Building New Health Services and Outcomes Knowledge: Predicting Re-Hospitalization

2.50
Hdl Handle:
http://hdl.handle.net/10755/157729
Type:
Presentation
Title:
Building New Health Services and Outcomes Knowledge: Predicting Re-Hospitalization
Abstract:
Building New Health Services and Outcomes Knowledge: Predicting Re-Hospitalization
Conference Sponsor:Western Institute of Nursing
Conference Year:2009
Author:Fischer, Brenda A., PhD, RN, MBA, CPHQ
P.I. Institution Name:Palomar Pomerado Health, The Center for Nursing Excellence
Title:Director-The Center for Nursing Excellence
Contact Address:15255 Innovation Drive, San Diego, CA, 92128, USA
Contact Telephone:858-675-5090
Purpose: The overall purpose of this study was to examine the predicative capability of the Outcome and Assessment Information Set (OASIS) for acute care re-hospitalization of home healthcare patients. Background/Conceptual Basis:  The emerging use of large clinical and administrative  databases to mine and uncover new knowledge about key relationships among current practices and the organizational structures and processes that support them (Mitchell, 2008) was integrated with the Quality Health Outcomes Model (QHOM). The QHOM was created specifically to address the question of nursing's contribution to patient outcomes and globally to guide health outcomes research (Mitchell et al, 1998). In recent years quality of services has been an increasing focus in the health system. For home healthcare the focus on outcomes has resulted in mandated federal requirements that all home healthcare agencies participating in Medicare collect and report patient data using a single core set of measures in OASIS. The phenomena of re-hospitalization remains the one outcome measure that has never improved despite it being a national priority. Method: Secondary data analysis using a logistic regression model building process was conducted on retrospective data from OASIS collected during the time period of to. This study was conducted in a Medicare certified Home Healthcare organization that is part of the largest public health system in. The sample of 1802 patients with complete episodes of care was derived from a data set of 5,523 patients. All patients were included in the analysis and logistic regression model and the disease specific independent variables included patients with a primary or secondary diagnosis of diabetes and an open skin lesion or wound. The study methodology related to the backwards method of logistic regression modeling was useful in being able to examine a large number of variables and their relationship to a dichotomous dependent variable. Results: The OASIS variables examined in the logistic regression model that showed significance as predictors of acute care re-hospitalization included a diagnosis of diabetes, overall prognosis, rehabilitation prognosis, existing dyspnea, existing urine and bowel incontinence, impairment in currently dressing the upper body and the ability to take own oral medications. Implications: This study adds to the growing body of research using OASIS.  Since this design and method has not been described in the literature prior to this study it has interesting implications for future research using OASIS. Home Healthcare nursing leaders can use the findings from this study to design focused interventions and delivery models to mediate or modify the effect of the predictive variables.
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.titleBuilding New Health Services and Outcomes Knowledge: Predicting Re-Hospitalizationen_GB
dc.identifier.urihttp://hdl.handle.net/10755/157729-
dc.description.abstract<table><tr><td colspan="2" class="item-title">Building New Health Services and Outcomes Knowledge: Predicting Re-Hospitalization</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">Fischer, Brenda A., PhD, RN, MBA, CPHQ</td></tr><tr class="item-institute"><td class="label">P.I. Institution Name:</td><td class="value">Palomar Pomerado Health, The Center for Nursing Excellence</td></tr><tr class="item-author-title"><td class="label">Title:</td><td class="value">Director-The Center for Nursing Excellence</td></tr><tr class="item-address"><td class="label">Contact Address:</td><td class="value">15255 Innovation Drive, San Diego, CA, 92128, USA</td></tr><tr class="item-phone"><td class="label">Contact Telephone:</td><td class="value">858-675-5090</td></tr><tr class="item-email"><td class="label">Email:</td><td class="value">brenda.fischer@pph.org, fischpond@cox.net</td></tr><tr><td colspan="2" class="item-abstract">Purpose: The overall purpose of this study was to examine the predicative capability of the Outcome and Assessment Information Set (OASIS) for acute care re-hospitalization of home healthcare patients. Background/Conceptual Basis:&nbsp; The emerging use of large clinical and administrative&nbsp; databases to mine and uncover new knowledge about key relationships among current practices and the organizational structures and processes that support them (Mitchell, 2008) was integrated with the Quality Health Outcomes Model (QHOM). The QHOM was created specifically to address the question of nursing's contribution to patient outcomes and globally to guide health outcomes research (Mitchell et al, 1998). In recent years quality of services has been an increasing focus in the health system. For home healthcare the focus on outcomes has resulted in mandated federal requirements that all home healthcare agencies participating in Medicare collect and report patient data using a single core set of measures in OASIS. The phenomena of re-hospitalization remains the one outcome measure that has never improved despite it being a national priority. Method: Secondary data analysis using a logistic regression model building process was conducted on retrospective data from OASIS collected during the time period of to. This study was conducted in a Medicare certified Home Healthcare organization that is part of the largest public health system in. The sample of 1802 patients with complete episodes of care was derived from a data set of 5,523 patients. All patients were included in the analysis and logistic regression model and the disease specific independent variables included patients with a primary or secondary diagnosis of diabetes and an open skin lesion or wound. The study methodology related to the backwards method of logistic regression modeling was useful in being able to examine a large number of variables and their relationship to a dichotomous dependent variable. Results: The OASIS variables examined in the logistic regression model that showed significance as predictors of acute care re-hospitalization included a diagnosis of diabetes, overall prognosis, rehabilitation prognosis, existing dyspnea, existing urine and bowel incontinence, impairment in currently dressing the upper body and the ability to take own oral medications. Implications: This study adds to the growing body of research using OASIS.&nbsp; Since this design and method has not been described in the literature prior to this study it has interesting implications for future research using OASIS. Home Healthcare nursing leaders can use the findings from this study to design focused interventions and delivery models to mediate or modify the effect of the predictive variables.</td></tr></table>en_GB
dc.date.available2011-10-26T20:08:55Z-
dc.date.issued2011-10-17en_GB
dc.date.accessioned2011-10-26T20:08:55Z-
dc.description.sponsorshipWestern Institute of Nursingen_GB
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