Terminology Services: Standard Terminologies to Control Health Vocabulary

System interoperability and data linkage in the era of health information management: A bibliometric analysis

Background: Across the world, health data generation is growing exponentially. The continuous rise of new and diversified technology to obtain and handle health data places health information management and governance under pressure. Lack of data linkage and interoperability between systems undermines best efforts to optimise integrated health information technology solutions. Objective: This research aimed to provide a bibliometric overview of the role of interoperability and linkage in health data management and governance. Method: Data were acquired by entering selected search queries into Google Scholar, PubMed, and Web of Science databases and bibliometric data obtained were then imported to Endnote and checked for duplicates. The refined data were exported to Excel, where several levels of filtration were applied to obtain the final sample. These sample data were analysed using Microsoft Excel (Microsoft Corporation, Washington, USA), WORDSTAT (Provalis Research, Montreal, Canada) and VOSviewer software (Leiden University, Leiden, Netherlands). Results: The literature sample was retrieved from 3799 unique results and consisted of 63 articles, present in 45 different publications, both evaluated by two specific in-house global impact rankings. Through VOSviewer, three main clusters were identified: (i) e-health information stakeholder needs; (ii) e-health information quality assessment; and (iii) e-health information technological governance trends. A residual correlation between interoperability and linkage studies in the sample was also found. Conclusion: Assessing stakeholders' needs is crucial for establishing an efficient and effective health information system. Further and diversified research is needed to assess the integrated placement of interoperability and linkage in health information management and governance. Implications: This research has provided valuable managerial and theoretical contributions to optimise system interoperability and data linkage within health information research and information technology solutions.

HL7 Fast Healthcare Interoperability Resources (HL7 FHIR) in digital healthcare ecosystems for chronic disease management: Scoping review

BACKGROUND

The prevalence of chronic diseases has shifted the burden of disease from incidental acute inpatient admissions to long-term coordinated care across healthcare institutions and the patient's home. Digital healthcare ecosystems emerge to target increasing healthcare costs and invest in standard Application Programming Interfaces (API), such as HL7 Fast Healthcare Interoperability Resources (HL7 FHIR) for trusted data flows.

OBJECTIVES

This scoping review assessed the role and impact of HL7 FHIR and associated Implementation Guides (IGs) in digital healthcare ecosystems focusing on chronic disease management.

METHODS

To study trends and developments relevant to HL7 FHIR, a scoping review of the scientific and gray English literature from 2017 to 2023 was used.

RESULTS

The selection of 93 of 524 scientific papers reviewed in English indicates that the popularity of HL7 FHIR as a robust technical interface standard for the health sector has been steadily rising since its inception in 2010, reaching a peak in 2021. Digital Health applications use HL7 FHIR in cancer (45 %), cardiovascular disease (CVD) (more than 15 %), and diabetes (almost 15 %). The scoping review revealed that references to HL7 FHIR IGs are limited to ∼ 20 % of articles reviewed. HL7 FHIR R4 was most frequently referenced when the HL7 FHIR version was mentioned. In HL7 FHIR IGs registries and the internet, we found 35 HL7 FHIR IGs addressing chronic disease management, i.e., cancer (40 %), chronic disease management (25 %), and diabetes (20 %). HL7 FHIR IGs frequently complement the information in the article.

CONCLUSIONS

HL7 FHIR matures with each revision of the standard as HL7 FHIR IGs are developed with validated data sets, common shared HL7 FHIR resources, and supporting tools. Referencing HL7 FHIR IGs cataloged in official registries and in scientific publications is recommended to advance data quality and facilitate mutual learning in growing digital healthcare ecosystems that nurture interoperability in digital health innovation.

Undercounting diagnoses in Australian general practice: a data quality study with implications for population health reporting

BACKGROUND

Diagnosis can often be recorded in electronic medical records (EMRs) as free-text or using a term with a diagnosis code. Researchers, governments, and agencies, including organisations that deliver incentivised primary care quality improvement programs, frequently utilise coded data only and often ignore free-text entries. Diagnosis data are reported for population healthcare planning including resource allocation for patient care. This study sought to determine if diagnosis counts based on coded diagnosis data only, led to under-reporting of disease prevalence and if so, to what extent for six common or important chronic diseases.

METHODS

This cross-sectional data quality study used de-identified EMR data from 84 general practices in Victoria, Australia. Data represented 456,125 patients who attended one of the general practices three or more times in two years between January 2021 and December 2022. We reviewed the percentage and proportional difference between patient counts of coded diagnosis entries alone and patient counts of clinically validated free-text entries for asthma, chronic kidney disease, chronic obstructive pulmonary disease, dementia, type 1 diabetes and type 2 diabetes.

RESULTS

Undercounts were evident in all six diagnoses when using coded diagnoses alone (2.57-36.72% undercount), of these, five were statistically significant. Overall, 26.4% of all patient diagnoses had not been coded. There was high variation between practices in recording of coded diagnoses, but coding for type 2 diabetes was well captured by most practices.

CONCLUSION

In Australia clinical decision support and the reporting of aggregated patient diagnosis data to government that relies on coded diagnoses can lead to significant underreporting of diagnoses compared to counts that also incorporate clinically validated free-text diagnoses. Diagnosis underreporting can impact on population health, healthcare planning, resource allocation, and patient care. We propose the use of phenotypes derived from clinically validated text entries to enhance the accuracy of diagnosis and disease reporting. There are existing technologies and collaborations from which to build trusted mechanisms to provide greater reliability of general practice EMR data used for secondary purposes.

Waiting for the next winter. Outpatient pediatric visits for respiratory infections before, during, and after the COVID-19 pandemic in the city of Buenos Aires

INTRODUCTION

During the COVID-19 pandemic, pediatric visits due to acute lower respiratory infections (ALRIs) decreased, but most reports are from hospitalized patients. There is little information on this phenomenon in outpatients, who are the majority in ALRI. We evaluated the impact of the COVID-19 pandemic on ALRI-related outpatient visits in the City of Buenos Aires.

METHODS

Observational study including all outpatient visits of children under 2 years of age to the public health system of the City of Buenos Aires, between 1 January 2018 and 31 December 2022. We analyzed the total number of visits and the ALRI-related visits, and their distribution throughout the study period.

RESULTS

A total of 704,426 visits were registered, 7.38% of them due to ALRI. ALRI-related visits decreased from the implementation of a national lockdown (2020) and increased again as the restriction measures decreased, particularly the return to full school attendance (2021). In general, the proportion of ALRI-related visits was significantly higher in the cold months than in the warm ones (9.8% vs. 5.5%; odds ratio: 1.76, 95% confidence interval: 1.73-1.79; p < .001). This difference was observed before (2018 and 2019) and after the pandemic (2022), but not in 2020-2021. The peak of ALRI-related visits occurred in the cold months in pre-pandemic years (2018-2019), did not appear in 2020, reappeared delayed in 2021, and recovered seasonality in 2022.

CONCLUSION

Outpatient ALRI-related visits decreased significantly in the city of Buenos Aires during the COVID-19 pandemic and currently seem to have recovered their magnitude and seasonality.

Comparison of the accuracy of Japanese synonym identifications using word embeddings in the radiological technology field

The terminology in radiological technology is crucial, encompassing a broad range of principles from radiation to medical imaging, and involving various specialists. This study aimed to evaluate the accuracy of automatic synonym detection considering the characteristics of the Japanese language by Word2vec and fastText in the radiological technology field for the terminology elaboration. We collected around 340 thousand abstracts in Japanese. First, preprocessing of the abstract data was performed. Then, training models were created with Word2vec and fastText with different architectures: continuous bag-of-words (CBOW) and skip-gram, and vector sizes. Baseline synonym sets were curated by two experts, utilizing terminology resources specific to radiological technology. A term in the dataset input into the generated models, and the top-10 synonym candidates which had high cosine similarities were obtained. Subsequently, precision, recall, F1-score, and accuracy for each model were calculated. The fastText model with CBOW at 300 dimensions was most precise in synonym detection, excelling in cases with shared n-grams. Conversely, fastText with skip-gram and Word2vec were favored for synonyms without common n-grams. In radiological technology, where n-grams are prevalent, fastText with CBOW proved advantageous, while in informatics, characterized by abbreviations and transliterations, Word2vec with CBOW was more effective.

Burden of lupus activity on health care resources utilization in Buenos Aires, Argentina

OBJECTIVE

The aim is to analyze health care resource utilization (HCRU) of patients with lupus (SLE) from a health management organization (HMO) in Buenos Aires, Argentina, compared with matched controls and comparing periods of flare, low disease activity, and remission.

METHODS

This is a retrospective observational study including all SLE incident cases (ACR 1997/SLICC 2012 criteria) between 2000 and 2020 and 5 matched controls. Clinical data and HCRU (medical and nonmedical consultations, lab and imaging tests performed, emergency room visits, hospitalizations, and drugs prescribed) were obtained from administrative databases and electronic medical records. For each patient with SLE, an activity state was determined in every month of follow-up: flare (BILAG A or 2 BILAG B); low disease activity (LLDAS); remission (DORIS definition); or intermediate activity (not fulfilling any of previous). Incidence rates for each HCRU item and incidence rate ratios between SLE and control patients were and between remission and flare periods were calculated. Multivariate negative binomial logistic regression analyses were performed for identification of variables associated with major resource use.

RESULTS

A total of 62 SLE and 310 control patients were included, 88.7% were women, the median age at diagnosis was 46 years, and were followed for more than 8 years. Patients with SLE contributed with 537.2 patient-years (CI 95% 461.1-613.3) and controls with 2761.9 patient-years (CI 95% 2600.9-2922.8). HCRU in patients with SLE was significantly higher than in controls in all items, even in remission periods. Patients with SLE remained 74.4% of the time in remission, 12.1% in LLDAS, 12.2% in intermediate activity, and 1.3% in flare (there were 64 flares in 36 patients). HCRU was significantly higher during flare periods compared with remission periods. Number of flares was independently associated with emergency department consultations, lab tests and X-ray performed, number of drugs prescribed, and hospitalizations.

CONCLUSION

Significantly more HCRU was observed in patients with SLE in flare compared to remission periods.

Establishing a consensus on wound infection definitions

OBJECTIVES

The aim of this study was to establish an international, interorganisational consensus on wound infection terminology.

METHODS

This project consisted of definition scoping and a Delphi process to produce a consensus glossary for 18 wound infection terms. Recent guidelines/consensus documents were reviewed to identify 2-4 definitions for each term. An online consensus process was undertaken using the RAND Appropriateness Method, a consensus method for panels to reach agreement. International wound organisations nominated experts to participate, from whom 21 participants were selected to represent different organisations, geographic regions and disciplines. In the first consensus round, each term was presented alongside 2-3 definitions and participants nominated their preferred definition, with the majority vote used to select a baseline definition. The consensus process then proceeded, with participants using a 9-point Likert scale to score their level of agreement or disagreement with the definition for each term. Participants also provided a justification outlining the reason behind their rating. At the end of each round, an index was calculated to provide a quantitative evaluation indicating whether agreement or disagreement had been reached.

RESULTS

Reasoning statements were summarised and the definitions were adjusted to incorporate concepts identified by participants. The adjusted definition was presented in the next consensus round, together with the reasoning statements. Terms for which a final definition was not achieved in three consensus rounds were finalised with preferential voting using 2-3 definitions that had reached consensus.

PROJECT PROGRESS AND SIGNIFICANCE

The project generated a glossary of wound infection terms, endorsed through participation of 15 international organisations, for dissemination of guidelines and clinical decision-making/teaching tools.

Development of an Interoperable and Easily Transferable Clinical Decision Support System Deployment Platform: System Design and Development Study

BACKGROUND

A clinical decision support system (CDSS) is recognized as a technology that enhances clinical efficacy and safety. However, its full potential has not been realized, mainly due to clinical data standards and noninteroperable platforms.

OBJECTIVE

In this paper, we introduce the common data model-based intelligent algorithm network environment (CANE) platform that supports the implementation and deployment of a CDSS.

METHODS

CDSS reasoning engines, usually represented as R or Python objects, are deployed into the CANE platform and converted into C# objects. When a clinician requests CANE-based decision support in the electronic health record (EHR) system, patients' information is transformed into Health Level 7 Fast Healthcare Interoperability Resources (FHIR) format and transmitted to the CANE server inside the hospital firewall. Upon receiving the necessary data, the CANE system's modules perform the following tasks: (1) the preprocessing module converts the FHIRs into the input data required by the specific reasoning engine, (2) the reasoning engine module operates the target algorithms, (3) the integration module communicates with the other institutions' CANE systems to request and transmit a summary report to aid in decision support, and (4) creates a user interface by integrating the summary report and the results calculated by the reasoning engine.

RESULTS

We developed a CANE system such that any algorithm implemented in the system can be directly called through the RESTful application programming interface when it is integrated with an EHR system. Eight algorithms were developed and deployed in the CANE system. Using a knowledge-based algorithm, physicians can screen patients who are prone to sepsis and obtain treatment guides for patients with sepsis with the CANE system. Further, using a nonknowledge-based algorithm, the CANE system supports emergency physicians' clinical decisions about optimum resource allocation by predicting a patient's acuity and prognosis during triage.

CONCLUSIONS

We successfully developed a common data model-based platform that adheres to medical informatics standards and could aid artificial intelligence model deployment using R or Python.

islEHR, a model for electronic health records interoperability

Objectives

Due to the diversity, volume, and distribution of ingested data, the majority of current healthcare entities operate independently, increasing the problem of data processing and interchange. The goal of this research is to design, implement, and evaluate an electronic health record (EHR) interoperability solution – prototype – among healthcare organizations, whether these organizations do not have systems that are prepared for data sharing, or organizations that have such systems.

Methods

We established an EHR interoperability prototype model named interoperability smart lane for electronic health record (islEHR), which comprises of three modules: 1) a data fetching APIs for external sharing of patients’ information from participant hospitals; 2) a data integration service, which is the heart of the islEHR that is responsible for extracting, standardizing, and normalizing EHRs data leveraging the fast healthcare interoperability resources (FHIR) and artificial intelligence techniques; 3) a RESTful API that represents the gateway sits between clients and the data integration services.

Results

The prototype of the islEHR was evaluated on a set of unstructured discharge reports. The performance achieved a total time of execution ranging from 0.04 to 84.49 s. While the accuracy reached an F-Score ranging from 1.0 to 0.89.

Conclusions

According to the results achieved, the islEHR prototype can be implemented among different heterogeneous systems regardless of their ability to share data. The prototype was built based on international standards and machine learning techniques that are adopted worldwide. Performance and correctness results showed that islEHR outperforms existing models in its diversity as well as correctness and performance.

Expressiveness of an International Semantic Standard for Wound Care: Mapping a Standardized Item Set for Leg Ulcers to the Systematized Nomenclature of Medicine-Clinical Terms

Background

Chronic health conditions are on the rise and are putting high economic pressure on health systems, as they require well-coordinated prevention and treatment. Among chronic conditions, chronic wounds such as cardiovascular leg ulcers have a high prevalence. Their treatment is highly interdisciplinary and regularly spans multiple care settings and organizations; this places particularly high demands on interoperable information exchange that can be achieved using international semantic standards, such as Systematized Nomenclature of Medicine–Clinical Terms (SNOMED CT).

Objective

This study aims to investigate the expressiveness of SNOMED CT in the domain of wound care, and thereby its clinical usefulness and the potential need for extensions.

Methods

A clinically consented and profession-independent wound care item set, the German National Consensus for the Documentation of Leg Wounds (NKDUC), was mapped onto the precoordinated concepts of the international reference terminology SNOMED CT. Before the mapping took place, the NKDUC was transformed into an information model that served to systematically identify relevant items. The mapping process was carried out in accordance with the ISO/TR 12300 formalism. As a result, the reliability, equivalence, and coverage rate were determined for all NKDUC items and sections.

Results

The developed information model revealed 268 items to be mapped. Conducted by 3 health care professionals, the mapping resulted in moderate reliability (κ=0.512). Regarding the two best equivalence categories (symmetrical equivalence of meaning), the coverage rate of SNOMED CT was 67.2% (180/268) overall and 64.3% (108/168) specifically for wounds. The sections general medical condition (55/66, 83%), wound assessment (18/24, 75%), and wound status (37/57, 65%), showed higher coverage rates compared with the sections therapy (45/73, 62%), wound diagnostics (8/14, 57%), and patient demographics (17/34, 50%).

Conclusions

The results yielded acceptable reliability values for the mapping procedure. The overall coverage rate shows that two-thirds of the items could be mapped symmetrically, which is a substantial portion of the source item set. Some wound care sections, such as general medical conditions and wound assessment, were covered better than other sections (wound status, diagnostics, and therapy). These deficiencies can be mitigated either by postcoordination or by the inclusion of new concepts in SNOMED CT. This study contributes to pushing interoperability in the domain of wound care, thereby responding to the high demand for information exchange in this field. Overall, this study adds another puzzle piece to the general knowledge about SNOMED CT in terms of its clinical usefulness and its need for further extensions.

Instance-Based Learning Following Physician Reasoning for Assistance during Medical Consultation

This article presents an automatic system for modeling clinical knowledge to follow a physician’s reasoning in medical consultation. Instance-based learning is applied to provide suggestions when recording electronic medical records. The system was validated on a real case study involving advanced medical students. The proposed system is accurate and efficient: 2.5× more efficient than a baseline empirical tool for suggestions and two orders of magnitude faster than a Bayesian learning method, when processing a testbed of 250 clinical case types. The research provides a framework to implement a real-time system to assist physicians during medical consultations.

Strategies for Adopting and Implementing SNOMED CT in Korea

Objectives

The objective of this study was to introduce the Systematized Nomenclature of Medicine Clinical Terms (SNOMED CT), to describe use cases of SNOMED CT with the barriers and facilitators, and finally, to propose strategies for adopting and implementing SNOMED CT in Korea as a member of SNOMED International.

Methods

We reviewed a collection of SNOMED CT documents, such as introductory materials, practical guides, technical specifications, and reference materials provided by SNOMED International and the literature on SNOMED CT published by researchers to identify use cases of SNOMED CT with barriers and facilitators. We also surveyed the attendees of SNOMED CT education and training series offered by the Korea Human Resource Development Institute for Health and Welfare to identify perceived barriers to adopting SNOMED CT in Korea.

Results

We identified the barriers and facilitators to adopt SNOMED CT experienced by international terminology experts and prospective Korean users. They were related to governance and infrastructure, support services for use, education and training programs, use cases, and vendor capability to implement SNOMED CT. Based on these findings, we identified strategies for adopting and implementing SNOMED CT in Korea. They included the establishment of SNOMED CT management infrastructure, the development of SNOMED CT education and training programs for various user groups, the provision of support services for SNOMED CT use, and the development of SNOMED CT use cases.

Conclusions

These strategies for the adoption and implementation of SNOMED CT need to be executed step by step.

What You Need to Know Before Implementing a Clinical Research Data Warehouse: Comparative Review of Integrated Data Repositories in Health Care Institutions

Background

Integrated data repositories (IDRs), also referred to as clinical data warehouses, are platforms used for the integration of several data sources through specialized analytical tools that facilitate data processing and analysis. IDRs offer several opportunities for clinical data reuse, and the number of institutions implementing an IDR has grown steadily in the past decade.

Objective

The architectural choices of major IDRs are highly diverse and determining their differences can be overwhelming. This review aims to explore the underlying models and common features of IDRs, provide a high-level overview for those entering the field, and propose a set of guiding principles for small- to medium-sized health institutions embarking on IDR implementation.

Methods

We reviewed manuscripts published in peer-reviewed scientific literature between 2008 and 2020, and selected those that specifically describe IDR architectures. Of 255 shortlisted articles, we found 34 articles describing 29 different architectures. The different IDRs were analyzed for common features and classified according to their data processing and integration solution choices.

Results

Despite common trends in the selection of standard terminologies and data models, the IDRs examined showed heterogeneity in the underlying architecture design. We identified 4 common architecture models that use different approaches for data processing and integration. These different approaches were driven by a variety of features such as data sources, whether the IDR was for a single institution or a collaborative project, the intended primary data user, and purpose (research-only or including clinical or operational decision making).

Conclusions

IDR implementations are diverse and complex undertakings, which benefit from being preceded by an evaluation of requirements and definition of scope in the early planning stage. Factors such as data source diversity and intended users of the IDR influence data flow and synchronization, both of which are crucial factors in IDR architecture planning.

A Review of Automatic Phenotyping Approaches using Electronic Health Records

Electronic Health Records (EHR) are a rich repository of valuable clinical information that exist in primary and secondary care databases. In order to utilize EHRs for medical observational research a range of algorithms for automatically identifying individuals with a specific phenotype have been developed. This review summarizes and offers a critical evaluation of the literature relating to studies conducted into the development of EHR phenotyping systems. This review describes phenotyping systems and techniques based on structured and unstructured EHR data. Articles published on PubMed and Google scholar between 2013 and 2017 have been reviewed, using search terms derived from Medical Subject Headings (MeSH). The popularity of using Natural Language Processing (NLP) techniques in extracting features from narrative text has increased. This increased attention is due to the availability of open source NLP algorithms, combined with accuracy improvement. In this review, Concept extraction is the most popular NLP technique since it has been used by more than 50% of the reviewed papers to extract features from EHR. High-throughput phenotyping systems using unsupervised machine learning techniques have gained more popularity due to their ability to efficiently and automatically extract a phenotype with minimal human effort.