Many people admitted to hospital with a provisional diagnosis of nonserious back pain are subsequently found to have serious pathology as the underlying cause

To determine the proportion of patients admitted to the hospital for back pain who have nonserious back pain, serious spinal, or serious other pathology as their final diagnosis. The proportion of nonserious back pain admissions will be used to plan for future ‘virtual hospital’ admissions. Electronic medical record data between January 2016 and September 2020 from three emergency departments (ED) in Sydney, Australia were used to identify inpatient admissions. SNOMED-CT-AU diagnostic codes were used to select ED patients aged 18 and older with an admitting diagnosis related to nonserious back pain. The inpatient discharge diagnosis was determined from the primary ICD-10-AM codes by two independent clinician-researchers. Inpatient admissions were then analysed by sociodemographic and hospital admission variables. A total of 38.1% of patients admitted with a provisional diagnosis of nonserious back pain were subsequently diagnosed with a specific pathology likely unsuitable for virtual care; 14.2% with a serious spinal pathology (e.g., fracture and infection) and 23.9% a serious pathology beyond the lumbar spine (e.g., pathological fracture and neoplasm). A total of 57% of admissions were identified as nonserious back pain, likely suitable for virtual care. A challenge for implementing virtual care in this setting is screening for patients with serious pathology. Protocols need to be developed to reduce the risk of patients being admitted to virtual hospitals with serious pathology as the cause of their back pain. Key Points• Among admitted patients provisionally diagnosed in ED with non-serious back pain, 38.1% were found to have ‘serious spinal pathologies’ or ‘serious pathologies beyond the lumbar spine’ at discharge.• Spinal fractures were the most common serious spinal pathology, accounting for 9% of all provisional ‘non-serious back pain’ admissions from ED.• 57% of back pain admissions were confirmed to be non-serious back pain and may be suitable to virtual hospital care; the challenge is discriminating these patients from those with serious pathology.

HESML: a real-time semantic measures library for the biomedical domain with a reproducible survey

Background Ontology-based semantic similarity measures based on SNOMED-CT, MeSH, and Gene Ontology are being extensively used in many applications in biomedical text mining and genomics respectively, which has encouraged the development of semantic measures libraries based on the aforementioned ontologies. However, current state-of-the-art semantic measures libraries have some performance and scalability drawbacks derived from their ontology representations based on relational databases, or naive in-memory graph representations. Likewise, a recent reproducible survey on word similarity shows that one hybrid IC-based measure which integrates a shortest-path computation sets the state of the art in the family of ontology-based semantic measures. However, the lack of an efficient shortest-path algorithm for their real-time computation prevents both their practical use in any application and the use of any other path-based semantic similarity measure. Results To bridge the two aforementioned gaps, this work introduces for the first time an updated version of the HESML Java software library especially designed for the biomedical domain, which implements the most efficient and scalable ontology representation reported in the literature, together with a new method for the approximation of the Dijkstra’s algorithm for taxonomies, called Ancestors-based Shortest-Path Length (AncSPL), which allows the real-time computation of any path-based semantic similarity measure. Conclusions We introduce a set of reproducible benchmarks showing that HESML outperforms by several orders of magnitude the current state-of-the-art libraries in the three aforementioned biomedical ontologies, as well as the real-time performance and approximation quality of the new AncSPL shortest-path algorithm. Likewise, we show that AncSPL linearly scales regarding the dimension of the common ancestor subgraph regardless of the ontology size. Path-based measures based on the new AncSPL algorithm are up to six orders of magnitude faster than their exact implementation in large ontologies like SNOMED-CT and GO. Finally, we provide a detailed reproducibility protocol and dataset as supplementary material to allow the exact replication of all our experiments and results.

Fast Healthcare Interoperability Resources (FHIR) for Interoperability in Health Research: A Systematic Review (Preprint)

BACKGROUND The standard Fast Healthcare Interoperability Resources (FHIR) is widely used in health information technology. However, its use as a standard for health research is still less prevalent. To use existing data sources more efficiently for health research, data interoperability becomes increasingly important. FHIR provides solutions by offering resource domains such as “Public Health & Research” and “Evidence-Based Medicine” while using already established web technologies. Therefore, FHIR could help to standardize data across different data sources and improve interoperability in health research. OBJECTIVE The aim of our study was to provide a systematic review of existing literature and determine the current state of FHIR implementations in health research and possible future directions. METHODS We searched PubMed/Medline, EMBASE, Web of Science, IEEE Xplore and the Cochrane Library databases for studies published from 2010 to 2021. Studies investigating the use of FHIR in health research were included. Articles published before 2010, abstracts, reviews, editorials and expert opinions were excluded. We followed the PRISMA guidelines and registered this study with PROSPERO, CRD42021235393. Data synthesis was done in tables and figures. RESULTS We identified a total of 674 studies, of which 28 studies were eligible for inclusion. Most studies covered the domain of clinical research (22/28) while the remaining studies focused on public health/ epidemiology (3/28) or did not specify their research domain (3/28). Studies used FHIR for data capture (11/28), standardization of data (7/28), analysis (4/28), recruitment (4/28) and consent management (2/28). Most studies had a generic approach (15/28) and nine of 13 studies focusing on specific medical specialties (infectious disease, genomics, oncology, environmental health, imaging, pulmonary hypertension) reported their solutions to be conferrable to other use cases. Half of the studies reported using additional data models or terminologies: SNOMED CT (8/14), LOINC (8/14), ICD-10 (6/14), OMOP CDM (3/14) and others (9/14). Only one study used a FHIR resource from the domain “Public Health & Research”. Limitations using FHIR included the possible change in the content of FHIR resources, safety and legal matters and the need for a FHIR server. CONCLUSIONS Our review found that FHIR can be implemented in health research and that the areas of application are broad and generalizable in most use cases. Implementation of international terminologies was common and other standards such as OMOP CDM could be used complementary with FHIR. Limitations such as change of FHIR content, lack of FHIR implementation, safety and legal matters need to be addressed in future releases to expand the use of FHIR and therefore interoperability in health research.

Mapping of Wound Infection Concepts

Wound infection is a serious health care complication. Standardized clinical terminologies could be leveraged to support the early identification of wound infection. The purpose of this study was to evaluate the representation of wound infection assessment and diagnosis concepts (N=26) in SNOMED CT and ICNP, using a synthesized procedural framework. A total of 13/26 (50%) assessment and diagnosis concepts had exact matches in SNOMED CT and 2/7 (29%) diagnosis concepts had exact matches in ICNP. This study demonstrated that the source concepts were moderately well represented in SNOMED CT and ICNP; however, further work is necessary to increase the representation of diagnostic infection types. The use of the framework facilitated a systematic, transparent, and repeatable mapping process, with opportunity to extend.

Combining word embeddings to extract chemical and drug entities in biomedical literature

Background Natural language processing (NLP) and text mining technologies for the extraction and indexing of chemical and drug entities are key to improving the access and integration of information from unstructured data such as biomedical literature. Methods In this paper we evaluate two important tasks in NLP: the named entity recognition (NER) and Entity indexing using the SNOMED-CT terminology. For this purpose, we propose a combination of word embeddings in order to improve the results obtained in the PharmaCoNER challenge. Results For the NER task we present a neural network composed of BiLSTM with a CRF sequential layer where different word embeddings are combined as an input to the architecture. A hybrid method combining supervised and unsupervised models is used for the concept indexing task. In the supervised model, we use the training set to find previously trained concepts, and the unsupervised model is based on a 6-step architecture. This architecture uses a dictionary of synonyms and the Levenshtein distance to assign the correct SNOMED-CT code. Conclusion On the one hand, the combination of word embeddings helps to improve the recognition of chemicals and drugs in the biomedical literature. We achieved results of 91.41% for precision, 90.14% for recall, and 90.77% for F1-score using micro-averaging. On the other hand, our indexing system achieves a 92.67% F1-score, 92.44% for recall, and 92.91% for precision. With these results in a final ranking, we would be in the first position.

DCC Terminology Service—An Automated CI/CD Pipeline for Converting Clinical and Biomedical Terminologies in Graph Format for the Swiss Personalized Health Network

One goal of the Swiss Personalized Health Network (SPHN) is to provide an infrastructure for FAIR (Findable, Accessible, Interoperable and Reusable) health-related data for research purposes. Semantic web technology and biomedical terminologies are key to achieving semantic interoperability. To enable the integrative use of different terminologies, a terminology service is a important component of the SPHN Infrastructure for FAIR data. It provides both the current and historical versions of the terminologies in an SPHN-compliant graph format. To minimize the usually high maintenance effort of a terminology service, we developed an automated CI/CD pipeline for converting clinical and biomedical terminologies in an SPHN-compatible way. Hospitals, research infrastructure providers, as well as any other data providers, can download a terminology bundle (currently composed of SNOMED CT, LOINC, UCUM, ATC, ICD-10-GM, and CHOP) and deploy it in their local terminology service. The distributed service architecture allows each party to fulfill their local IT and security requirements, while still having an up-to-date interoperable stack of SPHN-compliant terminologies. In the future, more terminologies and mappings will be added to the terminology service according to the needs of the SPHN community.

Introduction of Systematized Nomenclature of Medicine–Clinical Terms Coding Into an Electronic Health Record and Evaluation of its Impact: Qualitative and Quantitative Study

Background This study describes the conversion within an existing electronic health record (EHR) from the International Classification of Diseases, Tenth Revision coding system to the SNOMED-CT (Systematized Nomenclature of Medicine–Clinical Terms) for the collection of patient histories and diagnoses. The setting is a large acute hospital that is designing and building its own EHR. Well-designed EHRs create opportunities for continuous data collection, which can be used in clinical decision support rules to drive patient safety. Collected data can be exchanged across health care systems to support patients in all health care settings. Data can be used for research to prevent diseases and protect future populations. Objective The aim of this study was to migrate a current EHR, with all relevant patient data, to the SNOMED-CT coding system to optimize clinical use and clinical decision support, facilitate data sharing across organizational boundaries for national programs, and enable remodeling of medical pathways. Methods The study used qualitative and quantitative data to understand the successes and gaps in the project, clinician attitudes toward the new tool, and the future use of the tool. Results The new coding system (tool) was well received and immediately widely used in all specialties. This resulted in increased, accurate, and clinically relevant data collection. Clinicians appreciated the increased depth and detail of the new coding, welcomed the potential for both data sharing and research, and provided extensive feedback for further development. Conclusions Successful implementation of the new system aligned the University Hospitals Birmingham NHS Foundation Trust with national strategy and can be used as a blueprint for similar projects in other health care settings.