2.50
Hdl Handle:
http://hdl.handle.net/10755/157924
Type:
Presentation
Title:
Divide and Conquer: The Challenge of Defining Small Hospitals for Benchmarking
Abstract:
Divide and Conquer: The Challenge of Defining Small Hospitals for Benchmarking
Conference Sponsor:Western Institute of Nursing
Conference Year:2009
Author:Brown, Diane, PhD, FNAHQ, FAAN
P.I. Institution Name:Kaiser Permanente Northern California Region, Accreditation, Regulation & Licensing
Title:CalNOC Co-Principal Investigator; Kaiser Permanente Northern California Region Accreditation Clinical Practice Leader
Contact Address:1950 Franklin Street, 14th Floor, Oakland, CA, 94612, USA
Contact Telephone:(510) 987-3769
Co-Authors:Moshe Fridman, PhD, CalNOC Statistician, President and Owner of AMP Consulting
Purpose: With increasing public transparency on performance of nurse-sensitive quality indicators, selecting appropriate benchmark comparison groups has never been more important. While using administratively determined comparison groups based on average daily census (ADC), we have noted, over 10-years of unit-based hospital data, significant differences in falls and hospital acquired pressure ulcers (HAPU) between small and large hospitals. The purpose of this presentation is to examine statistically appropriate cutoff values to determine comparison groups for benchmarking hospital performance using nurse-sensitive quality indicators, thereby challenging the conventional use of arbitrary administrative comparison groups. Methods: Data from the 190 CalNOC participating hospitals reported during 2007 and the first two quarters of 2008 were used for these analyses. Included were 195 medical/ surgical nursing units (MS), 192 critical care (CC), and 124 step-down (SD) for the reported outcomes of total reported falls, falls with injury (both per 1000 patient days), HAPU, and HAPU stage 2 or higher. Data were aggregated to the facility level by taking the median outcome rate for each facility. For each outcome of interest, we calculated unit-type hospital size cut-points since the hospital size distribution for the distinct unit types was different, potentially affecting the resulting cutoff values. Our approach was to look for an optimal dichotomous classification of hospitals into "small" and "large" hospital size so that the resulting groups were the best predictors of outcome. This classification results in contiguous size and homogeneous (relative to the outcome) hospital groups. To obtain cut-points that are robust to extreme outcome values, we calculated the overall sample median rate of outcome (for each specific outcome and unit type) and classified facility rates as "low" (below median) or "high" (above median). For a given hospital size cut-point, this process created a two-by-two table of hospital size by outcome level. Logistic regression was used with varying hospital size groups as predictors of outcome level, using cut-points from lowest to highest size in increments of 10 beds. The optimal size cut-point was the one that resulted in the highest accuracy of outcome prediction based on the largest c-statistic. Results: Our results demonstrated that hospital size cut-points varied by both outcome and unit type. The identified cut-points were not consistent with historically identified administrative cut-points using ADC where broad categories typically have defined small hospitals as under 100 beds. For specialty units such as CC or SD, cut-points were not as stable as those identified in MS units. We will discuss the results in detail in our presentation. Implications: Understanding appropriate comparison groups for benchmarking nurse-sensitive quality indicators is an important nursing research issue in this era of transparency and accountability for outcomes. Traditional administrative categories may not be appropriate for all unit types and standard categories may not be appropriate for all outcome indicators.
Repository Posting Date:
26-Oct-2011
Date of Publication:
17-Oct-2011
Sponsors:
Western Institute of Nursing

Full metadata record

DC FieldValue Language
dc.typePresentationen_GB
dc.titleDivide and Conquer: The Challenge of Defining Small Hospitals for Benchmarkingen_GB
dc.identifier.urihttp://hdl.handle.net/10755/157924-
dc.description.abstract<table><tr><td colspan="2" class="item-title">Divide and Conquer: The Challenge of Defining Small Hospitals for Benchmarking</td></tr><tr class="item-sponsor"><td class="label">Conference Sponsor:</td><td class="value">Western Institute of Nursing</td></tr><tr class="item-year"><td class="label">Conference Year:</td><td class="value">2009</td></tr><tr class="item-author"><td class="label">Author:</td><td class="value">Brown, Diane, PhD, FNAHQ, FAAN</td></tr><tr class="item-institute"><td class="label">P.I. Institution Name:</td><td class="value">Kaiser Permanente Northern California Region, Accreditation, Regulation &amp; Licensing</td></tr><tr class="item-author-title"><td class="label">Title:</td><td class="value">CalNOC Co-Principal Investigator; Kaiser Permanente Northern California Region Accreditation Clinical Practice Leader</td></tr><tr class="item-address"><td class="label">Contact Address:</td><td class="value">1950 Franklin Street, 14th Floor, Oakland, CA, 94612, USA</td></tr><tr class="item-phone"><td class="label">Contact Telephone:</td><td class="value">(510) 987-3769</td></tr><tr class="item-email"><td class="label">Email:</td><td class="value">Diane.Brown@KP.ORG</td></tr><tr class="item-co-authors"><td class="label">Co-Authors:</td><td class="value">Moshe Fridman, PhD, CalNOC Statistician, President and Owner of AMP Consulting</td></tr><tr><td colspan="2" class="item-abstract">Purpose: With increasing public transparency on performance of nurse-sensitive quality indicators, selecting appropriate benchmark comparison groups has never been more important. While using administratively determined comparison groups based on average daily census (ADC), we have noted, over 10-years of unit-based hospital data, significant differences in falls and hospital acquired pressure ulcers (HAPU) between small and large hospitals. The purpose of this presentation is to examine statistically appropriate cutoff values to determine comparison groups for benchmarking hospital performance using nurse-sensitive quality indicators, thereby challenging the conventional use of arbitrary administrative comparison groups. Methods: Data from the 190 CalNOC participating hospitals reported during 2007 and the first two quarters of 2008 were used for these analyses. Included were 195 medical/ surgical nursing units (MS), 192 critical care (CC), and 124 step-down (SD) for the reported outcomes of total reported falls, falls with injury (both per 1000 patient days), HAPU, and HAPU stage 2 or higher. Data were aggregated to the facility level by taking the median outcome rate for each facility. For each outcome of interest, we calculated unit-type hospital size cut-points since the hospital size distribution for the distinct unit types was different, potentially affecting the resulting cutoff values. Our approach was to look for an optimal dichotomous classification of hospitals into &quot;small&quot; and &quot;large&quot; hospital size so that the resulting groups were the best predictors of outcome. This classification results in contiguous size and homogeneous (relative to the outcome) hospital groups. To obtain cut-points that are robust to extreme outcome values, we calculated the overall sample median rate of outcome (for each specific outcome and unit type) and classified facility rates as &quot;low&quot; (below median) or &quot;high&quot; (above median). For a given hospital size cut-point, this process created a two-by-two table of hospital size by outcome level. Logistic regression was used with varying hospital size groups as predictors of outcome level, using cut-points from lowest to highest size in increments of 10 beds. The optimal size cut-point was the one that resulted in the highest accuracy of outcome prediction based on the largest c-statistic. Results: Our results demonstrated that hospital size cut-points varied by both outcome and unit type. The identified cut-points were not consistent with historically identified administrative cut-points using ADC where broad categories typically have defined small hospitals as under 100 beds. For specialty units such as CC or SD, cut-points were not as stable as those identified in MS units. We will discuss the results in detail in our presentation. Implications: Understanding appropriate comparison groups for benchmarking nurse-sensitive quality indicators is an important nursing research issue in this era of transparency and accountability for outcomes. Traditional administrative categories may not be appropriate for all unit types and standard categories may not be appropriate for all outcome indicators.</td></tr></table>en_GB
dc.date.available2011-10-26T20:20:11Z-
dc.date.issued2011-10-17en_GB
dc.date.accessioned2011-10-26T20:20:11Z-
dc.description.sponsorshipWestern Institute of Nursingen_GB
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