The Perils of Large Non-clinical Data Sets in Retrospective Studies

2.50
Hdl Handle:
http://hdl.handle.net/10755/148299
Type:
Presentation
Title:
The Perils of Large Non-clinical Data Sets in Retrospective Studies
Abstract:
The Perils of Large Non-clinical Data Sets in Retrospective Studies
Conference Sponsor:Sigma Theta Tau International
Conference Year:2001
Conference Date:November 10 - 14, 2001
Author:Bernier, Mary, PhD
P.I. Institution Name:University of Texas Medical Branch
Objective: With the move toward evidenced-bases practice, large data sets are being used increasingly in nursing research. A cardiovascular disease and depression study is presented as a backdrop for discussing the pros and cons of using such data sets. The purpose of the study was to estimate the differences in the cost and length of stay between cardiac inpatients with a diagnosis of depression (n= 144) and cardiac inpatients without depression (n = 9099). Design: This retrospective, outcomes study used a descriptive design. Sample: The sample consisted of hospitalized cardiac inpatients (N= 9243). Setting: A large urban hospital during fiscal year 1995. Names of variables or concepts: Depression, cardiovascular disease, and clinical and non-clinical large data sets. Measures: A computerized charge capture system was used to detect the incidence of depression. Length of stay and cost were the outcome measures used to estimate the differences between cardiac inpatients with and without a diagnosis of depression. Findings: A major unanticipated finding was the extremely low incidence of depression detected in these patients when compared to depressed patients in other studies (1.6% vs. 40%). Conclusions: In the absence of large nursing data sets, many researchers are turning to non-clinical data sets, which are financial or administrative in nature, in order to achieve the desired sample sizes. The low incidence of depression documented in our study is likely due to the use of a financial, rather than a clinical data set. Objective psychometric measures are often better suited than financial measures for detecting psychiatric symptoms. Implications: Although large data sets are often desirable in terms of providing the statistical power necessary to detect subtle patient outcomes, the variables of interest must be accurately categorized to conduct analyses that yield trustworthy outcomes. Recommendations are made for increasing the suitability of non-clinical data for nursing research.
Repository Posting Date:
26-Oct-2011
Date of Publication:
10-Nov-2001
Sponsors:
Sigma Theta Tau International

Full metadata record

DC FieldValue Language
dc.typePresentationen_GB
dc.titleThe Perils of Large Non-clinical Data Sets in Retrospective Studiesen_GB
dc.identifier.urihttp://hdl.handle.net/10755/148299-
dc.description.abstract<table><tr><td colspan="2" class="item-title">The Perils of Large Non-clinical Data Sets in Retrospective Studies</td></tr><tr class="item-sponsor"><td class="label">Conference Sponsor:</td><td class="value">Sigma Theta Tau International</td></tr><tr class="item-year"><td class="label">Conference Year:</td><td class="value">2001</td></tr><tr class="item-conference-date"><td class="label">Conference Date:</td><td class="value">November 10 - 14, 2001</td></tr><tr class="item-author"><td class="label">Author:</td><td class="value">Bernier, Mary, PhD</td></tr><tr class="item-institute"><td class="label">P.I. Institution Name:</td><td class="value">University of Texas Medical Branch</td></tr><tr class="item-email"><td class="label">Email:</td><td class="value">mbernier@utmb.edu</td></tr><tr><td colspan="2" class="item-abstract">Objective: With the move toward evidenced-bases practice, large data sets are being used increasingly in nursing research. A cardiovascular disease and depression study is presented as a backdrop for discussing the pros and cons of using such data sets. The purpose of the study was to estimate the differences in the cost and length of stay between cardiac inpatients with a diagnosis of depression (n= 144) and cardiac inpatients without depression (n = 9099). Design: This retrospective, outcomes study used a descriptive design. Sample: The sample consisted of hospitalized cardiac inpatients (N= 9243). Setting: A large urban hospital during fiscal year 1995. Names of variables or concepts: Depression, cardiovascular disease, and clinical and non-clinical large data sets. Measures: A computerized charge capture system was used to detect the incidence of depression. Length of stay and cost were the outcome measures used to estimate the differences between cardiac inpatients with and without a diagnosis of depression. Findings: A major unanticipated finding was the extremely low incidence of depression detected in these patients when compared to depressed patients in other studies (1.6% vs. 40%). Conclusions: In the absence of large nursing data sets, many researchers are turning to non-clinical data sets, which are financial or administrative in nature, in order to achieve the desired sample sizes. The low incidence of depression documented in our study is likely due to the use of a financial, rather than a clinical data set. Objective psychometric measures are often better suited than financial measures for detecting psychiatric symptoms. Implications: Although large data sets are often desirable in terms of providing the statistical power necessary to detect subtle patient outcomes, the variables of interest must be accurately categorized to conduct analyses that yield trustworthy outcomes. Recommendations are made for increasing the suitability of non-clinical data for nursing research.</td></tr></table>en_GB
dc.date.available2011-10-26T09:43:09Z-
dc.date.issued2001-11-10en_GB
dc.date.accessioned2011-10-26T09:43:09Z-
dc.description.sponsorshipSigma Theta Tau Internationalen_GB
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