Biomedical subjects

L K Goodwin

Publications and source records attributed to L K Goodwin.

4 recordsLinked to original sources

PubMed2002

Web-based informatics education: lessons learned from five years in the trenches.

Duke University has a five-year history with high-quality and clinically oriented informatics web-based nursing informatics education. This paper highlights an overview of instructional methods used and pedagogical considerations for both students and faculty. To do the job well, faculty workload for web-based instruction has been more than double the time and effort required for teaching an on-campus course. Results suggest that virtual teamwork is difficult but possible for highly motivated students. Committed to excellence, Duke's program finds that most students do well in achieving their goals and achieving Duke's high standards of academic rigor, however some students are not successful with on-line courses.

Computer-Assisted Instruction↗

PubMed1998

Cost-effectiveness analysis in the nursing literature, 1992-1996.

PURPOSE: To review the recommendations by the U.S. Panel on Cost-Effectiveness in Health and Medicine (panel) for use in future nursing research. The review (a) provides a critique of the nursing cost-effectiveness and cost utility literature from the perspective of the recommendations set forth by the panel and other recognized authorities in cost-effectiveness analysis (CEA), (b) constructs an interdisciplinary framework to show the steps in the conduct of CEA, (c) makes the techniques and major findings of nursing CEA studies available and understandable, and (d) offers guidelines for the incorporation of CEA into the evaluation of future nursing intervention and research. DATA SOURCES: Seven nursing studies published between 1992 and 1996 that compared two or more interventions for costs and outcomes. ORGANIZING FRAMEWORK: For each study, the (a) perspective, (b) net costs, (c) net effect, (d) analysis of costs and effects, and (e) decision outcomes were analyzed. FINDINGS: If the panel's recommendations reflect the problems in the health care CEA literature in general, then on balance, the nursing CEA 1992-1996 studies are no more or less flawed than CEA studies in the health or medical care fields. CONCLUSIONS: Methodologic guidelines and interdisciplinary strategies are needed to advance the progress of nursing cost-effectiveness research.

Cost-Benefit Analysis↗

PubMed1997

Medical data mining: knowledge discovery in a clinical data warehouse.

Clinical databases have accumulated large quantities of information about patients and their medical conditions. Relationships and patterns within this data could provide new medical knowledge. Unfortunately, few methodologies have been developed and applied to discover this hidden knowledge. In this study, the techniques of data mining (also known as Knowledge Discovery in Databases) were used to search for relationships in a large clinical database. Specifically, data accumulated on 3,902 obstetrical patients were evaluated for factors potentially contributing to preterm birth using exploratory factor analysis. Three factors were identified by the investigators for further exploration. This paper describes the processes involved in mining a clinical database including data warehousing, data query and cleaning, and data analysis.

Data Interpretation, Statistical↗

PubMedDate not supplied

Data mining methods find demographic predictors of preterm birth.

BACKGROUND: Preterm births in the United States increased from 11.0% to 11.4% between 1996 and 1997; they continue to be a complex healthcare problem in the United States. OBJECTIVE: The objective of this research was to compare traditional statistical methods with emerging new methods called data mining or knowledge discovery in databases in identifying accurate predictors of preterm births. METHOD: An ethnically diverse sample (N = 19,970) of pregnant women provided data (1,622 variables) for new methods of analysis. Preterm birth predictors were evaluated using traditional statistical and newer data mining analyses. RESULTS: Seven demographic variables (maternal age and binary coding for county of residence, education, marital status, payer source, race, and religion) yielded a .72 area under the curve using Receiving Operating Characteristic curves to test predictive accuracy. The addition of hundreds of other variables added only a .03 to the area under the curve. CONCLUSION: Similar results across data mining methods suggest that results are data-driven and not method-dependent, and that demographic variables offer a small set of parsimonious variables with reasonable accuracy in predicting preterm birth outcomes in a racially diverse population.

Data Collection↗