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.

Towards achieving semantic interoperability of clinical study data with FHIR.

BACKGROUND: Observational clinical studies play a pivotal role in advancing medical knowledge and patient healthcare. To lessen the prohibitive costs of conducting these studies and support evidence-based medicine, results emanating from these studies need to be shared and compared to one another. Current approaches for clinical study management have limitations that prohibit the effective sharing of clinical research data. METHODS: The objective of this paper is to present a proposal for a clinical study architecture to not only facilitate the communication of clinical study data but also its context so that the data that is being communicated can be unambiguously understood at the receiving end. Our approach is two-fold. First we outline our methodology to map clinical data from Clinical Data Interchange Standards Consortium Operational Data Model (ODM) to the Fast Healthcare Interoperable Resource (FHIR) and outline the strengths and weaknesses of this approach. Next, we propose two FHIR-based models, to capture the metadata and data from the clinical study, that not only facilitate the syntactic but also semantic interoperability of clinical study data. CONCLUSIONS: This work shows that our proposed FHIR resources provide a good fit to semantically enrich the ODM data. By exploiting the rich information model in FHIR, we can organise clinical data in a manner that preserves its organisation but captures its context. Our implementations demonstrate that FHIR can natively manage clinical data. Furthermore, by providing links at several levels, it improves the traversal and querying of the data. The intended benefits of this approach is more efficient and effective data exchange that ultimately will allow clinicians to switch their focus back to decision-making and evidence-based medicines.

Using Australian medicines terminology (AMT) and SNOMED CT-AU to better support clinical research.

A large scale, long term clinical study faced significant quality issues with its medications use data which had been collected from participants using paper forms and manually entered into a data capture system. A method was developed that automatically mapped 72.2% of the unique medication names collected for the study to the AMT and SNOMED CT-AU using Ontoserver, a terminology server for clinical ontologies. These initial results are promising and, with further improvements to the algorithms and evaluation, are expected to greatly improve the analysis of medication data gathered from the study.

Identifying symptom groups from Emergency Department presenting complaint free text using SNOMED CT.

Patients presenting to Emergency Departments may be categorised into different symptom groups for the purpose of research and quality improvement. The grouping is challenging due to the variability in the way presenting complaints are recorded by clinical staff. This work proposes analysis of the presenting complaint free-text using the semantics encoded in the SNOMED CT ontology. This work demonstrates a validated prototype system that can classify unstructured free-text narratives into patient's symptom group. A rule-based mechanism was developed using variety of keywords to identify the patient's symptom group. The system was validated against the manual identification of the symptom groups by two expert clinical research nurses on 794 patient presentations from six participating hospitals. The comparison of system results with one clinical research nurse showed 99.3% sensitivity; 80.0% specificity and 0.9 F-score for identifying "chest pain" symptom group.

Mapping the Queensland Health iPharmacy Medication File to the Australian Medicines Terminology Using Snapper.

An exploratory exercise in mapping approximately 8000 medication terms from the Queensland Health iPharmacy Medication File to the Australian Medicines Terminology (AMT) was carried out to determine coverage, build specialist knowledge, and inform future clinical terminology strategies. Snapper was the mapping tool selected for this exercise. The Automap function of the tool mapped 39.2% of the items that were successfully mapped, and the remainder were manually mapped. A total of 51.8% of the sample items were mapped to a semantically equivalent AMT concept with 50.0% of terms being mapped to a satisfactory fully specified term, and 1.8% of terms being mapped to a fully specified term that was considered unsuitable for QH clinical purposes. Rules and guidelines on how to deal with the emerging differences between the two terminologies were developed during the course of the project. Snapper was found to be an appropriate tool for this exercise; its functionality is being constantly refined to assist users. As a result, this exercise will provide NEHTA with input for the national scope and content for AMT, and QH will endeavour to prepare the iPharmacy medication file for future interfaces with other terminologies.

Developing a national emergency department data reference set based on SNOMED CT.

Emergency departments around Australia use a range of software to capture data on patients' reason for encounter, presenting problem and diagnosis. The data collected are mainly based on descriptions and codes of the International Classification of Diseases, 10th revision, Australian modification (ICD-10-AM), with each emergency department having a tailored list of terms. The National E-Health Transition Authority is introducing a standard clinical terminology, the Systematized Nomenclature of Medicine--Clinical Terms (SNOMED CT), as one of the building blocks of an e-health infrastructure in Australia. The Australian e-Health Research Centre has developed a software platform, Snapper, which facilitates mapping of existing clinical terms to the SNOMED CT terminology. Using the Snapper software, reference sets of terms for emergency departments are being developed, based on the Australian version of SNOMED CT (SNOMED CT-AU). Existing software systems need to be able to implement these reference sets to support standardised recording of data at the point of care. As the terms collected will be part of a larger terminology, they will be useful for patients' admission and discharge summaries and for computerised clinical decision making. Mapping existing sets of clinical terms to a national emergency department SNOMED CT reference set will facilitate consistency between emergency department data collections and improve the usefulness of the data for clinical and analytical purposes.

Symbolic rule-based classification of lung cancer stages from free-text pathology reports.

OBJECTIVE: To classify automatically lung tumor-node-metastases (TNM) cancer stages from free-text pathology reports using symbolic rule-based classification. DESIGN: By exploiting report substructure and the symbolic manipulation of systematized nomenclature of medicine-clinical terms (SNOMED CT) concepts in reports, statements in free text can be evaluated for relevance against factors relating to the staging guidelines. Post-coordinated SNOMED CT expressions based on templates were defined and populated by concepts in reports, and tested for subsumption by staging factors. The subsumption results were used to build logic according to the staging guidelines to calculate the TNM stage. MEASUREMENTS: The accuracy measure and confusion matrices were used to evaluate the TNM stages classified by the symbolic rule-based system. The system was evaluated against a database of multidisciplinary team staging decisions and a machine learning-based text classification system using support vector machines. RESULTS: Overall accuracy on a corpus of pathology reports for 718 lung cancer patients against a database of pathological TNM staging decisions were 72%, 78%, and 94% for T, N, and M staging, respectively. The system's performance was also comparable to support vector machine classification approaches. CONCLUSION: A system to classify lung TNM stages from free-text pathology reports was developed, and it was verified that the symbolic rule-based approach using SNOMED CT can be used for the extraction of key lung cancer characteristics from free-text reports. Future work will investigate the applicability of using the proposed methodology for extracting other cancer characteristics and types.