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A health record database contains structured data fields that identify the patient, such as patient ID, patient name, e-mail and phone number. These data are fairly easy to de-identify, that is, replace with other identifiers. However, these data also occur in fields with doctors’ free-text notes written in an abbreviated style that cannot be analyzed grammatically. If we replace a word that looks like a name, but isn’t, we degrade readability and medical correctness. If we fail to replace it when we should, we degrade confidentiality. We de-identified an existing Danish electronic health record database, ending up with 323,122 patient health records. We had to invent many methods for de-identifying potential identifiers in the free-text notes. The de-identified health records should be used with caution for statistical purposes because we removed health records that were so special that they couldn’t be de-identified. Furthermore, we distorted geography by replacing zip codes with random zip codes. [ABSTRACT FROM AUTHOR]

Abstract In this article the authors investigated a visualization tool (uVis) for end-user developers, in order to see how end users actually use it. The tool was an early version and the investigation helped the authors to improve it. The investigation showed that users appreciated the simple formula language, the coordinated panels, and the drag-and-drop mechanism. However, the most important thing for them was the immediate response when they changed something, for instance part of a formula. The entire visualization was updated immediately without having to switch from development view to production view. With uVis, developers construct a visualization from simple visual components such as boxes, curvePoints, and textboxes. All component properties such as Top and BackColor can be complex formulas similar to spreadsheet formulas. The operands in the formula can address relational data in a database, other visual objects, and dialog data provided by the user. A special Rows property can bind to a database query and make the component replicate itself for each row in the query. In this way, traditional as well as novel visualizations can be constructed.

The most serious usability problems were data binding and not noticing errors (errors were shown in an error list, but not in the formula that had the error). There were many other usability problems. Removing them would speed up learning and make the tool more successful. [ABSTRACT FROM AUTHOR]

Use cases are widely used as a substantial part of requirements, also when little programming is expected (COTS-based systems, Commercial-Off-The-Shelf). Are use cases effective as requirements? To answer this question, we invited professionals and researchers to specify requirements for the same project: Acquire a new system to support a hotline. Among the 15 replies, eight used traditional use cases that specified a dialog between user and system. Seven used a related technique, task description, which specified the customer's needs without specifying a dialog. It also allowed the analyst to specify problem requirements-problems to be handled by the new system. It turned out that the traditional use cases covered the customer's needs poorly in areas where improvement was important but difficult. Use cases also restricted the solution space severely. Tasks did not have these problems and allowed an easy comparison of solutions. [ABSTRACT FROM AUTHOR]

Electronic health records (EHR) contain a large amount of structured data and free text. Exploring and sharing clinical data can improve healthcare and facilitate the development of medical software. However, revealing confidential information is against ethical principles and laws. We de-identified a Danish EHR database with 437,164 patients. The goal was to generate a version with real medical records, but related to artificial persons. We developed a de-identification algorithm that uses lists of named entities, simple language analysis, and special rules. Our algorithm consists of 3 steps: collect lists of identifiers from the database and external resources, define a replacement for each identifier, and replace identifiers in structured data and free text. Some patient records could not be safely de-identified, so the de-identified database has 323,122 patient records with an acceptable degree of anonymity, readability and correctness (F-measure of 95%). The algorithm has to be adjusted for each culture, language and database.