Modern healthcare depends on teamwork and communication. Information systems capable of providing the right information, at the right time and place in a safe and reliable manner, help to increase the efficiency and effectiveness of health services. Improved interoperability can help to increase the efficiency and effectiveness of health services. Semantically interoperable information facilitates quicker and more soundly based decision-making, it reduces waste by cutting out repeated work, and it improves safety due to fewer errors. All this leads to an improvement in the quality of care, optimization of resources and generation of wealth and investment.

To achieve the real semantic interoperability we need to incorporate several processes into the workflow of the organization, such as data quality analysis, integration, information modeling, model management, data transformation and standardization, normalization of medical terminologies and storage in standardized repositories. VeraTech helps your organization by providing consulting and technical services on each of the phases.



We define data integration as the problem of combining data residing in different systems and providing users with a unified view of them.

Combining our methodologies and software solutions, we offer consultancy, support and developments for data sharing or communication projects where semantic interoperability prevail or adoption of electronic health records or terminology standards.

Real use cases

PANGEA: We develop a unified viewer and light integration engine for the Hospital General Universitario de Valencia based on xml messages, which allows a mechanism of homogeneous access of the dispersed clinical information in heterogeneous systems. It is, therefore, a data access middleware capable of satisfying the implementation of the data layer of many modern multilayer applications.

Nodo: We created an interoperability platform between the Electronic Health Record of the Valencian Health Agency of the Generalitat Valenciana and the General Hospital of Valencia.



When sharing data it is basic to describe the information in the most accurate way. For example, the structure, tags, obligatory nature, cardinality, data types, correct values or terminologies to be used need to be precisely described. This facilitates the comprehension, quality and reuse of data, which eases the development of new information systems. We have methodologies and visual software tools for the design and management of health information models. Our tools allow

  • to specify these models based on electronic medical records standards if necessary and are oriented to facilitate their use by health professionals who are experts in the clinical domain to model

  • to manage the life cycle, collaborative editing and publication of clinical information models

Real use cases

Modeling Object Server: We developed a component for the Spanish Ministry of Health, Social Services and Equality that allows the government of clinical information systems as well as the access and distribution of the different semantic resources that are part of the semantic interoperability infrastructure of health information systems. VeraTech has also been in charge of modeling the set of reports of the Minimum Data Set of Clinical Reports (CMDIC) in ISO13606 and HL7 CDA

Unified National Electronic Health Records: For the Electronic Government Agency and the Information and Knowledge Society of Uruguay, we carry out the modeling and implementation of the clinical reports.




EHR standards

We have methodologies and tools to transform legacy data into other formats.  When the destination format is a standard then we talk about standardization. Our tools are oriented to the health sector and therefore have different standards (such as HL7 CDA, HL7 FHIR, ISO13606, openEHR, ...), also using the most important clinical terminologies. Apart from the data transformation programs, they automatically generate other processable artifacts that can be used directly in software projects such as validators, data entry forms or example instances.

Terminology standards

Lack of agreed medical terminology has been recognized as an issue for at least 250 years. When in the fifteenth century, Gutenberg’s invention of the movable type led to the mass production and dissemination of books and written information, language was still relatively unformalized. It took until the eighteenth century before the great dictionaries and nomenclatures had appeared. Medical terminology escaped formalization, leading to ambiguity problems that are now recognized as a patient safety significant risk.

Thus, we offer consultancy, support, developments, software solutions and methodologies to map local terminologies to standard terminologies such as ICD-10, LOINC or Snomed CT.

​​Real use cases


CTMAP: We created an assistant tool for coding ICD-10-MC / PCS / LOINC, which responds to the needs of automatic coding of each and every one of the different areas of the healthcare circuit. The tool supports the documentalist as a tool for assisted coding in CIE-10, since its objective is to ensure that the coding in ICD-10 is carried out maintaining the same levels of quality and productivity that are currently already offered in ICD-9.

SNQuery: We developed an engine for evaluating Snomed CT expression constraints . Besides of facilitating the query and search of subsets, includes validators, optimizers and a results analyzer. This main objective of this language is to define subsets intentionally (that is, by means of expressions whose evaluation generates the subset) as opposed to the explicit enumeration of subset members (extensionally). The tool offers a web interface and a HL7 FHIR web service API for remote invocation.



To put in value the data of an organization it is necessary to ensure its quality, converting it into usable information for its large-scale treatment and other uses. Having quality clinical data repositories for reuse is the objective of most national and international projects, and  great efforts are being invested in this matter.


Firstly, the use of repositories as a data source for research facilitates the capture and monitoring of subjects, minimizing the inconveniences and investments in time and money that this entails, being also a good source of data for the construction of predictive models.


On the other hand, it expresses its usefulness in the field of healthcare quality since repositories of clinical data are the primary source of data to guide strategic management decisions, monitor clinical processes or designing and evaluating interventions to ensure patient safety.

Our experience shows us that every organization needs a particular adaptation to the needs of each user, so we offer individual adjustments in the applications of the following data quality methodologies.

  • Uniqueness Dimension. Number of replicates in the set of all analyzed data

  • Completeness Dimension. How complete is the data in each column

  • Correction Dimension. Number of anomalous data

  • Consistency Dimension. How faithful is the data to the established rules (types, ranges, etc.)

  • Predictive value Dimension. How much predictive value has a variable

  • Temporal Stability Dimension. How similar are the probability distributions of the data over time

  • Multi-source stability Dimension. How similar are the probability distributions of the data between different sources (hospitals, departments, professionals, etc.)

Real use cases

BDCAP: We adapted the multi-source stability dimension to the Spanish Ministry of Health, Social Services and Equality needs in order to analyze how the morbidity data recorded in the primary care clinical database (BDCAP) vary according to their source

Perinatal: Pilot project to improve the quality of perinatal information (MSSSI). This project consisted in both a perinatal data standardization and a evaluation of data quality in two hospitals  (Hospital Virgen del Castillo - Yecla y Hospital 12 de Octubre de Madrid)



We provide consulting service and development of normalized clinical data repositories with quality measurement and control. Depending on the need, the repositories can be oriented to both care work, research or both. Our solutions maximize the use of metadata, flexibility, consulting capacity and are based on noSQL solutions.

Casos reales

DiaBD: Together with the Hospital Universitario Doctor Peset, we develop a normalized repository as the basis of a quality scorecard in the field of diabetes

Perinatal: In collaboration with the Hospital Virgen del Castillo we have developed a normalized repository with assured quality aimed at the care of the first 1000 days of life.