Joanne R. Duffy

A significant portion of health care resources are spent in intensive care units with, historically, up to two-fold variation in risk-adjusted mortality. Technological, demographic, and social forces are likely to lead to an increased volume of intensive care in the future. Thus, it is important to identify ways of more efficiently managing intensive care units and reducing the variation in patient outcomes. Based on data collected from 17,440 patients across 42 ICUs, the present study examines the factors associated with risk-adjusted mortality, risk-adjusted average length of stay, nurse turnover, evaluated technical quality of care, and evaluated ability to meet family member needs. Using the Apache III methodology for risk-adjustment, findings reveal that: 1) technological availability is significantly associated with lower risk-adjusted mortality (beta = -.42); 2) diagnostic diversity is significantly associated with greater risk-adjusted mortality (beta = .46); and 3) caregiver interaction comprising the culture, leadership, coordination, communication, and conflict management abilities of the unit is significantly associated with lower risk-adjusted length of stay (beta = .34), lower nurse turnover (beta = -.36), higher evaluated technical quality of care (beta = .81), and greater evaluated ability to meet family member needs (beta = .74). Furthermore, units with greater technological availability are significantly more likely to be associated with hospitals that are more profitable, involved in teaching activities, and have unit leaders actively participating in hospital-wide quality improvement activities. The findings hold a number of important managerial and policy implications regarding technological adoption, specialization, and the quality of interaction among ICU team members. They suggest intervention "leverage points" for care givers, managers, and external policy makers in efforts to continuously improve the outcomes of intensive care.

OBJECTIVE: To examine organizational practices associated with higher and lower intensive care unit (ICU) outcome performance. DESIGN: Prospective multicenter study. Onsite organizational analysis; prospective inception cohort. SETTING: Nine ICUs (one medical, two surgical, six medical-surgical) at five teaching and four nonteaching hospitals. PARTICIPANTS: A sample of 3,672 ICU admissions; 316 nurses and 202 physicians. MATERIALS AND METHODS: Interviews and direct observations by a team of clinical and organizational researchers. Demographic, physiologic, and outcome data for an average of 408 admissions per ICU; and questionnaires on ICU structure and organization. The ratio of actual/predicted hospital death rate was used to measure ICU effectiveness; the ratio of actual/predicted length of ICU stay was used to assess efficiency. MEASUREMENTS AND MAIN RESULTS: ICUs with superior risk-adjusted survival could not be distinguished by structural and organizational questionnaires or by global judgment following on-site analysis. Superior organizational practices among these ICUs were related to a patient-centered culture, strong medical and nursing leadership, effective communication and coordination, and open, collaborative approaches to solving problems and managing conflict. CONCLUSIONS: The best and worst organizational practices found in this study can be used by ICU leaders as a checklist for improving ICU management.

OBJECTIVES: Weight gain is a consistent sequela of smoking cessation. A successful intervention might attract smokers who fear weight gain. If the gain causes smoking relapse, such an intervention might reduce smoking relapse risk. METHODS: Using a sample of 158 smokers who completed a 2-week smoking treatment program, we compared an innovative weight gain prevention intervention with both a nonspecific treatment and standard treatment. Subjects were assessed on weight and smoking behavior and followed for 1 year. RESULTS: A disturbing, unexpected finding was that subjects in both the innovative and nonspecific conditions had a higher risk of smoking relapse than did standard treatment subjects. Some differences were observed between abstinent and smoking subjects in weight gain by treatment condition. CONCLUSIONS: Both active interventions may have been so complicated that they detracted from nonsmoking. Also, caloric restriction may increase the reinforcing value of nicotine, a psychoactive drug, thereby increasing smoking relapse risk. The magnitude of weight gain after smoking cessation may not merit interventions that increase smoking risk. Perhaps attitudinal modifications are the most appropriate.

Evidence supports associations between professional nursing and quality health outcomes. Yet, what specifically accounts for those linkages remains buried in the daily practice of nursing. The Quality-Caring Model exposes and demonstrates the value of nursing within the evidence-based practice milieu of modern health care. It favors a process, or way of being, that challenges modernist conventions and highlights the power of relationships. By reaffirming the nature of nursing's work as relationship-centered, the blended model describes the 2 dominant relationships that comprise professional encounters. Relationships characterized by caring are theorized to influence positive outcomes for patients/families, health care providers, and health care systems. Model components are clarified, assumptions described, and propositions stated. Conceptual-theoretical linkages in the model are identified and ties to empirical indicators provide the logical consistency necessary for validation. Clinical practice and research applications of the model are offered. The Quality-Caring Model helps to translate the hidden work of nursing into objective terms that can be tested. Scientifically demonstrating its worth will advance professional nursing while simultaneously improving the quality of health care.

Quitting smoking often results in weight gain. The causes of the gain are not known. The present study evaluated changes in calories, total sugars, sucrose, fat, protein, and nonsugar carbohydrates as well as changes in activity levels after quitting smoking. Ninety-five subjects were randomly assigned to either early (Week 2) or late (Week 6) quit dates. Subjects were assessed on weight, food intake, activity levels, and smoking levels at baseline, at Weeks 4 and 8, and at Weeks 12 and 26 postquit. The results indicated significant increases in calories, sucrose, and fats 2 weeks after the quit date. Changes for total sugars were less consistent. Activity levels did not change significantly. Early caloric increases predicted weight gain at 26 weeks for abstinent women. No relation was found for abstinent men, but interpretation of this finding is weakened by a small subsample size. Abstinent subjects gained over 9 lb by 26 weeks postquit. Despite this gain, Week 26 results showed that caloric intake for abstinent women was approximately equal to that observed at baseline, whereas that for abstinent men had dropped significantly.