measured the radiation exposure to the eyes, thyroid, and hands for each surgeon; measured the difference of radiation exposure between the two surgeons; the difference in radiation exposure with respect to the proximity of the surgeon to the X-ray tube, and the radiation exposure for each patient. METHODS: An electronic dosimeter was attached over the thyroid guard and a thermoluminescent dosimeter ring on both hands of each surgeon. The patients were monitored using the dose area product (DAP) measurements from the image intensifier, and their radiation exposure was calculated with the Monte Carlo calculation. RESULTS: The mean eye dose per procedure for the two surgeons S1 and S2 was 0.8 µSv and 1.3 µSv, respectively. The mean thyroid dose for S2 and S1 was 1.2 µSv and 1.4 µSv, respectively. The dose recorded by the surgeon on the same side of the patient as the X-ray tube was significantly higher than for the surgeon on the far side (p<.05). Mean DAP per procedure was 91.3 cGycm(2) and the mean radiation dose for patients was 252.9 µSv. The increase in cancer risk for patients and surgeons was 0.001% and 0.0005%, respectively, for each year of exposure. CONCLUSIONS: A significantly higher dose of radiation during scoliosis surgery was received by the surgeon standing on the same side as the X-ray tube. However, both surgeons received a total radiation dose of less than 1% of the recommended dose limit per year and, therefore, the total radiation exposure in both surgeons and patients was well within the recommended safe limits.