Utilization of the Boston Children's Hospital SRTR Cohort Visualization Tool to increase team understanding of reporting cohorts.

BACKGROUND: While reviewing outcomes metrics and data from the SRTR, it became apparent that prospective assessment of the SRTR reporting cohorts would be an important proactive strategy for internal quality control. It was particularly important to identify the number of patient deaths and graft failures within 1 year of transplant that would result in being flagged by the UNOS and the MPSC. METHODS: A simple Microsoft Excel line graph was created to visually display retrospective, current, and future SRTR cohorts. Data provided by the SRTR CUSUM (https://securesrtr.transplant.hrsa.gov/srtr-reports/cusum-charts/) Reports and the SRTR 1 Year Expected Survival Excel Worksheet (https://securesrtr.transplant.hrsa.gov/srtr-reports/current-release/) were leveraged to identify whether programs were in jeopardy of being flagged by UNOS/MPSC for outcomes. RESULTS & CONCLUSIONS: The creation of this visual tool has greatly improved team understanding of SRTR report cohorts, as well as the risk of being flagged by regulatory agencies, for adverse outcomes.

Predictors and correlates of dissatisfaction with intensive care.

OBJECTIVE: Dissatisfaction is an important threat to high-quality care. The aim of this study was to identify factors independently associated with dissatisfaction with critical care. DESIGN: Prospectively collected observational cohort study. SETTING: Nine intensive care units at a tertiary care university hospital in the United States. PARTICIPANTS: Four hundred forty-nine family members of adult intensive care unit patients who completed the Family Satisfaction with Care in the Intensive Care Unit instrument. INTERVENTION: None. MEASUREMENTS AND MAIN RESULTS: Four family-and patient-related factors ascertainable at intensive care unit admission independently predicted low overall satisfaction: living in the same city as the hospital, disagreement within the family regarding care, having a cardiac comorbidity but being hospitalized in a noncardiac-care intensive care unit, and living in a different household than the patient. When three or more risk factors were present, 63% (95% confidence interval 48%-78%) of families were dissatisfied. Among factors ascertained at the end of the intensive care unit stay, dissatisfaction with six items was independently associated with overall dissatisfaction: 1) perceived competence of nurses (odds ratio for dissatisfaction=5.9, 95% confidence interval 2.3-15.2); 2) concern and caring by intensive care unit staff (odds ratio 5.0, 95% confidence interval 1.9-12.6); 3) completeness of information (odds ratio 4.4, 95% confidence interval 2.4-8.1); 4) dissatisfaction with the decision-making process (odds ratio 3.0, 95% confidence interval 1.6- 5.6); 5) atmosphere of the intensive care unit (odds ratio 2.6, 95% confidence interval 1.4-4.8); and 6) atmosphere of the waiting room (odds ratio 2.7, 95% confidence interval 1.2-6.0). CONCLUSION: Specific factors ascertainable at intensive care unit admission identify families at high risk of dissatisfaction with care. Other discrete aspects of the patient/family experience that develop during the intensive care unit stay are also strongly associated with dissatisfaction with the critical care experience. These results may provide insight into the design of future evidence-based strategies to improve satisfaction with the intensive care unit experience.

Design and measurement of quality improvement indicators in ambulatory pulmonary care: creating a "culture of quality" in an academic pulmonary division.

BACKGROUND: Quality improvement (QI) measures often are cited as goals for individual practices and medical centers and may someday form a component of reimbursement guidelines. Relatively few QI metrics relevant to ambulatory pulmonary medicine have been published. We describe the development and implementation of a QI program in an academic pulmonary division, including progress to date and lessons learned. METHODS: Metrics for the pulmonary QI Dashboard were developed based on an extensive literature review. Patients were identified through International Classification of Diseases-based billing databases, and results data were obtained from a manual and automated review of the electronic medical record. The performance of the division was monitored and presented in regular faculty meetings. Quarterly, confidential, individual scorecards gave each clinician feedback about his or her performance and compared the feedback to that of the faculty of the entire division. RESULTS: Significant improvements were found in many QI measures during a 2-year period. The number of patients with asthma who received appropriately prescribed inhaled corticosteroids increased from a baseline of 76 to 92% to 98%. Flu shot and pneumococcal vaccine administration documentation for patients with COPD increased from baseline values of 11 to 32% and 11 to 34%, respectively, to 90% and 93%, respectively. The COPD Global Initiative for Obstructive Lung Disease pharmacotherapy guidelines adherence increased substantially for patients with all disease stages. Chest CT scan results notification documentation improved from a baseline of 67 to 76% to 98%. Comparison between baseline and QI periods yielded statistically significant increases for these indicators. CONCLUSIONS: QI measures for an ambulatory pulmonary practice can be designed, implemented, and monitored. Key components include a well-structured electronic medical record, measurable outcomes, strong QI leadership, and specific interventions, such as providing feedback through QI review meetings and individual "report cards."