Dual Implementation Guides in FHIR and CDA.

BACKGROUND: The Fast Healthcare Interoperability Resources (FHIR) and Clinical Document Architecture (CDA) are standards for the healthcare industry, designed to improve the exchange of health data by interoperability. Both standards are constrained through what are known as Implementation Guides (IG) for specific use. OBJECTIVES: Both of these two standards are widely in use and play an important role in the Austrian healthcare system. Concepts existing in CDA and FHIR must be aligned between both standards. METHODS: Many existing approaches are presented and discussed, none are fully suited to the needs in Austria. RESULTS: The IG Publisher has already been used for CDA IGs, beside of its intended FHIR support, but never for both in one IG. Even the International Patient Summary (IPS), existing as CDA and FHIR specification, does not solve the needed comparability between these two. CONCLUSION: As the IG Publisher is widely used and supports CDA, it should be used for Dual Implementation Guides. Further work and extension of IG Publisher is necessary to enhance the readability of the resulting IGs.

Interoperability in a Post-Roe Era: Sustaining Progress While Protecting Reproductive Health Information.

This Viewpoint proposes a solution to better safeguard reproductive health information in patient records that are now more complete owing to the interoperability of health information exchange networks.

Gobernanza de datos en salud: interoperabilidad y estándares

Dentro del marco de la gobernanza de datos, es necesario la adopción de arquitecturas empresariales que nos permitan tener un marco referencial para el abordaje de los distintos dominios en la gestión de los datos. La construcción de una infoestructura sólida es fundamentada en la integración de estándares y buenas prácticas que permitan a las organizaciones de salud mejora el ciclo de los datos: recolección, integración, transferencia, interoperabilidad y uso de los datos para la toma de decisiones, de una forma óptima, con calidad y seguridad.Por lo anterior, la adopción de estándares para la codificación de los datos de salud (Por ejemplo CIE 9,10 y 11), así como para la interoperabilidad de datos (FHIR/HL7 por ejemplo) y marcos de intercambio en salud en general (OpenHIE y otros más), se tornan fundamentales para que la infoestructura del sistema de información para la salud en el país funcione eficientemente con una arquitectura de salud pública robusta y acorde a los retos de la transformación digital del sector salud.

Panorama regional de estándares de informática en salud

Presentación hecha por Alejandro Benavides, Sub-coordinador de la Red Centroamericana de Informática en Salud, durante el Webinar "Panorama Regional de Estándares de Informática en Salud" realizado el día 4 de diciembre de 2019.

New Standards for Clinical Decision Support: A Survey of The State of Implementation.

OBJECTIVES: To review the current state of research on designing and implementing clinical decision support (CDS) using four current interoperability standards: Fast Healthcare Interoperability Resources (FHIR); Substitutable Medical Applications and Reusable Technologies (SMART); Clinical Quality Language (CQL); and CDS Hooks. METHODS: We conducted a review of original studies describing development of specific CDS tools or infrastructures using one of the four targeted standards, regardless of implementation stage. Citations published any time before the literature search was executed on October 21, 2020 were retrieved from PubMed. Two reviewers independently screened articles and abstracted data according to a protocol designed by team consensus. RESULTS: Of 290 articles identified via PubMed search, 44 were included in this study. More than three quarters were published since 2018. Forty-three (98%) used FHIR; 22 (50%) used SMART; two (5%) used CQL; and eight (18%) used CDS Hooks. Twenty-four (55%) were in the design stage, 15 (34%) in the piloting stage, and five (11%) were deployed in a real-world setting. Only 12 (27%) of the articles reported an evaluation of the technology under development. Three of the four articles describing a deployed technology reported an evaluation. Only two evaluations with randomized study components were identified. CONCLUSION: The diversity of topics and approaches identified in the literature highlights the utility of these standards. The infrequency of reported evaluations, as well as the high number of studies in the design or piloting stage, indicate that these technologies are still early in their life cycles. Informaticists will require a stronger evidence base to understand the implications of using these standards in CDS design and implementation.

Maturity assessment of Kenya's health information system interoperability readiness.

BACKGROUND: The use of digital technology in healthcare promises to improve quality of care and reduce costs over time. This promise will be difficult to attain without interoperability: facilitating seamless health information exchange between the deployed digital health information systems (HIS). OBJECTIVE: To determine the maturity readiness of the interoperability capacity of Kenya's HIS. METHODS: We used the HIS Interoperability Maturity Toolkit, developed by MEASURE Evaluation and the Health Data Collaborative's Digital Health and Interoperability Working Group. The assessment was undertaken by eHealth stakeholder representatives primarily from the Ministry of Health's Digital Health Technical Working Group. The toolkit focused on three major domains: leadership and governance, human resources and technology. RESULTS: Most domains are at the lowest two levels of maturity: nascent or emerging. At the nascent level, HIS activities happen by chance or represent isolated, ad hoc efforts. An emerging maturity level characterises a system with defined HIS processes and structures. However, such processes are not systematically documented and lack ongoing monitoring mechanisms. CONCLUSION: None of the domains had a maturity level greater than level 2 (emerging). The subdomains of governance structures for HIS, defined national enterprise architecture for HIS, defined technical standards for data exchange, nationwide communication network infrastructure, and capacity for operations and maintenance of hardware attained higher maturity levels. These findings are similar to those from interoperability maturity assessments done in Ghana and Uganda.

What do users think of the UK's three COVID-19 contact-tracing apps? A comparative analysis.

OBJECTIVES: Our goal was to gain insights into the user reviews of the three COVID-19 contact-tracing mobile apps, developed for the different regions of the UK: 'NHS COVID-19' for England and Wales, 'StopCOVID NI' for Northern Ireland and 'Protect Scotland' for Scotland. Our two research questions are (1) what are the users' experience and satisfaction levels with the three apps? and (2) what are the main issues (problems) that users have reported about the apps? METHODS: We assess the popularity of the apps and end users' perceptions based on user reviews in app stores. We conduct three types of analysis (data mining, sentiment analysis and topic modelling) to derive insights from the combined set of 25 583 user reviews of the aforementioned three apps (submitted by users until the end of 2020). RESULTS: Results show that end users have been generally dissatisfied with the apps under study, except the Scottish app. Some of the major issues that users have reported are high battery drainage and doubts on whether apps are really working. DISCUSSION: Towards the end of 2020, the much-awaited COVID-19 vaccines started to be available, but still, analysing the users' feedback and technical issues of these apps, in retrospective, is valuable to learn the right lessons to be ready for similar circumstances in future. CONCLUSION: Our results show that more work is needed by the stakeholders behind the apps (eg, apps' software engineering teams, public-health experts and decision makers) to improve the software quality and, as a result, the public adoption of these apps. For example, they should be designed to be as simple as possible to operate (need for usability).

Integrating Genomics and Clinical Data for Statistical Analysis by Using GEnome MINIng (GEMINI) and Fast Healthcare Interoperability Resources (FHIR): System Design and Implementation.

BACKGROUND: The introduction of next-generation sequencing (NGS) into molecular cancer diagnostics has led to an increase in the data available for the identification and evaluation of driver mutations and for defining personalized cancer treatment regimens. The meaningful combination of omics data, ie, pathogenic gene variants and alterations with other patient data, to understand the full picture of malignancy has been challenging. OBJECTIVE: This study describes the implementation of a system capable of processing, analyzing, and subsequently combining NGS data with other clinical patient data for analysis within and across institutions. METHODS: On the basis of the already existing NGS analysis workflows for the identification of malignant gene variants at the Institute of Pathology of the University Hospital Erlangen, we defined basic requirements on an NGS processing and analysis pipeline and implemented a pipeline based on the GEMINI (GEnome MINIng) open source genetic variation database. For the purpose of validation, this pipeline was applied to data from the 1000 Genomes Project and subsequently to NGS data derived from 206 patients of a local hospital. We further integrated the pipeline into existing structures of data integration centers at the University Hospital Erlangen and combined NGS data with local nongenomic patient-derived data available in Fast Healthcare Interoperability Resources format. RESULTS: Using data from the 1000 Genomes Project and from the patient cohort as input, the implemented system produced the same results as already established methodologies. Further, it satisfied all our identified requirements and was successfully integrated into the existing infrastructure. Finally, we showed in an exemplary analysis how the data could be quickly loaded into and analyzed in KETOS, a web-based analysis platform for statistical analysis and clinical decision support. CONCLUSIONS: This study demonstrates that the GEMINI open source database can be augmented to create an NGS analysis pipeline. The pipeline generates high-quality results consistent with the already established workflows for gene variant annotation and pathological evaluation. We further demonstrate how NGS-derived genomic and other clinical data can be combined for further statistical analysis, thereby providing for data integration using standardized vocabularies and methods. Finally, we demonstrate the feasibility of the pipeline integration into hospital workflows by providing an exemplary integration into the data integration center infrastructure, which is currently being established across Germany.

Using informatics to improve cancer surveillance.

OBJECTIVES: This review summarizes past and current informatics activities at the Centers for Disease Control and Prevention National Program of Cancer Registries to inform readers about efforts to improve, standardize, and automate reporting to public health cancer registries. TARGET AUDIENCE: The target audience includes cancer registry experts, informaticians, public health professionals, database specialists, computer scientists, programmers, and system developers who are interested in methods to improve public health surveillance through informatics approaches. SCOPE: This review provides background on central cancer registries and describes the efforts to standardize and automate reporting to these registries. Specific topics include standardized data exchange activities for physician and pathology reporting, software tools for cancer reporting, development of a natural language processing tool for processing unstructured clinical text, and future directions of cancer surveillance informatics.

Why real-world health information technology performance transparency is challenging, even when everyone (claims to) want it.

Crowdsourced ratings have driven increased performance transparency between consumers and suppliers. While many industries have benefitted from such transparency, crowdsourced ratings have struggled to scale in the healthcare domain. In theory, interoperability services offer an ideal setting for crowdsourced ratings: costs are high, performance is variable, and information asymmetries between provider organizations (customers) and vendors offering interoperability solutions exist. Via a Cooperative Agreement between the Office of the National Coordinator for Health Information Technology and University of California, San Francisco, we developed InteropSelect, a public website that allows crowdsourced ratings of interoperability service purchases. While we garnered broad engagement during the development process, the site failed to attract sufficient reviewers, which is fundamental to the success of crowdsourcing. Additional challenges included the lack of service commoditization that resulted in a complex rating form and lack of market dynamics that facilitated vendor engagement. Our lessons cast doubt on whether crowdsourcing and similar performance transparency efforts under the 21st Century Cures Act will succeed.

Public health reporting and outbreak response: synergies with evolving clinical standards for interoperability.

Public health needs up-to-date information for surveillance and response. As healthcare application programming interfaces become widely available, a novel data gathering mechanism could provide public health with critical information in a timely fashion to respond to a fast-moving epidemic. In this article, we extrapolate from our experiences using a Fast Healthcare Interoperability Resource-based architecture for infectious disease surveillance for sexually transmitted diseases to its application to gather case information for an outbreak. One of the challenges with a fast-moving outbreak is to accurately assess its demand on healthcare resources, since information specific to comorbidities is often not available. These comorbidities are often associated with poor prognosis and higher resource utilization. If the comorbidity data and other clinical information were readily available to public health workers, they could better address community disruption and manage healthcare resources. The use of FHIR resources available through application programming and filtered through tools such as described herein will give public health the flexibility needed to investigate rapidly emerging disease while protecting patient privacy.

Impact of interoperability of smart infusion pumps and an electronic medical record in critical care.

PURPOSE: To describe the benefits of smart infusion pump interoperability with an electronic medical record (EMR) system in an adult intensive care unit (ICU) setting. SUMMARY: In order to assess the impact of smart infusion pump and EMR interoperability, we observed whether there were changes in the frequency of electronic medication administration record (eMAR) documentation of dose titrations in epinephrine and norepinephrine infusions in the ICU setting. As a secondary endpoint, we examined whether smart pump/EMR interoperability had any impact on the rate of alerts triggered by the dose-error reduction software. Pharmacist satisfaction was measured to determine the impact of smart pump/EMR interoperability on pharmacist workflow. In the preimplementation phase, there were a total of 2,503 administrations of epinephrine and norepinephrine; 13,299 rate changes were documented, for an average of 5.31 documented rate changes per administration. With smart pump interoperability, a total of 13,024 rate changes were documented in association with 1,401 administrations, for an average of 9.29 documented rate changes per administration (a 74.9% increase). A total of 1,526 dose alerts were triggered in association with 76,145 infusions in the preimplementation phase; there were 820 dose alerts associated with 48,758 autoprogammed infusions in the postimplementation phase (absolute difference, -0.32%). ICU pharmacists largely agreed (75% of survey respondents) that the technology provided incremental value in providing patient care. CONCLUSION: Interoperability between the smart pump and EMR systems proved beneficial in the administration and monitoring of continuous infusions in the ICU setting. Additionally, ICU pharmacists may be positively impacted by improved clinical data accuracy and operational efficiency.

FHIR PIT: an open software application for spatiotemporal integration of clinical data and environmental exposures data.

BACKGROUND: Informatics tools to support the integration and subsequent interrogation of spatiotemporal data such as clinical data and environmental exposures data are lacking. Such tools are needed to support research in environmental health and any biomedical field that is challenged by the need for integrated spatiotemporal data to examine individual-level determinants of health and disease. RESULTS: We have developed an open-source software application-FHIR PIT (Health Level 7 Fast Healthcare Interoperability Resources Patient data Integration Tool)-to enable studies on the impact of individual-level environmental exposures on health and disease. FHIR PIT was motivated by the need to integrate patient data derived from our institution's clinical warehouse with a variety of public data sources on environmental exposures and then openly expose the data via ICEES (Integrated Clinical and Environmental Exposures Service). FHIR PIT consists of transformation steps or building blocks that can be chained together to form a transformation and integration workflow. Several transformation steps are generic and thus can be reused. As such, new types of data can be incorporated into the modular FHIR PIT pipeline by simply reusing generic steps or adding new ones. We validated FHIR PIT in the context of a driving use case designed to investigate the impact of airborne pollutant exposures on asthma. Specifically, we replicated published findings demonstrating racial disparities in the impact of airborne pollutants on asthma exacerbations. CONCLUSIONS: While FHIR PIT was developed to support our driving use case on asthma, the software can be used to integrate any type and number of spatiotemporal data sources at a level of granularity that enables individual-level study. We expect FHIR PIT to facilitate research in environmental health and numerous other biomedical disciplines.

Exploring JSON-LD as an Executable Definition of FHIR RDF to Enable.

This study developed and evaluated a JSON-LD 1.1 approach to automate the Resource Description Framework (RDF) serialization and deserialization of Fast Healthcare Interoperability Resources (FHIR) data, in preparation for updating the FHIR RDF standard. We first demonstrated that this JSON-LD 1.1 approach can produce the same output as the current FHIR RDF standard. We then used it to test, document and validate several proposed changes to the FHIR RDF specification, to address usability issues that were uncovered during trial use. This JSON-LD 1.1 approach was found to be effective and more declarative than the existing custom-code-based approach, in converting FHIR data from JSON to RDF and vice versa. This approach should enable future FHIR RDF servers to be implemented and maintained more easily.

Call for Data Standardization: Lessons Learned and Recommendations in an Imaging Study.

PURPOSE: Data sharing creates potential cost savings, supports data aggregation, and facilitates reproducibility to ensure quality research; however, data from heterogeneous systems require retrospective harmonization. This is a major hurdle for researchers who seek to leverage existing data. Efforts focused on strategies for data interoperability largely center around the use of standards but ignore the problems of competing standards and the value of existing data. Interoperability remains reliant on retrospective harmonization. Approaches to reduce this burden are needed. METHODS: The Cancer Imaging Archive (TCIA) is an example of an imaging repository that accepts data from a diversity of sources. It contains medical images from investigators worldwide and substantial nonimage data. Digital Imaging and Communications in Medicine (DICOM) standards enable querying across images, but TCIA does not enforce other standards for describing nonimage supporting data, such as treatment details and patient outcomes. In this study, we used 9 TCIA lung and brain nonimage files containing 659 fields to explore retrospective harmonization for cross-study query and aggregation. It took 329.5 hours, or 2.3 months, extended over 6 months to identify 41 overlapping fields in 3 or more files and transform 31 of them. We used the Genomic Data Commons (GDC) data elements as the target standards for harmonization. RESULTS: We characterized the issues and have developed recommendations for reducing the burden of retrospective harmonization. Once we harmonized the data, we also developed a Web tool to easily explore harmonized collections. CONCLUSION: While prospective use of standards can support interoperability, there are issues that complicate this goal. Our work recognizes and reveals retrospective harmonization issues when trying to reuse existing data and recommends national infrastructure to address these issues.

Campos MARC21 para interoperabilidade com a base de dados LILACS / MARC21 fields for interoperability with LILACS database

Tabela de conversão de dados para fins de interoperabilidade entre formato MARC21 e Metodologia LILACS para criação de registros bibliográficos

Interactive biomedical ontology matching.

Due to continuous evolution of biomedical data, biomedical ontologies are becoming larger and more complex, which leads to the existence of many overlapping information. To support semantic inter-operability between ontology-based biomedical systems, it is necessary to identify the correspondences between these information, which is commonly known as biomedical ontology matching. However, it is a challenge to match biomedical ontologies, which dues to: (1) biomedical ontologies often possess tens of thousands of entities, (2) biomedical terminologies are complex and ambiguous. To efficiently match biomedical ontologies, in this paper, an interactive biomedical ontology matching approach is proposed, which utilizes the Evolutionary Algorithm (EA) to implement the automatic matching process, and gets a user involved in the evolving process to improve the matching efficiency. In particular, we propose an Evolutionary Tabu Search (ETS) algorithm, which can improve EA's performance by introducing the tabu search algorithm as a local search strategy into the evolving process. On this basis, we further make the ETS-based ontology matching technique cooperate with the user in a reasonable amount of time to efficiently create high quality alignments, and make use of EA's survival of the fittest to eliminate the wrong correspondences brought by erroneous user validations. The experiment is conducted on the Anatomy track and Large Biomedic track that are provided by the Ontology Alignment Evaluation Initiative (OAEI), and the experimental results show that our approach is able to efficiently exploit the user intervention to improve its non-interactive version, and the performance of our approach outperforms the state-of-the-art semi-automatic ontology matching systems.

The qualculative dimension of healthcare data interoperability.

Research on interoperability and information exchange between information technology systems touts the use of secondary data for a variety of purposes, including research, management, quality improvement, and accountability. However, many studies have pointed out that this is difficult to achieve in practice. Hence, this article aims to examine the causes for this by reporting an ethnographic study of the data work performed by medical records coders and birth certificate clerks working in a hospital system to uncover the practices of creating administrative data (e.g. secondary data). The article illustrates that clerks and coders use situated qualitative judgments of the accuracy and authority of different primary medical accounts. Coders and clerks also employ their understandings of the importance of different future uses of data as they make crucial decisions about how much discretion to exercise in producing accurate data and how much effort to put toward clarifying problematic medical data. These findings suggest that information technology systems designed for interoperability and secondary data also need to be designed in ways that support the qualculative practices of data workers in order to succeed, including making future uses of data clear to data workers and finding ways to minimize conflicting data before data workers encounter it.