Semanticification in Connected Health.

The purpose of the implementation of nursing terminologies is to capture and process meaningful health data wherein facts about patients and nursing care can be recorded and inferences for nursing care, patient outcomes and associations with all care delivered by the interprofessional team can be made. This paper describes the clinical information landscape, implementation of semantic content, and competencies required for nurses. Health data can be outlined in a high level clinical information ecosystem where nursing terminologies can be represented for implementation. This ecosystem consists of both the structural and dynamic aspects of the support of care.

Clinical professional governance for detailed clinical models.

This chapter describes the need for Detailed Clinical Models for contemporary Electronic Health Systems, data exchange and data reuse. It starts with an explanation of the components related to Detailed Clinical Models with a brief summary of knowledge representation, including terminologies representing clinic relevant "things" in the real world, and information models that abstract these in order to let computers process data about these things. Next, Detailed Clinical Models are defined and their purpose is described. It builds on existing developments around the world and accumulates in current work to create a technical specification at the level of the International Standards Organization. The core components of properly expressed Detailed Clinical Models are illustrated, including clinical knowledge and context, data element specification, code bindings to terminologies and meta-information about authors, versioning among others. Detailed Clinical Models to date are heavily based on user requirements and specify the conceptual and logical levels of modelling. It is not precise enough for specific implementations, which requires an additional step. However, this allows Detailed Clinical Models to serve as specifications for many different kinds of implementations. Examples of Detailed Clinical Models are presented both in text and in Unified Modelling Language. Detailed Clinical Models can be positioned in health information architectures, where they serve at the most detailed granular level. The chapter ends with examples of projects that create and deploy Detailed Clinical Models. All have in common that they can often reuse materials from earlier projects, and that strict governance of these models is essential to use them safely in health care information and communication technology. Clinical validation is one point of such governance, and model testing another. The Plan Do Check Act cycle can be applied for governance of Detailed Clinical Models. Finally, collections of clinical models do require a repository in which they can be stored, searched, and maintained. Governance of Detailed Clinical Models is required at local, national, and international levels.

Bridging the HL7 template - 13606 archetype gap with detailed clinical models.

The idea of two level modeling has been taken up in healthcare information systems development. There is ongoing debate which approach should be taken. From the premise that there is a lack of clinician's time available, and the need for semantic interoperability, harmonization efforts are important. The question this paper addresses is whether Detailed Clinical Models (DCM) can bridge the gap between existing approaches. As methodology, a bottom up approach in multilevel comparison of existing content and modeling is used. Results indicate that it is feasible to compare and reuse DCM with clinical content from one approach to the other, when specific limitations are taken into account and precise analysis of each data-item is carried out. In particular the HL7 templates, the ISO/CEN 13606 and OpenEHR archetypes reveal more commonalties than differences. The linkage of DCM to terminologies suggests that data-items can be linked to concepts present in multiple terminologies. This work concludes that it is feasible to model a multitude of precise items of clinical information in the format of DCM and that transformations between different approaches are possible without loss of meaning. However, a set of single or combined clinical items and assessment scales have been tested. Larger groupings of clinical information might bring up more challenges.

Detailed clinical models: a review.

OBJECTIVES: Due to the increasing use of electronic patient records and other health care information technology, we see an increase in requests to utilize these data. A highly level of standardization is required during the gathering of these data in the clinical context in order to use it for analyses. Detailed Clinical Models (DCM) have been created toward this purpose and several initiatives have been implemented in various parts of the world to create standardized models. This paper presents a review of DCM. METHODS: Two types of analyses are presented; one comparing DCM against health care information architectures and a second bottom up approach from concept analysis to representation. In addition core parts of the draft ISO standard 13972 on DCM are used such as clinician involvement, data element specification, modeling, meta information, and repository and governance. RESULTS: SIX INITIATIVES WERE SELECTED: Intermountain Healthcare, 13606/OpenEHR Archetypes, Clinical Templates, Clinical Contents Models, Health Level 7 templates, and Dutch Detailed Clinical Models. Each model selected was reviewed for their overall development, involvement of clinicians, use of data types, code bindings, expressing semantics, modeling, meta information, use of repository and governance. CONCLUSIONS: Using both a top down and bottom up approach to comparison reveals many commonalties and differences between initiatives. Important differences include the use of or lack of a reference model and expressiveness of models. Applying clinical data element standards facilitates the use of conceptual DCM models in different technical representations.

ROC van Twente: nursing education in care and technology.

The ROC van Twente offers nursing education at the diploma level (MBO), and is innovating the program to include a major/minor structure for education about care and technology. In order to achieve this, a new position was created: the Master Docent, Care and Technology. The task of the master docent includes development of education for nursing about technology, multidisciplinary cooperation, and service to health care institutions among others. The first development concerns a module about electronic patient records, standards, and semantic interoperability for continuity of care. The module is delivered to nursing students and to students from the information technology department, who work jointly in 'development teams'. This paper describes the background, the development of the educational material and program, and the core content of the module. The core content are the care information models that link clinical materials with health care information standards. The program has started end November 2006. At the Medinfo 2007 conference the results of the course for the first group of about 40 students will be presented.

Evaluation of documents that integrate knowledge, terminology and information models.

Healthcare depends on evidence for practice and for electronic exchange of clinical patient information. To standardize the information that is being exchanged we created a format for describing care related information: a care information model. This model integrates knowledge, terminology and an information model. During a meeting between experts on nursing, standards and electronic patient records we evaluated the format of the care information model by use of an evaluation form and a group discussion. In general, the experts were enthusiastic about the format of the care information model. However they missed specific information about the purpose of the documents, and clarity on copyright issues. In addition, detailed comments on the existing structure and suggestions for additional categories were given. Also, experts suggested representing the integration in both HL7 message model format and in OpenEHR archetype format to allow different implementations of the same intelligence. From this we can conclude that the combination of these aspects in one document creates a valuable content for development of messages and systems. However, some adjustments are needed.

Using SNOMED CT codes for coding information in electronic health records for stroke patients.

For a project on development of an Electronic Health Record (EHR) for stroke patients, medical information was organised in care information models (templates). All (medical) concepts in these templates need a unique code to make electronic information exchange between different EHR systems possible. When no unique code could be found in an existing coding system, a code was made up. In the study presented in this article we describe our search for unique codes in SNOMED CT to replace the self made codes. This to enhance interoperability by using standardized codes. We wanted to know for how many of the (self made) codes we could find a SNOMED CT code. Next to that we were interested in a possible difference between templates with individual concepts and concepts being part of (scientific) scales. Results of this study were that we could find a SNOMED CT code for 58% of the concepts. When we look at the concepts with a self made code, 54.9% of these codes could be replaced with a SNOMED CT code. A difference could be detected between templates with individual concepts and templates that represent a scientific scale or measurement instrument. For 68% of the individual concepts a SNOMED CT could be found. However, for the scientific scales only 26% of the concepts could get a SNOMED CT code. Although the percentage of SNOMED CT codes found is lower than expected, we still think SNOMED CT could be a useful coding system for the concepts necessary for the continuity of care for stroke patients, and the inclusion in Electronic Health Records. Partly this is due to the fact that SNOMED CT has the option to request unique codes for new concepts, and is currently working on scale representation.