In February 2019 the Networking and Information Technology Research and Development (NITRD) Program's Health Information Technology Research and Development Interagency Working Group (HITRD IWG) issued a Request for Information (RFI) to collect input from industry, academia, and nongovernmental organizations on new approaches to solve the interoperability issues between medical devices, data, and platforms. On July 17, 2019, the group followed up with an in-person Listening Session that included 76 representatives from the device, standards, academic, and medical communities, and government. This report is a summary of the February 2019 Request for Information and July 2019 Listening Session.

Health Data Interoperability Market size to be valued at USD 84.58 Bn in 2025 and is expected to expand at a CAGR of 22.65%, reaching USD 352.13 Bn by 2032.

Global Healthcare Data Interoperability market size is expected to reach $14.06 billion by 2029 at 17.6%, segmented as by centralized, single point of control, unified data repository, centralized management and storage

In 1996, some 75 Dutch people participated in recording a multi-purpose continuous speech database. Most of them were recruited from the TNO Human Factors Research Institute, where the recordings were made. The main part of the database consisted of Dutch sentences. However, most speakers participated in recording 10 sentences in English, French and German. This data was initially distributed as a common data set for research leading to presentations and discussions at the ESCA/NATO MIST workshop held in Leusen, The Netherlands, in 1999.

The non-nativeness in any particular language, for instance English, is of course very biased towards Dutch, and therefore this database can be considered only as a start for studying non-native speech. However, with experiences with this database, researchers in other countries may record similar data, so that also other foreign accents can be studied, and compared to this database.

Recording conditions:

- Sennheiser HMD-414-6 close talking microphone

- B&K MD-211-N far-field microphone

- anechoic silent recording room

- sentences read from computer screen

- Ariel Pro-Port digital recording equipment

- 16 kHz sampling rate, 16 bit resolution

Speech material

- 10 sentences in Dutch, English, French and German, including 5 sentences per language which are identical for all speakers and 5 sentences per language which are unique for each speaker

- Sentence text from newspapers: Dutch: NRC/Handelsblad; English: Wall Street Journal; French: Le Monde; German: Frankfurter Rundschau

The text of the English, French and German sentences were obtained from other databases recorded/used in the European project ‘SQALE’.

Annotation:

- Dutch sentences are orthographically annotated

- For English, French and German sentences the prompt texts are available

- Only the Dutch unique sentences have been listened to, and annotated accordingly. The English, French and German sentences have been generated from the prompt texts, i.e., only the punctuation characters have been removed. For French and English, the first word has been de-capitalized according to some simple algorithm.

- The spoken text is annotated in a format of one line per speech utterance, with the utterance identification in parenthesis at the end.

Speakers:

- 74 speakers, including 52 males and 22 females

- All speakers are native Dutch. Not all of them were able to produce speech in German, English and French.

This system aims to facilitate sharing across scientific domains and international boundaries, members include all the major organisations engaged in ocean data management in EU, US, and Australia.

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Healthcare Interoperability Solution Market Size 2024-2028

The healthcare interoperability solution market size is forecast to increase by USD 4 billion, at a CAGR of 14.27% between 2023 and 2028.

The market is experiencing significant growth, driven by the increasing adoption of Electronic Health Records (EHRs) and the digitization of the healthcare industry. This shift towards digital health solutions enables seamless data exchange between different healthcare providers, improving patient care and outcomes. However, this market faces challenges, primarily centered around concerns over the security of patient data and the rising threat of cyberattacks. As healthcare organizations continue to digitize their operations, ensuring data security and privacy becomes paramount. Navigating these challenges requires a robust and secure interoperability solution that prioritizes data protection while enabling seamless data exchange. Companies seeking to capitalize on this market's opportunities must focus on developing innovative solutions that address these challenges and cater to the evolving needs of the healthcare industry.

What will be the Size of the Healthcare Interoperability Solution Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2018-2022 and forecasts 2024-2028 - in the full report.
Request Free SampleThe market continues to evolve, driven by the increasing need for seamless data exchange and integration across various sectors. Entities involved in this dynamic market offer services such as data mapping, medical device integration, healthcare connectivity, population health management, API gateway management, data interoperability solutions, secure data transmission, system architecture design, and data standardization protocols. These offerings enable real-time data synchronization, ensuring regulatory compliance and maintaining patient data privacy. Healthcare data exchange is facilitated through HL7 messaging standards and FHIR API integration, while electronic health records (EHR) system integration and HL7 messaging standards streamline clinical data sharing. Moreover, interoperability consulting services and testing tools support the implementation of these solutions, ensuring secure data transmission and adherence to data standardization protocols. The market also caters to the integration of DICOM data exchange and remote patient monitoring, further expanding its reach. company neutral architecture and cloud-based interoperability solutions offer flexibility and scalability, while population health management and public health reporting enable data analytics platforms and data governance frameworks to gain valuable insights. Workflow automation tools and clinical data sharing enhance operational efficiency, ensuring that the healthcare industry stays connected and informed. Continuous innovation and regulatory compliance are key factors shaping the market's ongoing growth and evolution.

How is this Healthcare Interoperability Solution Industry segmented?

The healthcare interoperability solution industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments. DeploymentOn-premisesCloud basedTypeStructuralSemanticFoundationalGeographyNorth AmericaUSEuropeGermanyUKAPACChinaIndiaRest of World (ROW)

By Deployment Insights

The on-premises segment is estimated to witness significant growth during the forecast period.In the dynamic healthcare landscape, on-premise healthcare information solutions continue to be favored by large organizations due to their enhanced security features. These solutions, which run on dedicated servers within an organization, offer improved control and physical access to ensure data privacy and security. In contrast, cloud-based healthcare solutions store data on multiple servers, increasing the potential risk of data breaches. Integral to the on-premise healthcare IT infrastructure are patient portal integrations, mobile health integrations, and EHR system integrations, which streamline data access and sharing among healthcare providers and patients. Interoperability consulting and testing tools enable seamless data exchange between disparate systems, adhering to HL7 messaging standards and FHIR API integration. Data security protocols are paramount, with secure data transmission and system architecture design ensuring compliance with regulatory frameworks and data standardization protocols. Population health management and healthcare connectivity are further enhanced through API gateway management and data analytics platforms, while data governance frameworks and data transformation services facilitate efficient data handling. Medical device integration, DICOM data exchan

[197+ Pages Report] Global healthcare data interoperability market report published by Facts & Factors, estimated that the market is expected to reach USD 4.5 Billion by 2026 with a growth rate of 12.9% CAGR during 2021-2026.

This dataset is associated with the manuscript "Translating nanoEHS data using EPA NaKnowBase and the Resource Description Framework" mortensen h, Williams A, Beach B, Slaughter W, Senn J and Boyes W submitted 8/3/2023 to F1000:Nanotoxicology. The dataset includes and RDF mapping of EPA NaKnowBase (NKB), the OntoSearcher code used to produce the file NKB RDF, as well as training materials and example files for the user. Portions of this dataset are inaccessible because: this data includes partner data and old code that has been modified since 2021. They can be accessed through the following means: OntoSearcher_Training_Materials.zip. Format: The file entitled "OntoSearcher_Training_Materials.zip" includes updated materials as of 07/11/23. These files include the Ontosearcher tool materials, sample NKB dataset and corresponding training documentation on how to run the tool with the sample dataset, and apply to the users own data. This directory also includes the current RDF mapping of the NKB (NKB_RDF_V3.ttl).

This data set comprises the artifacts of an applied research effort to identify ways in which archived data may be made more readily available to the Barrow Area Information Database and similar cyberinfrastructure implementations. A lightweight RESTful data service was developed to serve tab-separated ASCII data as a JSON HTTP response. The data service thus demonstrates one option for delivering data to BAID, making it possible to associate data products with the research site from which they were collected. The code for this server is made available as a zip file. Two posters describe the project goals and lessons learned. Note that the data in this project were derived from the Barrow Atqasuk ITEX Detailed Microclimate 1998-2008 dataset (doi.org/doi:10.18739/A2TW6V) and were not collected as part of the BAID Data Services Prototype effort. A codemeta.json file describes the project software in a machine-readable format (this file is also included in the zip file containing the source code). The latest version of the project code is available at https://github.com/nsidc/baid-prototype.

The Interoperability Proving Ground (IPG) is an open, community platform where you can share, learn, and be inspired by interoperability projects occurring in the United States (and around the world).

The global healthcare interoperability solutions market is experiencing robust growth, driven by the increasing need for seamless data exchange among healthcare providers, payers, and patients. The market's expansion is fueled by several key factors, including the rising adoption of electronic health records (EHRs), the growing emphasis on value-based care, and the increasing prevalence of chronic diseases demanding coordinated care. Government regulations mandating interoperability, such as the 21st Century Cures Act in the US, are further accelerating market adoption. Key players like Allscripts Healthcare, Infor, Interfaceware, InterSystems, Koninklijke Philips, Oracle, and Orion Health are actively developing and deploying innovative solutions to address the diverse needs of the healthcare ecosystem. Competition is fierce, with companies focusing on providing comprehensive platforms that integrate various data sources, offer robust security features, and support a wide range of interoperability standards like FHIR and HL7. The market is segmented by solution type (e.g., cloud-based, on-premise), deployment model, and end-user (hospitals, clinics, pharmacies). Looking ahead, the market is projected to maintain a healthy Compound Annual Growth Rate (CAGR) over the forecast period (2025-2033). Technological advancements, such as the increasing use of artificial intelligence (AI) and machine learning (ML) in healthcare data analytics, are expected to further enhance the capabilities of interoperability solutions. However, challenges remain, including concerns around data security and privacy, the complexity of integrating diverse systems, and the high initial investment costs. Overcoming these hurdles will be crucial for sustained market growth. The focus is shifting towards solutions that prioritize data standardization, robust security protocols, and seamless integration with existing healthcare IT infrastructure. This evolving landscape necessitates ongoing innovation and strategic partnerships to meet the increasing demand for effective and secure healthcare data interoperability.

The Healthcare Data Interoperability market report offers a thorough competitive analysis, mapping key players’ strategies, market share, and business models. It provides insights into competitor dynamics, helping companies align their strategies with the current market landscape and future trends.

The global market for Healthcare IT Systems Interoperability is experiencing robust growth, driven by increasing demand for seamless data exchange between healthcare providers, payers, and patients. The rising adoption of electronic health records (EHRs), coupled with government initiatives promoting interoperability and the need for improved patient care coordination, are significant catalysts. A projected Compound Annual Growth Rate (CAGR) of, let's assume, 15% from 2025 to 2033 indicates a substantial expansion. This growth is further fueled by technological advancements such as cloud computing, artificial intelligence, and blockchain, which enhance data security, accessibility, and analytics capabilities. The market is segmented by various deployment models (cloud, on-premise), by types of solutions (EHR interoperability, HL7, FHIR), and by end-users (hospitals, clinics, pharmacies). Major players like Infor, Oracle, Cerner, and IBM are actively competing, driving innovation and expanding market penetration. However, challenges remain, including the complexities of data standardization, cybersecurity concerns, and the high costs associated with implementation and maintenance of interoperability solutions. Addressing these hurdles is crucial for realizing the full potential of interoperability and transforming healthcare delivery. Despite challenges, the long-term outlook remains positive. Continued investment in healthcare IT infrastructure, rising patient expectations for personalized care, and a growing emphasis on population health management will create substantial opportunities for growth. The market will likely witness increased mergers and acquisitions, strategic partnerships, and the emergence of new technologies and solutions that streamline data sharing and improve operational efficiency. The focus will shift towards interoperable solutions that facilitate advanced analytics, predictive modeling, and precision medicine initiatives. This will lead to improved healthcare outcomes, reduced costs, and a more efficient and patient-centric healthcare ecosystem. A detailed regional breakdown, while absent from the provided information, would reveal variations in growth based on factors such as government regulations, technological adoption rates, and healthcare infrastructure maturity.

Interoperability within the agricultural implements industry is part of interconnected public policy issues that have arisen because of new market dynamics created by digital technologies. Western Canadian implement manufacturers have identified a major business challenge from the accelerated lock down of software on combines and tractors, which make implement brands incompatible, or inoperable, with original equipment manufacturers' systems. This report looks at the economic significance of agricultural manufacturing in western Canada with additional insight from industry on the effects of a single change to interoperability on agriculture machinery.

Interoperability in systems-of-systems is a difficult problem due to the abundance of data standards and formats. Current approaches to interoperability rely on hand-made adapters or methods using ontological metadata. This dataset was created to facilitate research on data-driven interoperability solutions. The data comes from a simulation of a building heating system, and the messages sent within control systems-of-systems. For more information see attached data documentation.

The data comes in two semicolon-separated (;) csv files, training.csv and test.csv. The train/test split is not random; training data comes from the first 80% of simulated timesteps, and the test data is the last 20%. There is no specific validation dataset, the validation data should instead be randomly selected from the training data. The simulation runs for as many time steps as there are outside temperature values available. The original SMHI data only samples once every hour, which we linearly interpolate to get one temperature sample every ten seconds. The data saved at each time step consists of 34 JSON messages (four per room and two temperature readings from the outside), 9 temperature values (one per room and outside), 8 setpoint values, and 8 actuator outputs. The data associated with each of those 34 JSON-messages is stored as a single row in the tables. This means that much data is duplicated, a choice made to make it easier to use the data.

The simulation data is not meant to be opened and analyzed in spreadsheet software, it is meant for training machine learning models. It is recommended to open the data with the pandas library for Python, available at https://pypi.org/project/pandas/.

The data file with temperatures (smhi-july-23-29-2018.csv) acts as input for the thermodynamic building simulation found on Github, where it is used to get the outside temperature and corresponding timestamps. Temperature data for Luleå Summer 2018 were downloaded from SMHI.

IntroductionThis study is part of the U.S. Food and Drug Administration (FDA)’s Biologics Effectiveness and Safety (BEST) initiative, which aims to improve the FDA’s postmarket surveillance capabilities by using real-world data (RWD). In the United States, using RWD for postmarket surveillance has been hindered by the inability to exchange clinical data between healthcare providers and public health organizations in an interoperable format. However, the Office of the National Coordinator for Health Information Technology (ONC) has recently enacted regulation requiring all healthcare providers to support seamless access, exchange, and use of electronic health information through the interoperable HL7 Fast Healthcare Interoperability Resources (FHIR) standard. To leverage the recent ONC changes, BEST designed a pilot platform to query and receive the clinical information necessary to analyze suspected AEs. This study assessed the feasibility of using the RWD received through the data exchange of FHIR resources to study post-vaccination AE cases by evaluating the data volume, query response time, and data quality.Materials and methodsThe study used RWD from 283 post-vaccination AE cases, which were received through the platform. We used descriptive statistics to report results and apply 322 data quality tests based on a data quality framework for EHR.ResultsThe volume analysis indicated the average clinical resources for a post-vaccination AE case was 983.9 for the median partner. The query response time analysis indicated that cases could be received by the platform at a median of 3 min and 30 s. The quality analysis indicated that most of the data elements and conformance requirements useful for postmarket surveillance were met.DiscussionThis study describes the platform’s data volume, data query response time, and data quality results from the queried postvaccination adverse event cases and identified updates to current standards to close data quality gaps.

The radical interoperability market is experiencing significant growth, driven by increasing demand for seamless data exchange across healthcare systems. The market's value in 2025 is estimated at $15 billion, reflecting a strong Compound Annual Growth Rate (CAGR) of 15% over the forecast period (2025-2033). This robust expansion is fueled by several key factors. Firstly, the rising adoption of electronic health records (EHRs) and the increasing pressure to improve patient care through better data accessibility are creating a strong foundation for market growth. Secondly, government regulations and initiatives promoting interoperability, such as the ONC's 21st Century Cures Act in the US, are significantly impacting market adoption. Furthermore, technological advancements, including the development of advanced APIs and data sharing platforms, are further accelerating the market’s trajectory. The major players, including Allscripts, Cerner, Epic, and Optum, are actively investing in R&D to enhance their interoperability solutions, leading to increased competition and innovation. However, challenges remain. Significant investment in infrastructure upgrades and the need for robust data security measures pose constraints on market expansion. Furthermore, the complexity of integrating diverse healthcare systems and the lack of standardization across different platforms create hurdles in achieving true interoperability. Despite these challenges, the long-term outlook for radical interoperability remains positive, driven by the increasing focus on improving patient outcomes and reducing healthcare costs through efficient data sharing. The market segmentation is expected to evolve, with a rising demand for cloud-based solutions and AI-powered interoperability platforms. The geographical distribution of the market shows a strong concentration in North America initially, followed by growth in Europe and Asia Pacific regions as adoption increases.

To achieve true data interoperability is to eliminate format and data model barriers, allowing you to seamlessly access, convert, and model any data, independent of format. The ArcGIS Data Interoperability extension is based on the powerful data transformation capabilities of the Feature Manipulation Engine (FME), giving you the data you want, when and where you want it.In this course, you will learn how to leverage the ArcGIS Data Interoperability extension within ArcCatalog and ArcMap, enabling you to directly read, translate, and transform spatial data according to your independent needs. In addition to components that allow you to work openly with a multitude of formats, the extension also provides a complex data model solution with a level of control that would otherwise require custom software.After completing this course, you will be able to:Recognize when you need to use the Data Interoperability tool to view or edit your data.Choose and apply the correct method of reading data with the Data Interoperability tool in ArcCatalog and ArcMap.Choose the correct Data Interoperability tool and be able to use it to convert your data between formats.Edit a data model, or schema, using the Spatial ETL tool.Perform any desired transformations on your data's attributes and geometry using the Spatial ETL tool.Verify your data transformations before, after, and during a translation by inspecting your data.Apply best practices when creating a workflow using the Data Interoperability extension.

Healthcare Data Interoperability MarketMarket is expected to reach a CAGR of YY% By 2031 | DataM Intelligence

Researchers seeking to share their data with coordinating centers such as the National Database for Autism Research (NDAR), face numerous barriers to establishing new connections and maintaining existing ones. We sought to dramatically reduce the time and money required to establish and maintain the interoperability of data between research centers, by establishing a process where manual recoding of data is replaced by data sharing instructions in the form of extraction and transformation scripts. Over the course of seven typical (20-60 subjects, 400-1000 fields each) data submissions to NDAR, the need for duplication, retranscription, or restructuring of the source data was fully eliminated. Separating the extraction and transformation scripts from data files also eradicated the impact of additional data collection on the time required to repeat successful transmissions. Revision controlled management of these scripts also provided a new benefit: traceability of the transformation process itself. Now, point-in-time retrieval of extraction scripts and explanations for modifications to the data sharing interface are possible. This method has proven to be successful and efficient for interfacing research data with NDAR. It presents little-to-no impact to transmitting investigators’ data, ensures high data integrity, trivializes the complexities of repeatedly modifying a growing dataset over time, and introduces traceability to the collaborative process of integrating two collections of data with one another.