Interoperable E-Health System Using Structural and Semantic Interoperability Approaches in CAREPATH

Technical and semantic interoperability are broadly used components of interoperability technology in healthcare. Technical Interoperability provides interoperability interfaces to enable data exchange within different healthcare systems, despite any underlying heterogeneity. Semantic interoperability make different healthcare systems understand and interpret the meaning of the data that is exchanged, by using and mapping standardized terminologies, coding systems, and data models to describe the concept and structure of data. We propose a solution using Semantic and Structural Mapping techniques within CAREPATH; a research project designed to develop ICT solutions for the care management of elderly multimorbid patients with mild cognitive impairment or mild dementia. Our technical interoperability solution supplies a standard-based data exchange protocol to enable information exchange between local care systems and CAREPATH components. Our semantic interoperability solution supplies programmable interfaces, in order to semantically mediate different clinical data representation formats and incorporating data format and terminology mapping features. The solution offers a more reliable, flexible and resource efficient method across EHRs.

IMG-11. A COMPUTERISED CLINICAL DECISION SUPPORT SYSTEM FOR DIAGNOSING CHILDREN’S BRAIN TUMOURS USING FUNCTIONAL IMAGING AND MACHINE LEARNING

INTRODUCTION: Magnetic resonance imaging is a key investigation in the diagnosis of childhood solid tumours. Advanced techniques such as diffusion weighted imaging (DWI), magnetic resonance spectroscopy (MRS) and perfusion imaging probe the underlying cellular, chemical and vascular nature of the disease. Coupled with machine learning these scanning methods show improvement in diagnostic accuracy compared with conventional imaging. Advanced image analysis is not routinely available in hospitals. We present a clinical decision support system (CDSS) developed for advanced MR analysis and interpretation. METHOD: The CDSS was developed in house. The Children’s Cancer and Leukaemia Group Functional Imaging Group (CCLGFIG) Database, a national resource, was used to provide a repository of cases together with their advanced imaging and machine learning diagnostic classifiers. A new case is displayed alongside cases in the repository with known diagnoses, including summary statistics for relevant diagnostic categories. The CDSS was made available to radiologists, in their clinical environment for technical and clinical evaluation. Structured interviews were undertaken. The CDSS was developed as a computer app for multi-centre distribution. RESULTS: 436 MRS, 240 DWI and 85 perfusion cases were available for building repositories. Machine learning classifiers showed diagnostic accuracies for the major childhood brain tumour types of 85-95%. Comparison of MRS with a data repository was found to improve non-invasive diagnosis. Results from the CDSS can be uploaded to the CCLGFIG to support multicentre research. Positive feedback on the CDSS from clinicians included: ready access to advanced analysis; simple and efficient integration into clinical workflow; and assisted interpretation of advanced analysis. DISCUSSION: Advanced MR analysis techniques provide improved non-invasive diagnostic accuracy but are difficult to implement on clinical systems due to technical, infrastructure and training limitations. CONCLUSION: We have successfully released a CDSS for paediatric cancer within the hospital environment and assessed its suitability for clinical use.

Liquid Chromatographic Determination of Glyphosate Technical and Its Formulation: Collaborative Study

An HPLC method for the determination of glyphosate in formulation and technical samples has been subjected to a collaborative study with 12 laboratories participating. This method requires no sample pretreatment. Samples were dissolved in mobile phase, injected directly using a fixed-volume loop, and quantitated by an external standard technique. Compounds were separated on a strong anion exchange column with a water-methanol (96 + 4) mobile phase that was 0.0062M in KH2P04 and adjusted to pH 1.9 with 85% H3PO4, and detected with a variable wavelength UV detector at 195 nm. Calculations were made using peak areas. The collaborative study involved 3 pairs of matched samples with single determinations on each sample: Roundup herbicide, a technical intermediate, and technical glyphosate. The coefficients of variation for the 3 pairs were 1.70, 0.88, and 0.90%, respectively. The method has been adopted official first action.