Inside the Black Box: The Impact of Processes on Care on Hospital Acquired Pressure Ulcers and Falls

2.50
Hdl Handle:
http://hdl.handle.net/10755/201663
Type:
Presentation
Title:
Inside the Black Box: The Impact of Processes on Care on Hospital Acquired Pressure Ulcers and Falls
Abstract:
(41st Biennial Convention)       Research Objective The primary research question guiding this study was, “how do unit level characteristics of RN workload and clinical processes of care interact to predict variance in selected nursing sensitive outcomes?”  This report is part of a larger study examining the Impact of Medical Surgical Acute Care Microsystem Nurse Characteristics and Practices on Patient Outcome, funded by the Robert Wood Johnson Foundation, Interdisciplinary Nursing Quality Research Initiative (INQRI).       Study Population and Design We created an empirically derived predictive model examining individual and collective effects of unit level nurse workload, staff nurse characteristics and selected risk assessment and preventive intervention processes of care on variance in nurse sensitive acute care patient outcomes at the microsystem level. The sample included data submitted to an established nursing sensitive benchmarking registry by 219 hospitals with 827 medical surgical units. We modeled event counts per year for Injury Falls/1000 patient days and Hospital Acquired Pressure Ulcer (HAPU) prevalence using the discrete Poisson with zero-inflation (ZIP) regression model suited to data with excess zeroes above what would be expected with a Poisson distribution. Falls/1000 patient days was modeled with an ordinary least squares regression. Principal Findings  We found that patient outcomes were predicted by combinations of all elements in our model, including: unit/patient characteristics, nursing workload, RN expertise, and clinical processes, and predictors were different for each outcome. Falls and Injury Falls were predicted by patient characteristics and clinical process variables. HAPU prevalence was predicted by a combination of all elements in our model: unit/patient characteristics, nursing workload, RN expertise, and clinical processes. Restraint Use was also predicted by a combination of all elements: unit/patient characteristics, nursing workload, RN expertise, and clinical processes Conclusions and Implications Staffing adequacy is multifaceted and complex. The content of nursing actions is powerful predictor of outcomes.
Keywords:
clinical microsystems; nursing sensitive outcomes; processes of care impacts on outcomes
Repository Posting Date:
11-Jan-2012
Date of Publication:
4-Jan-2012
Sponsors:
Sigma Theta Tau International

Full metadata record

DC FieldValue Language
dc.typePresentationen_GB
dc.titleInside the Black Box: The Impact of Processes on Care on Hospital Acquired Pressure Ulcers and Fallsen_GB
dc.identifier.urihttp://hdl.handle.net/10755/201663-
dc.description.abstract(41st Biennial Convention)       Research Objective The primary research question guiding this study was, “how do unit level characteristics of RN workload and clinical processes of care interact to predict variance in selected nursing sensitive outcomes?”  This report is part of a larger study examining the Impact of Medical Surgical Acute Care Microsystem Nurse Characteristics and Practices on Patient Outcome, funded by the Robert Wood Johnson Foundation, Interdisciplinary Nursing Quality Research Initiative (INQRI).       Study Population and Design We created an empirically derived predictive model examining individual and collective effects of unit level nurse workload, staff nurse characteristics and selected risk assessment and preventive intervention processes of care on variance in nurse sensitive acute care patient outcomes at the microsystem level. The sample included data submitted to an established nursing sensitive benchmarking registry by 219 hospitals with 827 medical surgical units. We modeled event counts per year for Injury Falls/1000 patient days and Hospital Acquired Pressure Ulcer (HAPU) prevalence using the discrete Poisson with zero-inflation (ZIP) regression model suited to data with excess zeroes above what would be expected with a Poisson distribution. Falls/1000 patient days was modeled with an ordinary least squares regression. Principal Findings  We found that patient outcomes were predicted by combinations of all elements in our model, including: unit/patient characteristics, nursing workload, RN expertise, and clinical processes, and predictors were different for each outcome. Falls and Injury Falls were predicted by patient characteristics and clinical process variables. HAPU prevalence was predicted by a combination of all elements in our model: unit/patient characteristics, nursing workload, RN expertise, and clinical processes. Restraint Use was also predicted by a combination of all elements: unit/patient characteristics, nursing workload, RN expertise, and clinical processes Conclusions and Implications Staffing adequacy is multifaceted and complex. The content of nursing actions is powerful predictor of outcomes.en_GB
dc.subjectclinical microsystemsen_GB
dc.subjectnursing sensitive outcomesen_GB
dc.subjectprocesses of care impacts on outcomesen_GB
dc.date.available2012-01-11T10:46:01Z-
dc.date.issued2012-01-04en_GB
dc.date.accessioned2012-01-11T10:46:01Z-
dc.description.sponsorshipSigma Theta Tau Internationalen_GB
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