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Cynthia Brandt

Publications and source records attributed to Cynthia Brandt.

8 recordsLinked to original sources
PubMed2006

Pivoting approaches for bulk extraction of Entity-Attribute-Value data.

Entity-Attribute-Value (EAV) data, as present in repositories of clinical patient data, must be transformed (pivoted) into one-column-per-parameter format before it can be used by a variety of analytical programs. Pivoting approaches have not been described in depth in the literature, and existing descriptions are dated. We describe and benchmark three alternative algorithms to perform pivoting of clinical data in the context of a clinical study data management system. We conclude that when the number of attributes to be returned is not too large, it is feasible to use static SQL as the basis for views on the data. An alternative but more complex approach that utilizes hash tables and the presence of abundant random-access-memory can achieve improved performance by reducing the load on the database server.

Algorithms↗
PubMed2005

The GuideLine Implementability Appraisal (GLIA): development of an instrument to identify obstacles to guideline implementation.

BACKGROUND: Clinical practice guidelines are not uniformly successful in influencing clinicians' behaviour toward best practices. Implementability refers to a set of characteristics that predict ease of (and obstacles to) guideline implementation. Our objective is to develop and validate a tool for appraisal of implementability of clinical guidelines. METHODS: Indicators of implementability were identified from the literature and used to create items and dimensions of the GuideLine Implementability Appraisal (GLIA). GLIA consists of 31 items, arranged into 10 dimensions. Questions from 9 of the 10 dimensions are applied individually to each recommendation of the guideline. Decidability and Executability are critical dimensions. Other dimensions are Global, Presentation and Formatting, Measurable Outcomes, Apparent Validity, Flexibility, Effect on Process of Care, Novelty/Innovation, and Computability. We conducted a series of validation activities, including validation of the construct of implementability, expert review of content for clarity, relevance, and comprehensiveness, and assessment of construct validity of the instrument. Finally, GLIA was applied to a draft guideline under development by national professional societies. RESULTS: Evidence of content validity and preliminary support for construct validity were obtained. The GLIA proved to be useful in identifying barriers to implementation in the draft guideline and the guideline was revised accordingly. CONCLUSION: GLIA may be useful to guideline developers who can apply the results to remedy defects in their guidelines. Likewise, guideline implementers may use GLIA to select implementable recommendations and to devise implementation strategies that address identified barriers. By aiding the design and operationalization of highly implementable guidelines, our goal is that application of GLIA may help to improve health outcomes, but further evaluation will be required to support this potential benefit.

Benchmarking↗
PubMed2005

Validation of a system for quality improvement: preliminary data.

Electronic Health Records (EHRs) are designed for patient-centered care and not for cross-patient analyses. Thus to ensure that measurements of quality of care derived from data extracted from EHRs are meaningful, they must be validated.1 We present preliminary data regarding validation of measurements of quality of care in a pediatric clinic.

Humans↗
PubMed2005

Designing a tracking system based on cognitive theory of error.

We will present an application for tracking research samples that has been designed based upon prior research in cognitive theories on error that has been applied successfully to fields such as aviation and anesthesiology. By anticipating where the errors are likely to occur in the human-computer interaction and workflow, we hope to reduce number of errors and minimize the effects of inevitable errors.

Biomedical Research↗
PubMed2003

Temporal query of attribute-value patient data: utilizing the constraints of clinical studies.

We describe an interface and architecture for ad hoc temporal query of TrialDB, a clinical study data management system (CSDMS). A clinical study focuses primarily on the effect of therapy on a group of patients, who have individually enrolled in a study at different times. Relative times (chronological offsets from the time of enrollment) are therefore more useful than absolute times when collectively describing therapeutic or adverse events. For logistic reasons, study parameter values are typically recorded at fixed relative times ('study events'), which serve as time-stamps and can be used by CSDMS temporal query algorithms to simplify temporal computations. The entity-attribute-value model of clinical data storage, used by both CSDMSs and clinical patient record systems, complicates temporal query. To apply temporal operators, data for parameters of interest must first be transiently converted into conventional relational form, with one column per parameter.

Academic Medical Centers↗
PubMed2003

The integration of similar clinical research data collection instruments.

We devised an algorithm for integrating similar clinical research data collection instruments to create a common measurement instrument. We tested this algorithm using questions from several similar surveys. We encountered differing levels of granularity among questions and responses across surveys resulting in either the loss of granularity or data. This algorithm may make survey integration more systematic and efficient.

Algorithms↗
PubMedDate not supplied

Metadata-driven ad hoc query of patient data: meeting the needs of clinical studies.

Clinical study data management systems (CSDMSs) have many similarities to clinical patient record systems (CPRSs) in their focus on recording clinical parameters. Requirements for ad hoc query interfaces for both systems would therefore appear to be highly similar. However, a clinical study is concerned primarily with collective responses of groups of subjects to standardized therapeutic interventions for the same underlying clinical condition. The parameters that are recorded in CSDMSs tend to be more diverse than those required for patient management in non-research settings, because of the greater emphasis on questionnaires for which responses to each question are recorded separately. The differences between CSDMSs and CPRSs are reflected in the metadata that support the respective systems' operation, and need to be reflected in the query interfaces. The authors describe major revisions of their previously described CSDMS ad hoc query interface to meet CSDMS needs more fully, as well as its porting to a Web-based platform.

Computer Security↗