Patterns of care: an analysis of the quality of nutritional care routinely provided to elderly hospitalized veterans

To assess the adequacy of the nutritional care provided, a prospective noninterventional study was carried out on 250 randomly selected patients aged 65 and over who were admitted to a Veterans Administration Hospital. Of this group, 97 patients (39%) were found to be at high risk of having clinically significant protein-energy malnutrition. In 43 cases (17%), an assessment of the patient's nutritional status was not possible because the available data were inadequate. No patient had a diagnosis of malnutrition recorded on the problem list. Only 13 percent of the high-risk patients received some form of nutrition support therapy; 10 (10%) received oral supplements, and four (4%) were started on enteral feedings. Patients who received enteral feedings experienced a high rate of complications resulting from use of the feeding tubes. Over all, none of the high risk study patients received optimal nutrition support therapy. These findings indicate that elderly patients hospitalized in the Veterans Administration hospital in this study are usually not screened appropriately for protein-energy malnutrition, the diagnosis is frequently missed or ignored, and nutrition support therapy is underutilized and often ineffectually managed.     

Just-in-Time Evidence-Based E-mail “Reminders” in Home Health Care: Impact on Patient Outcomes

OBJECTIVE: To assess the impact and cost-effectiveness of two information-based provider reminder interventions designed to improve self-care management and outcomes of heart failure (HF) patients. DATA SOURCES/STUDY SETTING: Interview and agency administrative data on 628 home care patients with a primary diagnosis of HF. STUDY DESIGN: Patients were treated by nurses randomly assigned to usual care or one of two intervention groups. The basic intervention was an e-mail to the patient's nurse highlighting six HF-specific clinical recommendations. The augmented intervention supplemented the initial nurse reminder with additional clinician and patient resources. DATA COLLECTION: Patient interviews were conducted 45 days post admission to measure self-management behaviors, HF-specific outcomes (Kansas City Cardiomyopathy Questionnaire-KCCQ), health-related quality of life (EuroQoL), and service use. PRINCIPAL FINDINGS: Both interventions improved the mean KCCQ summary score (15.3 and 12.9 percent, respectively) relative to usual care (p< or =.05). The basic intervention also yielded a higher EuroQoL score relative to usual care (p< or =.05). In addition, the interventions had a positive impact on medication knowledge, diet, and weight monitoring. The basic intervention was more cost-effective than the augmented intervention in improving clinical outcomes. CONCLUSIONS: This study demonstrates the positive impact of targeting evidence-based computer reminders to home health nurses to improve patient self-care behaviors, knowledge, and clinical outcomes. It also advances the field's limited understanding of the cost-effectiveness of selected strategies for translating research into practice.     

Integrating Decision Making and Mental Health Interventions Research: Research Directions

The importance of incorporating patient and provider decision-making processes is in the forefront of the National Institute of Mental Health (NIMH) agenda for improving mental health interventions and services. Key concepts in patient decision making are highlighted within a simplified model of patient decision making that links patient-level/"micro" variables to services-level/"macro" variables via the decision-making process that is a target for interventions. The prospective agenda for incorporating decision-making concepts in mental health research includes (a) improved measures for characterizing decision-making processes that are matched to study populations, complexity, and types of decision making; (b) testing decision aids in effectiveness research for diverse populations and clinical settings; and (c) improving the understanding and incorporation of preference concepts in enhanced intervention designs.