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Augmenting program data with seconda...

Haley, Valerie Benson.

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Augmenting program data with secondary data sources to improve the quality of existing statistics: four examples from the New York State Department of Health Hospital-Acquired Infection Reporting Program.

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Augmenting program data with secondary data sources to improve the quality of existing statistics: four examples from the New York State Department of Health Hospital-Acquired Infection Reporting Program./
作者:	Haley, Valerie Benson.
面頁冊數:	102 p.
附註:	Source: Dissertation Abstracts International, Volume: 75-04(E), Section: B.
Contained By:	Dissertation Abstracts International75-04B(E).
標題:	Biology, Biostatistics. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3606976
ISBN:	9781303643323

Augmenting program data with secondary data sources to improve the quality of existing statistics: four examples from the New York State Department of Health Hospital-Acquired Infection Reporting Program.
Haley, Valerie Benson.

Augmenting program data with secondary data sources to improve the quality of existing statistics: four examples from the New York State Department of Health Hospital-Acquired Infection Reporting Program. - 102 p.

Source: Dissertation Abstracts International, Volume: 75-04(E), Section: B.

Thesis (Ph.D.)--State University of New York at Albany, 2013.

The objective of this dissertation is to illustrate the novel application of methods that can be used to improve the accuracy of hospital-acquired infection (HAI) rates. Public reporting of HAI rates is relatively new. New York was one of the first states to mandate reporting in acute care hospitals (2007), followed by national pay-for-reporting in 2011, and national value-based purchasing in 2013. Given the financial ramifications of public reporting, it is critical that the data are validated and adjusted for differences in underlying risk among patient populations so that hospital performance can be fairly compared. However, limited information on the quality of the HAI data and the impact of different risk adjustment strategies are available.

ISBN: 9781303643323Subjects--Topical Terms:

1018416
Biology, Biostatistics.

Augmenting program data with secondary data sources to improve the quality of existing statistics: four examples from the New York State Department of Health Hospital-Acquired Infection Reporting Program.
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245 1 0 $a Augmenting program data with secondary data sources to improve the quality of existing statistics: four examples from the New York State Department of Health Hospital-Acquired Infection Reporting Program.
300 $a 102 p.
500 $a Source: Dissertation Abstracts International, Volume: 75-04(E), Section: B.
500 $a Adviser: A. Gregory DiRienzo.
502 $a Thesis (Ph.D.)--State University of New York at Albany, 2013.
520 $a The objective of this dissertation is to illustrate the novel application of methods that can be used to improve the accuracy of hospital-acquired infection (HAI) rates. Public reporting of HAI rates is relatively new. New York was one of the first states to mandate reporting in acute care hospitals (2007), followed by national pay-for-reporting in 2011, and national value-based purchasing in 2013. Given the financial ramifications of public reporting, it is critical that the data are validated and adjusted for differences in underlying risk among patient populations so that hospital performance can be fairly compared. However, limited information on the quality of the HAI data and the impact of different risk adjustment strategies are available.
520 $a In four analyses, patient level HAI events were matched to secondary data sources to obtain more extensive patient information. 1) Additional risk factors from a clinical database improved coronary artery bypass graft surgical site infection (SSI) rates risk adjustment. 2) Additional risk factors from an administrative database improved Clostridium difficile risk adjustment. 3) Using an administrative database to target validation increased the efficiency of identifying missed infections and improved data accuracy. 4) Administrative data was used to correct for verification bias when evaluating trends in the accuracy of colon SSI data. These analyses substantiated the importance of data validation and risk adjustment in providing fair HAI rate comparisons and the use of secondary datasets in increasing the efficiency of HAI surveillance.
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