Lessons learned while collecting ANA indicator data.

Realizing the importance of linking nursing's contribution to quality patient care, a pilot study was conducted to determine whether data regarding the quality indicators proposed by the American Nurses' Association (ANA) could be collected from five acute-care inpatient units at one medical center that is part of a multisite managed care system. Although it was determined that data regarding the ANA quality indicators could be collected at the study site, a variety of unanticipated findings emerged. These findings reflect both discrepancies and congruities between how the investigative team expected the ANA indicators to operate versus what was actually experienced. The lessons learned while collecting ANA indicator data are shared to assist future users and to advance the evolution of the ANA indicators.

A classification scheme for outcome indicators.

PURPOSE: To provide a framework for classifying outcome indicators for a more comprehensive view of outcomes and quality. METHODS: Review of outcomes literature published since 1974 from medicine, nursing, and health services research to identify indicators. Outcome indicators were clustered inductively. FINDINGS: Three groups of outcome indicators were identified: patient-focused, provider-focused, and organization-focused. Although investigators tend to focus on a select few outcome indicators, such as patient satisfaction, quality of life, and mortality, many indicators exist to measure outcomes. CONCLUSIONS: Selecting and integrating a wide array of outcome indicators from the various categories will provide a more balanced view of health care delivery as compared with focusing on a few common indicators or only one category.

A neural network model for predicting pancreas transplant graft outcome.

OBJECTIVE: To compare the results of a neural network versus a logistic regression model for predicting early (0-3 months) pancreas transplant graft survival or loss. RESEARCH DESIGN AND METHODS: This study was a cross-sectional, secondary analysis of demographic and clinical data from 117 simultaneous pancreas-kidney (SPK), 35 pancreas-after-kidney (PAK), and 8 pancreas-transplant-alone (PTA) patients (n = 160). The majority of patients were men (57%) and were white (90.1%), with a mean age of 39 +/- 8.09 years. Of the patients, 23 (14.4%) experienced early graft loss, which included any loss owing to technical or immunological causes, and death with a functional graft. Data were analyzed with a logistic regression model for multivariate analysis and a backpropagation neural network (BPNN) model. RESULTS: A total of 12 predictor variables were chosen from literature and transplant surgeon recommendations. A logistic model with all predictor variables included correctly classified 93.53% of cases. Model sensitivity was 35.71%; specificity was 100% (pseudo-R2 0.24). Of the predictors, history of alcohol abuse (odds ratio [OR] 32.39; 95% CI 1.67-626.89), having a PAK or PTA (OR 13.6; 95% CI 2.20-84.01), and use of a nonlocal organ procurement center (OPO) (OR 4.51; 95% CI 0.78-25.96) were most closely associated with early graft loss. The BPNN model with the same 12 predictor variables correctly predicted 92.50% of cases (R2 0.71). Model sensitivity was 68%; specificity was 96%. Of the predictors, the three variables most closely associated with graft outcome in this model were recipient/donor weight difference >50 lb, having a PAK or PTA, and use of a nonlocal OPO. CONCLUSIONS: First, the BPNN model correctly predicted 92.5% of graft outcomes versus the logistic model (93.53%). Second, the BPNN model rendered more accurate predictions (>0.70 = loss; <0.30 = survival) versus the logistic model (>0.50 = loss; <0.50 = survival). Third, the BPNN model was more sensitive (68%) than the logistic model (35.71%) to graft failures and demonstrated an almost threefold increase in explained variance (R2 = 0.71 vs. 0.24). These results suggest that the BPNN model is a more powerful tool for predicting early pancreas graft loss than traditional multivariate statistical models.

Clinical case management in the Army medical department.

Clinical case management is a system that has the potential to improve the efficiency and effectiveness of health care delivery. Given this potential, the Army Medical Department (AMEDD) is examining this process as a means to achieve goals of quality care and cost effectiveness. In this article, the authors describe the concept of case management, the AMEDD system of clinical case management, the implementation of clinical case management in the AMEDD, and the role of the case manager.