Biomedical subjects

Sacha E Bleeker

Publications and source records attributed to Sacha E Bleeker.

4 recordsLinked to original sources

PubMed2006

Structured data entry for narrative data in a broad specialty: patient history and physical examination in pediatrics.

BACKGROUND: Whereas an electronic medical record (EMR) system can partly address the limitations, of paper-based documentation, such as fragmentation of patient data, physical paper records missing and poor legibility, structured data entry (SDE, i.e. data entry based on selection of predefined medical concepts) is essential for uniformity of data, easier reporting, decision support, quality assessment, and patient-oriented clinical research. The aim of this project was to explore whether a previously developed generic (i.e. content independent) SDE application to support the structured documentation of narrative data (called OpenSDE) can be used to model data obtained at history taking and physical examination of a broad specialty. METHODS: OpenSDE was customized for the broad domain of general pediatrics: medical concepts and its descriptors from history taking and physical examination were modeled into a tree structure. RESULTS: An EMR system allowing structured recording (OpenSDE) of pediatric narrative data was developed. Patient history is described by 20 main concepts and physical examination by 11. In total, the thesaurus consists of about 1800 items, used in 8648 nodes in the tree with a maximum depth of 9 levels. Patient history contained 6312 nodes, and physical examination 2336. User-defined entry forms can be composed according to individual needs, without affecting the underlying data representation. The content of the tree can be adjusted easily and sharing records among different disciplines is possible. Data that are relevant in more than one context can be accessed from multiple branches of the tree without duplication or ambiguity of data entry via "shortcuts". CONCLUSION: An expandable EMR system with structured data entry (OpenSDE) for pediatrics was developed, allowing structured documentation of patient history and physical examination. For further evaluation in other environments, the tree structure for general pediatrics is available at the Erasmus MC Web site (in Dutch, translation into English in progress) 1. The generic OpenSDE application is available at the OpenSDE Web site 2.

Child↗

PubMed2006

Paper versus computer: feasibility of an electronic medical record in general pediatrics.

BACKGROUND: Implementation of electronic medical record systems promises significant advances in patient care, because such systems enhance readability, availability, and data quality. Structured data entry (SDE) applications can prompt for completeness, provide greater accuracy and better ordering for searching and retrieval, and permit validity checks for data quality monitoring, research, and especially decision support. A generic SDE application (OpenSDE) to support documentation of patient history and physical examination findings was developed and tailored for the domain of general pediatrics. OBJECTIVE: To evaluate OpenSDE for its completeness, uniformity of reporting, and usability in general pediatrics. METHODS: Four (trainee) pediatricians documented data for 8 first-visit patients in the traditional, paper-based, medical record and immediately thereafter in OpenSDE (electronic record). The 32 paper records obtained served as the common data source for data entry in OpenSDE by the other 3 physicians (transcribed record). Data entered by 2 experienced users, with all patient information present in the paper record, served as the control record. Data entry times were recorded, and a questionnaire was used to assess users' experiences with OpenSDE. RESULTS: Clinicians documented 44% of all available patient information identically in the paper and electronic records. Twenty-five percent of all patient information was documented only in the paper record, and 31% was present only in the electronic record. Differences were found in patient history and physical examination documentation in the electronic record; more information was missing for patient history (38%) than for physical examination (15%). Furthermore, physical examination contained more additional information (39%) than did patient history (21%). The interobserver agreement of documentation of patient information from the same data source was fair to moderate, with kappa values of 0.39 for patient history and 0.40 for physical examination. Data entry times in OpenSDE decreased from 25 minutes to <15 minutes, indicating a learning effect. The questionnaire revealed a positive attitude toward the use of OpenSDE in daily practice. CONCLUSION: OpenSDE seems to be a promising application for the support of physician data entry in general pediatrics.

Documentation↗

PubMed2003

Internal and external validation of predictive models: a simulation study of bias and precision in small samples.

We performed a simulation study to investigate the accuracy of bootstrap estimates of optimism (internal validation) and the precision of performance estimates in independent validation samples (external validation). We combined two data sets containing children presenting with fever without source (n=376+179=555; 120 bacterial infections). Random samples were drawn from this combined data set for the development (n=376) and validation (n=179) of logistic regression models. The models included statistically significant predictors for infection selected from a set of 57 candidate predictors. Model development, including the selection of predictors, and validation were repeated in a bootstrapping procedure. The resulting expected optimism estimate in the receiver operating characteristic (ROC) area was compared with the observed optimism according to independent validation samples. The average apparent ROC area was 0.74, which was expected (based on bootstrapping) to decrease by 0.07 to 0.67, whereas the observed decrease in the validation samples was 0.09 to 0.65. Omitting the selection of predictors from the bootstrap procedure led to a severe underestimation of the optimism (decrease 0.006). The standard error of the observed ROC area in the independent validation samples was large (0.05). We recommend bootstrapping for internal validation because it gives reasonably valid estimates of the expected optimism in predictive performance provided that any selection of predictors is taken into account. For external validation, substantial sample sizes should be used for sufficient power to detect clinically important changes in performance as compared with the internally validated estimate.

Bias↗

PubMed2003

Diagnostic research on routine care data: prospects and problems.

A diagnosis in practice is a sequential process starting with a patient with a particular set of signs and symptoms. To serve practice, diagnostic research should aim to quantify the added value of a test to clinical information that is commonly available before the test will be applied. Routine care databases commonly include all documented patient information, and therefore seem to be suitable to quantify a tests' added value to prior information. It is well known, however, that retrospective use of routine care data in diagnostic research may cause various methodologic problems. But, given the increased attention of electronic patient records including data from routine patient care, we believe it is time to reconsider these problems. We discuss four problems related to routine care databases. First, most databases do not label patients by their symptoms or signs but by their final diagnosis. Second, in routine care the diagnostic workup of a patient is by definition determined by previous diagnostic (test) results. Therefore, routinely documented data are subject to so-called workup bias. Third, in practice, the reference test is always interpreted with knowledge of the preceding test information, such that in scientific studies using routine data the diagnostic value of a test under evaluation is commonly overestimated. Fourth, routinely documented databases are likely to suffer from missing data. Per problem we discuss methods that are presently available and may (partly) overcome each problem. All this could contribute to more frequent and appropriate use of routine care data in diagnostic research. The discussed methods to overcome the above problems may well be similarly useful to prospective diagnostic studies.

Bias↗