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Biomedical subjects

A M van Ginneken

Publications and source records attributed to A M van Ginneken.

At least 19 recordsLinked to original sources
PubMed2006

Mapping OpenSDE domain models to SNOMED CT. Applied to the domain of cardiovascular disease.

UNLABELLED: To explore the strengths and pitfalls of mapping structured EPR (electronic patient record) terms (OpenSDE) to SNOMED codes. METHODS: The OpenSDE model was developed for cardiovascular diseases in the context of the I4C project. We employed 35 patient records as references to adjust the model. We then performed automated and manual matches following the design of matching terms in the thesaurus of the resulting OpenSDE domain model to SNOMED concepts. Subsequently, we assessed what number of OpenSDE terms within the domain model can be matched to SNOMED concepts. RESULTS: The OpenSDE domain tree contains 3230 nodes, involving 689 unique terms (terms can be associated with more than one node in different parts of a domain model tree). After final manual work for the 689 tree terms, 616 resulted in a good match, 31 in a partial match, and 42 in no match. Of the good matches, 23 produced multiple matches. The matches were used to represent the mapping of each node in the domain tree by concatenation of the matching terms. CONCLUSIONS: Mapping predefined terms in OpenSDE domain models to SNOMED Clinical Terms (CT) concepts eliminates laborious mapping for each individual patient record. The assignment of SNOMED codes to OpenSDE tree nodes facilitates exchange, aggregation, and research involving patient data. The mapping will serve the construction of queries at higher semantic levels than explicitly modeled in an OpenSDE domain model. However, the usefulness of the mapping result depends on the completeness of the mapping to SNOMED CT, for which there is no gold standard.

Cardiovascular Diseases↗
PubMed2005

Are structured data structured identically? Investigating the uniformity of pediatric patient data recorded using OpenSDE.

OBJECTIVE: OpenSDE is an application that supports structured recording of narrative patient data to enable use of the data in both clinical practice and clinical research. Reliability and accuracy of collected data are essential for subsequent data use. In this study we analyze the uniformity of data entered with OpenSDE. Our objective is to obtain insight into the consensus and differences of recorded data. METHODS: Three pediatricians transcribed 20 paper patient records using OpenSDE. The transcribed records were compared and all recorded findings were classified into one of six categories of difference. RESULTS: Of all findings 22% were recorded identically; 17% of the findings were recorded differently (predominantly as free text); 61% was omitted, inferred, or in conflict with the paper record. CONCLUSION: The results of this study show that recording patient data using structured data entry does not necessarily lead to uniformly structured data.

Humans↗
PubMed2003

Considerations for the representation of meta-data for the support of structured data entry.

OBJECTIVES: Research and decision support require patient data to be structured. Flexible data representation, to cope with expansion and new recording demands, can be achieved through abstraction of the data model. Such a model, however, does not include explicit information for the support of structured data entry (SDE), i.e. the definition of what is relevant to say in a specific context. The objective of this paper is to provide considerations for an intuitive conceptual representation of such meta-data for domain experts. METHODS: The content of meta-data for SDE is compared with that of controlled medical terminologies in the context of its purpose. RESULTS: Upon analysis of medical descriptions, a network and a tree both emerge as an intuitive structure to represent meta-data for the support of SDE. The pros and cons of a tree versus a network are dominated by the challenges of representing multiple contexts for description. Meta-data for SDE only partially overlap with the content of controlled medical terminologies. CONCLUSIONS: Integration of meta-data for SDE and controlled terminologies would benefit standardization and retrieval, but the requirements for flexible data entry are at odds with the rigidity and limited granularity of a standard. The preferred strategy seems to be a mapping between the concepts from a terminology standard and meta-data for SDE.

Data Display↗
PubMed2002

Principles of structured data entry applied to reference sources.

Our Structured Data Entry application makes use of a domain specific data model. The principles governing the modeling of this data model, are similar to the requirements of the criteria underlying taxonomic systems. Using the DSM-IV as reference source to create a data model for psychiatry, revealed a number of flaws in the criteria of this system, which potentially influence the reliability and validity of this taxonomic system. We conclude that the modeling process provides us with a powerful tool which can be used during the revision process of such systems.

Child↗
PubMed2001

Structured physical examination data: a modeling challenge.

The success of systems facilitating collection of structured data by clinicians is largely dependent on the flexibility of the interface. The Open Record for CAre (ORCA) makes use of a generic model to support knowledge-based structured data entry for a variety of medical domains. An endeavor undertaken recently aimed to cover the broader area of Physical Examination by expanding the contents of the knowledge base. The model was found to be adequately expressive for supporting this task. Maintaining the balance between flexibility of the interface and constraints dictated by reliable retrieval, however, proved to be a considerable challenge. In this paper we illustrate through specific examples the effect of this trade off on the modeling process, together with the rationale for the chosen solutions and suggestions for future research focus.

Artificial Intelligence↗
PubMed2001

A multi-disciplinary approach to a user interface for structured data entry.

Physician data entry (PDE) is still an obstacle to the adoption of a CPR that replaces the paper chart. Most interfaces have been designed primarily on the basis of the functional requirements. Few studies document methods to elicit interface preferences from the clinician-user. We used insights from the fields of both medicine and Human Computer Interfaces to explore interface alternatives for structured data entry (SDE). We present and discuss three designs as alternatives for the SDE module in ORCA (Open Record for CAre). The methodology is applied to SDE in particular, but many aspects also apply to CPRs in general.

Humans↗
PubMed1999

ORCA: the versatile CPR.

The introduction of computer-based patient records (CPRs) that fully replace paper records proves especially difficult in specialized care, despite the potential advantages of CPRs for patient care and research. Improved data legibility, availability, sharing of records, and decision support may directly benefit patient care. Barriers to the introduction of CPR applications at institutions may be caused by lack of infrastructure, or by financial or organizational issues. To have clinicians interactively enter data at the point of care is still a big challenge. This paper presents an overview of ORCA (Open Record of CAre): a generic CPR, designed for integration with existing systems, presentation of multi-media patient data, and the collection of structured data, directly by clinicians. ORCA can easily be tailored to the needs of a variety of medical specialists without the need for changes to its data model, functionality, or interface. The paper describes the essence of the architecture of ORCA and the user benefits with emphasis on the support of structured data entry.

Medical Records Systems, Computerized↗
PubMed1998

Integration and communication for the continuity of cardiac care (I4C).

The project I4C (Integration and Communication for the Continuity of Cardiac Care) is carried out for the advancement of cardiac care, from prevention to follow-up. The goals of I4C are: (1) integrated access to patient data, wherever they are stored; (2) support of evidence-based care; (3) consistent recording of patient data (eg, patient history, electrocardiograms IECGs] or cine-angios) in a multimedia patient record; and (4) a documented reference data set for research. In several clinics, workstations are being installed to serve the four goals. Integration with other information systems in clinical care is realized by encapsulation. A computer-based patient record (ORCA) has been developed to support the collection, consultation, and sharing of patient data. In I4C, ORCA is intended for use in a research setting as well as routine patient care. The functionality of ORCA covers the collection of patient history data in a highly structured manner, the recording of drug prescriptions, an overview of laboratory test results, and viewers for ECGs and angiographic images. At present, structured data entry and consultation is supported in six European languages.

Cardiac Care Facilities↗
PubMed1998

Virtual electronic patient records for shared care.

Systems that primarily serve health-care organizations are changing into systems that support patient care. The core of this change is shaped by systems for computer-based patient records (CPRs), which are part of local or regional networks, giving access to data in different information systems. In principle, it should not matter where the patient data are located as long as data can be transferred to the physical location where patients and clinicians meet. Networking and electronic communication enable to realize an environment that makes all systems where patient data reside, acting as one integrated, virtual CPR-system from the user's perspective. The patient record itself needs not to be physically located at one place, but may be virtual. A development in this direction is the European 14C project, which aims at integrating patient record data, images, and biosignals from whatever system they are stored and on whatever computer they are processed in the network.

Computer Communication Networks↗
PubMed1998

Clinical data entry.

Routine capture of patient data for a computer-based patient record system remains a subject of study. Time constraints that require fast data entry and maximal expression power are in favor of free text data entry. However, using patient data directly for decision support systems, for quality assessment, etc. requires structured data entry, which appears to be more tedious and time consuming. In this paper, a prototype clinical data entry application is described that combines free text and structured data entry in one single application and allows clinicians to smoothly switch between these two different input styles. A knowledge base involving a semantic network of clinical data entry terms and their properties and relationships is used by this application to support structured data entry. From structured data, sentences are generated and shown in a text processor together with the free text. This presentation metaphor allows for easy integrated presentation of structured data and free text.

Artificial Intelligence↗
PubMed1998

Self-contained patient data in ORCA to cope with an evolving vocabulary.

Because of the benefits of standardization in healthcare data for research, decision support, and quality assessment, much research effort focuses on collection of structured patient data. Many strategies to obtain such data are based on controlled vocabularies to guide data entry in a far more flexible way than a fixed-form approach. Medical controlled vocabularies evolve, but change is difficult to reconcile with standardization. Retrieval of data, collected with different versions of vocabularies, is not straightforward and has consequences for patient care and research. There are several strategies to cope with these problems: keep each version, keep a record of changes, or conversion of previously collected data. Each of these strategies has pros and cons regarding storage consumption, performance during patient care, and research. The approach in ORCA (Open Record for Care) is based on self-contained patient data and combines the strengths of these strategies.

Humans↗
PubMed1997

Restructuring routinely collected patient data: ORCA applied to andrology.

Hospital information systems do not always cover all required detail per specialty. This may lead to scattering of data over disparate systems and the paper record. The ORCA (Open Record for CAre) CPR offers a generic structure for record sharing, and record keeping tailored to specific needs. We studied whether a semantic integration of existing and new data was possible, using the ORCA structure. Existing andrology data, originating from separate sources, were utilized for this purpose. During normalization, validation and explication steps, latent problems in the source data were exposed and removed, followed by a merge with new data items. By conversion of source data to ORCA, a unique representation of medical concepts in the database was attained, facilitating retrieval of univocal data for multiple purposes. We conclude that the expansion to the andrology domain, including transparent integration of existing data, provides support for the generality of ORCA.

Ambulatory Care Information Systems↗
PubMed1997

Can data representation and interface demands be reconciled? Approach in ORCA.

Research in the domain of computer-based patient records had always faced the conflicting demands of efficiency for the practicing physician and suitability of the record contents for data analysis in view of decision support, research, and quality assessment. Interface and contents pose different demands on the data model underlying the record. The challenge is to combine the most suitable model for data representation with the interface that best fits the clinical setting. ORCA (Open Record for CAre) provides a solution by making the distinction between domain dependent and domain independent data and letting domain dependence be decisive for the choice of model. Interactive definition of custom-views provides interface flexibility for domain dependent data. Views on domain independent data need not cope with the limitations of multiple table views in relational DBMSs. A standard set of single table queries can support recording of domain independent data, irrespective of the clinical setting.

Artificial Intelligence↗
PubMed1996

A multi-strategy approach for medical records of specialists.

Despite a number of well recognized shortcomings of paper medical records, the use of a Computer Patient Record (CPR) is not widespread among specialists. The complexity of specialized care combined with the diversity of their domains of expertise, make it a challenge to design a CPR that satisfies the needs of a specialist. Ideally, CPRs are tailored to the specific tasks of each user, and yet general enough to permit exchange and sharing of information. The basic philosophy behind our CPR is a 'mother' record, which is extended with specialized sub-records. Two different types of subrecords are discussed: one to accommodate standardized data entry in the context of a specialty or research protocol, and another for structured recording of accidental findings outside one's own domain of expertise. The CPR supports the entry of free text and does not impose structured data entry on the physician, but stimulates him to do so by confronting him with the benefits of a structured CPR.

Data Display↗
PubMed1996

Structured data entry in ORCA: the strengths of two models combined.

The capture of patient data in a structured format receives increasing attention. Data can be extracted from free text using natural language processing techniques, but it can also be collected in a structured fashion at the time of data entry. The latter has the advantage that completeness and unambiguity can be promoted by offering predefined terms and options for description of findings. The paper discusses two models for supporting structured data entry. In the direct model, there is an immediate relationship between the terms and options for data entry and the structure of the underlying database. In the indirect model, terms and options for data entry are based on a controlled vocabulary and not directly related to the structure in which actual data is represented. Both models have been utilized by ORCA (Open Record for CAre). We discuss the pros and cons of these two models in relation to the type of patient data and the task involved. It is concluded that a strategic combination of both models has more strengths and less weaknesses than the use of each model only.

Data Collection↗