Astrid M. van Ginneken

The computerized patient record: balancing effort and benefit

Promise and reality: this review addresses two questions. First, why is the introduction of the computerized patient record (CPR) so slow, while its potential for improved quality of care and reduction of cost is well recognized? Second, what, in this respect, is the role of record architecture and standardization? Barriers: the impediments for CPR adoption are put in a larger context by addressing the relationship among effort, benefit, and the parties involved. An important financial impediment is insufficient return of investment. Other hurdles related to the use of CPRs are lack of integration and flexibility, which cause clinicians to experience insufficient reward to motivate them for data entry and changes in working style. Effort and benefit have to be balanced for each party involved. Requirements for improvement: lack of standardization impedes exchange and sharing of medical data, and new developments cause fear of applications to become outdated. Flexibility in content and use, integration, and adaptability to change, are key requirements for CPR systems. These requirements can most effectively be met through an architecture that separates content and structure, such that the road to standardization is not paved with frequent expensive adaptations. Strategies for implementation: successful implementation and acceptance require reliable evaluation of applications by independent professional groups. Users need to be involved in setting priorities and planning for actual implementation.

OpenSDE: A strategy for expressive and flexible structured data entry

PURPOSE This description focuses on the expressiveness and flexibility of OpenSDE: an application that supports recording of structured narrative data. METHODS OpenSDE enables data entry with (customizable) forms based on trees of medical concepts. The relevant scope for data entry can be tailored per medical domain by construction of a domain-specific tree. OpenSDE is intended for structuring narrative data to make these available for both care and research. RESULTS The OpenSDE application is currently in use at several departments in our academic hospital, including radiology, neurology, pediatrics, and child psychiatry. OpenSDE is available for all in open source.

Extracting data recorded with OpenSDE: Possibilities and limitations

PURPOSE OpenSDE is an application intended to support structured data entry in a variety of settings, such as routine care and clinical research. The past years development has focused on data entry to support expressiveness and flexibility. The focus is now shifting to data extraction: what are the possibilities for extracting the data and does the adopted strategy pose limitations? METHODS Data extraction is supported by presenting the concepts for extraction in the same tree structure as for data entry. Users can select all or a sub selection of these concepts for extraction. Selected concepts are extracted and converted to a table format that can be queried using conventional tools. RESULTS The extraction tool (entity export) provides a successful technical solution for data extraction. Using the extracted data, however, leads to obstacles that are a result of a fundamental design principle of OpenSDE.

Why are structured data different? Relating differences in data representation to the rationale of OpenSDE

OpenSDE is an application that supports clinicians with structured recording of narrative patient data to enable use of data in both clinical practice and research. OpenSDE is based on a rationale and requirements for structured data entry. In this study, we analyse the impact of the rationale and the requirements on data representation using OpenSDE. Three paediatricians transcribed 20 paper patient records using OpenSDE. The transcribed records were compared; the findings that were the same in content but differed in representation (e.g. recorded as free text instead of in a structured manner) were categorized in one of three categories of difference in representation. The transcribed records contained 1764 findings in total. The medical content of 302 of these findings was represented differently by at least one clinician and was thus included in this study. In OpenSDE, clinicians are free to determine the degree of detail at which patient data are described. This flexibility accounts for 87% of the differences in data representation. Thirteen per cent of the differences are due to clinicians interpreting and translating phrases from the source text and transcribing these to (different) concepts in OpenSDE. The differences in data representation largely result from initial design decisions for OpenSDE.

Descriptive features of gastric ulcers: do endoscopists agree on what they see?

BACKGROUND Little is known about the interobserver variation between endoscopists on descriptive morphologic features. METHODS This study describes the agreement among 10 endoscopists on their description of 12 morphologic features, using 10 photographs of gastric ulcers, and on their eventual interpretation. The endoscopists used a form with predefined options for description. RESULTS Kappa value was on average 0.36 for descriptive features and 0.31 for interpretation. The proportion of endoscopists agreeing on descriptive features was on average 84%, and 81% on interpretations. The chance of an endoscopist describing all 12 morphologic features of an ulcer on a photograph exactly the same as a colleague ranged from 4% to 46% (average 15%). A positive correlation between agreement in description and interpretation (0.75, p < 0.05) was found. CONCLUSIONS These results indicate a poor agreement between endoscopists in their translation of visual observations into descriptive terms. The positive correlation between agreement in description and interpretation suggests disagreement in description as an important cause for disagreement in interpretation. We believe that the use of more explicit descriptive terms will improve agreement in description and in subsequent interpretation.

Is OpenSDE an alternative for dedicated medical research databases? An example in coronary surgery

BackgroundWhen using a conventional relational database approach to collect and query data in the context of specific clinical studies, a study with a new data set usually requires the design of a new database and entry forms. OpenSDE (SDE = Structured Data Entry) is intended to provide a flexible and intuitive way to create databases and entry forms for the collection of data in a structured format.This study illustrates the use of OpenSDE as a potential alternative to a conventional approach with respect to data modelling, database creation, data entry, and data extraction.MethodsA database and entry forms are created using OpenSDE and MSAccess to support collection of coronary surgery data, based on the Adult Cardiac Surgery Data Set of the Society of Thoracic Surgeons. Data of 52 cases are entered and nine different queries are designed, and executed on both databases.ResultsDesign of the data model and the creation of entry forms were experienced as more intuitive and less labor intensive with OpenSDE. Both resulting databases provided sufficient expressiveness to accommodate the data set. Data entry was more flexible with OpenSDE. Queries produced equal and correct results with comparable effort.ConclusionFor prospective studies involving well-defined and straight forward data sets, OpenSDE deserves to be considered as an alternative to the conventional approach.

Design of the diagnostic encyclopedia workstation (DEW)

The Diagnostic Encyclopedia Workstation (DEW) contains reference knowledge for diagnostic support in pathology. Illustrations are accessible via a video disc device. DEW can hold more knowledge, pictures and case histories than books, and its information is accessible via several entries. Software for data entry has been written in MUMPS with use of the relational database toolkit AIDA, which is particularly suited for manipulation of free text. The graphical mouse-driven user interface is written in C using MetaWindows. The DEW contains 85 diagnoses in ovarian pathology, covering all frequent cases and many rarities, illustrated by approximately 3000 pictures, divided among 158 cases.