2.50
Hdl Handle:
http://hdl.handle.net/10755/161462
Type:
Presentation
Title:
Understanding Nursing Complexity
Abstract:
Understanding Nursing Complexity
Conference Sponsor:Midwest Nursing Research Society
Conference Year:2003
Author:Lee, Jan
Contact Address:SON, 400 N. Ingalls, Ann Arbor, MI, 48109-0482, USA
Co-Authors:Betty L. Chang; Marjorie L. Pearson; Marc N. Elliott; Katherine L. Kahn; Lisa V Rubenstein
As hospital stays have shortened and intensified, the burden of illness presented by patients has increased. Little objective information exists about which patient characteristics comprise nursing complexity. Purpose: The purpose of this paper is to explore the relationships between nursing complexity, as judged by nurses, and patient characteristics, including functional status, disease-specific severity on admission, and patient demographics. Conceptual Framework: Donabedian’s classic model of quality of care guided this study. Data Source: Medical records of 291 patients with CHF and 290 with CVA were reviewed. Records were randomly selected from 301 hospitals in five states; hospitals were chosen by zip code and were nationally representative in size, rural status, teaching intensity, ownership and percentage of Medicare admissions. Methods: Nursing complexity was measured by expert nurse raters and based on structured implicit review of medical records. The nursing complexity scale demonstrated inter-rater reliability and validity. Patient characteristics were measured by independent explicit review of the same records using previously validated and reliable items and scales. Results: Nursing complexity predicted important clinical outcomes, even when controlling for sickness at admission. For both diseases, nursing complexity was a significant predictor of death at 30 days and death at 180 days, discharge to nursing home, and length of stay. In addition, for CVA, nursing complexity predicted instability at discharge. Conclusions: These findings indicate that it is important to adjust for nursing complexity in determining staffing resources required to provide quality, cost-efficient care, and that sickness-at-admission measures are not sufficient proxies for this task. It is critical that future staffing decisions be based on an appropriate combination of nursing care resources to meet both the sickness needs, and the nursing complexity needs, of the patient population to be served. AN: MN030364
Repository Posting Date:
26-Oct-2011
Date of Publication:
17-Oct-2011
Sponsors:
Midwest Nursing Research Society

Full metadata record

DC FieldValue Language
dc.typePresentationen_GB
dc.titleUnderstanding Nursing Complexityen_GB
dc.identifier.urihttp://hdl.handle.net/10755/161462-
dc.description.abstract<table><tr><td colspan="2" class="item-title">Understanding Nursing Complexity</td></tr><tr class="item-sponsor"><td class="label">Conference Sponsor:</td><td class="value">Midwest Nursing Research Society</td></tr><tr class="item-year"><td class="label">Conference Year:</td><td class="value">2003</td></tr><tr class="item-author"><td class="label">Author:</td><td class="value">Lee, Jan </td></tr><tr class="item-address"><td class="label">Contact Address:</td><td class="value">SON, 400 N. Ingalls, Ann Arbor, MI, 48109-0482, USA</td></tr><tr class="item-co-authors"><td class="label">Co-Authors:</td><td class="value">Betty L. Chang; Marjorie L. Pearson; Marc N. Elliott; Katherine L. Kahn; Lisa V Rubenstein </td></tr><tr><td colspan="2" class="item-abstract">As hospital stays have shortened and intensified, the burden of illness presented by patients has increased. Little objective information exists about which patient characteristics comprise nursing complexity. Purpose: The purpose of this paper is to explore the relationships between nursing complexity, as judged by nurses, and patient characteristics, including functional status, disease-specific severity on admission, and patient demographics. Conceptual Framework: Donabedian&rsquo;s classic model of quality of care guided this study. Data Source: Medical records of 291 patients with CHF and 290 with CVA were reviewed. Records were randomly selected from 301 hospitals in five states; hospitals were chosen by zip code and were nationally representative in size, rural status, teaching intensity, ownership and percentage of Medicare admissions. Methods: Nursing complexity was measured by expert nurse raters and based on structured implicit review of medical records. The nursing complexity scale demonstrated inter-rater reliability and validity. Patient characteristics were measured by independent explicit review of the same records using previously validated and reliable items and scales. Results: Nursing complexity predicted important clinical outcomes, even when controlling for sickness at admission. For both diseases, nursing complexity was a significant predictor of death at 30 days and death at 180 days, discharge to nursing home, and length of stay. In addition, for CVA, nursing complexity predicted instability at discharge. Conclusions: These findings indicate that it is important to adjust for nursing complexity in determining staffing resources required to provide quality, cost-efficient care, and that sickness-at-admission measures are not sufficient proxies for this task. It is critical that future staffing decisions be based on an appropriate combination of nursing care resources to meet both the sickness needs, and the nursing complexity needs, of the patient population to be served. AN: MN030364</td></tr></table>en_GB
dc.date.available2011-10-26T23:21:44Z-
dc.date.issued2011-10-17en_GB
dc.date.accessioned2011-10-26T23:21:44Z-
dc.description.sponsorshipMidwest Nursing Research Societyen_GB
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